essay about drug addiction in youth

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Essay on Drug Addiction in Youth

Essay on the Signs of Drug Addiction

Essay on the causes of drug addiction, essay on the effects of drug addiction.

• Essay on the Prevention of Drug Addiction
• Essay on the Treatment of Drug Addiction

The most disturbing thing about drug addiction is that people in different countries of the world are becoming addicted to all kinds of drugs. There are different types of street drugs such as – cocaine, meth, marijuana, crack, heroin etc. Heroin is one of the dangerous drugs that suppress your heart’s work and is appropriate to achieve narcotic effect.

The alarming rate of drug consumption has always been a problem and has detrimental effects on the society. Personal and family problems also lead to drug abuse among youngsters who fail to deal with personal problems. The physiological effects of drug addiction can be difficult to endure and this is why the addict must be treated for their condition. The worst thing is that drugs are that they affect youth in every country of the world.

The term drug not only means medicine, but fatal narcotics with different specifications. These drugs have their evil effects on mind and body cells of the addicts. The addict becomes dependent on the drug to a great extent that he/she cannot stop using it. Despite of having full knowledge of its effects on health, addicts use it on a regular basis.

Drug addiction is basically a brain disease that changes the functioning of brain. There is an uncontrollable desire to consume drugs, as a result of which addicted people engage in compulsive behavior to take drugs. The addicts find it impossible to control the intake of drugs, as a result of which they fail to fulfill day-to-day responsibilities in efficient manner. Drug addiction is also referred as drug dependency, as the addict develops dependency for particular substance.

Drug addiction is a compulsive disorder that leads an individual to use substance habitually to achieve desired outcome. Millions of people in the world are suffering with drug addiction and the number is expected to increase in the coming years. If the person is using drugs for a longer period, the outcome may change. For example – early experimentation with drugs is rooted in curiosity. However, as the frequency of substance becomes frequent – the body starts to depend in it to function properly.

The most common signs and symptoms of drug addiction are – obsession with a particular substance, loss of control over the usage of drugs, abandoning the activities which you used to enjoy, etc. Drug addiction may have long term impact on life and one may develop severe symptoms such as – fatigue, trembling, depression, anxiety, headache, insomnia, chills and sweating, paranoia, behavior changes, dilated pupils, poor coordination problems, nausea etc.

There are a number of reasons why youth and teenagers are addicted to drugs or related substances. Lack of self-confidence is considered as one of the primary causes of drug addiction. It can also be due to excessive stress, peer pressure, lack of parental involvement in child’s activities etc. some people consider drug addiction can be the cause of drug use and ignorance. The ignorance of drug addiction along with physical pain of condition becomes a primary cause of drug addiction. Here are some of the causes of drug addiction.

High Level Stress

Young people who have just started their college life or moved to a new city in search of job often face problems with life change. They are more likely to alleviate stress through the use of drugs and similar substances. Finding an easy fix often seems easier than facing the real problem and dealing with it. Trying illegal drugs can lead to addiction and becomes a long term habit.

Social Pressure

Today, we are living in a highly competitive world and it is difficult to grow in such world. There is always a peer pressure in young and old people. However, it is never visible. A lot of young people expect to experience the pressure to use drugs, smoke and drink alcohol. Young people find it difficult to be the person who doesn’t drink or smoke. As they feel isolated and like a social outcast, they make a habit of taking drugs.

Mental Health Conditions

Another primary reason for trying drugs is mental health condition. People who are emotionally weaker tend to feel depressed about the facts of the world. They look for ways to feel free and live life in a normal way as they go through the period of growing up. In such situation, they make a habit of taking drugs and can lead to addiction.

Psychological Trauma

A history of psychological trauma appears to increase the risk of substance abuse. More than 75% of people who suffered from psychological trauma use drugs as a part of self-medicating strategy or provide an avenue towards self-destructive behaviors. Women are more sensitive to drugs than men, and hence need less exposure to similar effects. The availability of these drugs plays an integral role in perpetuation of addictive behaviors within families.

Exposure to Drug Abuse

Exposure to drug abuse in which the young people are raised is another cause why young people get addicted to drugs. If the individuals grow up in an area where adults use drugs, then the person is likely to try the substance themselves. Setting a good example is extremely important to keep them off drugs and related substances. Providing genuine information about drugs is the best way to prevent drug addiction.

There are many negative effects of drug addiction on physical and mental health. As said, drug addiction refers to compulsive and repeated use of dangerous substances. The effects of drug addiction are wide and profound. The psychological effects of drug addiction comes form the reason that the user is addicted to drugs as well as the changes that take place in brain. Many people start using drugs to handle stress. However, the psychological effects of drug addiction involves craving of the substance and using it to the exclusion of all else.

Emotional Effects

The emotional effects of drug addiction include – mood swings, depression, violence, anxiety, decrease in everyday activities, hallucinations, confusion, psychological tolerance to drug effects etc. Besides these, there are many physical effects of drug addiction that are seen in the systems of the body. The primary effects of drug addiction take place in brain, which changes the brain functions and impacts how the body perceives pleasure.

Physical Effects

Other effects of drug addiction include – heart attack, irregular heartbeat, and contraction of HIV, respiratory problems, lung cancer, abdominal pain, kidney damage, liver problem, brain damage, stroke, seizures, and changes in appetite. The impact of drug addiction can be far-reaching and affects every organ of the body. Excessive usage of drugs can weaken immune system and increase susceptibility to infection.

Brain & Liver Damage

The effects of drug addiction are seen in people because the drug floods the brain repeatedly with chemicals such as – serotonin and dopamine. The brain becomes highly dependent on these drugs and cannot function without them. The effects of drug addiction are also seen in babies of drug abusers and can be affected throughout their life.

Drug addiction can cause the liver to work harder, causing significant liver failure or damage. Regarding brain function, drugs can impact daily activities by causing problems with memory, decision making, mental confusion and even permanent brain damage.

Short Term Effects

Different drugs affect body in different ways. There are some short term effects that occur in drug users depending on the amount of substance used, its purity and potency. Drugs can affect the person’s thinking, mood and perception to a great extent. Drugs can temporarily impair motor functioning and interfere with decision making and even reduce inhibition. The most common substances of drug addiction include – opiates, alcohol, barbiturates, inhalants etc.

A lot of people do not realize the damage caused by drug addiction because the short term effects are not apparent at first. The individual may feel quite invincible and unaware that drugs can actually affect almost every system in the body. The long lasting effects of drug addiction may not be known to addict. If treatment is not sought in time, the physical and emotional health will deteriorate.

Long Term Effects

The long term effects of drug addiction can have disastrous consequences on physical and mental health. As the body adapts to the substance, it needs increasing amount of it to experience the desired outcome. As the individual continues to increase the dosage, he/she may develop physical dependence. The individual may face deadly withdrawal symptoms, once he/she stops using the substance.

Legal Consequences

Drug abuse not only causes negative effects on your physical and mental health, but can have legal consequences. Individuals may have to deal with the legal consequences for the rest of their life. A lot of companies require the employees to take drug test before offering job. Driving under the influence of drugs can lead to serious legal action and even heavy fines.

By understanding the physical impact of the substance, individuals can make informed decision regarding their health. Remember that it is never late to seek help, when it comes to treat drug addiction. There are many rehabilitation centers that help you combat drug addiction in a supportive environment.

Essay on the P revention of Drug Addiction

As said, prevention is always better than cure. It is always best option to deter people from drug abuse. Though it is practically impossible to prevent everyone from using drugs, there are things we can do to avoid drug addiction. Here are some effective tips to prevent drug addiction.

Deal with Peer Pressure

The biggest reason why people start using drugs is because of their friends or colleagues who utilize per pressure. No one in this world likes to be left out, especially teens and youngsters. If you are in such situation, you should find a better group of friends who won’t pressure you into harmful things. You should plan ahead of time or prepare a good excuse to stay away from tempting situations.

Treat Emotional Illness

Individuals suffering with any mental condition such as – anxiety, depression, post-traumatic stress etc. should seek help from a physiatrist. There is a strong connection between mental illness and drug addiction. Those with weak emotional status may easily turn to drugs.

Learn to Deal with Pressure

People of today’s generation are overworked and often feel like taking a good break. However, they make the mistake of turning to drugs and end up making life more stressful. Many of us fail to recognize this. The best way is to find other ways to handle stress. Whether it is taking up exercising or reading a good book, you should try positive things that help in relieving stress.

Understand the Risk Factors

If you are not aware of the risk factors of drug addiction, you should first know about drug abuse. Individuals who are aware of the physical and emotional effects of drug addiction are likely to overcome them. People take up drugs when something in their life is not going well and they are unhappy about their life. One should always look at the big picture and focus on priorities, instead of worrying about short term goals.

Develop Healthy Habits

Eating a well-balanced diet and doing regular exercise is the best way to prevent drug addiction. A healthy body makes it easier for people to deal with stress and handle life effectively, which eventually reduces the temptation to use drugs.

The above tips are a just a few ideas that can help prevent drug addiction. However, if the person has already developed drug addiction, he/she should seek drug detox treatment at the earliest.

Essay on the T reatment of Drug Addiction

Drug addiction can be managed effectively like other chronic diseases such as diabetes, heart disease, asthma etc. Treatment of drug addiction is becoming personalized. The comprehensive treatment options not only address addiction, but treat the underlying issues resulting in addiction.

Though there are many options to treat drug addiction, it is not easy. Drug addiction is a chronic disease and one can’t stop using drugs within a few days. A lot of patients need long term or repeated care to stop using drugs completely. Drug addiction treatment depends on the severity of drug abuse. The treatment must stop the person from using drugs as well as keep him away from drugs.

Different treatment methodologies are employed in treating drug abuse. The treatment plan will be devised as per the condition of the addict. It is essential that the treatment is tailored to the unique individual as there is no single treatment that works for all.

Inpatient drug abuse treatment is one of the options that allow the addict to focus on his/her recovery. Attending this treatment facility can increase the chances of completing the drug addiction rehabilitation program, especially if the addict does not have good support system at home.

Outpatient drug abuse treatment is ideal for those addicts who have a supportive environment at home. It is usually recommended for those who want to attend short-term inpatient treatment program.

Cognitive behavioral therapy is another treatment option that is highly effective in treating drug addiction issues. CBT helps in controlling negative thought patterns that lead to drug abuse. Patients can identify the triggers that cause them to use drugs and learn to respond without the need to turn to the substance.

Drug addiction is a complex disease that results from a number of factors such as genetic predisposition, history of violence at home and stress. Researchers have been able to identify the factors that lead to drug abuse. Understanding the root cause of drug addiction is one of the best ways to improve treatment options and outcomes of drug addiction in future.

A lot of people do not understand why people get addicted to drugs and related substances. They mistakenly view drug abuse as a social problem and characterize the addict as a weak person. Though there is no scientific evidence on how exactly drugs work in brain, it can be successfully treated to help people stop abusing drugs. There are many treatments that help people counteract the disruptive effects of drug addiction and regain complete control over life.

Behavioral therapy is the best way to ensure success in most of the drug addicts. The treatment approaches are tailored to meet the drug abuse pattern of patients. It is not uncommon for an individual to relapse and start drug abuse again. In such case, an alternate treatment is required to regain control and recover completely.

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Teenage Drug Addiction: An Overview

• Substance Use Statistics
• Why Teens Use Drugs
• Drug Effects
• Specific Health Risks
• Symptoms and Warning Signs
• Four Stages of Addiction

Many teens experiment with substances but don’t continue to use them. For some adolescents, however, trying a substance like alcohol, marijuana, or illicit drugs leads to regular use. Once withdrawal and cravings set in, a teen dealing with addiction and dependence may not be able to stop using a substance, even if they want to.

Caregivers can prevent teen drug abuse by knowing the signs and talking to their children about the consequences of using substances. This article reviews statistics, risk factors, health effects, signs, and treatment for teenage  drug addiction .

Sturti / Getty Images

Teenage Substance Use Statistics

Public health experts track the rates of substance use in people of all ages. One group that they pay particular attention to is teens.

Basic Statistics

Here are some of the key statistics from the Monitoring the Future survey, which has been tracking youth substance use in the United States for over 40 years.

In 2023, here’s how many teens in the U.S. reported any illicit drug use in the last year:

• Eighth graders: 10.9%
• 10th graders: 19.2%
• 12th graders: 31.2%

In addition:

• By the time they reach 12th grade, 21.3% of teens have tried an illicit drug at least once.
• From 2016 to 2020, drug use among eighth graders increased by 61%.
• In a year, around 4,477 15-to-24-year-olds die of illicit drug overdoses (about 11.2% of all overdose deaths are in this age group).

Substances Used

Here is how many teens reported using a specific substance in the last year:

• Eighth graders: 15.1%
• 10th graders: 30.6%
• 12th graders: 45.7%
• Eighth graders: 8.3%
• 10th graders: 17.8%
• 12th graders: 29%
• Any illicit drugs:
• 10th graders: 19.8%
• 12th graders: 31.2 %
• Cigarettes:
• Eighth graders: 5.8%
• 10th graders: 9.4%
• 12 t thgraders: 15%
• Vaping nicotine (e-cigarettes):
• Eighth graders: 11.4%
• 10th graders: 17.6%
• 12th graders: 23.2%

Prescription Medications

Alcohol is the most commonly abused substance among teens, but rates of nicotine and prescription medication abuse are increasing. Examples of prescription drugs teens may misuse include stimulants like Adderall and benzodiazepines like Xanax .

What Causes Teens to Use Drugs?

The reasons why any person uses drugs are complex, and the same is true for teens. Wanting to fit in with peers, feeling overwhelmed by their changing brains and bodies, and pressure to perform in school or sports are just a few reasons why teens may start experimenting with drugs. Teens may not seek drugs out but are instead introduced to substances by someone they know, such as a friend, teammate, or even a family member.

In addition, teens often don’t know or understand the dangers of substance abuse. They may see occasional use as being safe and don’t believe they could become addicted to drugs or face consequences. They may also assume that they can stop using if they want to.

Other risk factors for drug use in teens include:

• Family history of substance use 
• Academic pressure
• Adverse childhood events ( ACES )
• Lack of supervision
• Mental health disorders
• Peer pressure
• Desire to escape (e.g., external situation like home life or internal situation like complex feelings)
• Social acceptance (e.g., fitting in with peers)
• Low  self-esteem
• Increased access to substances
• Transitional periods (e.g., starting puberty or attending a new school)

While drug use can lead to mental health disorders, sometimes it’s the other way around. Teens may use substances to self-medicate or numb emotional pain.

What Are the Effects of Using Drugs During Adolescence?

The body sends out a “feel good” chemical called  dopamine  when using a substance. This response tells the brain that it is worth using the substance again to get that feeling. As a result, a person starts having cravings for the substance. Addiction happens when cravings don’t stop,  withdrawal  occurs without the substance, and use continues even when there are negative consequences. Since the physical and mental urge to use is so strong, it becomes very hard to stop using a substance.

Teenagers who misuse substances can experience drug dependence ( substance use disorder ). Developmentally, adolescents are at the highest risk for drug dependence and severe addiction.  

Effects on Brain Development and Growth

The human brain continues to develop until about the age of 25. Using substances during adolescence can change brain structure and negatively affect brain functions like learning, processing emotions, and decision-making. It can also lead to the following:

• More risky behaviors : Substance abuse makes teens more likely to engage in risky behaviors like unprotected sex (or "condomless sex") or dangerous driving.
• Higher risk for adult health problems : Teenagers who abuse substances have a higher risk of heart disease, high blood pressure, and sleep disorders.
• Mental health disorders : It is common for teens with substance abuse disorders to have mental health conditions (and vice versa).
• Impaired academic performance : Substance use affects a teen’s concentration and memory, which may negatively affect their schoolwork.

Substance Misuse and Mental Health

A study showed that 60% of teens in a community-based substance use treatment program were also diagnosed with a mental health disorder.

What Are the Health Risks of Drug Abuse?

Drug and alcohol use can lead to substance use disorder as well as the specific health risks of the substance being abused.

Alcohol use can lead to an increased risk of:

• Liver disease, cirrhosis, and cancer
• Heart disease and stroke
• Depression 
• Lack of focus 
• Alcohol poisoning
• Increased risky behavior

Alcohol Statistics

In the United States, 29.5 million people ages 12 and older have an alcohol use disorder.

Marijuana can impair concentration, worsen mental health, interfere with prescription medications, lead to risky sexual behaviors, or contribute to dangerous driving. Smoking marijuana can also negatively affect lung health.

Marijuana is often thought of as not being "as bad" as other drugs and, in some cases, even good for you. However, marijuana can be harmful to teens because their brains are still developing. Marijuana use in teens is linked to difficulty with problem-solving, memory and learning issues, impaired coordination, and problems with maintaining attention.

Vaping and Edible Marijuana Use Is on the Rise

Recent data shows a shift from teens smoking marijuana to using vaping devices and edibles instead.

Opioids include legal prescription medications such as hydrocodone, oxycontin, and fentanyl, as well as illegal drugs such as heroin. These drugs carry a high risk of overdose and death. The annual rate of opioid overdose deaths for those aged 15 to 24 years is 12.6 per 100,000 people.

Over-the-Counter and Prescription Medications

Over-the-counter (OTC) and prescription medications can be misused more easily than others because they’re often easy for teens to obtain. Diet pills, caffeine pills, and cold and flu products with dextromethorphan are just a few examples of OTC substances teens may use. They may also have access to family member’s prescriptions for drugs like opiate painkillers and stimulants or get them from friends who do.

There are serious health risks to misusing OTC cold and cough products, including increased blood pressure, loss of consciousness, and overdose. There can also be legal issues if a teen is using someone else’s prescriptions.

Tobacco can lead to multiple chronic illnesses, including:

• Lung disease 
• Heart disease
• Vision loss
• Decreased fertility

E-Cigarettes (Vaping)

Vaping  is attractive to teens because e-cigarettes are often flavored like fruit, candy, or mint. These products may contain nicotine or other synthetic substances that damage the brain and lungs. The teenage brain is vulnerable to the harmful effects of nicotine, including anxiety and addiction.

E-cigarettes come in a variety of shapes and sizes and might be disguised as everyday items, such as:

• USB Flash Drives
• Hoodie (sweatshirt) strings
• Smartwatches
• Toys (e.g., fidget spinners)
• Phone cases

Cocaine  carries a risk of overdose and withdrawal. It causes decreased impulse control and poor decision-making. Withdrawal symptoms from cocaine include restlessness, paranoia, and irritability. Snorting cocaine can cause nosebleeds and a loss of smell. Using cocaine can lead to heart attacks, lung problems, strokes, seizures, and coma.

Cocaine Can Be Fatal With First Use

There have been reports of people dying the first time they use cocaine, often from sudden cardiac arrest, respiratory arrest, or seizures.

Ecstasy (MDMA)

Ecstasy is a stimulant that causes an increased heart rate, blurred vision, and nausea. It can also lead to brain swelling, seizures, and organ damage.

Ecstasy is also known as:

Inhalants are fumes from gases, glue, aerosols, or solvents that can damage the brain, heart, lungs, kidneys, and liver. Using inhalants even once can lead to overdose, suffocation, seizures, and death.

Methamphetamine

Methamphetamine (crystal meth) is a highly addictive stimulant that has multiple health consequences, including:

• Severe weight loss
• Lack of sleep
• Dental problems
• Change in brain structure
• Paranoia and hallucinations

Disease Transmission Risk

Injecting drugs with shared needles increases the risk of contracting HIV, hepatitis B, and hepatitis C.

What Are the Signs a Teen Is Using Drugs?

Being on the lookout for drug paraphernalia and signs and symptoms of drug abuse can help adults recognize at-risk teens. 

Behavioral warning signs of drug use in teens include:

• Personality changes 
• Irritability 
• Difficulty sleeping
• Inappropriate or odd behavior (e.g., laughing randomly)
• Loss of interest in hobbies or extracurricular activities
• Avoiding eye contact
• Acting secretive or like they’re hiding something
• Staying out late
• Social withdrawal (e.g., from family, friends)
• Poor academic performance
• Hanging out with new friends or no longer hanging out with their usual friend group
• Poor hygiene
• Skipping school
• Isolation (e.g., staying in their room, refusing family meals)

Not All Warning Signs Indicate Drug Use

These warning signs do not necessarily mean a teen is using drugs. Other health problems like allergies, sinus infections, hormone imbalances, or mental disorders can also cause these symptoms in teens.

Physical signs  of drug use in teens may include:

• Persistent cough
• Dilated pupils
• Increased or decreased energy
• Sleeping all the time or not at all
• Mood swings
• Memory problems
• Talking very fast or slowly
• Runny nose or nosebleeds
• Increased/decreased appetite
• Weight loss
• Smells like smoke or alcohol (e.g., on clothes, skin, or breath)

Other than behavior and physical signs in a teen, you should also be aware of objects that can be used to do drugs. Examples of drug paraphernalia include:

• Mirrors with white powder
• Razorblades
• Rolled dollar bills
• Crack pipes and spoons
• Needles and syringes
• Rolling paper

Substance Abuse Screening

The American Academy of Pediatrics (AAP) recommends that teens be screened at each annual medical exam appointment with questionnaires that ask them about substance use and their knowledge of the risks.

What Are the Four Stages of Drug Addiction?

You should also be aware of the four stages of addiction. The earlier teen drug use is recognized, the sooner they can get help.

• Experimentation: A teen tries one or more substances. Some teens will only try a substance once. Others will continue to experiment and increase their use.
• Regular or “social” use: A teen begins to use one or more substances regularly. At this stage, they may limit their use to just when they’re with friends or only in situations where they feel it’s needed—e.g., before a test.
• Risky use: A teen continues to use a substance that they have regularly been using, even if it’s caused problems for them at school, at home, and in their relationships. They crave the substance, both physically and mentally. At this stage, the substance has become central to a teen’s life, and they’ll take risks to get and use it.
• Dependence and Addiction: A teen is addicted to a substance, and most of their time and energy is devoted to getting and using it. At this stage, they would need intervention and treatment to quit, as they may not be able to stop on their own, even if they wanted to. 

How Can Parents Prevent Teenage Drug Use?

While they may not express it, teens do value bonds with the adults in their lives. Nurturing that connection with them includes being involved in their lives and having open, honest communication. 

How to Talk to Your Teen About Drug Use

Open communication starts by showing an interest in and talking to your teen about everything. This dialogue builds trust and respect, making it easier for you to talk about difficult topics.

Giving teens your undivided attention, without distractions, helps them feel special and heard. This quality time could be during chores, dinner, walks, car rides, or a fun family game night.

Here are some general tips to keep in mind when you’re talking about drugs with your teen:

• Stay curious and show interest.
• Ask open-ended questions.
• Actively listen.
• Don’t interrupt.
• Give compliments.
• Stay up late to talk.
• Chat over their favorite food. 

If you’re trying to start a conversation with your teen because you think they may be using drugs, their response to being confronted will determine how you’ll need to approach the conversation.

If your teen admits to using drugs, stay calm. Be supportive and willing to listen. Find out as much as you can about their drug use—what substances they’re using, how often they’re using them, and how they’re getting them. Be clear that the risks of drugs are serious and that drug use will not be tolerated. At the same time, make sure that you reassure your teen that you love them and that you want to help.

If your teen denies using drugs and you think they are lying , communicate the negative consequences of drug and alcohol use. Be clear that you want them to be safe and that experimenting with substances is dangerous—even if it’s just one time. If you are not able to keep the line of communication open with your teen, talk to their healthcare provider. They can help connect you to resources and support you in taking more decisive action, like drug testing.

Other Strategies

Talking to your teen openly and often is key, but there are also other steps you can take:

• Model responsible behavior for them.
• Stay involved with their activities but let them express their boundaries.
• Meet their friends and their parents.
• Teach them how to make good decisions when under pressure.

Protect Teens From Prescription Medications

Prescription drugs are generally safe when they're taken as prescribed. However, any time a person takes medication for reasons other than what they were prescribed for, it is considered medication abuse. Strategies to protect teens from prescription medication misuse include:

• Storing prescription medications in a safe place
• Locking up controlled substances 
• Getting rid of old medications

Safe Medication Disposal

Do not dispose of medications by flushing them down the toilet or pouring them down the sink. Medications can be crushed and mixed into the trash (to keep them away from children and pets) or returned to your local pharmacy or community drug take-back program.

Drug Addiction Treatment for Teens

Even if the adults in their lives try to prevent it, some teens will develop substance use disorders. Support for teens with drug addiction includes treating withdrawal or underlying mental health conditions, and addressing emotional needs, usually with a qualified mental health professional such as a psychiatrist or psychologist.

Treatment for teens experiencing substance use disorder includes a combination of the following:

• Outpatient clinics
• 12-step programs
• Inpatient mental health or substance use units 
• Medications
• Therapy (individual, group, or family)

Substance Use Helpline

If your teen is struggling with substance use or addiction, contact the Substance Abuse and Mental Health Services Administration (SAMHSA) National Helpline at 1-800-662-4357 for information on support and treatment facilities in your area.

If you are having suicidal thoughts, dial 988 to contact the 988 Suicide & Crisis Lifeline and connect with a trained counselor. If you or a loved one are in immediate danger, call 911 .

For more mental health resources, see our National Helpline Database .

Talk to your teen’s healthcare provider about what treatment would be best for them. Here are a few topics to discuss:

• Underlying health problems
• Benefits of treatment
• Credentials of team members
• Side effects 
• Family involvement
• Schoolwork during treatment
• Length of treatment
• Follow-up care

Experimenting with drugs or alcohol is tempting for teenagers because they may not know or understand the dangers of using substances—even just once. Academic pressure, low self-esteem, and peer pressure are just a few factors that increase their risk of substance use.

Caregivers need to have an open line of communication with their teens and teach them about the risks of using drugs. It’s also important to know the signs of drug use and intervene early to help teens who are at risk for or have already developed substance use disorders.

While drug use may increase the risk of mental health disorders, it’s also important to note that these disorders can lead to substance abuse to self-medicate or numb the emotional pain. If you suspect that a teenager is experiencing either, consult a pediatrician or mental health professional as soon as possible. 

Frequently Asked Questions

Depending on the substance and severity, a tube may be placed through the nose to suction drugs from the stomach. Activated charcoal is given through the tube to bind with the drug to release it from the body, decreasing the amount released into the bloodstream. If an antidote (reversal agent) such as Narcan is available for that substance, it may be given. 

National surveys from the National Institute on Drug Abuse show adolescent drug use rates have remained steady. However, the survey’s detected a shift in the types of drugs used by teens. Alcohol is still the most often abused substance, but the rates are decreasing. Instead, nicotine use and misuse of prescription medications are on the rise.  

University of Michigan. Teen drug use remains below pre-pandemic levels .

National Center for Drug Abuse Statistics. Drug use among youth: facts & statistics .

Monitoring the Future. National Survey Results on Drug Use, 1975-2023: Secondary School Students.

NCDAS. Drug use among youth: facts & statistics .

Monitoring the Future. Alcohol: Trends in last 12 months prevalence of use in 8 th , 10 th , and 12 th grade .

Monitoring the Future. Marijuana: Trends in last 12 months prevalence of use in 8 th , 10 th , and 12 th grade .

Monitoring the Future. Any illicit drug: Trends in last 12 months prevalence of use in 8 th , 10 th , and 12 th grade .

Monitoring the Future. Cigarettes: Trends in last 12 months prevalence of use in 8 th , 10 th , and 12 th grade.

Monitoring the Future. Vape nicotine (e-cigarettes): Trends in last 12 months prevalence of use in 8 th , 10 th , and 12 th grade.

DEA. Prescription for disaster: How teens abuse medicines .

National Institute of Health: National Institute on Drug Abuse, Advancing Addiction Science. NIH-funded study finds overall rate of drug use among 10-14 year-olds remained stable during the 2020 COVID-19 pandemic .

Scholastic and the National Institute of Drug Abuse (NIDA). How nicotine affects the teen brain .

Steinfeld M, Torregrossa MM. Consequences of adolescent drug use .  Translational Psychiatry . 2023;13(1). doi:10.1038/s41398-023-02590-4

University of Rochester Medical Center. Understanding the teen brain .

National Institute of Health: National Institute on Drug Abuse, Advancing Addiction Science. Common comorbidities with substance use disorders research report: part 1: the connection between substance use disorders and mental illness .

National Institute on Alcohol Abuse and Alcoholism. Alcohol use in the United States .

NIH. Alcohol use disorder (AUD) in the United States: Age groups and demographic characteristics.

American Lung Association. Marijuana and lung health .

Centers for Disease Control and Prevention. What you need to know about marijuana use in teens .

Sharma P, Mathews DB, Nguyen QA, Rossmann GL, A Patten C, Hammond CJ. Old dog, new tricks: A review of identifying and addressing youth cannabis vaping in the pediatric clinical setting .  Clin Med Insights Pediatr . 2023;17:11795565231162297. Published 2023 Mar 25. doi:10.1177/11795565231162297

NCDAS. Drug overdose death rates .

NIDA. Over-the-counter medicines .

Centers for Disease Control and Prevention. Smoking & tobacco use: health effects .

Center for Disease Control and Prevention. Smoking and tobacco use: Quick facts on the risks of e-cigarettes for kids, teens, and young adults .

NYC Health. Cocaine abuse and addiction .

Nemours Teens Health. MDMA (ecstasy) .

Medline Plus. Inhalants .

National Institute of Health: National Institute on Drug Abuse, Advancing Addiction Science. Methamphetamine drug facts .

CDC. Injection drug use .

Levy S, Williams JF, Ryan S, et al. Substance Use Screening, Brief Intervention, and Referral to Treatment .  Pediatrics . 2016;138(1). doi:10.1542/peds.2016-1211

• AAP. Bright Futures Toolkit: Links to Commonly Used Screening Instruments and Tools .

Orlando Recovery Center.  The four stages of addiction – what are they?.

Casa Palmera. The four stages of drug addiction.

Partnership to End Addiction. Preventing drug use: connecting and talking with your teen .

SAMHSA. Talking with teens about alcohol and other drugs .

American Academy of Child & Adolescent Psychiatry (AACAP). Substance abuse treatment for children and adolescents: questions to ask .

National Council Against Prescription Drug Abuse (NCAPDA). Drug overdose response: know the signs .

American Academy of Child & Adolescent Psychiatry (AACAP). Teens: alcohol and other drugs .

Center for Disease Control and Prevention, Fetal Alcohol Spectrum Disorders (FASDs). Teen substance use & risks . 

National Center for Drub Abuse Statistics. Drug use among youth: facts & statistics .

Substance Abuse and Mental Health Services Administration (SAMHSA). Tips for teens: cocaine .

By Brandi Jones, MSN-ED RN-BC Jones is a registered nurse and freelance health writer with more than two decades of healthcare experience.

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• Tween and teen health

Teen drug abuse: Help your teen avoid drugs

Teen drug abuse can have a major impact on your child's life. Find out how to help your teen make healthy choices and avoid using drugs.

The teen brain is in the process of maturing. In general, it's more focused on rewards and taking risks than the adult brain. At the same time, teenagers push parents for greater freedom as teens begin to explore their personality.

That can be a challenging tightrope for parents.

Teens who experiment with drugs and other substances put their health and safety at risk. The teen brain is particularly vulnerable to being rewired by substances that overload the reward circuits in the brain.

Help prevent teen drug abuse by talking to your teen about the consequences of using drugs and the importance of making healthy choices.

Why teens use or misuse drugs

Many factors can feed into teen drug use and misuse. Your teen's personality, your family's interactions and your teen's comfort with peers are some factors linked to teen drug use.

Common risk factors for teen drug abuse include:

• A family history of substance abuse.
• A mental or behavioral health condition, such as depression, anxiety or attention-deficit/hyperactivity disorder (ADHD).
• Impulsive or risk-taking behavior.
• A history of traumatic events, such as seeing or being in a car accident or experiencing abuse.
• Low self-esteem or feelings of social rejection.

Teens may be more likely to try substances for the first time when hanging out in a social setting.

Alcohol and nicotine or tobacco may be some of the first, easier-to-get substances for teens. Because alcohol and nicotine or tobacco are legal for adults, these can seem safer to try even though they aren't safe for teens.

Teens generally want to fit in with peers. So if their friends use substances, your teen might feel like they need to as well. Teens also may also use substances to feel more confident with peers.

If those friends are older, teens can find themselves in situations that are riskier than they're used to. For example, they may not have adults present or younger teens may be relying on peers for transportation.

And if they are lonely or dealing with stress, teens may use substances to distract from these feelings.

Also, teens may try substances because they are curious. They may try a substance as a way to rebel or challenge family rules.

Some teens may feel like nothing bad could happen to them, and may not be able to understand the consequences of their actions.

Consequences of teen drug abuse

Negative consequences of teen drug abuse might include:

• Drug dependence. Some teens who misuse drugs are at increased risk of substance use disorder.
• Poor judgment. Teenage drug use is associated with poor judgment in social and personal interactions.
• Sexual activity. Drug use is associated with high-risk sexual activity, unsafe sex and unplanned pregnancy.
• Mental health disorders. Drug use can complicate or increase the risk of mental health disorders, such as depression and anxiety.
• Impaired driving. Driving under the influence of any drug affects driving skills. It puts the driver, passengers and others on the road at risk.
• Changes in school performance. Substance use can result in worse grades, attendance or experience in school.

Health effects of drugs

Substances that teens may use include those that are legal for adults, such as alcohol or tobacco. They may also use medicines prescribed to other people, such as opioids.

Or teens may order substances online that promise to help in sports competition, or promote weight loss.

In some cases products common in homes and that have certain chemicals are inhaled for intoxication. And teens may also use illicit drugs such as cocaine or methamphetamine.

Drug use can result in drug addiction, serious impairment, illness and death. Health risks of commonly used drugs include the following:

• Cocaine. Risk of heart attack, stroke and seizures.
• Ecstasy. Risk of liver failure and heart failure.
• Inhalants. Risk of damage to the heart, lungs, liver and kidneys from long-term use.
• Marijuana. Risk of impairment in memory, learning, problem-solving and concentration; risk of psychosis, such as schizophrenia, hallucination or paranoia, later in life associated with early and frequent use. For teens who use marijuana and have a psychiatric disorder, there is a risk of depression and a higher risk of suicide.
• Methamphetamine. Risk of psychotic behaviors from long-term use or high doses.
• Opioids. Risk of respiratory distress or death from overdose.
• Electronic cigarettes (vaping). Higher risk of smoking or marijuana use. Exposure to harmful substances similar to cigarette smoking; risk of nicotine dependence. Vaping may allow particles deep into the lungs, or flavorings may include damaging chemicals or heavy metals.

Talking about teen drug use

You'll likely have many talks with your teen about drug and alcohol use. If you are starting a conversation about substance use, choose a place where you and your teen are both comfortable. And choose a time when you're unlikely to be interrupted. That means you both will need to set aside phones.

It's also important to know when not to have a conversation.

When parents are angry or when teens are frustrated, it's best to delay the talk. If you aren't prepared to answer questions, parents might let teens know that you'll talk about the topic at a later time.

And if a teen is intoxicated, wait until the teen is sober.

To talk to your teen about drugs:

• Ask your teen's views. Avoid lectures. Instead, listen to your teen's opinions and questions about drugs. Parents can assure teens that they can be honest and have a discussion without getting in trouble.
• Discuss reasons not to use drugs. Avoid scare tactics. Emphasize how drug use can affect the things that are important to your teen. Some examples might be sports performance, driving, health or appearance.
• Consider media messages. Social media, television programs, movies and songs can make drug use seem normal or glamorous. Talk about what your teen sees and hears.
• Discuss ways to resist peer pressure. Brainstorm with your teen about how to turn down offers of drugs.
• Be ready to discuss your own drug use. Think about how you'll respond if your teen asks about your own drug use, including alcohol. If you chose not to use drugs, explain why. If you did use drugs, share what the experience taught you.

Other preventive strategies

Consider other strategies to prevent teen drug abuse:

• Know your teen's activities. Pay attention to your teen's whereabouts. Find out what adult-supervised activities your teen is interested in and encourage your teen to get involved.
• Establish rules and consequences. Explain your family rules, such as leaving a party where drug use occurs and not riding in a car with a driver who's been using drugs. Work with your teen to figure out a plan to get home safely if the person who drove is using substances. If your teen breaks the rules, consistently enforce consequences.
• Know your teen's friends. If your teen's friends use drugs, your teen might feel pressure to experiment, too.
• Keep track of prescription drugs. Take an inventory of all prescription and over-the-counter medications in your home.
• Provide support. Offer praise and encouragement when your teen succeeds. A strong bond between you and your teen might help prevent your teen from using drugs.
• Set a good example. If you drink, do so in moderation. Use prescription drugs as directed. Don't use illicit drugs.

Recognizing the warning signs of teen drug abuse

Be aware of possible red flags, such as:

• Sudden or extreme change in friends, eating habits, sleeping patterns, physical appearance, requests for money, coordination or school performance.
• Irresponsible behavior, poor judgment and general lack of interest.
• Breaking rules or withdrawing from the family.
• The presence of medicine containers, despite a lack of illness, or drug paraphernalia in your teen's room.

Seeking help for teen drug abuse

If you suspect or know that your teen is experimenting with or misusing drugs:

• Plan your action. Finding out your teen is using drugs or suspecting it can bring up strong emotions. Before talking to your teen, make sure you and anyone who shares caregiving responsibility for the teen is ready. It can help to have a goal for the conversation. It can also help to figure out how you'll respond to the different ways your teen might react.
• Talk to your teen. You can never step in too early. Casual drug use can turn into too much use or addiction. This can lead to accidents, legal trouble and health problems.
• Encourage honesty. Speak calmly and express that you are coming from a place of concern. Share specific details to back up your suspicion. Verify any claims your child makes.
• Focus on the behavior, not the person. Emphasize that drug use is dangerous but that doesn't mean your teen is a bad person.
• Check in regularly. Spend more time with your teen. Know your teen's whereabouts and ask questions about the outing when your teen returns home.
• Get professional help. If you think your teen is involved in drug use, contact a health care provider or counselor for help.

It's never too soon to start talking to your teen about drug abuse. The conversations you have today can help your teen make healthy choices in the future.

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• Dulcan MK, ed. Substance use disorders and addictions. In: Dulcan's Textbook of Child and Adolescent Psychiatry. 3rd ed. American Psychiatric Association Publishing; 2021. https://psychiatryonline.org. Accessed Jan. 24, 2023.
• 6 parenting practices: Help reduce the chances your child will develop a drug or alcohol problem. Partnership to End Addiction. https://drugfree.org/addiction-education/. Accessed Jan. 24, 2023.
• Why do teens drink and use substances and is it normal? Partnership to End Addiction. https://drugfree.org/article/why-do-teens-drink-and-use-substances/. Accessed Jan. 24, 2023.
• Teens: Alcohol and other drugs. American Academy of Child & Adolescent Psychiatry. https://www.aacap.org/aacap/families_and_youth/facts_for_families/fff-guide/Teens-Alcohol-And-Other-Drugs-003.aspx. Accessed Dec. 27, 2018.
• Drugged driving. National Institute on Drug Abuse. https://www.drugabuse.gov/publications/drugfacts/drugged-driving. Accessed Jan. 24, 2023.
• Marijuana talk kit. Partnership for Drug-Free Kids. https://drugfree.org/drugs/marijuana-what-you-need-to-know/. Accessed Jan. 24, 2023.
• Drug guide for parents: Learn the facts to keep your teen safe. Partnership for Drug-Free Kids. https://www.drugfree.org/resources/. Accessed Dec. 12, 2018.
• Vaping: What you need to know and how to talk with your kids about vaping. Partnership to End Addiction. https://drugfree.org/addiction-education/. Accessed Jan. 24, 2023.
• How to listen. Partnership for Drug-Free Kids. https://www.drugfree.org/resources/. Accessed Dec. 12, 2018.
• Drug abuse prevention starts with parents. American Academy of Pediatrics. https://publications.aap.org/patiented/article/doi/10.1542/peo_document352/81984/Drug-Abuse-Prevention-Starts-With-Parents. Accessed Jan. 24, 2023.
• How to talk to your kids about drugs if you did drugs. Partnership for Drug-Free Kids. https://www.drugfree.org/resources/. Accessed Dec. 12, 2018.
• My child tried drugs, what should I do? Partnership to End Addiction. https://drugfree.org/article/my-child-tried-drugs-what-should-i-do/. Accessed Jan. 24, 2023.
• Gage SH, et al. Association between cannabis and psychosis: Epidemiologic evidence. Biological Psychiatry. 2016;79:549.
• Quick facts on the risks of e-cigarettes for kids, teens and young adults. Centers for Disease Control and Prevention. https://www.cdc.gov/tobacco/basic_information/e-cigarettes/Quick-Facts-on-the-Risks-of-E-cigarettes-for-Kids-Teens-and-Young-Adults.html. Accessed Jan. 30, 2023.
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Student Essays

Essays-Paragraphs-Speeches

Essays on Drug Addiction | Causes & Impacts of Drug Addiction in Youth

Drug addiction is the curse. It eats out the very fabric of our society. The following essay discusses the drug addiction with its underlying causes, its impacts and possible solutions for our youth. The essay is in simple language with easy to understand way. It would surely help primary, high school and college level students.

List of Topics

Drug addiction Essay; Major Causes, Impacts & Possible Solution

Drugs are very dangerous for health, addiction of drugs destroys the health.

Habitual drug users spend lot of money on buying drugs and they spend their accumulated wealth on drugs and when they become bankrupt they adopt illegal means of earning money.

The drugs which cause addiction are cocaine, meth, Marijuana, crack and heroin. All types of narcotics are fatal.

Causes of drug Addiction

The consumption of drugs often is observed when an individual specially youngster fail to cope up with personal problems.

Sometimes family issues are give birth to addiction of drugs. The youth throughout the world is vulnerable to drugs, mostly youngsters chose drugs to satiate their desires. Lack of self confidence is the root cause of addiction of drugs.

Due to pressure and excessive stress man often chose drugs and tries to lessen his or her stress by sing drugs. The high level stress compels an individual to use drugs. The social and personal pressure often result in smoking and drinking. It means when an individual start feeling isolated or is ignored in society he or she develop habit of using drugs.

The lack of parental involvement in child’s activities is also a cause of drug addiction in youngsters. Those who are emotionally weak they become drug addict. The availability and exposure of drugs is also a cause of addiction. An individual living in an area where drugs are available and people consume drugs there that individual will also develop habit of consuming drugs.

Effects of drug Addiction on Youth

The addiction of drugs leave adverse effects on the mind and body of an addict. It is a type of brain disease, regular consumption of drugs disrupts the proper functioning of brain.

The uncontrollable desire to consume drugs become worse day by day ultimately an addict find it impossible to control the intake of drugs.

A regular user of drug loses the efficiency of working. One who is drug addict can’t fulfill his or her responsibilities in good manner. The personal health of an individual is entirely lost when he or she become a drug addict. One who consumes more drugs often experiences anxiety, depression, fatigue, headache, sweating, insomnia etc.

The repeated and regular use of drugs leave psychological effects on an individual too. Many physical and mental disorders appear in an individual who uses drugs on regular basis.

Many respiratory diseases, heart attack, lung cancer, kidney failure, liver problems and brain damage are often caused of intake of drugs in excess. The immune system of man is badly affected because of drugs.

Solutions; How to Control Drug Addiction

Drug addiction is very hard to quit, those who are addicted they must be treated tenderly to quit bad habit. One who consumes more drugs he or she must be informed of ill effects of drugs. It is necessary to keep drugs off so that one who is not indulged in it remain far from it.

Though addiction of drugs is very difficult to prevent but there are some steps that can be taken to help stop consumption of drugs.

All individuals who are suffering from mental disorders or are victim of depression and stress must be taken to psychiatrist so that their mental illness is cured and they become able to quit drugs.

People must learn to deal with pressure and stress, the best way to get rid of stress is to handle it properly not to take drugs. There is ignorance among people, they are not known of the risk factors of addiction of drugs, they don’t know the abuse of drugs.

Drug addiction is one the gravest issues that our youths are facing these days. It brings a lot of problems in our lives. Therefore, every possible effort must be made in order to contain this issue forever.

Paragraph On Drug Addiction | Short & Long Paragraphs On Drug Addiction, Causes & Impacts

Any substance consumed by a person which is harmful to his health is called a drug. When one consume these dangerous substances regularly is called an addiction.

Users are mostly addicted in alcohol, cocaine, heroin, nicotine, opioid, painkillers etc. All these drugs are very harmful for physical and mental health. Drugs affect the mental cognition of a user, an addict can’t take good decisions nor he can retain information.

Signs of a drug Addict

The most vivid signs of a drug addict are red eyes, increased heart rate, anxiety, depression, paranoia and inactivity. Their memory power reduces, they feel difficulty in remembering something.

A drug addict can’t work properly without injecting it, he lack to properly coordinate with others. Due to drug addiction, the user become victim of erratic sleep patterns.

Apart from it a drug addict become happy and sad quickly. Sometimes they lose their consciousness, they are not aware of their surroundings and they forget their very existence.

Why Addiction of Drugs is Caused?

Drug addiction is mainly caused to feel happier, when an individual faces loss in life or fails to get something. He feel dejected, sad and unhappy.

In order to overcome this condition the individual start using drugs to feel happy because drugs contain a chemical called dopamine which induces happiness in the consumer and he feel happy. Slowly and gradually he become addicted and doesn’t feel happy until and unless he doesn’t consume the drug.

Effects of Drug Addiction

Drug addiction is very harmful, it not only destroy health but also leave many negatively influences on the psyche of the user.

Mostly drug addicts engage in reckless activities like gambling, stealing, adultery etc. Because of these activities they lose their respect and lose many relationships. Due to addiction of drugs many problems in personal and public relationships are created.

Their personality is badly affected by the excessive consumption of drugs, they stop caring of their hygiene. In both conditions while injecting any drug or without injecting it, a drug addict can’t communicate properly nor can Converse with anyone soundly.

It is observed that as the addiction increases the user lose interest in doing all activities which he loved to do. The addiction of drugs is fatal, it is a life-threatening act because it can kill a person.

All fatal and deadly diseases like kidney failure, lung diseases, heart diseases, brain damage, respiratory problems etc are caused by addiction of drugs.

A drug consumer feel difficulty in breathing, he feel lazy and inactive all the time and can’t perform any work in good way. Memory loss and speech problems affect the user’s personality.

Above all, the users of drugs become moody, hyperactive and victim of hallucinations.

Drug addiction is fatal, we must take steps to control addiction of drugs. Behavioral counseling is the most effective way to treat this disease, it is very important to have counseling with the user and motivate him to quit it before it takes his or her life.

Only the family members and friends can do this, if you find your loved ones addicted make a behavioral counseling with them and motivate them to quit it. Family members and friends can encourage them and can help them to get rid of bad addiction.

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• Open access
• Published: 28 May 2022

A qualitative study exploring how young people perceive and experience substance use services in British Columbia, Canada

• Roxanne Turuba 1 , 2 ,
• Anurada Amarasekera 1 , 2 ,
• Amanda Madeleine Howard 1 , 2 ,
• Violet Brockmann 1 , 2 ,
• Corinne Tallon 1 , 2 ,
• Sarah Irving 1 , 2 ,
• Steve Mathias 1 , 2 , 3 , 4 , 5 ,
• Joanna Henderson 6 , 7 ,
• Kirsten Marchand 1 , 4 , 5 , 8 &
• Skye Barbic 1 , 2 , 3 , 4 , 5 , 8  

Substance Abuse Treatment, Prevention, and Policy volume  17 , Article number:  43 ( 2022 ) Cite this article

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Metrics details

Substance use among youth (ages 12–24) is troublesome given the increasing risk of harms associated. Even more so, substance use services are largely underutilized among youth, most only accessing support when in crisis. Few studies have explored young people’s help-seeking behaviours to address substance use concerns. To address this gap, this study explored how youth perceive and experience substance use services in British Columbia (BC), Canada.

Participatory action research methods were used by partnering with BC youth (under the age of 30) from across the province who have lived and/or living experience of substance use to co-design the research protocol and materials. An initial focus group and interviews were held with 30 youth (ages 12–24) with lived and/or living experience of substance use, including alcohol, cannabis, and illicit substances. The discussions were audio-recorded, transcribed verbatim, and analyzed thematically using a data-driven approach.

Three main themes were identified and separated by phase of service interaction, starting with: Prevention/Early intervention , where youth described feeling unworthy of support; Service accessibility , where youth encountered many barriers finding relevant substance use services and information; and Service delivery , where youth highlighted the importance of meeting them where they are at, including supporting those who have milder treatment needs and/or do not meet the diagnosis criteria of a substance use disorder.

Conclusions

Our results suggest a clear need to prioritize substance use prevention and early interventions specifically targeting youth and young adults. Youth and peers with lived and/or living experience should be involved in co-designing and co-delivering such programs to ensure their relevance and credibility among youth. The current disease model of care leaves many of the needs of this population unmet, calling for a more integrated youth-centred approach to address the multifarious concerns linked to young people’s substance use and service outcomes and experiences.

Substance use initiation is common during adolescence and young adulthood [ 1 ]. In North America, youth (defined here as aged 12–24) report the highest prevalence of substance use compared to older age groups [ 2 , 3 ], alcohol being the most common (youth 15–19: 57%; youth 20–24: 83%), followed by cannabis (youth 15–19: 19%; youth 20–24: 33%), and illicit substances (youth 15–19: 4%; youth 20–24: 10%) [ 2 ]. High rates of substance use among youth are worrisome given the ample evidence linking early onset to an increased risk of developing a substance use disorder (SUD) and further mental health and psychosocial problems [ 4 , 5 , 6 ]. Youth are also more likely to use more heavily and in riskier ways than adults, making them especially vulnerable to substance use related harms [ 2 , 7 ]. For example, polysubstance use is more common and increasing among youth [ 8 , 9 , 10 ], which has been associated with an increase in youth overdose hospitalizations [ 11 ]. Substance use is also associated with several leading causes of death among youth (e.g., suicide, unintentional injury, violence) [ 12 , 13 ], demonstrating an urgent need to provide effective substance use services to this population.

Current evidence-based recommendations to address substance use issues among youth include a range of comprehensive services, including family-oriented treatments, behavioural therapy, harm reduction services, pharmacological treatments, and long-term recovery services [ 14 , 15 , 16 , 17 ]. Like with adults, these services should be tailored based on young people’s individual needs and circumstances and should consider concurrent mental health disorders which are common among youth who use substances [ 3 , 15 , 18 ]. Merikangas et al. [ 18 ] reported rates of co-occurring mental health disorders as high as 77% among a community sample of youth with a SUD diagnosis. Regardless of precedence, both mental health and SUD can have exacerbating effects on each other if not treated, highlighting the importance of early diagnosis and early access to care [ 19 ]. However, current practices utilizing an integrative approach to diagnose and treat SUD and concurrent mental health disorders have yet to be widely implemented [ 20 , 21 , 22 ]. Further, the current substance use service landscape has been largely designed to treat SUD in adult populations [ 17 ], who often require more intensive treatment compared to youth [ 15 ].

Literature suggests that there are differences between how youth and adults perceive and present substance use issues, suggesting different approaches may be needed to address substance use concerns [ 15 ]. For example, youth have shorter substance use histories and therefore often express fewer negative consequences related to their substance use, which may reduce their perceived need for services [ 15 ]. Further, the normalization of substance use among younger populations and the influence of peers and family members may also play a factor in reducing young people’s ability to recognize problems that arise due to their substance use [ 9 , 23 ]. Confidentiality concerns may also prevent youth from accessing services when needed [ 23 ]. Youth are therefore unlikely to access substance use services before they are in crisis. The 2019 National Survey on Drug Use and Health [ 24 ] reported that only 7.2% of youth ages 12–25 who were identified as needing specialized substance use treatment (defined as substance use treatment received at a hospital (inpatient), rehabilitation facility (inpatient or outpatient), or a mental health centre) accessed appropriate services and that 92% of youth did not feel they needed to access specialized services for substance use. In 2020, the percentage of youth who received specialized treatment dropped to 3.6 and 98% of youth did not perceive the need for it [ 3 ], demonstrating the exacerbating effects the pandemic has had on young people’s service trajectory and experiences.

Although help-seeking behaviours to address mental health concerns among youth have been explored [ 25 , 26 ], few studies have been specifically designed to explore young people’s experiences with substance use services. Existing evidence has largely focused on the experiences of street entrenched youth and youth who specifically use illicit substances (e.g., opioids, heroin, fentanyl) ([ 1 , 27 , 28 , 29 , 30 ], (Marchand K, Fogarty O, Pellat KM, Vig K, Melnychuk J, Katan C, et al: “We need to build a better bridge”: findings from a multi-site qualitative analysis of opportunities for improving opioid treatment services for youth, Under review)), which remains an important research focus, but may not be representative of those who have milder treatment needs. As such, this qualitative study aims to understand how youth perceive and experience substance use services in British Columbia (BC) more broadly. This study also explored young people’s recommendations to improving current models of care to address substance use concerns.

Study design & setting

This study is part of the Building capacity for early intervention: Increasing access to youth-centered, evidence-based substance use and addictions services in BC and Ontario project, which aims to create youth-informed substance use training for peer support workers and other service providers working within an integrated care model. The project is being led by Foundry Central Office and the Youth Wellness Hubs Ontario (YWHO), two youth integrated health service hubs in BC and Ontario respectively. As part of this project, the BC project team conducted a qualitative research study, entitled The Experience Project , to support the development of substance use training. This paper focuses on this BC study, which follows standards for reporting qualitative research (SRQR) [ 31 ].

In May 2020, we applied participatory action research (PAR) methods [ 32 , 33 ], by partnering with 14 youth (under the age of 30) throughout the course of the project, who had lived and/or living experience of substance use and lived in BC. Youth advisors were recruited through social media and targeted outreach (i.e., advisory councils from Indigenous-led organizations and rural and remote communities) in order to engage a diverse group of young people. A full description of our youth engagement methods has been described elsewhere (Turuba R, Irving S, Turnbull H, Howard AM, Amarasekera A, Brockmann V, et al: Practical considerations for engaging youth with lived and/or living experience of substance use as youth advisors and co-researchers, Under review). British Columbia has a population of approximately 4.6 million people, 88% of which reside within a metropolitan area; only 12% live in rural and remote communities across a vast region of land. Nationally, BC has been disproportionately impacted by the opioid crisis, counting 1782 illicit drug overdose deaths in 2021 alone, 84% of which were due to fentanyl poisoning [ 34 ]. Although more than half of BC’s population reside in the Metro Vancouver area, rates of illicit drug overdose deaths are similar across all health regions [ 34 ].

The youth partners formed a project advisory which co-created and revised the research protocol and materials. The initial focus group questions were informed by Foundry’s Clinician Working Group, based on what Foundry clinicians wanted to know about youth who use substances and how best to support them. The subsequent interview guide was developed based on the focus group learnings and debriefing sessions with the project youth advisory (see Data Collection section below). Three advisory members were also hired as youth research assistants to support further research activities including data collection, transcription, and analysis.

Participants

Participants were defined as youth between the ages of 12–24 who had lived and/or living experience of substance use (including alcohol, cannabis, and/or illicit substance use) in their lifetime and lived in BC. Substance use service experience was not a requirement as we wanted to understand young people’s perception of services and barriers to accessing them. Youth were recruited through Foundry’s social media pages and targeted advertisements. Organizations serving youth across the province were contacted about the study and asked to share recruitment adverts with youth clients. Organizations were identified by our youth advisors and Foundry service teams from across the province in order to recruit a geographically diverse sample of youth. This included mental health services, child and family services, social services, crisis centres, youth shelters, harm reduction services, treatment centres, substance use research partners, community centres, friendship centres, schools, and youth advisories. Interested youth contacted the research coordinator (author RT) to confirm their eligibility. Youth under the age of 16 required consent from a parent or legal guardian and gave their assent in order to participate, while youth ages 16–24 consented on their own behalf. Verbal consent was obtained from participants/legal guardians over the phone or Zoom after being read the consent form, prior to the focus group/interview. A hard copy of their consent form was signed by the research coordinator and sent to the participant/legal guardian for their records.

Data collection

Data collection began in November 2020 until April 2021. An initial semi-structured 2-h focus group with 3 youth (ages 16–24) was facilitated by 2 trained research team members, including a youth research assistant with lived/living experience. A peer support worker was also available for further support. The focus group discussion highlighted youth participants’ multifarious experiences with substance use services and the variety of substances used, which led us to change our data collection methods to individual in-depth interviews. Two interview guides were developed based on the focus group learnings to reflect the different range of service experiences. Interviews questions were reviewed and modified with the project youth advisory. Semi-structured interviews were held with 27 youth participants, which were facilitated by 1–2 members of the research team and lasted 30-min to an hour. In an effort to promote a safe and inclusive space for youth to share their experiences, participants were given the option to request a focus group/interview facilitator who identified as a person of color if preferred. The focus group/interviews began with introductions and the development of a community agreement to ensure youth felt safe to share their experiences. Participants were also sent a demographic survey to fill out prior to the focus group/interview, which was voluntary and not a requirement for participating in the qualitative focus group/interview. Due to the COVID-19 pandemic, the discussions were conducted virtually over Zoom. Participants were provided with a $30 or $50 honoraria for taking part in an interview or focus group, respectively.

Data analysis

The focus group and interviews were audio-recorded, transcribed verbatim, and analyzed thematically using NVivo (version 12) following an inductive approach using Braun and Clarke’s six step method [ 35 ]. The research coordinator led the analysis and debriefed regularly with author KM, who has extensive experience with qualitative health research in substance use [ 36 , 37 ]. The transcripts were read multiple times and initial memos were taken. A data driven approach was used to generate verbatim codes and identify themes. Meetings were also held with the youth research assistants to discuss the data and review and refine the themes to strengthen the credibility and validity of the findings, given their role as facilitators and their lived/living experience with substance use. This included selecting supporting quotes to highlight in the manuscript and conference presentations.

We interviewed a total of 30 youth participants. Socio-demographics, substance use patterns and service experiences are listed in Table  1 . Participants’ median age was 21 and primarily identified as women (55.6%) and white/Caucasian (66.7%). Most youth had used multiple substances in their lifetime and over the past 12-months, with alcohol being the most common, followed by marijuana/cannabis, psychedelics, amphetamines (e.g., MDMA, ecstasy) and other stimulants, non-prescription or illicit opioids, depressants, and inhalants. More than half (55.6%) had some post-secondary education and almost all participants were either in school and/or employed (94.4%). Seventy-five percent of participants had experience accessing substance use services.

Three overarching themes of youths’ substance use service perceptions and experiences were identified (see Fig.  1 ). These themes were specific to the phase of service interaction youth described, given that they were all at different phases of their substance use journeys and had different levels of interaction with substance use services. For example, some youth had never accessed substance use services but described their perceptions of services based on the information available to them, while others described specific service interactions they had. The themes were therefore separated by phase of service interaction, starting with 1. Prevention/Early intervention, where youth describe feeling unworthy of support; 2. Service accessibility, where youth encounter many barriers finding relevant services and information; and 3. Service delivery, where youth highlight the importance of meeting them where they are at.

Overarching themes describing young people’s experiences with substance use services

Prevention/early intervention: youth feel unworthy of support

Many youth described feeling unworthy of health and social services, especially when they did not identify as having a SUD. Young people’s perception of SUD typically revolved around the use of “ harder substances”, which participants defined as heroin, crack cocaine, intravenous drugs, and being in crisis situations, such as being homeless or at risk of an overdose. Youth perceived that most services were geared towards this population and therefore not for them. Many described suffering from “ imposter syndrome ” fearing that they would be taking space away from others who needed it more or judged by services providers for accessing services they did not ‘need’:

“...that idea that you could go get help for your drug use without it – without you being some stereotype of an addict, right?... like there’s different severities of addiction, or you could not have an addiction but also still have some sort of issue related to substance use that should be dealt with. I think my biggest fear as a person with anxiety, through all aspects of accessing health care, is that...I am gonna go to the doctor and they’re going to say ‘Oh my god what an idiot, she doesn’t need to be here, I’m just going to give her something to shut her up’.”

Youth described feeling embarrassed or afraid of how people in the community (including friends, family, and service providers), would react to their substance use, not wanting to disappoint anyone or be stereotyped as an “ addict ”, a “ bad person ” or a “ criminal ”. Alternately, some youth were simply not ready to change their substance use behaviours and assumed this would be expected of them if they reached out for support. As one participant described: “A lot of people are under the idea that if they tell people about their problems, they’re just going to ship them off somewhere, and the only form of recovery is abstinence based, which is not at all helpful and way too intimidating.”

Youth also felt that substance use adverts were often irrelevant to their experiences, and that public health messaging was polarizing and unconvincing:

“I feel like maybe there could be a larger conversation about how drugs are fun, and we should stop – like that’s the thing, if everyone pretends that they’re not and that it’s all bad – that’s why people don’t believe you, they don’t believe what you’re saying, right? Drugs are really fun, that’s why they’re dangerous. That’s why people have addiction problems. They’re really fun until they’re not.”

“I think if they had signs that spoke more to the average college student who is maybe getting black out every weekend or popping zanies...instead I’m hearing about a 40-year old who’s been using hard drugs for like 20 years”.

Further, youth described how marijuana/cannabis and stimulant use were often disregarded, which are commonly used among youth and young adults [ 24 ]. For example, participants described the lack of recognition marijuana/cannabis has as being an addictive substance for some people, which invalidated their experiences. Hence, youth struggled to understand when their substance use “hit a threshold of bad enough to bother public health services” and therefore often only reached out for support when in crisis: “What stopped me from accessing services after this initial attempt was me just second-guessing that I actually had an issue ”.

Youth expressed wanting more information about the neuroscience of addiction, and how to differentiate between substance use, abuse, and disorder to reduce feelings of shame and increase their ability to identify when they should reach out for support. Youth also appreciated learning that substances affect people differently, which validated their experiences : “I learned that it’s very different for everyone....and I was like ‘Oh, I didn’t think there was anybody like me’. So it was this amazing thing, learning that I’m not the only high schooler struggling with this.”

Youth were more likely to reach out to friends for support; however, participants reported that the normalization of substance use among youth meant peers often did not take issues seriously and therefore could not be an effective source of support long-term. This also strengthened participants’ self-doubt about whether their issues warranted support from health and social services, often delaying accessing to care.

Service accessibility: youth encounter many barriers finding substance use services and information “zero to 100”

When youth were ready to access services and information for their substance use, they encountered many barriers. Youth expressed not knowing what services and supports were available, or which services they would benefit from: “It seems like through my searching, it’s either you can get counselling, or you can reach out for people – to health professionals to chat with on a hotline. Or it goes from zero to 100 where you have to get admitted to a rehab treatment program.”

Youth expressed a lack of available information about substance use and services and identified a need to reach those who were not already actively accessing services. This included advertising about different service options in schools, coffee shops, bars, and social media. “I would’ve never went up and asked somebody about it [information about substance use services] or looked it up on the internet. That just wasn’t an interest at all.... I feel like it’s got to be in schools where you can just plain and broad see it in the office or have school counsellors talk about it.” Youth also wanted more information provided in schools about the long-term effects of different substances, harm reduction, and how lifestyle choices and emotional regulation can play a role in substance use behaviours.

Having information more widely available was also identified to “ help break the stigma” by increasing people’s awareness about substance use and available supports. Youth often had to research information independently, which had its own barriers. This included not knowing what to look for or where to start, a lack of information about services listed on service websites, requiring further research through phone calls and emails, and a lack of service options available. As one youth described:

“When I saw people talking about their problems on social media...it just made me realize there’s so much other treatments out there that are just very simple. Like, you can honestly learn breathing techniques...or like cognitive behavioural therapy or all these other things...I guess for people to be able to talk about it – people don’t really see what is cognitive behavioural therapy online, you have to search it up yourself. But for some companies being able to express what it is, express what their services are, it would be able to give an idea to some people.”

When trying to access services, youth described encountering other challenges, including long wait times, challenges getting to appointments (e.g., lack of transportation), limited hours of operation, and a lack of services available, including a lack of affordable services, especially for specialized care (e.g., service providers specializing in substance use, LGBTQ2S+, etc.). A lack of referrals between services was also a barrier to receiving care, placing the responsibility on the youth to reconnect with care, which required them to continuously retell their story. Youth also felt like service providers tended to withhold information about service options based on their level of perceived need, which was often inaccurate, and thus, felt they needed to appear more in crisis to receive more options:

“They [service providers] will withhold certain information from you based on what your need is, because I feel like they try to assess people, and they place them on a sliding scale of like, “Who needs one more?” Which is why I didn’t really like that because … a lot of… supports only became available to me after I had been in the hospital, when I feel like I would’ve benefitted from the support even more, like beforehand.”

Service delivery: importance of meeting youth where they are at

For youth who accessed substance use services, their care experiences varied widely depending on their interactions with their service providers, with some who “ genuinely listened ” and “ took their time to make a connection ”, while others were described as “ uncompassionate ” and ‘ don’t really understand what I’m going through’ . Youth wanted to be “ treated with the same respect and dignity like anyone ” but described being treated like children, as though they were being “ lectured by a parent ” or treated as though incapable of making good decisions for themselves. Youth described “ not being taken seriously” and their issues often “ pushed aside” for not fitting a certain “ stereotype ”. For example, one participant expressed: “I was a really good student, I had a really good home life, and everything was, on the outside, literally perfect. And there was kind of that stigma around “You don’t have any problems, why would you have problems?”.” This strengthened youths’ perceptions that substance use services were not for them and prevented them from accessing further support. As one youth described their experience after an overdose:

“When they had asked me my age and I had told them my age, they were like, ‘Oh my goodness. What are you doing?’ And it was just a random nurse. It wasn’t actually anyone trained, but I just felt like, ‘Wow. Maybe I should go home’. Even though I really needed to be there, it was just hard to not get up and run.”

Youth recognized the importance of crisis-oriented services; however they expressed that “the goal should be preventing crisis rather than just helping people when they get there.” This implied taking youth’s concerns at face value, regardless of how service providers perceived their situation:

“Yeah, I guess assuming that people are asking for help because they really need it, and because... people that are good at holding it together, that have extreme privilege, that look like they’re healthy and making it work, they’re still accessing services for a reason and maybe to include more of a preventative mind frame in their model of care in the sense that, this person may be not be at their worst right now, and that’s actually wonderful that they’re here before that happens, so let’s take this seriously and try to work with them before, you know, they look like they need help.”

Having a service provider who took additional steps to support them, such as providing rides, meeting them in more casual settings, and checking in with them regularly, made youth feel genuinely cared for and increased their likelihood of returning. As one youth described:

“I found that they checked in a lot and it made me feel like they actually cared. You know what I mean? It’s not like just because I’m not there in that moment seeing them... Sometimes, I’d get a text or a phone call being like, “Hey, what are you doing? I haven’t you seen in a while.” You know what I mean? And I had a period of time with the counsellor that I was seeing that I literally ignored her calls for 2 months and [she] was still calling me and leaving voice mails. Even though I wasn’t answering and speaking to her, I still felt like, "Wow, she actually gives a shit. She's still trying to communicate and be there even though I’m not putting the same effort back.”

Being able to connect with someone of similar age, gender, and race/ethnicity generally made it easier for youth to relate to their service provider, however this varied and highlighted the importance of providing youth with options to choose from. Youth described being more comfortable talking to someone who could relate to them and had their own lived experiences. Hearing about similar experiences helped youth feel “ normal ” and validated. This came in the form of peer support, friends, support groups, and online forums such as Reddit and Facebook groups. However, some youth described hesitancy accessing peer support services given that peers may not have received any formal substance use training. Meanwhile, some youth assumed their problems would not compare to the lived experiences of peer support workers, and therefore did not see its value. As one youth described “Hearing [about] other people’s problems...[it] reminds me that other people have gone through wars and made it out of wars, which is like, would be comforting for some people, but for me, makes me feel like [I should] “get over it”.”

Youth desired a holistic approach to care, where all aspects of their life were considered rather than solely focusing on their substance use. As one participant describes: “It wasn’t just substance abuse going on for me, so programs kind of like CBT again, it kind of helps you deal with emotions no matter what way you choose to cope...I think just more effort to get to the root of the problem instead of just trying to stop the symptom.” Focusing on accomplishments rather than abstinence was important, as abstinence was not always young people’s objective for accessing services. Setting more attainable and flexible goals also reduced pressures associated with potential relapses, which were often a source of shame. Having providers who rejected the “ all or nothing approach ” made youth feel more confident and comfortable admitting setbacks.

Addressing mental health concerns was also a priority for most youth, many for whom it had been the primary reason for their service visit. “When I started talking about my mental health as a factor in substance abuse rather than two different things...once I figured out what works for me...and that [mental health] was more stable, everything fell into place after that.” Other factors youth wanted service providers to consider included traumatic experiences, parental substance use, school and work stress, social pressures, and relationship issues. Youth also found it helpful when service providers helped them build recovery capital, including helping them meet their basic needs, recommending school and employment programs, and finding activities and healthy habits. As one youth described “We talked about lots of different ways to cope and things that do not necessarily have anything to do with my substance use, such as eating habits and exercising and study habits when I’m in school. Those really impact me. When those are going well, then it is easier for me to heal from my substance use.”

Youth experience many challenges engaging with existing substance use services in BC as they are currently delivered. Participants in our study described their perceptions towards substance use and their experiences trying to navigate services, and they reflected on multi-level barriers associated with accessing information and support. Throughout these discussions, youth described how the crisis-oriented state of the current health care system leaves many of their needs unmet, calling for a more youth-centred and driven preventative and early intervention approach for diverse youth across BC.

In accordance with the Canadian Drugs and Substances Strategy [ 38 ], all three themes demonstrate a clear need to prioritize substance use prevention and early intervention specifically targeting youth. Youth are in the early phase of substance use, which presents a critical opportunity to reduce potential related harms, including SUDs. However, many existing prevention programs and early interventions have shown limited effectiveness in reducing substance use and associated harms among youth [ 39 ], and very few youths receive evidence-based substance use prevention and education [ 40 , 41 ]. Hanley et al. [ 41 ] reported only 35% of schools in the United States used evidence-based programing, and that only 14% used evidence-based strategies as their primary source of programming. Programs like D.A.R.E. are still being used [ 42 ], which focus on the potential negative consequences associated with substance use to deter young people from using, rather than acknowledging their place in society [ 43 , 44 ]. This approach fails to acknowledge that youth often use substances for enjoyment and social benefits, rather than solely responding to distress [ 44 , 45 ], leading to unconvincing public health messages that fail to resonate with youth.

Following the principles of the Canadian Standards for Community-Based Youth Substance Abuse Prevention [ 46 ], substance use prevention and education should be informed by youth to ensure messaging is relevant to their experiences and is effective in providing youth with the tools needed to make informed decisions about substance use. Moffat et al. [ 47 ] reported that involving youth in prevention efforts helped develop public health recommendations about cannabis that were less ambiguous and stimulated productive conversations among youth about the associated risks. A systematic review on the involvement of youth in substance use prevention efforts also reported that these practices increased youths’ knowledge about substance use and supported the development of prevention interventions that were specifically tailored to the needs of the community [ 48 ].

Youth participants also highlighted the benefits of hearing from peer experiences and advocated for more opportunities for peers to talk in schools. Although there has been increasing evidence supporting the effectiveness of peer-led programs in reducing substance use and associated harms, peers remain largely underutilized in substance use prevention efforts [ 49 , 50 ]. These findings underline the importance of reducing stigma and discrimination against people who use substances, so that peers can be actively engaged in programs design and delivery. However, the findings from this study also indicates that youth may worry about peers invalidating their own experiences through self-disclosure, highlighting the different preferences among youth. This also suggests that the purpose of self-disclosure may need to be better conveyed to youth as a tool to help build common humanity and trust rather than the focus of peer roles.

The study also highlighted that preventative efforts are not only important in school settings but should also be applied in other healthcare settings. As youth from this study explained, services should address the motivations for using substances from a holistic perspective rather than trying to treat substance use alone, requiring an individualized approach. Concurrent mental health disorders, including internalizing (e.g., anxiety, depression) and externalizing disorders (e.g., attention deficit hyperactivity disorder, conduct disorder) are common among youth and are often linked to substance use issues, highlighting the importance of diagnosing and treating substance use and mental health concerns simultaneously [ 22 , 51 ]. However, our results emphasized that the current fragmented state of the healthcare system makes this approach challenging for young people and their families. As many youths access the healthcare system for reasons other than substance use concerns, substance use screening and brief interventions need to occur in a variety of health care settings, accompanied with proper staff training. This approach has been proven to be effective in reducing substance use and violence among youth by screening for substance use in schools, emergency departments, and primary care settings among high-risk youth [ 52 ]. However, this study suggests that substance use screening should be applied more broadly and intentionally integrated as youth may not present external signs of problematic substance use and may not feel comfortable bringing it up unless explicitly asked or in crisis. Providing service providers with training on how to provide culturally safe care to youth who use substances is imperative for this approach to be effective and maintain trusting relationships with youth, given young people’s fears of being stigmatized and judged when accessing services [ 53 , 54 ].

There has been increasing evidence supporting the benefits of an integrated approach to address substance use and mental health concerns among youth, which would facilitate the early identification of possible substance use issues [ 21 ]. Although several barriers can impede the implementation of such services (e.g., organizational-level barriers, distinct health financing systems, and having to train providers in multiple disciplines) [ 54 ], this model of care has been successfully implemented in Australia (Headspace) [ 55 ], Ireland (Jigsaw) [ 56 ], and Canada (Foundry, Youth Wellness Hubs Ontario, ACCESS Open Minds, and YouthCAN Impact) [ 21 , 57 ]. This framework has the potential to increase service provider awareness about the complexities associated with substance use and facilitate the delivery of a wide range of services to support recovery, such as primary care, financial assistance, supportive housing, employment, education, and family support. Given youths’ hesitancy to discuss substance use issues with health care providers, this framework should also integrate peer support services to provide youth with a relatable point of contact to discuss issues without fear of judgment or negative consequences [ 21 ]. Although peer support has been associated with positive treatment outcomes [ 58 ], this study suggests that these services need to be better integrated and conveyed to youth who may benefit.

The service accessibility barriers described by youth in this study reflect the undeniable need to increase the service system’s capacity to provide substance use services. These barriers are consistent with other Canadian studies [ 26 , 59 , 60 ], including a study conducted with youth in urban, rural, and remote Ontario [ 59 ] who described a general lack of substance use services available, low service awareness by youth, and a lack of coordination and collaboration between services. Family members in this study validated these challenges as they described trying to navigate the system for and/or with their young person, which was further substantiated by caregivers trying to navigate youth opioid treatment services in BC (Marchand KM, Turuba R, Katan C, Brasset C, Fogarty O, Tallon C, et al: Becoming our young people’s case managers:Caregivers’ experiences, needs, and ideas for improving opioid use treatments for young people using opioids, Under review). Given the increasing harms associated with the opioid crisis [ 7 ], coordinated efforts across all levels of government and multiple sectors are imperative to improving young people’s access to substance use services and create space, not only for youth in dire need of these services, but for those trying to address substance use concerns proactively.

This study had several limitations. Participants were recruited through Foundry social media channels and targeted advertisements, therefore youth who had access to a phone or a computer and followed mental health and/or substance use organizations were more likely to hear about the study. Consequently, our sample mainly included youth who were actively employed and in school and living in stable living environments. Yet, similar accessibility barriers are described by street-entrenched youth in Ontario [ 27 ] and British Columbia [ 30 ], including long wait times and difficulties seeking support due to stigma, as well as negative experiences with abstinent-based approaches, highlighting young people’s desire for holistic care regardless of substance use patterns. Although we tried to recruit through several health and social services across the province, the COVID-19 pandemic likely limited organizations’ capacity to support with local promotion. Further, we were only able to recruit 1 youth between the ages of 12–15, likely due to our inability to recruit through schools and need for parental consent, which hindered our ability to identify potential differences in substance use service perceptions and experiences between adolescents and young adults. Given the important life transitions that occur between adolescence and young adulthood, future studies exploring these differences are important as different prevention and early intervention approaches may be warranted. Exploring how perceptions and experiences differ across communities could also be an important consideration for future research to better understand how geographic location, including urban and rural differences, impacts young peoples’ access to services. Despite these limitations, the findings of this study have important implications in the way we co-design and deliver substance use services to youth. They also have important considerations for policy makers who are considering how to shape substance use services for diverse youth in their jurisdictions.

This study highlights the many challenges youth experience when engaging with substance use services and emphasizes a need for a more preventative approach. The lack of integration and capacity among service providers to provide substance use services implies that youth who have milder treatment needs and/or do not meet the diagnosis criteria of SUD often do not have access to adequate substance use service interventions. Research, health service, and policy efforts should focus on substance use prevention and early interventions to address young people’s concerns before they are in crisis and increase their ability to perceive the need to reach out for support. Moving forward, it is critical that diverse youth and peers with lived and/or living experience be involved in these efforts, including the co-design of new services and evaluation of impact of prevention and early intervention services, including quality improvement efforts. Intentional, sustained investment in youth substance use services will optimize the health outcomes and experiences of young people across BC, transformation that young people can no longer patiently wait for.

Availability of data and materials

The datasets generated and analysed during the current study are not publicly available due to the potential for identifying participants but are available from the corresponding author on reasonable requests.

Abbreviations

British Columbia

Drug Abuse Resistance Education

3,4-Methylenedioxymethamphetamine

• Participatory action research

Substance use disorder

Barbic SP, Jones AA, Woodward M, Piercy M, Mathias S, Vila-Rodriguez F, et al. Clinical and functional characteristics of young adults living in single room occupancy housing: preliminary findings from a 10-year longitudinal study. Can J Public Health. 2018;109(2):204–14. https://doi.org/10.17269/s41997-018-0087-9 .

Article   PubMed   PubMed Central   Google Scholar  

Health Canada. Canadian tobacco, alcohol and drugs survey (CTADS): summary of results for 2017. Ottawa: Health Canada; 2019. Available from: https://www.canada.ca/en/health-canada/services/canadian-alcohol-drugs-survey/2017-summary.html . [updated 2021 Aug 12; cited 2021 Nov 16]

Google Scholar  

Substance Abuse and Mental Health Services Administration [SAMHSA]. Key substance use and mental health indicators in the United States: Results from the 2020 National Survey on Drug Use and Health (HHS Publication No. PEP21–07–01-003, NSDUH Series H-56). Rockville: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration, 2021 Available from: https://www.samhsa.gov/data/ . [updated 2021 Oct;e cited 18 Nov 2021]

Poudel A, Gautam S. Age of onset of substance use and psychosocial problems among individuals with substance use disorders. BMC Psychiatry. 2017;17(1):10. https://doi.org/10.1186/s12888-016-1191-0 .

McCabe SE, West BT, Morales M, Cranford JA, Boyd CJ. Does early onset of non-medical use of prescription drugs predict subsequent prescription drug abuse and dependence? Results from a national study. Addiction. 2007;102(12):1920–30. https://doi.org/10.1111/j.1360-0443.2007.02015.x .

Chen CY, Storr CL, Anthony JC. Early-onset drug use and risk for drug dependence programs. Addict Behav. 2009;34(3):319–22. https://doi.org/10.1016/j.addbeh.2008.10.021 .

Article   CAS   PubMed   Google Scholar  

Canadian Institute for Health Information. Opioid-related harms in Canada, December 2018. Ottawa: CIHI; 2018. p. 79. Report No.: ISBN 978–1–77109-767-3.

Choi HJ, Lu Y, Schulte M, Temple JR. Adolescent substance use: latent class and transition analysis. Addict Behav. 2018;77:160–5. https://doi.org/10.1016/j.addbeh.2017.09.022 .

Article   PubMed   Google Scholar  

Breda C, Heflinger CA. Predicting incentives to change among adolescents with substance abuse disorder. Am J Drug Alcohol Abuse. 2004;30(2):251–67. https://doi.org/10.1081/ADA-120037377 .

Zuckermann AME, Williams G, Battista K, de Groh M, Jiang Y, Leatherdale ST. Trends of poly-substance use among Canadian youth. Addict Behav Rep. 2019;10:100189. https://doi.org/10.1016/j.abrep.2019.100189 .

White AM, Hingson RW, Pan IJ, Yi HY. Hospitalizations for alcohol and drug overdoses in young adults ages 18-24 in the United States, 1999-2008: results from the nationwide inpatient sample. J Stud Alcohol Drugs. 2011;72(5):774–86. https://doi.org/10.15288/jsad.2011.72.774 .

Wong SS, Zhou B, Goebert D, et al. The risk of adolescent suicide across patterns of drug use: a nationally representative study of high school students in the United States from 1999 to 2009. Soc Psychiatry Psychiatr Epidemiol. 2013;48(10):1611–20. https://doi.org/10.1007/s00127-013-0721-z PMID: 23744443.

Keyes KM, Brady JE, Li G. Effects of minimum legal drinking age on alcohol and marijuana use: evidence from toxicological testing data for fatally injured drivers aged 16 to 25 years. Inj Epidemiol. 2015;2. https://doi.org/10.1186/s40621-014-0032-1 .

Hadland SE, Yule AM, Levy SJ, Hallett E, Silverstein M, Bagley SM. Evidence-based treatment of young adults with substance use disorders. Pediatrics. 2021;147(S2):204–14. https://doi.org/10.1542/peds.2020-023523D .

Article   Google Scholar  

Winters KC, Tanner-Smith EE, Bresani E, Meyers K. Current advances in the treatment of adolescent drug use. Adolesc Health Med Ther. 2014;5:199–210. https://doi.org/10.2147/AHMT.S48053 .

British Columbia Centre on Substance Use. A Guideline for the Clinical Management of Opioid Use Disorder—Youth Supplement. B.C. Ministry of Health, & B.C. Ministry of Mental Health and Addictions; 2018. Available from: http://www.bccsu.ca/care-guidance-publications/ [updated 2018 June 13, cited 2021 Nov 18]

Christie GIG, Cheetham A, Lubman DI. Interventions for alcohol and drug use disorders in young people: 10 key evidence-based approaches to inform service delivery. Curr Addict Rep. 2020;7:464–74. https://doi.org/10.1007/s40429-020-00336-6 .

Merikangas KR, He J, Burstein M, Swanson SA, Avenevoli S, Cui L, et al. Lifetime prevalence of mental disorders in US adolescents: results from the National Comorbidity Study-Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry. 2010;49(1):980–9. https://doi.org/10.1016/j.jaac.2010.05.017 .

Otasowie J. Co-occurring mental health disorder and substance use disorder in young people: aetiology, assessment and treatment. BJPsych Adv. 2021;27(4):272–81. https://doi.org/10.1192/bja.2020.64 .

Sterling S, Weisner C, Hinman A, Parthasarathy S. Access to treatment for adolescents with substance use and co-occurring disorders: challenges and opportunities. J Am Acad Child Adolesc Psychiatry. 2010;49:637–46. https://doi.org/10.1016/j.jaac.2010.03.019 .

Halsall T, Manion I, Iyer SN, Mathias S, Purcell R, Henderson J. Trends in mental health system transformation: integrating youth services within the Canadian context. Healthc Manage Forum. 2019;32(2):51–5. https://doi.org/10.1177/0840470418808815 .

Brownlie E, Beitchman JH, Chaim G, Wolfe DA, Rush B, Henderson J. Early adolescent substance use and mental health problems and service utilization in a school-based sample. C J Psychiatry. 2019;64(2):116–25. https://doi.org/10.1177/0706743718784935 .

Winters KC, Botzet AM, Fahnhorst T, Stinchfield R, Koskey R. Adolescent substance abuse treatment: a review of evidence-based research. In: Leukefeld C, Gullota T, Tindall MS, editors. Adolescent substance abuse: evidence-based approaches to prevention and treatment. New York: Springer Science + Buisness Media; 2009.

Substance Abuse and Mental Health Services Administration [SAMHSA]. Key substance use and mental health indicators in the United States: results from the 2019 National Survey on drug use and health (HHS publication no. PEP20-07-01-001, NSDUH series H-55). Rockville: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration, 2020 Available from: https://www.samhsa.gov/data/ . [updated 2020 Sept; cited 18 Nov 2021]

Tylee A, Haller DM, Graham T, Churchill R, Sanci LA. Youth-friendly primary-care services: how are we doing and what more needs to be done? Lancet. 2007;369:1565–73. https://doi.org/10.1016/S0140-6736(07)60371-7 .

Gulliver A, Griffiths KM, Christensen H. Perceived barriers and facilitators to mental health help-seeking in young people: a systematic review. BMC Psychiatry. 2010;10:113. https://doi.org/10.1186/1471-244X-10-113 .

Kozloff N, Cheung AH, Ross LE, Winer H, Ierfino D, Bullock H, et al. Factors influencing service use among homeless youths with co-occurring disorders. Psychiatr Serv. 2013;64(9):925–8. https://doi.org/10.1176/appi.ps.201200257 .

Bozinoff N, Small W, Long C, DeBeck K, Fast D. Still “at risk”: an examination of how street-involved young people understand, experience, and engage with “harm reduction” in Vancouver’s inner city. Int J Drug Policy. 2017;45:33–9. https://doi.org/10.1016/j.drugpo.2017.05.006 .

Giang V, Thulien M, McNeil R, Sedgemore K, Anderson H, Fast D. Opioid agonist therapy trajectories among street entrenched youth in the context of a public health crisis. SSM Popu Health. 2020;11:1006009. https://doi.org/10.1016/j.ssmph.2020.100609 .

Boyd J, Fast D, Hobbins M, McNeil R, Small W. Social-structural factors influencing periods of injection cessation among marginalized youth who inject drugs in Vancouver, Canada: an ethno-epidemiological study. Harm Reduct J. 2017;14:31. https://doi.org/10.1186/s12954-017-0159-9 .

O’Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: a synthesis of recommendations. Acad Med. 2014;89(9):1245–51. https://doi.org/10.1097/ACM.0000000000000388 .

Kindon S, Pain R, Kesby M. Participatory action research approaches and methods: connecting people, participation and place. Oxfordshire: Routledge; 2010.

Hawke LD, Relihan J, Miller J, McCann E, Rong J, Darnay K, et al. Engaging youth in research planning, design and exercise: practical recommendations for researchers. Health Expect. 2018;21(6):944–9. https://doi.org/10.1111/hex.12795 .

BC Coroner’s Service. Illicit Drug Toxicity Deaths in BC. January 1, 2011 – October 31, 2021. 2021. Available from: https://www2.gov.bc.ca/assets/gov/birth-adoption-death-marriage-and-divorce/deaths/coroners-service/statistical/illicit-drug.pdf

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101. https://doi.org/10.1191/1478088706qp063oa .

Marchand K, Foreman J, MacDonald S, Harrison S, Schechter MT, Oviedo-Joekes E. Building healthcare provider relationships for patient-centered care: a qualitative study of the experiences of people receiving injectable opioid agonist treatment. Subst Abuse Treat Prev Policy. 2020;15(1):7. https://doi.org/10.1186/s13011-020-0253-y .

Palis H, Marchand K, Spike G, Westfall PJ, Lock K, MacDonald S. Exploring the effectiveness of dextroamphetamine for treatment of stimulant use disorder: a qualitative study with patients receiving injectable opioid agonist treatment. Subst Abuse Treat Prev Policy. 2021;16(68). https://doi.org/10.1186/s13011-021-00399-2 .

Health Canada. Canadian drugs and substances strategy. Ottawa: Health Canada; 2019. Available from: https://www.canada.ca/en/health-canada/services/substance-use/canadian-drugs-substances-strategy.html#shr-pg0 [updated 2019 July; cited 18 Nov 2021]

Stockings E, Hall WD, Lynskey M, Morley K, Reavley N, Strang J, et al. Prevention, early intervention, harm reduction, and treatment of substance use in young people. Lancet Psychiatry. 2016;3(3):280–96. https://doi.org/10.1016/S2215-0366(16)00002-X .

Cho H, Dion Hallfors D, Iritani BJ, Hartman S. The influence of “no child left behind” legislation on drug prevention in US schools. Eval Rev. 2009;33(5):446–63. https://doi.org/10.1177/0193841X09335050 .

Hanley SM, Ringwalt C, Ennett ST, Vincus AA, Bowling JM, Haws SW, et al. The prevalence of evidence-based substance use prevention curricula in the nation’s elementary schools. J Drug Educ. 2010;40(1):51–60. https://doi.org/10.2190/DE.40.1.d .

Tremblay M, Baydala L, Khan M, Currie C, Morley K, Burkholder C, et al. Primary substance use prevention programs for children and youth: a systematic review. Pediatrics. 2020;146(3):e20192747. https://doi.org/10.1542/peds.2019-2747 .

Moreland AD, Lopez CM, Gilmore AK, Borkman AL, McCauley JL, Rheingold AA, et al. Substance use prevention programming for adolescents and young adults: a mixed-method examination of substance use perceptions and use of prevention services. Subst Use Misuse. 2020;55(14):2341–7. https://doi.org/10.1080/10826084.2020.1817079 .

Farrugia A. Assembling the dominant accounts of youth drug use in Australian harm reduction drug education. Int J Drug Policy. 2014;25(4):663–72. https://doi.org/10.1016/j.drugpo.2014.04.019 .

Fry ML. Seeking the pleasure zone: understanding young adult’s intoxication culture. AMJ. 2011;19:65–70. https://doi.org/10.1016/j.ausmj.2010.11.009 .

Canadian Centre on Substance Abuse. Stronger together: Canadian standards for community-based youth substance abuse prevention. Ottawa: Canadian Centre on Substance Abuse; 2010. Available from: https://www.drugsandalcohol.ie/19993/1/Canada_Stronger_together.pdf [cited 18 Nov 2021]

Moffat BM, Jenkins EK, Johnson JL. Weeding out the information: an ethnographic approach to exploring how young people make sense of the evidence on cannabis. Harm Reduct J. 2013;10(34):1–9. https://doi.org/10.1186/1477-7517-10.34 .

Valdez EA, Skobic I, Valdez L, Garcia DO, Korchmaros J, Stevens S, et al. Youth participatory action research for youth substance use prevention: a systematic review. Subst Use Misuse. 2020;55(2):314–28. https://doi.org/10.1080/10826084.2019.1668014 .

Ti L, Tzemis D, Buxton JA. Engaging people who use drugs in policy and program development: a review of the literature. Subst Abuse Treat Prev Policy. 2012;7(47):1–9.

Greer AM, Luchenski SA, Amlani AA, Lacroix K, Burmeister C, Buxton JA. Peer engagement in harm reduction strategies and services: a critical case study and evaluation framework from British Columbia, Canada. BMC Public Health. 2016;16(450):1–9. https://doi.org/10.1186/s12889-016-3136-4 .

Article   CAS   Google Scholar  

Henderson JL, Wilkins LK, Hawe LD, Wang W, Sanches M, Brownlie EB, et al. Longitudinal emergence of concurrent mental health and substance use concerns in an Ontario school-based sample: the research and action for teens study. J Can Acad Child Adolesc Psychiatry. 2021;30(4):249–63.

PubMed   PubMed Central   Google Scholar  

Sterling S, Valkanoff T, Hinman A, Weisner C. Integrating substance use treatment into adolescent health care. Curr Psychiatry Rep. 2012;14:453–61. https://doi.org/10.1007/s11920-012-0304-9 .

Urbanoski K, Pauly B, Inglis D, Cameron F, Haddad T, Phillips J, et al. Defining culturally safe primary care for people who use substances: a participatory concept mapping study. BMC Health Serv Res. 2020;20:1060. https://doi.org/10.1186/s12913-030-05915-x .

Sterling S, Chi F, Hinman A. Integrating care for people with co-occurring alcohol and other drug, medical, and mental health conditions. Alcohol Res Health. 2011;33(4):338–49.

Hilferty F, Cassells R, Muir K, Duncan A, Christensen D, Mitrou F, et al. Is headspace making a difference to young people’s lives? Final report of the independent evaluation of the headspace program. Sydney: Social Policy Research Centre, University of New South Whales; 2015.

O’Keeffe L, O’Reilly A, O’Brien G, Buckley R, Illback R. Description and outcome evaluation of jigsaw: an emergent Irish mental health early intervention programme for young people. Ir J Psychol Med. 2015;32(1):72–7. https://doi.org/10.1017/ipm.2014.86 .

Mathias S, Tee K, Helfrich W, Gerty K, Chan G, Barbic SP. Foundry: early learnings from the implementation of an integrated youth service network. Early Interv. 2021:1–9. https://doi.org/10.1111/eip.13181 .

Barton J, Henderson J. Peer support and youth recovery: a brief review of the theoretical underpinnings and evidence. CJFY. 2016;8(1):1–17. https://doi.org/10.29173/cjfy27140 .

Brownlie EB, Chaim G, Heffernan O, Herzog T, Henderson J. Youth services system review: moving from knowledge gathering to implementation through collaboration, youth engagement, and exploring local community needs. Can J Community Ment Health. 2017;36(2):1–17. https://doi.org/10.7870/cjcmh-2017-018 .

Russell C, Neufeld M, Sabioni P, Varatharajan T, Ali F, Miles S, et al. Assessing service and treatment needs and barriers of youth who use illicit and non-medical prescription drugs in northern Ontario, Canada. PLoS One. 2019;14(2):e0225548. https://doi.org/10.1371/journal.pone.0225548 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

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Acknowledgements

The Experience Project is grateful to have taken place on the ancestral lands of many different Indigenous Nations and Peoples across what we now call British Columbia. We are also very grateful to the Youth4Youth Advisory Committee who supported the research and the participants who shared their experiences and insights with us.

The Experience Project has been made possible through the financial contributions of Health Canada under their Substance Use and Addiction Program. The views herein do not necessarily represent the views of Health Canada. Author Kirsten Marchand is supported by a Michael Smith Foundation for Health Research/Centre for Health Evaluation & Outcome Sciences Research Trainee award and author Skye Barbic by a Scholar grant funded by the Michael Smith Foundation for Health Research.

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All authors contributed to the conception and design of the Experience Project. Authors RT, AA, AH, VB and CT collected the data and RT, AA, AH, VB and KM contributed to the analysis and interpretation of the data. All authors contributed to the manuscript and approved the submitted version.

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Turuba, R., Amarasekera, A., Howard, A.M. et al. A qualitative study exploring how young people perceive and experience substance use services in British Columbia, Canada. Subst Abuse Treat Prev Policy 17 , 43 (2022). https://doi.org/10.1186/s13011-022-00456-4

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A Conversation With …

Teen Drug Use Habits Are Changing, For the Good. With Caveats.

Dr. Nora Volkow, who leads the National Institutes of Drug Abuse, would like the public to know things are getting better. Mostly.

By Matt Richtel

Historically speaking, it’s not a bad time to be the liver of a teenager. Or the lungs.

Regular use of alcohol, tobacco and drugs among high school students has been on a long downward trend.

In 2023, 46 percent of seniors said that they’d had a drink in the year before being interviewed; that is a precipitous drop from 88 percent in 1979, when the behavior peaked, according to the annual Monitoring the Future survey, a closely watched national poll of youth substance use. A similar downward trend was observed among eighth and 10th graders, and for those three age groups when it came to cigarette smoking. In 2023, just 15 percent of seniors said that they had smoked a cigarette in their life, down from a peak of 76 percent in 1977 .

Illicit drug use among teens has remained low and fairly steady for the past three decades, with some notable declines during the Covid-19 pandemic.

In 2023, 29 percent of high school seniors reported using marijuana in the previous year — down from 37 percent in 2017, and from a peak of 51 percent in 1979.

There are some sobering caveats to the good news. One is that teen overdose deaths have sharply risen, with fentanyl-involved deaths among adolescents doubling from 2019 to 2020 and remaining at that level in the subsequent years.

Dr. Nora Volkow has devoted her career to studying use of drugs and alcohol. She has been the director of the National Institute on Drug Abuse since 2003. She sat down with The New York Times to discuss changing patterns and the reasons behind shifting drug-use trends.

What’s the big picture on teens and drug use?

People don’t really realize that among young people, particularly teenagers, the rate of drug use is at the lowest risk that we have seen in decades. And that’s worth saying, too, for legal alcohol and tobacco.

What do you credit for the change?

One major factor is education and prevention campaigns. Certainly, the prevention campaign for cigarette smoking has been one of the most effective we’ve ever seen.

Some of the policies that were implemented also significantly helped, not just making the legal age for alcohol and tobacco 21 years, but enforcing those laws. Then you stop the progression from drugs that are more accessible, like tobacco and alcohol, to the illicit ones. And teenagers don’t get exposed to advertisements of legal drugs like they did in the past. All of these policies and interventions have had a downstream impact on the use of illicit drugs.

Does social media use among teens play a role?

Absolutely. Social media has shifted the opportunity of being in the physical space with other teenagers. That reduces the likelihood that they will take drugs. And this became dramatically evident when they closed schools because of Covid-19. You saw a big jump downward in the prevalence of use of many substances during the pandemic. That might be because teenagers could not be with one another.

The issue that’s interesting is that despite the fact schools are back, the prevalence of substance use has not gone up to the prepandemic period. It has remained stable or continued to go down. It was a big jump downward, a shift, and some drug use trends continue to slowly go down.

Is there any thought that the stimulation that comes from using a digital device may satisfy some of the same neurochemical experiences of drugs, or provide some of the escapism?

Yes, that’s possible. There has been a shift in the types of reinforcers available to teenagers. It’s not just social media, it’s video gaming, for example. Video gaming can be very reinforcing, and you can produce patterns of compulsive use. So, you are shifting one reinforcer, one way of escaping, with another one. That may be another factor.

Is it too simplistic to see the decline in drug use as a good news story?

If you look at it in an objective way, yes, it’s very good news. Why? Because we know that the earlier you are using these drugs, the greater the risk of becoming addicted to them. It lowers the risk these drugs will interfere with your mental health, your general health, your ability to complete an education and your future job opportunities. That is absolutely good news.

But we don’t want to become complacent.

The supply of drugs is more dangerous, leading to an increase in overdose deaths. We’re not exaggerating. I mean, taking one of these drugs can kill you.

What about vaping? It has been falling, but use is still considerably higher than for cigarettes: In 2021, about a quarter of high school seniors said that they had vaped nicotine in the preceding year . Why would teens resist cigarettes and flock to vaping?

Most of the toxicity associated with tobacco has been ascribed to the burning of the leaf. The burning of that tobacco was responsible for cancer and for most of the other adverse effects, even though nicotine is the addictive element.

What we’ve come to understand is that nicotine vaping has harms of its own, but this has not been as well understood as was the case with tobacco. The other aspect that made vaping so appealing to teenagers was that it was associated with all sorts of flavors — candy flavors. It was not until the F.D.A. made those flavors illegal that vaping became less accessible.

My argument would be there’s no reason we should be exposing teenagers to nicotine. Because nicotine is very, very addictive.

Anything else you want to add?

We also have all of this interest in cannabis and psychedelic drugs. And there’s a lot of interest in the idea that psychedelic drugs may have therapeutic benefits. To prevent these new trends in drug use among teens requires different strategies than those we’ve used for alcohol or nicotine.

For example, we can say that if you take drugs like alcohol or nicotine, that can lead to addiction. That’s supported by extensive research. But warning about addiction for drugs like cannabis and psychedelics may not be as effective.

While cannabis can also be addictive, it’s perhaps less so than nicotine or alcohol, and more research is needed in this area, especially on newer, higher-potency products. Psychedelics don’t usually lead to addiction, but they can produce adverse mental experiences that can put you at risk of psychosis.

Matt Richtel is a health and science reporter for The Times, based in Boulder, Colo. More about Matt Richtel

Essay on Drugs On Youth

Students are often asked to write an essay on Drugs On Youth in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Drugs On Youth

Introduction.

Drugs are harmful substances that can hurt our bodies. When young people use drugs, it can cause big problems. This essay will talk about how drugs affect youth.

Why Youth Use Drugs

Many young people start using drugs because of peer pressure or stress. They might think it’s cool or a way to escape problems. But, it’s not a good solution and can lead to serious issues.

Effects of Drugs on Youth

Drugs can harm a person’s mind and body. They can make a young person feel sick, act differently, and have trouble in school. Over time, it can even lead to addiction.

Prevention and Help

It’s important to teach young people about the dangers of drugs. If someone is using drugs, they should seek help from a trusted adult or a professional. There are many resources available to help.

In conclusion, drugs can have a negative impact on youth. It’s important to understand the risks and seek help if needed. We must work together to prevent drug use and help those in need.

250 Words Essay on Drugs On Youth

Drugs can harm young people in many ways. They can change how the brain works, making it hard for youth to think, learn, and make good choices.

Drugs and Health Risks

Drugs are risky for everyone, but they’re especially dangerous for young people. This is because their bodies and brains are still growing. Drugs can harm this growth, leading to long-term health problems. For example, drugs can harm the heart, lungs, and other important parts of the body.

Drugs and Behavior

Drugs can also change how young people behave. They can make youth act in ways they normally wouldn’t, like being violent or taking risks. This can lead to problems at school, with friends, or with the law.

Drugs and Addiction

Drugs can be very addictive. This means that once a young person starts using drugs, it can be hard for them to stop. This can lead to a life-long struggle with drug use.

It’s important for young people to understand the risks of drug use. This can help them make good choices and stay healthy. Remember, saying no to drugs is always the best choice.

500 Words Essay on Drugs On Youth

Drugs are harmful substances that can change the way our body works. When we talk about ‘Drugs On Youth’, we mean the impact of these substances on young people. This is a serious issue because drugs can harm young people’s health, their school work, and their relationships.

The Attraction of Drugs

Many young people start using drugs out of curiosity or because friends are doing it. They might think that drugs can help them forget their problems or feel more relaxed and happy. But this is not true. Drugs can make problems worse and can lead to new problems.

Health Problems

One of the main impacts of drugs on youth is health problems. Drugs can damage important parts of the body like the brain, heart, and lungs. They can also make young people feel tired, confused, or scared. Some drugs can even lead to death.

Impact on School Work

Another impact of drugs on youth is on their school work. Drugs can make it hard for young people to concentrate, learn, and remember things. This can lead to poor grades, trouble with teachers, and even dropping out of school.

Relationship Problems

Drugs can also harm young people’s relationships. They can lead to fights with family and friends, and can make it hard to trust others. Young people who use drugs might also start hanging out with other drug users, which can lead to more problems.

Prevention is Key

To stop the impact of drugs on youth, we need to prevent young people from starting to use drugs in the first place. This can be done by teaching them about the dangers of drugs, and by giving them healthy ways to deal with stress and problems. Parents, teachers, and friends can all help in this.

In conclusion, drugs can have a big impact on young people’s health, school work, and relationships. It’s important to prevent young people from starting to use drugs, and to help those who are already using drugs to stop. By doing this, we can protect our youth and help them to have a bright and healthy future.

(Word Count: 325)

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If you’re looking for more, here are essays on other interesting topics:

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Substance Abuse: Adolescent Issues and Interventions Essay

Introduction, the development of substance abuse, risk factors related to substance abuse, the influence of addiction on self-identity, interventions, reference list.

This paper has been aimed at discussing the problem of substance abuse among adolescents. In particular, it focuses on the factors that lead to dependence on drugs on alcohol, for instance, peer-pressure, low self-esteem, family problems, experiences of sexual abuse, and so forth. The proposed interventions include the following measures: 1) regular communication with both parents; 2) paying more attention to the friends of a teenager; 3) limiting the access to money; 4) organizing extra curriculum activities for teenagers.

Substance abuse among adolescents is the problem that requires the joint effort of parents, teachers, and social workers. It can affect families that have various social, racial, or economic characteristics and no one can say that he or she is insured against this risk. This paper is aimed at discussing the factors that contribute to substance abuse among teenagers. Moreover, it is necessary to propose interventions that can prevent adolescents from experimenting with drugs or drinking alcohol.

The development of substance abuse among adolescents can be explained in different ways. The researchers point out that more than 40 percent of teenagers begin to take drugs or drink alcohol in the company of their peers (Ahmad, Khalique, & Khan, 2009, p. 401). These people may believe that drug use is a sign of belonging to the group.

As a rule, they do not want to criticize the behavior of their peers who may believe that drugs and alcohol are acceptable. In their opinion, the rejection of group values can make them outcasts. An adolescent, who has a strong attachment to the group, can abuse substance either to acquire new experiences or in effort to impress his or her friends (Ramirez et al, 2012, p. 39).

Thus, peer pressure is one way to explain this problem. Certainly, peer pressure can also be a positive force, especially when a teenager’s friends lead a healthy lifestyle and do not approve of alcohol or drug abuse. This is the first issue that parents and teachers should take into account.

Secondly, one should remember that this behavior can be caused by some problems within the family. Chassin and Handley believe, teenagers can resort to alcohol and drugs when they do not feel the emotional support and encouragement of parents (2006, p. 136). For them, substance abuse becomes a substitute for normal family relations. Such behavior is more typical of teenagers suffering from domestic violence (Caple & Schub, 2012, p. 1).

Additionally, one should remember that remember that for many teenagers attempt to raise their perceived status and self-esteem by drinking alcoholic beverages or using substances (Ahmad, Khalique, & Khan, 2009, p. 401). In their opinion, this behavior signifies adulthood and independence.

Certainly, this assumption is false but this is how many of them believe. Thus, this problem can be related to the way in which adolescents perceive themselves. These examples suggest that there are different paths that lead to substance abuse problems and intervention should account for various possibilities.

Overall, researchers single out several characteristics of adolescents that may take drugs, namely, low self-esteem, psychological distress, lack of meaningful relations with parents, or contacts with people, usually peers who abuse drugs (Newcomb, 1986, p. 525). These teenagers believe that only peers can offer them help or encouragement. Usually, this belief is not justified, but it is very strong.

However, there are other important indicators that should not be overlooked. For example, statistical evidence suggests that teenagers, who suffered sexual or physical abuse, tend to drink alcohol or use drugs much earlier that their peers (Caple & Schub, 2012, p. 1). These adolescents cannot give vent to their feelings, and they view drugs or alcohol as the only solution available to them. Additionally, one should remember about such a factor as the structure of the family.

A teenager growing up in a two-family household is less likely to get addicted to alcohol or drugs (Caple & Schub, 2012, p. 1). Surely, one cannot assume that adolescents from single-parent families are always prone to drug abuse or alcohol consumption. However, it is easier for teenagers to cope with stress when they can communicate with both parents.

Parenting style can either increase or decrease the likelihood of substance abuse among teenagers. Over-permissiveness is strongly correlated with alcohol consumption (Caple & Schub, 2012, p. 2). For example, adolescents are more likely to use drugs if their parents give them unlimited access to money (McCrystal, Percy, &Higgins, 2007, p. 26). Yet, authoritarian parenting can also lead to substance abuse.

So, parents should find a balance between control over their children and permissiveness. The risk factors that have been discussed cannot be applied to every case of substance abuse among adolescents; yet, they are very widespread. Therefore, educators should pay more attention to the needs of these teenagers, because they are more exposed to the risk of substance abuse.

Drug abuse and subsequent addiction affects the self-identity of teenagers. In part, this influence can be explained with the help of social learning theory developed by Albert Bandura. It postulates that an individual learns behavioral norms by observing those people who are close to him or her (Wodarski, 1990, 670). A teenager, who wants to appear stronger or more independent, may emulate the habits of older peers and these people may abuse drugs or alcohol (Wodarski, 1990, 670).

Moreover, one should not forget that mass media, especially television are full of images suggesting alcohol consumption is acceptable for adults. Thus, this person begins to think that substance is an attribute of adulthood. Such teenagers may believe that they are strong, self-sufficient, and independent of their parents. However, later they are not able to perceive themselves in this way. They understand that they have become addicted to drugs or alcohol.

They see that they do not have any control over the situation. The awareness of this fact can lead to low self-esteem and feeling of worthlessness. Thus, it is possible to argue that addiction distorts the self-identity of an individual. At first, it creates an illusion of power or independence, but eventually results into the feeling of helplessness. These are the main effects of addiction on the self-identity of a teenager.

There are several interventions that can reduce the risk of substance abuse among adolescent. First of all, parents should remember that regular communication with both parents reduces the risk of deviant behavior, including drug abuse (Caple & Schub, 2012, p. 1). Thus, parents should make sure that they can talk to a child at least once a day.

Divorced parents should not prevent one another from seeing a child. A teenager should know that he or she can rely on other members of the family. As a result, this person will not feel the need to use drugs and seek the support of peers.

Secondly, parents should learn more about the friends of their children. For example, they should know how they spend time, and what they are interested in. By doing so, parents can determine whether their children’s friends have a good or bad influence over them. This is why parents should not prohibit their children from inviting their friends to the house. Moreover, parents can even occasionally organize small parties for them. In this way, they learn much more about the friends of their children.

The third intervention that researchers recommend is to limit a teenager’s access to money (McCrystal, Percy, &Higgins, 2007, p. 26). The findings suggest that uncontrolled access to money at the age of 13 or 14 increases the probability of drug use (McCrystal, Percy, &Higgins, 2007, p. 26).

Thus, parents should be attentive to how their children spend money. Certainly, people cannot always know for what kind of purposes their children need money. However, they should be very careful when a child asks for extra cash because this cash can be needed for drugs or alcohol.

In turn, teachers should encourage children’s participation in school life when it is possible. Special attention should be paid to extra curriculum activities because a student, who has certain interests or goals, will be less attracted to alcohol and drugs. For example, schools can establish study groups for children who may be interested in different subjects like biology, chemistry, mathematics, and so forth. In this way, they can divert adolescents’ attention from drugs.

Additionally, they should be very attentive to academic performance of students. The thing is that poor grades and continuous absence from school may indicate at some emotional problems or even substance abuse. At any rate, parents should be warned about these issues as soon as possible.

On the whole, drug abuse and alcohol consumption are the problems that can affect the life of almost any family irrespective of its income level, ethnic origins , or education background. The examples discussed in this paper suggest that teenagers are torn between their need for independence and the need for support.

The proposed intervention are premised on the idea that the child, who feels the support of parents and who has some interests, is less interested in alcohol or drugs because they create no value for him or her. Moreover, this adolescent will be more resistant to peer pressure.

Ahmad, A., Khalique, N., & Khan, Z. (2009). Analysis of Substance Abuse in Male Adolescents. Iranian Journal of Pediatrics, 19 (4), 399-403.

Caple, C., & Schub, T. (2012). Substance Abuse in Adolescence: Risk/Protective Factors. CINAHL Nursing Guide, 1-2.

Chassin, L., & Handley, E. D. (2006). Parents and Families as Contexts for the Development of Substance Use and Substance Use Disorders. Psychology Of Addictive Behaviors, 20 (2), 135.

McCrystal, P., Percy, A., & Higgins, K. (2007). The cost of drug use in adolescence: Young people, money and substance abuse. Drugs: Education, Prevention & Policy, 14 (1), 19-28.

Newcomb, M. M. (1986). Risk Factors for Drug Use among Adolescents: Concurrent and Longitudinal Analyses. American Journal Of Public Health, 76 (5), 525-540.

Ramirez, R., Hinman, A., Sterling, S., Weisner, C., & Campbell, C. (2012). Peer Influences on Adolescent Alcohol and Other Drug Use Outcomes. Journal Of Nursing Scholarship, 44 (1), 36-44.

Wodarski, J. S. (1990). Adolescent substance abuse: Practice implications. Adolescence, 25 (99), 667-688.

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Essay on Drug Abuse

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Essay on Drug Abuse in 150 words

Essay on drug abuse in 250-300 words, essay on drug abuse in 500-1000 words.

Drug abuse is a global issue that poses serious risks to individuals and society. It involves the harmful and excessive use of drugs, leading to physical and mental health problems. Drug abuse can result in addiction, organ damage, cognitive impairment, and social and economic difficulties. Prevention efforts should focus on education, raising awareness about the dangers of drug abuse, and promoting healthy lifestyles. Access to quality healthcare and addiction treatment services is crucial for recovery. Strengthening law enforcement measures against drug trafficking is necessary to address the supply side of the problem. Creating supportive environments and opportunities for positive engagement can help prevent drug abuse. By taking collective action, we can combat drug abuse and build healthier communities.

Drug abuse is a growing global concern that poses significant risks to individuals, families, and communities. It refers to the excessive and harmful use of drugs, both legal and illegal, that have negative effects on physical and mental health.

Drug abuse has severe consequences for individuals and society. Physically, drug abuse can lead to addiction, damage vital organs, and increase the risk of overdose. Mentally, it can cause cognitive impairment, and psychological disorders, and deteriorate overall well-being. Additionally, drug abuse often leads to social and economic problems, such as strained relationships, loss of employment, and criminal activities.

Preventing drug abuse requires a multi-faceted approach. Education and awareness programs play a crucial role in informing individuals about the dangers of drug abuse and promoting healthy lifestyle choices. Access to quality healthcare and addiction treatment services is vital to help individuals recover from substance abuse. Strengthening law enforcement efforts to curb drug trafficking and promoting international cooperation is also essential to address the supply side of the issue.

Community support and a nurturing environment are critical in preventing drug abuse. Creating opportunities for individuals, especially young people, to engage in positive activities and providing social support systems can serve as protective factors against drug abuse.

In conclusion, drug abuse is a significant societal problem with detrimental effects on individuals and communities. It requires a comprehensive approach involving education, prevention, treatment, and enforcement. By addressing the root causes, raising awareness, and providing support to those affected, we can combat drug abuse and create a healthier and safer society for all.

Title: Drug Abuse – A Global Crisis Demanding Urgent Action

Introduction :

Drug abuse is a pressing global issue that poses significant risks to individuals, families, and communities. It refers to the excessive and harmful use of drugs, both legal and illegal, that have detrimental effects on physical and mental health. This essay explores the causes and consequences of drug abuse, the social and economic impact, prevention and treatment strategies, and the importance of raising awareness and fostering supportive communities in addressing this crisis.

Causes and Factors Contributing to Drug Abuse

Several factors contribute to drug abuse. Genetic predisposition, peer pressure, stress, trauma, and environmental influences play a role in initiating substance use. The availability and accessibility of drugs, as well as societal norms and cultural acceptance, also influence drug abuse patterns. Additionally, underlying mental health issues and co-occurring disorders can drive individuals to self-medicate with drugs.

Consequences of Drug Abuse

Drug abuse has devastating consequences on individuals and society. Physically, drug abuse can lead to addiction, tolerance, and withdrawal symptoms. Substance abuse affects vital organs, impairs cognitive function, and increases the risk of accidents and injuries. Mental health disorders, such as depression, anxiety, and psychosis, are often associated with drug abuse. Substance abuse also takes a toll on relationships, leading to strained family dynamics, social isolation, and financial instability. The social and economic costs of drug abuse include increased healthcare expenses, decreased productivity, and the burden on criminal justice systems.

Prevention and Education

Preventing drug abuse requires a comprehensive and multi-faceted approach. Education and awareness programs are essential in schools, communities, and the media to inform individuals about the risks and consequences of drug abuse. Promoting healthy coping mechanisms, stress management skills, and decision-making abilities can empower individuals to resist peer pressure and make informed choices. Early intervention programs that identify at-risk individuals and provide support and resources are crucial in preventing substance abuse.

Treatment and Recovery

Access to quality healthcare and evidence-based addiction treatment is vital in addressing drug abuse. Treatment options include detoxification, counseling, behavioral therapies, and medication-assisted treatments. Rehabilitation centers, support groups, and outpatient programs provide a continuum of care for individuals seeking recovery. Holistic approaches, such as addressing co-occurring mental health disorders and promoting healthy lifestyles, contribute to successful long-term recovery. Support from family, friends, and communities plays a significant role in sustaining recovery and preventing relapse.

Law Enforcement and Drug Policies

Effective law enforcement efforts are necessary to disrupt drug trafficking and dismantle illicit drug networks. International cooperation and collaboration are crucial in combating the global drug trade. Additionally, drug policies should focus on a balanced approach that combines law enforcement with prevention, treatment, and harm reduction strategies. Shifting the emphasis from punitive measures toward prevention and rehabilitation can lead to more effective outcomes.

Creating Supportive Communities:

Fostering supportive communities is vital in addressing drug abuse. Communities should provide resources, social support networks, and opportunities for positive engagement. This includes promoting healthy recreational activities, providing vocational training, and creating safe spaces for individuals in recovery. Reducing the stigma associated with drug abuse and encouraging empathy and understanding are crucial to building a compassionate and supportive environment.

Conclusion :

Drug abuse remains a complex and multifaceted issue with far-reaching consequences. By addressing the causes, raising awareness, implementing preventive measures, providing quality treatment and support services, and fostering supportive communities, we can combat drug abuse and alleviate its impact. It requires collaboration and a collective effort from individuals, communities, governments, and organizations to build a society that is resilient against the scourge of drug abuse. Through education, prevention, treatment, and compassion, we can pave the way toward a healthier and drug-free future.

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Adolescent Substance Use and the Brain: Behavioral, Cognitive and Neuroimaging Correlates

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Adolescence is an important ontogenetic period that is characterized by behaviors such as enhanced novelty-seeking, impulsivity, and reward preference, which can give rise to an increased risk for substance use. While substance use rates in adolescence are generally on a decline, the current rates combined with emerging trends, such as increases in e-cigarette use, remain a significant public health concern. In this review, we focus on the neurobiological divergences associated with adolescent substance use, derived from a cross-sectional, retrospective, and longitudinal studies, and highlight how the use of these substances during adolescence may relate to behavioral and neuroimaging-based outcomes. Identifying and understanding the associations between adolescent substance use and changes in cognition, mental health, and future substance use risk may assist our understanding of the consequences of drug exposure during this critical window.

Introduction

Adolescence is characterized by a series of developmental changes occurring roughly between 10–19 years, with the timing of onset highly impacted by social, cultural, and nutritional influences (Spear, 2000 ). During this time, the body experiences increased production of gonadal steroids that contribute to growth and sexual development (Spear, 2000 ). Additionally, a vast array of neurodevelopmental changes occur during this time, including cortical thinning and gray matter volume (GMV) reductions, increases in white matter volume, synaptic pruning, and reorganization within cortical and limbic regions (Schneider, 2013 ; Spear, 2014 ; Jaworska and MacQueen, 2015 ; Dumontheil, 2016 ; Thorpe et al., 2020 ). These neurodevelopmental changes give rise to characteristic behaviors during adolescence, such as improvements in cognition and executive functions; increases in reward sensitivity, novelty-seeking, risk-taking behavior; as well as a tendency to spend more time with peers (Spear, 2000 ; Choudhury et al., 2006 ; Romer, 2010 ). Some of these behavioral characteristics, in turn, contribute to a greater likelihood of initiating substance use (Lisdahl et al., 2018 ). The temporal overlap between substance use initiation and the vulnerable neurodevelopmental windows makes this an important period to study (Spear, 2000 ; Thorpe et al., 2020 ).

Substance use (used broadly to include alcohol and other drugs) by adolescents remains a significant public health concern. According to the most recent National Epidemiologic Survey on Alcohol and Related Conditions, more than 50% of substance use initiation cases occur between the ages 15–19 (Blanco et al., 2018 ). Moreover, an earlier age of onset of use is significantly associated with the risk of developing a substance use disorder later in life (Taioli and Wynder, 1991 ; Viner and Taylor, 2007 ). While the prevalence of substance use has declined in recent years from historical highs, recent surveys show that there have been some specific increases in the past year and that some concerning patterns may be emerging. According to the University of Michigan’s Monitoring the Future Survey in 2019, the prevalence of cannabis use as well as any illicit drug use in students in grades 8–12 have remained consistently high across prior decades (Johnston et al., 2020 ). Furthermore, nicotine vaping continued to be a concern with over one in three grade 12 students reporting past-year use (with 25.5% of these students indicating past month use), and this prevalence remains substantially higher than other forms of tobacco, including cigarettes, which continues to decline (Johnston et al., 2020 ). Another emerging trend from the survey suggested that the declining trends in alcohol use and binge drinking may be leveling off (Johnston et al., 2020 ). Despite the declines from historical highs, by the end of high school, four out of every 10 students reported consuming alcohol in their lifetime. In addition to the increased risk for future substance use, adolescent drug use can also negatively impact ongoing neurodevelopment, which might contribute to the risk for cognitive impairments and psychopathology. A growing body of research predominantly consisting of findings from magnetic resonance imaging (MRI) studies is beginning to unravel the structural and functional changes associated with these clinical outcomes.

This review will outline the cognitive, psychopathological, and future drug use related associations with adolescent substance use, especially related to the emerging trends in this use that have not been addressed in previous reviews. We will also present brain-imaging based neurobiological correlates of these findings when applicable, providing a unique perspective on these associations and potential interactions between behavioral and neural domains. While the specific behaviors under each of the reviewed domains may differ between the drug classes (depending on the availability of research findings), this approach helps to contrast the similarities and differences between the different drugs. We focus on findings from studies of substances most commonly used during adolescence, namely tobacco and e-cigarettes, alcohol, and cannabis (Johnston et al., 2020 ); while other less prevalent drug classes (e.g., stimulants, ecstasy) are not addressed in this review (for a review see Squeglia et al., 2009a ), we chose to include opioids and drug co-use as additional drug classes due to the lack of existing syntheses on these topic. Although brain development continues well into adulthood (Spear, 2014 ), we limit this review to studies using adolescent sample populations with a mean age of 19-years-old or lower to capture the potential effects of drug use during the most dynamic stages of post-childhood development. This review comes at a time of recreational cannabis legalization and decriminalization by government bodies across the globe despite our somewhat incomplete understanding of its causal impacts on the developing brain alone, or in combination with other drugs commonly used by youth. Importantly, we also summarize the currently available findings surrounding the potential consequences of vaping, which has quickly become one of the most common methods of nicotine and cannabis delivery in youth, one that is still under-represented in the literature to date.

Tobacco and E-Cigarettes

In 2017, it was estimated that 4.9% of adolescents in the United States aged 12–17 were current users of tobacco products, including cigarettes, cigars, smokeless tobacco (i.e., snuff, chew), and pipe tobacco (Substance Abuse and Mental Health Services Administration, 2018 ). Recent estimates suggest 3.7% of adolescents regularly use cigarettes ( Figure 1A ; Johnston et al., 2020 ). These estimates, along with results from the US National Survey on Drug Use and Health, indicate that the prevalence of tobacco use is at its lowest levels since 1991 (Substance Abuse and Mental Health Services Administration, 2019 ; Johnston et al., 2020 ). These declining trends in tobacco use, however, contrast with nicotine vaping rates among teens; more adolescents in grades 8, 10, and 12 are estimated to be vaping nicotine than smoking combustible cigarettes ( Figure 1B ; Johnston et al., 2020 ), and the rate of use has been steadily increasing since 2011 (US Department of Health and Human Services, 2016 ). In this age group, nicotine vaping is often perceived as less harmful than traditional smoking (Parker et al., 2018 ; Jun et al., 2019 ), likely contributing to the growing proportion of adolescents who experiment with, and regularly use e-cigarettes. Traditional smoking habits are initiated almost exclusively between early adolescence and young adulthood (Substance Abuse and Mental Health Services Administration, 2019 ), and initiating e-cigarette use in later adulthood is unlikely relative to those under the age of 25 ( National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health, 2016 ).

Prevalence of substance use and substance use disorder in adolescents. (A) Collated data from the 2017 National Survey on Drug Use and Health, 2018 National Survey on Drug Use and Health, and 2019 Monitoring the Future Survey showing the past 30-day substance use by U.S. adolescents, along with the reported percentage of adolescents with specific substance use disorders (Substance Abuse and Mental Health Services Administration, 2018 ; Substance Abuse and Mental Health Services Administration, 2018 ; Johnston et al., 2020 ). (B) Adolescent substance use by school grade (8, 10, 12) as per the 2019 Monitoring the Future report (Johnston et al., 2020 ). All categories represent self-reported substance use in the past 30 days except for heavy alcohol use (five or more drinks in a row) in the past two weeks. Emerging substance use behaviors (i.e., nicotine and cannabis vaping) are highlighted by a red box.

Nicotine, the primary psychoactive component of cigarette smoke and e-cigarette liquid, is highly addictive and can impact brain development when its use is initiated during adolescence (Thorpe et al., 2020 ). Nicotine interacts with nicotinic acetylcholine receptors within the body; however, there is a paucity of studies investigating human nicotinic acetylcholine receptors activity and development in the context of adolescent smoking owing to methodological and ethical limitations (e.g., use of radioisotopes in positron emission tomography). As such, most neurobiological studies utilize structural MRI to investigate gross brain morphology; functional MRI (fMRI) to infer brain region activity based on dynamic cerebral blood flow measured through blood oxygenation level-dependent (BOLD) imaging; and diffusion tensor imagining (DTI) to investigate white matter microstructure via water diffusivity across axon bundles (Beres, 2017 ; Yousaf et al., 2018 ). Below, we synthesize findings from studies that suggest potential cognitive-, psychopathology-, and future drug use susceptibility-related outcomes associated with nicotine use during the adolescent period, be it through combustible cigarette consumption or e-cigarette use and relate these findings to neural correlates. Summaries of these studies can be found in Supplementary Table S1 .

Adolescence is a period of attentional development and is characterized by impulsive and risk-taking behaviors (Romer, 2010 ). Several longitudinal (Treur et al., 2015 ; Akkermans et al., 2017 ) and cross-sectional (Tercyak et al., 2002 ; Jacobsen et al., 2005 , 2007b , c ) reports implicate a relationship between adolescent smoking and worsened attentional performance relative to non-smoking youth. Though not always significant (Jacobsen et al., 2007b , c ), studies consistently report more symptoms of inattention in smokers compared to non-smokers that persist into adulthood (Tercyak et al., 2002 ; Treur et al., 2015 ; Akkermans et al., 2017 ). Performance during selective and divided attention tasks are similarly observed to be poorer in smoking adolescents compared to their non-smoking peers (Jacobsen et al., 2005 , 2007b , c ; Bi et al., 2017 ; Li et al., 2017 ), especially in males (Jacobsen et al., 2005 ), although divided, but not selective, attentional deficits may be related to nicotine withdrawal (Jacobsen et al., 2005 , 2007c ). Although performance deficits in some of these attentional tasks may stem from smoking-associated working memory impairments (Jacobsen et al., 2005 , 2007c ), findings from neural correlate studies conducted in smoking and non-smoking youth suggest that smoking behaviors impact the development and function of attentional brain circuits. Many studies have shown morphological and functional differences between smoking and non-smoking adolescents in the prefrontal cortex (PFC), inferior parietal cortex, and anterior insula that in part comprise the selective and divided attention neural circuits (Elsey et al., 2016 ). Gray matter loss in the cortex may be exacerbated by smoking; smokers reportedly have lower amounts of gray matter in the frontal cortex (Li et al., 2015 ; Akkermans et al., 2017 ; Chaarani et al., 2019 ), inferior parietal lobe (Li et al., 2015 ; Akkermans et al., 2017 ), and insula (Li et al., 2015 ) than non-smoking controls, and gray matter in the dorsolateral PFC (DLPFC) was negatively correlated with smoking dependency (Li et al., 2015 ). A recent fMRI study of adolescent smokers found that resting-state functional connectivity (RSFC) was lower between the anterior insula and the DLPFC, amygdala, and striatum of smokers compared to non-smokers (Bi et al., 2017 ). The activity of the DLPFC appears to be important for divided attention performance, such that greater activation of this brain region is associated with worse performance accuracy when multiple sensory modalities are required (Johnson and Zatorre, 2006 ), and RSFC between the anterior insula with the DLPFC and inferior parietal cortex may be reduced during acute smoking abstinence (Fedota et al., 2018 ). Although DLPFC activity has not been monitored during task performance in smoking adolescents, resting-state deficits in the DLPFC during minimal nicotine deprivation conditions found by Bi et al. ( 2017 ) suggest smoking-induced functional changes to networks important to divided attention, though the appearance of cognitive impairments such as those found by Jacobsen et al. ( 2005 ) may depend on smoking recency. Collectively, these results suggest that the neurotoxic effects of smoking may interfere with the normal developmental trajectory and function of attention-related brain regions and consequently manifest as attentional deficits.

Tobacco use is also suggested to have long-term impacts on inhibitory control, which could prevent future abstinence from smoking through a failure to suppress smoking urges. However, adolescents consistently report fewer withdrawal symptoms relative to adult smokers (McNeill et al., 1986 ; Rojas et al., 1998 ) and studies measuring inhibitory control and impulsivity behaviors on adolescent smoking patterns have been conflicting. Counterintuitively, some studies have found that impulsivity (Tercyak et al., 2002 ) and distractibility (DiFranza et al., 2007 ) are protective factors against current cigarette use and the loss of control over smoking relative to adolescents without symptoms of impulsivity, whereas others have identified a positive association between impulsivity and cigarette use (Leventhal et al., 2015 ). These inconsistent findings, as suggested by DiFranza et al. ( 2007 ), may be attributed to only some studies controlling for medication status in individuals with co-occurring psychiatric disorders affecting impulsivity such as attention-deficit/hyperactivity disorder (ADHD). Furthermore, there is conflicting evidence surrounding the association between adolescent smoking and inhibitory control performance, with one study finding that smokers commit more errors in the Go/No-Go task (Yin et al., 2016 ), whereas another study found that adolescent smokers do not show inhibitory control deficits during the Stop-Signal task (Galván et al., 2011 ) compared to non-smoking peers. However, negative correlations between successful Stop-Signal inhibition trial reaction times and BOLD activation in regions important to inhibitory control have been reported such that greater activation was associated with faster responding (Galván et al., 2011 ). These correlations indicate that inhibitory control regions (Goldstein and Volkow, 2002 ; Zhang et al., 2017 ) are possibly affected by adolescent smoking, supported by findings of smoking-associated abnormalities in the adolescent anterior cingulate (ACC; Lee et al., 2005 ; Rubinstein et al., 2011b ; Bi et al., 2017 ; Li et al., 2017 ), insula (Lee et al., 2005 ; Jacobsen et al., 2007c ; Rubinstein et al., 2011a ; Li et al., 2015 , 2017 ; Bi et al., 2017 ), and orbitofrontal cortex (OFC; Dinn et al., 2004 ; Li et al., 2015 ; Akkermans et al., 2017 ). The Go/No-Go and Stop-Signal tasks are thought to rely on unique neural correlates despite sharing a common core network, which may explain the discrepant cognitive results between studies (Zhang et al., 2017 ; Raud et al., 2020 ). Taken together, these findings suggest that baseline inhibitory control and impulsive behavior may determine the risk for adolescent smoking, and likewise adolescent smoking may be a detriment to inhibitory control processing.

Aside from potential smoking-induced deficits in attentional and inhibitory processes, there is some evidence that adolescent smoking alters intelligence. A longitudinal study of older adolescent male current smokers, former smokers, and non-smokers found that cognitive abilities related to intelligence quotient (IQ) were negatively correlated with the number of cigarettes smoked per day, that performance deficits were more pronounced in current smokers than non-smokers and former smokers, and that cognitive performance was lower in former smokers than non-smokers (Weiser et al., 2010 ). Among discordant smoking sibling pairs, smokers were also more likely to have a lower IQ than their non-smoking counterparts. Furthermore, future smoking was more likely in males with lower baseline cognitive scores compared to those who did not initiate smoking, suggesting lower IQ may be predictive of future smoking, which has been supported (Corley et al., 2012 ; Wraw et al., 2018 ) and contended (Batty et al., 2007 ) by other studies comparing childhood IQ with smoking in adulthood.

Adolescent smoking may also impact working memory in a sensory modality-dependent fashion. Auditory working memory accuracy was found to be worse in adolescent smokers compared to non-smokers (Jacobsen et al., 2005 , 2007a ). These auditory cognitive deficiencies were later recapitulated by the same group, which showed greater smoking-associated deficits in auditory relative to visual cognitive performance (Jacobsen et al., 2007c ). These auditory working memory deficits are supported by fMRI findings suggesting that brain regions supporting auditory working memory, such as the inferior frontal gyrus and parietal lobes, show greater activation with worse task performance, suggesting network inefficiency in smokers (Jacobsen et al., 2007a ). Likewise, DTI findings suggest that smoking youth have altered white matter integrity compared to non-smokers, as indicated by greater fractional anisotropy (FA), an indirect measure of axonal organization and coherence, in auditory corticothalamic tracts (Jacobsen et al., 2007b ). This is in line with findings of a recent meta-analysis of smokers under 30-years-old suggesting brain-wide increases in FA compared to non-smokers, which could represent greater white matter integrity or myelination, or deleterious vasogenic swelling in these tracts (Gogliettino et al., 2016 ). In addition, smoking-associated differences in hippocampal (Jacobsen et al., 2007c ; Rubinstein et al., 2011b ) and parahippocampal (Rubinstein et al., 2011b ; Li et al., 2015 ) function and morphology have also been reported in smoking youth, further supporting potential effects of smoking during adolescence on alterations in memory performance.

Age of initiation is an important factor in the trajectory of potential negative outcomes of smoking. Attentional (Treur et al., 2015 ) and working memory (Jacobsen et al., 2005 ) performance impairments are less pronounced in those who initiated smoking at a later age. Also, an earlier onset of cigarette use initiation and regular use are both consistently associated with heavier smoking patterns and craving in later adolescence (Stanton, 1995 ; Everett et al., 1999 ; Colder et al., 2001 ; Riggs et al., 2007 ; Dierker et al., 2012 ; Buchmann et al., 2013 ) and adulthood (Taioli and Wynder, 1991 ; Klein et al., 2013 ; Lanza and Vasilenko, 2015 ), as well as greater smoking cue reactivity in adulthood (Mashhoon et al., 2018 ). Greater cognitive deficits associated with an earlier age of smoking initiation may, therefore, reflect a unique early adolescent vulnerability to the effects of nicotine exposure and/or a cumulative impact of smoking duration on cognition. Although dependence and withdrawal symptoms are reportedly lower in adolescents compared to adults (McNeill et al., 1986 ; Rojas et al., 1998 ), dependence in smoking adolescents could lead to loss of smoking autonomy (McNeill et al., 1986 ; Rojas et al., 1998 ; DiFranza et al., 2000 , 2002 ). One theory suggests that adolescents are at higher risk for the future negative consequences of smoking because they are less likely to experience negative feelings associated with tobacco use, and thus will continue their habits despite the known health risks, subsequently leading to more damage to the brain through the neurotoxic effects of nicotine (O’Dell et al., 2004 ). Considering this, early-onset adolescent smoking may cause a greater deviation in the developmental trajectory of attentional-, memory-, inhibitory control-associated brain regions than those who are late-onset users, and consequently worsen the management of withdrawal symptoms during cessation attempts. Together, these studies highlight the importance of early cessation interventions for adolescent smokers, especially for those who initiate their smoking habits at younger ages, to mitigate the potential cognitive impairments that arise from adolescent smoking as well as the known health risks associated with chronic smoking in adulthood.

Psychopathology

Schizophrenia and psychosis.

Heavy nicotine dependence is prevalent in 16–46% of those in the prodromal phase of schizophrenia (Gogos et al., 2019 ), leading researchers to question if there is a causal relationship between schizophrenia and smoking (i.e., does smoking increase the risk for schizophrenia, or does having schizophrenia promote smoking habits to alleviate disease symptoms?), if the risk for smoking and schizophrenia share common mechanistic underpinnings, or both (Khokhar et al., 2018 ). The link between adolescence, schizophrenia, and nicotine use has been intensely investigated; almost all schizophrenia diagnoses occur during adolescence and young adulthood, and neurobiological systems that develop during adolescence include those that are implicated in both schizophrenia and smoking (Selemon and Zecevic, 2015 ). While few studies have found no (Dinn et al., 2004 ) or a negative association (Zammit et al., 2003 ) between adolescent smoking and psychosis outcomes, most studies indicate that smoking during adolescence and young adulthood is associated with increased risk for the development of schizophrenia (Weiser et al., 2004 ; Myles et al., 2012b ; McGrath et al., 2016 ; Mustonen et al., 2018 ). This increased risk is especially prominent in individuals who engage in heavy smoking behaviors (Weiser et al., 2004 ; Mustonen et al., 2018 ) and initiate smoking during early adolescence as compared with older youth (McGrath et al., 2016 ; Mustonen et al., 2018 ). Importantly, unaccounted for confounders in these studies may contribute extensively to the observed relationship between smoking onset and future psychotic experiences (Jones et al., 2018 ). However, the age of smoking onset does not appear to alter the temporal course of psychosis development, as a meta-analysis found that while an earlier age of smoking onset predicted diagnosis, smoking status did not predict an earlier disease onset (Myles et al., 2012a ).

Although the etiology of schizophrenia is complex and disrupted the development of many brain regions has been implicated in its emergence, neurobiological abnormalities and cognitive impairments associated with adolescent smoking overlap with those observed in schizophrenia. For instance, patients with schizophrenia present with deficits in cognitive processes such as inhibition, attention, and working memory, and show the aberrant activity of brain regions such as the DLPFC, ACC, and parietal lobes, all of which are implicated in adolescent smoking effects (Selemon and Zecevic, 2015 ). As previously mentioned, adolescence is a critical period of cortical development, and gray matter loss occurs into adulthood as synapses are pruned. Cortical gray matter reductions undergo steeper declines in those with schizophrenia than healthy individuals, suggesting a link between synapse refinement and development of the disease (Selemon and Zecevic, 2015 ). Since cortical thickness and GMV is lower in smoking adolescents (Li et al., 2015 ; Akkermans et al., 2017 ; Chaarani et al., 2019 ) and the rate of cortical thinning, though non-significant, is greater in novel smokers compared to non-smokers (Akkermans et al., 2017 ), it is possible that smoking may exacerbate gray matter declines in youth with a genetic predisposition to develop schizophrenia. Longitudinal MRI studies of adolescent smokers and non-smokers with schizophrenia risk (genetic or environmental) would help to elucidate the potential for additive effects of these factors on gray matter development.

Attention-Deficit/Hyperactivity Disorder

Like schizophrenia, problematic nicotine consumption disproportionately affects individuals with ADHD. Multiple theories, such as common mechanistic underpinnings, disease-associated predisposition, and the self-medication hypothesis, have been presented to address why this is the case (Van Amsterdam et al., 2018 ). While it is clear that ADHD is a risk factor in smoking initiation and dependence (see Glass and Flory, 2010 ), there is sparse and conflicting evidence surrounding the potential for smoking to impact ADHD trajectory and symptomology in adolescents. Some studies found that ADHD symptoms are more apparent in smokers compared to non-smokers (Dinn et al., 2004 ; Akkermans et al., 2017 ) but conflict on which symptoms were associated with regular smoking. Specifically, the longitudinal study by Akkermans et al. ( 2017 ) investigating smoking and non-smoking older adolescents with or without an ADHD diagnosis found that symptoms of inattention but not hyperactivity/impulsivity were greater in smokers at baseline, whereas the cross-sectional study by Dinn et al. ( 2004 ) of college-aged participants that did not specifically target those with an established ADHD diagnosis found that only hyperactivity/impulsivity symptoms were more likely in smokers compared to non-smokers. Additionally, Akkermans et al. ( 2017 ) did not find that the trajectory of inattention symptom count was correlated with smoking status between study time-points. Given the paucity of studies on the topic and conflicting evidence between existing studies, there is little to suggest that smoking during adolescence exacerbates or alleviates ADHD symptoms or alters symptom trajectory. Future longitudinal studies in smoking and non-smoking adolescents with an ADHD diagnosis should be conducted to delineate if this is truly the case considering the significant overlap between smoking and ADHD, and that the self-medication hypothesis is one of the predominant theories in the field to explain why those with ADHD smoke.

Recently, ADHD research has focused on disruptions to cortical thickness and white matter development within the brain that appear to be a characteristic of the disease (van Ewijk et al., 2012 ; Bouziane et al., 2018 ; Albajara Sáenz et al., 2019 ). As with the brains of patients with schizophrenia, imaging studies show that cortical thickness is reduced in the brains of patients with ADHD (Albajara Sáenz et al., 2019 ), and smoking during adolescence may have additive effects on cortical thickness. Current evidence does not support this theory, but it is important to highlight that only one small cohort longitudinal study to date has investigated cortical thickness in ADHD-affected and non-affected smokers and non-smokers, which may not have had the statistical power needed to observe a relationship (Akkermans et al., 2017 ). There are also notable overlaps in white matter abnormalities seen in smokers and those with ADHD. Although the direction of differences compared to control subjects conflicted between studies, meta-analysis of white matter integrity in ADHD patients indicated that ADHD individuals have abnormal FA within tracts of the basal ganglia (i.e., caudate nucleus, anterior corona radiata, internal capsule), as well as the cerebellum, corpus callosum, and right forceps minor (van Ewijk et al., 2012 ). Adolescent smokers show increased FA in the corpus callosum, internal capsule, and inferior longitudinal fasciculus (Jacobsen et al., 2007b ; Yu et al., 2016 ), as well as the corona radiata (Yu et al., 2016 ) and forceps minor (Jacobsen et al., 2007b ) relative to non-smokers. However, the exact relationship between smoking and FA in the corpus callosum remains unclear considering FA in this region has been negatively, rather than positively, correlated with the extent of youth smoking history (Chaarani et al., 2019 ). These overlapping regional differences in white matter integrity between young smokers and those with ADHD could suggest that the ADHD brain is differentially sensitive to tobacco’s potential effects on white matter compared to those without ADHD. Interestingly, Van Ewijk et al. ( 2015 ) found abnormal white matter characteristics in both smoking and ADHD individuals, albeit in opposing directions; that is, lower FA was associated with ADHD, while FA was increased in smokers. Although the significance of these findings is unclear, it has been suggested that pre-morbid differences in white matter integrity in the brains of those with ADHD may contribute to confounding study results and may also be a causal factor in smoking initiation and maintenance as opposed to a consequence of smoking (Groenman et al., 2013 ; Van Ewijk et al., 2015 ). Also, a recent study has suggested that the developmental trajectory of white matter during young adolescence (10- to 12-years-old) is only reduced in those taking medications to treat symptoms of ADHD, but not medication-naïve patients (Bouziane et al., 2018 ). Prior and current treatment of ADHD with medication should, therefore, be included as a variable in investigations of white matter integrity in the ADHD brain of adolescent smokers, as it is unclear whether there is a synergistic effect of smoking and medication history on white matter microstructure across the span of adolescent brain development.

Depression and Anxiety

Studies reliably point to an association between adolescent smoking with depression. Depression and depressive symptoms are consistently observed in smoking adolescents compared to their non-smoking peers (Wu and Anthony, 1999 ; Goodman and Capitman, 2000 ; Albers and Biener, 2002 ; Jacobsen et al., 2007c ; Needham, 2007 ; Ilomäki et al., 2008 ; Audrain-Mcgovern et al., 2009 ; Morrell et al., 2010 ; Slomp et al., 2019 ). Most studies of this age group found smoking positively predicted the development of depression and depressive symptoms (Brown et al., 1996 ; Stein et al., 1996 ; Choi et al., 1997 ; Goodman and Capitman, 2000 ; Windle and Windle, 2001 ; Albers and Biener, 2002 ; Brook et al., 2002 , 2004 ; Galambos et al., 2004 ; Duncan and Rees, 2005 ; Rodriguez et al., 2005 ; Boden et al., 2010 ; Moon et al., 2010 ; Jamal et al., 2011 ; Beal et al., 2014 ; Gage et al., 2015 ), but not all findings have supported this association (Wang et al., 1996 ; Repetto et al., 2005 ; Clark et al., 2007 ; Munafò et al., 2008 ; Hu et al., 2011 ; Strong et al., 2014 ). Importantly, the relationship between smoking and depression in adolescence has been suggested to be bidirectional, such that baseline depression contributes to the risk for future smoking habits just as baseline smoking predicts depression (Brown et al., 1996 ; Windle and Windle, 2001 ; Galambos et al., 2004 ; Needham, 2007 ; Audrain-Mcgovern et al., 2009 ; Moon et al., 2010 ). Some studies also indicate that baseline depression is a considerable factor in the trajectory of depression symptom development in that smoking may mitigate symptom number acceleration, supporting the notion that a subgroup of adolescents smokes to self-medicate (Rodriguez et al., 2005 ; Needham, 2007 ; Audrain-Mcgovern et al., 2009 ). In comparison to depression and depressive symptoms, there is little evidence to suggest adolescent smoking is a predictor of future anxiety (Brown et al., 1996 ; Gage et al., 2015 ), but one retrospective, cross-sectional study did observe that an earlier onset of smoking (<15-years-old) was associated with an earlier anxiety diagnosis compared to late-onset smokers (Jamal et al., 2011 ). However, the cross-sectional and retrospective nature of this study, and that it only included participants that smoked before diagnosis, is a considerable limitation of this finding in concluding the relationship between tobacco use and anxiety disorder. As is the case with the other psychopathologies associated with adolescent smoking, the relationship between smoking, depression, and anxiety is unclear, with evidence supporting that smoking is a causative factor in the development of depression and anxiety, that pre-existing poor mental health facilitates smoking behavior, or that there is an underlying predisposition for smoking, depression, and anxiety to arise independently of each other.

A prevalent theory in the field of smoking, depression, and anxiety is that adolescents smoke to self-medicate, although some argue against this idea (Boden et al., 2010 ; Beal et al., 2014 ). Monoamine systems implicated in depression may be indirectly modulated by nicotine’s effects on cholinergic neurotransmission differentially in baseline depressed and non-depressed youth (Dao et al., 2011 ; Rendu et al., 2011 ; Pitsillou et al., 2020 ). This could explain why smoking is generally associated with more depressive symptoms, but deceleration of symptom progression in those with depressive symptomology preceding smoking onset (Rodriguez et al., 2005 ; Needham, 2007 ; Audrain-Mcgovern et al., 2009 ). This is further complicated by the dynamic development of cortical and limbic receptor expression observed during the adolescent critical period in animal models (Thorpe et al., 2020 ). The introduction of exogenous receptor ligands, such as nicotine may have consequences on neurotransmission that can impact youth behavior and cognition immediately, including the production of positive feelings (e.g., relaxation). However, repetitive insults to these systems by the actions of nicotine may also modulate the expression profile of neurotransmitter receptors, synthesizing enzymes, and metabolizing enzymes, ultimately changing neural activity that could contribute to the risk for depression and anxiety (Thorpe et al., 2020 ). The possibility of reciprocal feedback between depression and smoking should also be considered in those with smoking onset preceding depression, such that smoking may lead to the emergence of depressive symptoms that are alleviated by maintaining smoking habits.

Future Substance Use

Future drug and alcohol use.

Chronic nicotine exposure may lead to an increased risk for neurochemical and pathological changes in the brain, and adolescent smoking is also associated with future substance use. As previously mentioned, adolescent smoking is a strong predictor of future smoking, and this risk is greater with a younger age of use (Taioli and Wynder, 1991 ; Stanton, 1995 ; Everett et al., 1999 ; Colder et al., 2001 ; Riggs et al., 2007 ; Dierker et al., 2012 ; Buchmann et al., 2013 ; Klein et al., 2013 ; Lanza and Vasilenko, 2015 ). Similarly, adolescent and young adult smokers are reported to consume more alcohol (Galván et al., 2011 ) and cannabis (Caris et al., 2009 ), and adolescent smoking is a predictor of future substance use (Lewinsohn et al., 1999 ; Dinn et al., 2004 ; Ilomäki et al., 2008 ), especially when smoking is initiated in early adolescence (Brown et al., 1996 ; Lewinsohn et al., 1999 ; Ilomäki et al., 2008 ). The increased risk for future substance use may be a consequence of alterations to the brain’s reward circuit (Rubinstein et al., 2011a , b ; Li et al., 2015 ). For instance, multiple DTI studies have reported higher FA within the basal ganglia of smokers (Jacobsen et al., 2007b ; Van Ewijk et al., 2015 ; Yu et al., 2016 ), including in fibers of the external capsule that terminate in the ventral striatum (Van Ewijk et al., 2015 ). The ventral striatum plays an integral role in motivation and reward, and the activity of dopaminergic neurons in this region is thought to be modulated by acute and chronic drug use (Volkow and Morales, 2015 ). Therefore, disruption of white matter tracts in this region, possibly caused by regular smoking, may leave adolescents susceptible to the rewarding potential of tobacco and other substances. This vulnerability may also extend to a future attention bias toward smoking cues; fMRI studies by Rubinstein et al. ( 2011a , b ) suggest that even adolescent light smokers have blunted neural responses to naturally reinforcing stimuli (i.e., food; Rubinstein et al., 2011a ) while simultaneously showing greater neural activation to smoking cues (Rubinstein et al., 2011b ) in comparison to non-smokers.

E-Cigarette Use and Transition to Combustible Tobacco Smoking

Although e-cigarettes potentially offer a less harmful alternative to traditional smoking, the use of e-cigarettes may increase the susceptibility for cigarette smoking in youth that would otherwise have not begun smoking (Barrington-Trimis et al., 2016 , 2018 ; Azagba et al., 2017 ; Miech et al., 2017 ; Soneji et al., 2017 ; Spindle et al., 2017 ; Wills et al., 2017 ; Parker et al., 2018 ; Berry et al., 2019 ; Vogel et al., 2019 , 2020 ), alongside the risk for vaping cannabis (Cassidy et al., 2018 ; Dai et al., 2018 ). Adolescents are attracted to e-cigarette flavors, especially those with fruit- and candy-like tastes, and a desire to use e-cigarettes for their taste is frequently cited as a reason for use (Vogel et al., 2019 ; Jackson et al., 2020 ). Also, recent findings by Chen et al. ( 2018 ) demonstrate that smoking and non-smoking youth report urges to smoke and have greater activation of reward-related brain regions following the presentation of e-cigarette advertisements in comparison to neutral cues. As such, e-cigarette advertisements and the availability of flavored e-cigarette liquids may make use of these devices enticing to youth, encourage initiation, and subsequently lead to the transition to combustibles and other drugs.

The rising popularity of next-generation e-cigarette devices are concerning due to their ability to deliver higher nicotine concentrations in the form of nicotine salts (Boykan et al., 2019 ). Adolescents who use high nicotine concentration liquids with e-cigarettes are more susceptible to higher frequency and intensity of combustible and e-cigarette use in the future relative to adolescents that do not engage in e-cigarette use or use liquids with lower nicotine concentrations (Goldenson et al., 2017 ; Boykan et al., 2019 ). One study suggests that urinary levels of cotinine, a metabolite of nicotine, are higher in adolescent e-cigarette users, relative to levels observed in another study of those who consume combustibles (Benowitz et al., 2018 ; Goniewicz et al., 2019 ). However, e-cigarettes can vary widely in the amount of nicotine they deliver per puff (Wagener et al., 2017 ) and this finding may depend on the model of e-cigarette used by the study population. While the use of newer model e-cigarettes results in almost double the mean urinary cotinine levels compared to traditional smoking, adolescents who use any model of e-cigarettes have lower urinary cotinine levels, than those who smoke combustibles (Boykan et al., 2019 ). Greater nicotine delivery efficiency apparent in newer generation e-cigarettes (Wagener et al., 2017 ; Boykan et al., 2019 ) coupled with the unique vulnerability of adolescents to rewarding substances may result in youth using e-cigarettes consuming higher levels of nicotine when compared to traditional smokers. Despite their potential for harm reduction relative to traditional smoking, the high risk for adolescents to transition from e-cigarettes to combustibles and other drugs, and the possible modulation of neural activity by e-cigarette use, must be considered in future research as well as in the context of marketing and health policies surrounding these devices.

According to the Monitoring the Future Survey conducted in 2019, 18.2% of adolescents in grades 8, 10, and 12 were current alcohol users ( Figure 1A ; Johnston et al., 2020 ). Of these, 1.6% reportedly had an alcohol use disorder (AUD; Substance Abuse and Mental Health Services Administration, 2019 ). Given the overlap between the high degree of neural reorganization and alcohol use initiation during adolescence (Zahr and Pfefferbaum, 2017 ), it is imperative to consider what impact this interaction may have on neurodevelopment. Alcohol acts primarily via γ-aminobutyric acid type-A and N-methyl-D-aspartate receptors, which regulate inhibitory and excitatory signaling within the brain, respectively (Chandrasekar, 2013 ; Mallard et al., 2018 ). An extensive body of evidence suggests that these neurotransmitter systems are affected by alcohol exposure, which may have long-lasting implications on overall neurocircuitry within the brain (Banerjee, 2014 ). The potential cognitive, psychopathological, and future substance use vulnerability outcomes associated with adolescent alcohol use are summarized in Supplementary Table S2 .

Several studies have evaluated and identified potential impairments related to adolescent alcohol use on many neurocognitive domains, including attention and inhibitory control. For instance, heavy-drinking adolescents exhibit impulsive choice and attentional bias for alcohol-related cues compared to light-drinkers (Field et al., 2007 ). Attentional bias for alcohol-related cues was also observed in adolescent social drinkers, suggesting that attentional bias may still emerge with limited alcohol use (Melaugh McAteer et al., 2015 ). The association between alcohol use and attention has also been demonstrated in a longitudinal study of adolescents who were first assessed before initiation of drinking and followed over three years. In this study, greater hangover symptoms in males predicted worsening of sustained attention (Squeglia et al., 2009b ). Similarly, adolescent alcohol drinking altered the developmental trajectory of impulsivity, whereby improvement in impulsivity decelerated following the onset of binge drinking (Ruan et al., 2019 ). Interestingly, a family history of alcoholism was shown to be protective concerning impulsivity by Jones et al. ( 2017 ). Adolescents with a family history of alcoholism who remained alcohol-naïve exhibited a greater decrease in impulsive choice across an eight-year follow-up period compared to those who went on to binge drink. Furthermore, a greater escalation of drinking was associated with greater impulsive choice in this study. The protective effect of a family history of alcoholism is not supported by earlier studies that suggest that youth with a family history of alcoholism exhibit developmental delay in executive functioning, including heightened impulsivity (see review by Cservenka, 2016 ). Therefore, future studies should focus on the extent to which familial alcoholism interacts with adolescent alcohol use to alter cognition. This may help uncover unique characteristics that may potentially help address some of the discrepant findings related to adolescent alcohol use throughout this section.

Supporting the cognitive differences related to attention and inhibition in adolescent alcohol users, youth who consume alcohol also exhibit neural activity differences. For instance, heavy-drinking adolescents exhibited attenuated activation in the left supplementary motor area, bilateral parietal lobule, right hippocampus, bilateral middle frontal gyrus, left superior temporal gyrus, and the ACC compared to light drinkers during a response inhibition task (Ahmadi et al., 2013 ). Similarly, Aloi et al. ( 2018 ) reported an association between increasing AUD severity and reduced BOLD responses within the ACC and the dorsomedial PFC during the affective Stroop task assessing emotional interference on cognitive functioning. Effects on inhibitory control may be dose-dependent as a longitudinal study of adolescents with low alcohol use did not find any impairments in the development of inhibitory control across adolescence and activation in related networks, such as the dorsal ACC, DLPFC, pre-supplementary motor area, and the posterior parietal cortex (Jurk et al., 2018 ). However, in a longitudinal assessment of adolescents aged 12–14 with very limited substance use histories at baseline, reduced activation in regions that largely overlapped with the Ahmadi et al. ( 2013 ) study during the same inhibitory task predicted transition into heavy alcohol use after approximately four years (Norman et al., 2011 ). This suggests that activation differences may predate, and possibly contribute to, the initiation of alcohol use. Another study has revealed a bidirectional relationship with reduced activation in frontal, temporal, and parietal regions during inhibitory tasks predicting future heavy drinking, and heavy drinking, in turn, predicting increased activation in frontal, parietal, subcortical, and cerebellar regions over time (Wetherill et al., 2013 ). Together, these findings suggest that neural vulnerabilities in regions implicated in inhibitory control predict alcohol use, and heavy drinking subsequently may lead to additional alterations. Similarly, Squeglia et al. ( 2014 ) have reported a bidirectional relationship with smaller cingulate and rostral ACC volumes at baseline predicting later transition to heavy drinking, and heavy drinking, in turn, predicting greater volume reductions in the left inferior/middle temporal gyrus and left caudate. Another study has demonstrated the reverse relationship between alcohol use and morphological differences, whereby smaller left dorsal and rostral paralimbic ACC volumes predicted later alcohol-related problems (Cheetham et al., 2014 ). These findings suggest that the relationship between alcohol use and neural differences is complex and on-going prospective studies (like the Adolescent Brain Cognitive Development study of the National Institute on Drug Abuse) that follow adolescents before the initiation of alcohol use and across development may help further clarify directionality.

Adolescent alcohol drinkers appear to exhibit poorer working and verbal memory (Brown et al., 2000 ; Hanson et al., 2011 ; Parada et al., 2012 ), suggesting that alcohol use during this critical window may predispose youth to memory impairments. However, adverse memory-related outcomes may improve after prolonged drinking abstinence. In a longitudinal study, interruption of binge-drinking patterns led to a partial cognitive recovery, with ex-binge drinkers having greater memory consolidation deficits than non-binge drinkers but fewer deficits than continued binge drinkers (Carbia et al., 2017a ). In a separate analysis by this group, binge drinkers showed improvements in working memory span but maintained consistent deficits in perseveration errors (Carbia et al., 2017b ). However, it is difficult to predict whether these differences in adolescent drinkers compared to their relatively abstinent peers were present before the initiation of alcohol use. In a study of adolescents first assessed at 11-years-old, working memory impairment predicted both baselines and increased frequency of alcohol use over a four-year follow-up period, while there was no evidence supporting the reverse relationship (Khurana et al., 2013 ). However, in adolescents first assessed before initiation of substance use, extreme-binge drinkers exhibited poorer performance in measures of verbal learning and memory despite equivalent performances at baseline (Nguyen-Louie et al., 2016 ). The latter study suggests that the effects of alcohol on learning and memory may be mediated by dose. Dose-dependent neurotoxicity of alcohol use is also observed in other neurocognitive domains that were previously discussed, including attention and impulsive choice (Squeglia et al., 2009b ; Jones et al., 2017 ). Therefore, more research is needed to develop strategies to reduce alcohol intake severity that may help temper the neurocognitive consequences related to adolescent alcohol use.

Adolescent alcohol users also differ in the degree of neural recruitment during a memory task performance from non-users. For instance, during verbal recall, non-drinking adolescents show activation of the left hippocampus whereas adolescents who engage in binge drinking do not and recall fewer words. Binge drinking adolescents also show greater activation in the right superior frontal and bilateral parietal cortices, areas implicated in working memory, compared to non-drinkers, suggesting heavier reliance on alternate memory networks (Schweinsburg et al., 2010 ). Similarly, adequate performance in the spatial working memory task required greater response in prefrontal and temporal regions compensating for diminished activity in the bilateral cerebral areas and the left precentral gyrus in adolescents with AUD (Tapert et al., 2004 ). The relationship between adolescent drinking and memory may be bidirectional, as the extent of memory-related brain region activation during working memory tasks has been shown to predict future heavy drinking, and heavy drinking, in turn, predicted increased activation over time (Squeglia et al., 2012a ). Female adolescents with AUD may be especially vulnerable to abnormal activity patterns, with Caldwell et al. ( 2005 ) suggesting greater compensatory activation in the temporal areas for a reduced frontal and cingulate response to the spatial working memory task. In a subsequent study, attenuated frontal, temporal, and cerebellar responses to a spatial working memory task corresponded to deficits in sustained attention and working memory in female binge drinkers. Meanwhile, male binge drinkers’ spatial working memory performance was positively correlated with activation of related brain regions and these individuals showed better spatial working performance compared to controls, suggesting an engagement of compensatory mechanisms (Squeglia et al., 2011 ). Aberrant neural recruitment during cognitive processes, in turn, may suggest functional compensation for differences in structural connectivity. For instance, adolescent binge drinkers exhibited lower connectivity in major white matter tracts implicated in neurocognitive functioning, whereby FA was reduced in the corpus callosum, corona radiata, superior longitudinal fasciculus, and fronto-occipital fasciculus compared to controls (Jacobus et al., 2009 ; McQueeny et al., 2009 ). These results conflict with those by Cardenas et al. ( 2013 ), who reported higher FA in the posterior corpus callosum in adolescents with AUD and did not report any regions with lower FA according to drinking status. Since higher FA in the corpus callosum was not related to any measure of alcohol use, it might predict vulnerability to AUD, rather than being a direct consequence of alcohol use. Lower FA reported by the former studies may suggest alcohol’s toxic effects on white matter microstructure as a longitudinal assessment of adolescents aged 14–19 revealed drinking-associated blunted white matter microstructure development, evidenced by decreased FA in the left caudate and inferior frontal occipital fasciculus over than years (Luciana et al., 2013 ). The participants in this study had no experience with alcohol and did not have any significant premorbid differences at the baseline assessment.

Morphological differences in alcohol-using adolescents relative to abstinent adolescents have also been observed in brain regions implicated in neurocognitive functioning, such as smaller hippocampal, PFC, and cerebellar volumes, as well as thicker frontal cortices (De Bellis et al., 2000 , 2005 ; Nagel et al., 2005 ; Medina et al., 2008 ; Squeglia et al., 2012b ; Lisdahl et al., 2013 ), but the directionality of these findings is debated. In one longitudinal study of baseline “alcohol-naive youth” aged 12–21, and another of youth aged 18–23, heavy drinkers exhibited accelerated gray matter loss in the superior frontal gyrus, caudal middle frontal gyrus, and rostral middle frontal gyrus (Pfefferbaum et al., 2018 ), as well as in the parahippocampus (Meda et al., 2017 ) compared to no/low drinking controls over two years. A similar observation was made by Squeglia et al. ( 2015 ) in lateral frontal and temporal GMV in addition to attenuated white matter growth of the corpus callosum in heavy adolescent drinkers who were followed over eight years. Other studies have demonstrated the reverse relationship between adolescent alcohol use and morphological differences, whereby thinner DLPFC and inferior frontal cortex (Brumback et al., 2016 ), and higher GMV in the caudate nucleus and the left cerebellum (Kuhn et al., 2019 ) predicted later increases in alcohol use and alcohol-related problems.

The age of drinking onset may also have important implications for future cognitive and neurobiological abnormalities. An earlier age of first drinking onset predicted worse psychomotor speed and visual attention functioning, but only when the model accounted for drinking duration (Nguyen-Louie et al., 2017 ). Consistently, participants with an earlier age of weekly drinking onset performed poorer on measures of cognitive inhibition and working memory than those with a later onset age. In light of this evidence, it is suggested that early onset of drinking increases the risk for alcohol-related neurocognitive vulnerabilities and that initiation of alcohol use at younger ages appears to be a risk factor for poorer subsequent neuropsychological functioning. The impact of early adolescent alcohol use upon later working memory was also observed in a larger study of 3,300 participants, with the frequent/binge drinking group displaying impaired working memory at three-year follow-up (Mahedy et al., 2018 ). While each of the above studies attempted to control for confounding variables, including comorbid substance use, sociodemographic status, and baseline neuropsychological performance, the impact of these confounds was mixed across studies. Nevertheless, even after controlling for these variables, the association between earlier alcohol use and poorer neurocognitive performance remained across both studies. The variability in confounding influences and the different neuropsychological measures taken across studies highlight the need for high-quality, long-term prospective cohort studies with standardized measures to better understand the lasting consequences of adolescent drinking.

Several studies have investigated the association between psychiatric illness and alcohol use (see detailed reviews: Fiorentini et al., 2011 ; Addington et al., 2014 ). However, whether this association is causal or arises from shared pathophysiology has remained difficult to parse (Khokhar et al., 2018 ). This notion is further supported by a comprehensive, prospective longitudinal study of participants interviewed from ages 16 through 30 that showed a high prevalence of comorbidity of major depressive disorder (MDD) and AUD. Prospectively, adolescent AUD predicted early adult MDD and early adult MDD predicted adult AUD, suggesting that MDD and AUD are inter-related in a complex manner (Brière et al., 2011 ). This association has been shown at even sub-clinical levels of alcohol use, with adolescent alcohol use at the age of 13–15 predicting depression at age 17 (Edwards et al., 2014 ). Interestingly, another study has suggested that the relationship between alcohol use and depression may be mediated by a specific measure of alcohol involvement, whereby problematic use (defined by adverse consequences of alcohol use), but not alcohol intake (defined by the level of alcohol consumption) predicted young adult MDD (Mason et al., 2008 ). Self-reported alcohol use in adolescence has also been prospectively associated with hypomanic/manic symptoms at age 23 (Fasteau et al., 2017 ); however, these results solely relied on self-reported alcohol intake and problematic use and will need to be confirmed in future studies with more robust designs. Although the neural basis for the association between adolescent alcohol use and mood disorders has been largely unexplored, AUD symptom severity in adolescents was associated with increased amygdala responses to emotional compared to neutral stimuli (Aloi et al., 2018 ). However, the directional implications of these findings on the relationship between alcohol use and mood disorders are unclear, highlighting the need for more studies to identify neural markers to help characterize their comorbidity. For instance, activity within neural circuitry that underlie both alcohol use and mood disorders, such as the reward circuit (Russo and Nestler, 2013 ), in response to paradigms measuring emotional processing should be assessed through neuroimaging techniques.

Socially anxious adolescents have been shown to use alcohol to cope with their symptoms, supporting the self-medication hypothesis (Blumenthal et al., 2010 ). Furthermore, in a recent large cohort longitudinal study that tracked girls aged 13–17, higher baseline depression severity predicted an increased likelihood of future alcohol use. There was also evidence for an inconsistent, reciprocal relationship with the consumption of one full drink at ages 14 and 16 predicting decreased depression in the next year. However, the latter finding should be interpreted with caution as this association was inconsistent across time and low levels of alcohol drinking are not necessarily pathological and may constitute normative behavior among adolescents (Schleider et al., 2019 ). A bidirectional relationship has also been reported between alcohol use and internalizing symptoms (e.g., anxiety and depressive symptoms) among adolescents who were prospectively assessed from age 14–16, where alcohol use or internalizing symptoms at age 14 predicted the other at age 16. Another important finding emerged from this study when internalizing symptoms were examined in clusters related to either anxiety or depression. While the Anxious Arousal scale showed a consistent reciprocal relationship with alcohol use, the association between alcohol use and Anhedonic Depression disappeared after controlling for delinquency, highlighting that symptoms of anxiety and depression in the internalizing domain are not interchangeable, which should be considered in future studies. There was also variation within symptoms unique to anxiety as measures from the Anxiety scale was not associated with alcohol use, contrasting what was observed with the Anxious Arousal scale (Parrish et al., 2016 ). This is consistent with another study showing that while early generalized anxiety symptomology predicted an increased risk for initiation of alcohol use, separation anxiety symptomology predicted decreased risk (Kaplow et al., 2001 ). It is also important to consider co-occurring externalizing symptoms (e.g., aggression and impulsivity) when assessing the relationship between alcohol use and internalizing symptoms, as externalizing symptoms have been previously shown to mediate this relationship (Colder et al., 2017 ). Current evidence relating to the association between alcohol and mood disorders is mixed with some supporting the self-medication hypothesis, while others suggesting that adolescent alcohol use may be a risk factor for developing mood disorders.

Adolescent alcohol drinking may also contribute to the risk for subsequent alcohol or drug use and dependence in adulthood; adolescent binge drinking predicts an increased risk of adult alcohol dependence, persistent cannabis, and other illicit drug use (Viner and Taylor, 2007 ; Pampati et al., 2018 ). The association between early alcohol use and subsequent alcohol-related problems has been further supported by data drawn from two large population studies conducted in two countries with distinct alcohol use policies and cultures. After controlling for a comprehensive number of potential confounders, both early-onset drinking and early onset of excessive drinking in adolescence (aged 14–16) were related to increased risk of alcohol-related problems when assessed at 18- to 25-years-old (Enstad et al., 2019 ). Impaired decision-making and underlying neural mechanisms in adolescent alcohol users may mediate the relationship between alcohol use and future substance use vulnerability. For instance, adolescent binge drinkers cross-sectionally exhibited poorer performance compared to controls in the Iowa gambling task used to assess effective decision-making, and higher activity in regions implicated in the emotional and incentive-related aspects of decision-making, such as the amygdala and insula. Similarly, connectivity between the OFC and amygdala predicted increases in alcohol use and increased connectivity between these regions has previously been shown to be protective against risk-taking (Peters et al., 2017 ). Activation differences in response to risky decision-making may both predict and be a consequence of adolescent alcohol drinking. While adolescent binge drinkers showed reduced activation in the dorsal caudate during risky decision-making, reduced frontoparietal activation in binge drinkers was present before they initiated alcohol use (Jones et al., 2016 ). In another study, an opposite pattern of increased activation in the nucleus accumbens, precuneus, and occipital cortex during risky decision-making predicted earlier initiation of binge drinking (Morales et al., 2018 ).

Adolescent binge drinking may also alter neural activity during reward processing, with Aloi et al. ( 2019 ) showing a cross-sectional association between AUD symptom severity and reduced activity in the posterior cingulate cortex and the striatum. Furthermore, among adolescents who were alcohol-naïve at baseline, those who transitioned into binge drinking after a two-year follow-up period exhibited reduced activity in the left cerebellum compared to controls during reward processing (Cservenka et al., 2015 ). This cerebellar activity was negatively associated with the average number of drinks consumed/drinking days, suggesting a dose-dependent effect. Differential activation patterns in reward-related regions may also predict increases in alcohol use from age 16–18 in a gender-specific manner; higher ventral striatum activity during reward anticipation was observed in boys, and higher dorsomedial PFC activity and decreased ventral striatum activity during reward anticipation was found in girls (Swartz et al., 2020 ). Greater activation to alcohol cues in adolescent alcohol users have also been reported, indicating a more intense desire and craving for alcohol, potentially putting them at risk for greater alcohol use in the future (Tapert et al., 2003 ; Dager et al., 2014 ; Brumback et al., 2015 ). Together, these findings suggest that neural markers may both predict alcohol use initiation, and also be a consequence of alcohol’s neurotoxic effects on reward circuitry; these differences may ultimately predispose adolescent alcohol users to excessive drinking in the future. However, research investigating adolescent alcohol use and vulnerability to alcohol and other drugs is scarce and requires considerable attention.

In 2019, approximately 15.6% of U.S. adolescents were current users of cannabis, making it the second most commonly used substance by this age group ( Figure 1A ; Johnston et al., 2020 ), and one that requires further attention. Adolescence marks a period in which extensive cortical reorganization and synaptic pruning occur, and mounting evidence points to chronic cannabis use interfering with this process (Renard et al., 2014 ). Δ 9 -tetrahydrocannabinol, the primary psychoactive ingredient of cannabis, acts primarily as a partial agonist at the cannabinoid type 1 receptor. Given that cannabinoid type 1 receptors are widely expressed throughout the brain, structural and functional consequences of cannabis exposure are a subject of interest (Pertwee, 1997 ). Herein, we review the possible consequences of cannabis use during adolescence related to cognition, psychopathology, and future substance use risk, and studies investigating these associations are summarized in Supplementary Table S3 .

Numerous studies have suggested that adolescent cannabis users are at a heightened risk for adverse cognitive outcomes (see review by Lubman et al., 2015 ). For instance, cross-sectional studies have reported that adolescent cannabis use is associated with impairments in inhibitory control and attention (Harvey et al., 2007 ; Lane et al., 2007 ; Medina et al., 2007a ). A longitudinal assessment by Infante et al. ( 2020 ) supports this relationship as greater lifetime adolescent cannabis use over 14 years was associated with impairments in inhibitory control and visuospatial functioning. Inhibitory control impairments may be, in turn, related to increased connectivity between the parietal and cerebellar regions, which comprise part of the inhibitory circuit (Behan et al., 2014 ). Adolescent cannabis users also exhibited hyper-activations in DLPFC and parietal regions during a Go/No-Go task in the absence of group differences in performance, instead suggesting functional compensation (Tapert et al., 2007 ). The effects of cannabis use on attention in adolescence may be dose-dependent. In a recent large cross-sectional study of adolescents aged 14–21, frequent, but not occasional cannabis users exhibited poorer sustained attention compared to non-users. Interestingly, earlier age of onset of cannabis use appeared to be a risk factor for sustained attention deficits in occasional cannabis users (Scott et al., 2017 ). This dose-dependency is also apparent in fMRI studies with adolescent chronic cannabis users exhibiting impairments in executive attention and greater activation of the right PFC compared to non-using controls (Abdullaev et al., 2010 ). Also, cannabis users may be more vulnerable to the adverse effects of cannabis on attention compared to other executive functions as Hanson et al. ( 2010 ) showed that working memory and verbal learning deficits improved after three weeks of abstinence in cannabis users, while attention deficits persisted. This study highlights the importance of considering the periods of abstinence from cannabis in cross-sectional studies that differ from one study to another, making it difficult to disentangle acute and lasting effects of adolescent cannabis use on cognition. Therefore, harmonization of protocols relating to the period of abstinence is necessary, in addition to assessing the effect of abstinence from cannabis longitudinally.

Although not specific to inhibitory control or attention, adolescent cannabis users also displayed larger cerebellar vermis volumes compared to controls, which was associated with poorer executive functioning assessed by subsets of the Delis-Kaplan executive function test. This suggests that morphological differences in brain regions may underlie abnormalities related to deficits in higher-level cognitive skills (Medina et al., 2010 ). Female adolescent cannabis users may be at a greater risk for such differences as Medina et al. ( 2009 ) reported larger PFC volumes in female cannabis users, with smaller PFC volumes predicting better executive functioning among cannabis users. Reduced right medial PFC volume (Churchwell et al., 2010 ) and greater left hippocampal volumes (Ashtari et al., 2011 ) have also been observed in adolescent heavy cannabis users; however, functional correlates of these morphological differences have yet to be studied in adolescent heavy cannabis users. The hippocampal volume findings conflict with Weiland et al. ( 2015 ), who showed that adolescent daily cannabis users did not differ from non-users in hippocampal volumes. Similarly, Scott et al. ( 2019 ) reported non-significant differences between frequent and occasional cannabis users, as well as non-users in global or regional brain volumes, cortical thickness, and gray matter density. These findings suggest that adolescent heavy cannabis users may be at a heightened risk for impairments in neurocognitive functioning, and future studies should focus on investigating the functional correlates of the structural differences observed in heavy cannabis users.

Cannabis use during adolescence is also associated with deficits in intelligence. In adolescents first assessed at 13 years of age before the onset of cannabis use and again at 20-years-old, poor short-term and working memory predicted earlier age of onset of cannabis use. Conversely, an earlier age of onset and more frequent use during adolescence was associated with declines in performance in verbal IQ as well as trial and error learning and conditional association learning (Castellanos-Ryan et al., 2017 ). Similarly, adolescent cannabis use was associated with greater IQ decline and working memory impairments and cessation of cannabis use did not restore neurocognitive functioning (Meier et al., 2012 ). However, findings regarding adolescent cannabis-associated declines in IQ remain conflicting with one study showing no evidence for IQ decline from ages 12–18, while another indicated that cigarette smoking may be a confounder (Mokrysz et al., 2016 ; Meier et al., 2018 ). Familial factors may also contribute to the observed differences in IQ decline between cannabis users and non-users. In a large longitudinal twin-pair study of participants aged 9–11 at baseline, cannabis-using twins did not exhibit greater IQ decline relative to their non-user co-twin when assessed at 18–20 years of age (Jackson et al., 2016 ). However, a neuroimaging study by Camchong et al. ( 2017 ) converges on the former findings with adolescents with cannabis use disorder (CUD) showing decreased caudal ACC RSFC with the left DLPFC and OFC, as well as lower IQ and slower cognitive function across an 18-month follow-up period. Adolescent cannabis use is not consistently associated with deficits in IQ, which may be explained by familial factors and the use of other drugs; therefore, the extent to which these factors interact with the effect of cannabis on the adolescent brain should be considered for other behavioral and neurobiological domains.

Greater amounts of cannabis use have also been prospectively associated with declines in immediate, but not delayed, memory performance (Duperrouzel et al., 2019 ) and persistent verbal learning impairments (Becker et al., 2015 ). The latter study also investigated the association between cannabis use and white matter microstructure and found that adolescent cannabis users aged 18–20 at baseline exhibited attenuated FA growth in the superior longitudinal fasciculus, an association fiber that has been largely implicated in cognitive functions (Becker et al., 2015 ). Also, male adolescent heavy cannabis users exhibited decreased FA in the left temporal lobe, an area implicated in verbal memory. FA reductions were accompanied by complementary increases in radial diffusivity and trace values, all suggestive of decreased myelination (Ashtari et al., 2009 ). This study reported minimal baseline differences in FA between cannabis users and controls, suggesting that white matter microstructure differences did not predate cannabis use. Interestingly, attenuated loss of cortical thickness across adolescent development (Epstein and Kumra, 2015 ) and greater GMV (Orr et al., 2019 ) have been observed in adolescent cannabis users in several regions of the brain bilaterally, both of which have reciprocal relationships with myelination. Cross-sectional studies have reported that adolescent cannabis users also show impairments in working memory, problem-solving, and planning (Harvey et al., 2007 ; Medina et al., 2007a ; Vo et al., 2014 ). Compensatory hyper-functioning has been reported by fMRI studies in the brains of adolescent cannabis users during task performance, complementing these behavioral findings. For instance, hyper-activation in the DLPFC and the right basal ganglia (Padula et al., 2007 ; Jager et al., 2010 ), as well as failure to reduce activation in the right hippocampus (Jacobsen et al., 2004 ), have been observed in adolescent cannabis users compared to non-users during working memory tasks. In the former studies, activation differences between groups were present despite adequate performance on the task, suggesting that adolescent cannabis users require more neural recruitment to perform the tasks at a comparable level to non-users.

The age of onset also plays a critical role in the effects of adolescent cannabis use on cognition. For instance, adolescent early-onset cannabis use has been associated with poorer sustained attention, impulse control, and verbal IQ compared to a later onset of use in current adult cannabis users (Pope et al., 2003 ; Fontes et al., 2011 ). Females may be more susceptible to the effects of earlier initiation of cannabis use on neurocognitive functions, as female adolescents exhibited more spatial working memory deficits compared to males across a five-year follow-up period from a baseline age of 12 (Noorbakhsh et al., 2020 ). Interestingly, in a longitudinal study that tracked cannabis use across adolescence and into adulthood, earlier onset of cannabis use was associated with longer reaction times during a working memory task, which was mediated by reduced activity in the posterior parietal cortex compared to late-onset use (Tervo-Clemmens et al., 2018 ). This suggests that early onset of cannabis use may predispose those who continue to use cannabis into adulthood to executive function impairments. Also, Wilson et al. ( 2000 ) reported a smaller percentage of cortical gray matter, and a larger percentage of white matter across the whole brain, in adults who initiated cannabis use before age 17 compared to those who initiated their use later. These differences were most prominent in the frontal lobes. However, in a study of adolescent boys followed prospectively into adulthood, no differences were observed in both cortical and subcortical region morphology between non-users and users across different trajectories of cannabis use ranging across infrequent to chronic use (Meier et al., 2019 ). The mixed findings could be attributed to the differences in study design, whereby some of the aforementioned studies were retrospective, and are therefore susceptible to recall bias. Therefore, the longevity of the effects of adolescent cannabis use on cognitive functions and their neurobiological correlates need to be further elucidated through currently on-going and future prospective longitudinal studies.

Cannabis use is common among first-episode psychosis patients (Katz et al., 2016 ; Abdel-Baki et al., 2017 ), and cannabis use has been hypothesized to be a causal factor in these disorders (Toftdahl et al., 2016 ). More recent data appears to confirm this positive association between adolescent cannabis use and schizophrenia spectrum disorders (Arseneault et al., 2002 ; Jones et al., 2018 ), particularly in that cannabis both hastens the onset and amplifies the severity of schizophrenia (Shahzade et al., 2018 ). However, Hanna et al. ( 2016 ) reported better cognitive function in adolescent cannabis users with schizophrenia/schizoaffective disorders, suggesting a potential protective role of cannabis in psychosis-related cognitive dysfunction. Structural MRI studies are not consistent with a neuroprotective effect and have suggested that processes underlying gray matter and cortical maturation may mediate the association between adolescent cannabis use and risk for schizophrenia. Among adolescents aged 10–21, those with CUD and early-onset schizophrenia exhibited decreased GMV in the left superior parietal cortex compared to controls (Kumra et al., 2012 ). Greater cannabis consumption across an 18-month follow-up period in adolescents with CUD predicted a greater decrease in the left inferior longitudinal fasciculus (Kumra et al., 2012 ), a white matter tract that was previously shown to be disrupted in adolescents with schizophrenia (Ashtari et al., 2007 ). Moreover, gender may interact with structural abnormalities mediating the association between cannabis use and schizophrenia. For instance, male adolescent cannabis users, with a high polygenic risk score for schizophrenia across 108 genetic loci exhibited decreased cortical thickness, which was not observed in low-risk male, or high- and low-risk female participants (French et al., 2015 ). However, gender differences need to be investigated further as current studies report mixed findings. For instance, in a study of Australian adolescents, girls who started using cannabis before the age of 16 displayed higher levels of introvertive anhedonia, a negative schizotypy, than girls who started using cannabis later in adolescence, whereas this association was not present in boys (Albertella et al., 2017 ). Also, the causal direction of the relationship between adolescent cannabis use and schizophrenia is called into question as Hiemstra et al. ( 2018 ) found stronger evidence for a reverse association, showing that schizophrenia genetic risk was predictive of increased cannabis use from age 16 to 20. This study, combined with those outlined above, suggests that the association between adolescent cannabis use with psychosis, while strong, may not be causal, and further study of the functional contributions of the risk of loci identified in these studies might help to unravel this “chicken-or-egg” problem.

Adolescent cannabis users, particularly females, maybe at a heightened risk for mood disorders. Among Norwegian adolescents aged 13–17, cannabis users reported more anxiety and depressive symptoms compared to non-users. Girls reported slightly more symptoms compared to boys despite the lower prevalence of cannabis use among girls (Kaasbøll et al., 2018 ). Similarly, more internalizing symptoms in female adolescent cannabis users were associated with larger amygdalar volumes (McQueeny et al., 2011 ); this association was not observed in male participants. Conversely, other studies have found no association between adolescent cannabis use and differences in amygdala volumes between adolescent cannabis users and non-using controls (Ashtari et al., 2011 ; Weiland et al., 2015 ). It is important to note that the number of female participants in the McQueeny et al. ( 2011 ) study was small and future studies with more female participants would be needed to confirm these results. Despite limited evidence for differences in amygdalar morphology between adolescent cannabis users and non-users, amygdalar hypersensitivity in response to angry faces has been reported in adolescent cannabis users, which could predispose individuals to future mood disorders (Spechler et al., 2015 ). However, these results are conflicted by a more recent study that showed no differences in amygdalar responsivity to emotional stimuli in adolescents with CUD (Aloi et al., 2018 ). Psychiatric comorbidity may have masked any association between CUD symptomology and amygdala responsiveness in the latter study. Furthermore, in adolescent cannabis users, depressive symptoms were positively associated with increased connectivity between the left OFC and left parietal regions, while anxiety symptoms were negatively associated with increased connectivity between bilateral OFC with right occipital and temporal regions (Subramaniam et al., 2018 ). Similarly, decreased FA and increased radial diffusivity and trace in the thalamic radiation were observed in older adolescents with a history of heavy cannabis use (Ashtari et al., 2009 ); decreased FA in the thalamic radiation has also been previously shown in young adult patients with depression (Lai and Wu, 2014 ). Also, smaller global white matter volumes were associated with more depressive symptoms in adolescent cannabis users (Medina et al., 2007b ), suggesting that white matter abnormalities may extend beyond what is observed at a microstructure level.

A recent meta-analysis of longitudinal studies indicated that adolescent cannabis use is associated with a modest risk of developing depression in young adulthood (Gobbi et al., 2019 ). A recent population-based cohort of young adults who were retrospectively assessed for adolescent cannabis use and followed over 30 years has also captured this. Adolescent cannabis use, particularly an earlier onset of use, as well as more frequent use was associated with adult depression, independent of adult cannabis and other substance use (Hengartner et al., 2020 ). Adolescent cannabis use may further exacerbate depressive symptoms in males with mild depression at baseline, with limited evidence to support the self-medication hypothesis, whereby depressive symptoms predicted only slight increases in later cannabis use (Womack et al., 2016 ). Similarly, anxiety symptoms do not appear to predate adolescent cannabis use and may instead depend on the frequency of use. In a recent longitudinal study, adolescents with higher levels of cannabis use reported more persisting anxiety over the next year compared to less frequent users; anxiety levels at baseline did not predict differences in cannabis use between the groups (Duperrouzel et al., 2018 ). In longitudinal studies, gender differences in the relationship between adolescent cannabis use and anxiety/depressive symptoms have shown an opposite trend to those reported by cross-sectional studies discussed above. In a large adolescent sample balanced for gender, baseline cannabis use at age 16 predicted increases in depressive symptoms in over three years among male, but not, female African American adolescents (Assari et al., 2018 ). Another study found an association between escalating cannabis use and decreased connectivity between nucleus accumbens and medial PFC that predicted higher levels of depressive symptoms (Lichenstein et al., 2017 ). Decreased growth in FA in the right anterior thalamic radiation was also observed over three years in adolescent cannabis users (Becker et al., 2015 ), suggesting possible shared pathophysiology with young adult patients with depression (Lai and Wu, 2014 ). Overall, both imaging and behavioral findings support a strong relationship between adolescent cannabis use and mood disorders that appear to uniquely interact with gender; neural markers that may give rise to these differences between males and females should be investigated in future studies.

In addition to the relationships between adolescent cannabis use and the risk for schizophrenia and mood disorders, longitudinal studies have revealed that occasional and early-onset cannabis use in adolescence predicts nicotine use and dependence, harmful alcohol consumption, and other illicit drug use in adulthood (Degenhardt et al., 2010 ; Swift et al., 2012 ; Scholes-Balog et al., 2016 ; Jin et al., 2017 ; Taylor et al., 2017 ; Pampati et al., 2018 ). When examining the risk of future drug dependence as a consequence of adolescent cannabis use, it may also be important to consider different cannabis use behaviors, such as using cannabis in social settings vs. solitary use. Solitary cannabis use may present as a risk factor for future cannabis dependence as a recent study showed that compared to social-only use, solitary use is associated with greater cannabis use, as well as CUD symptoms in young adulthood (Creswell et al., 2015 ). However, these results should be interpreted with caution as the association between solitary cannabis use and future CUD symptoms disappeared after controlling for adolescent CUD symptoms. Interestingly, early-onset cannabis use has previously been associated with anti-social behavior, which may, in turn, promote solitary use (Scholes-Balog et al., 2016 ). Similar findings have been observed for cannabis vaping, which has been relatively under-studied compared to combustible cannabis use and nicotine vaping despite its prevalence. Cassidy et al. ( 2018 ) recently observed that youth entering college are more likely to initiate cannabis vaping if they have a prior history of any cannabis or e-cigarette use, and the risk for vaping cannabis scales with the number of peers also engaging in use. However, the frequency and intensity of use among those who initiate cannabis vaping in social settings and the risk for the development of CUD in these populations are not defined, nor is the use of cannabis vaping in younger adolescent populations.

Earlier onset and greater duration of cannabis use were also associated with risky and impulsive decision-making in adolescent users (Solowij et al., 2012 ), and impaired decision-making, in turn, may promote substance use. Neuroimaging studies suggest functional compensation as De Bellis et al. ( 2013 ) reported that adolescents with CUD exhibit higher activity in the left superior parietal lobule, left lateral occipital cortex, and bilateral precuneus during risky decision-making despite no group differences in performance. Despite cross-sectional associations between cannabis use and poor decision-making in youth aged 14–17 at baseline, cannabis use was not associated with changes in decision-making over a one-year follow-up period (Duperrouzel et al., 2019 ). This suggests that impaired decision-making may predate cannabis use initiation in adolescence. This is in line with a structural MRI study showing that smaller OFC volume, implicated in decision-making, predicted the initiation of cannabis use by the age of 16 (Cheetham et al., 2012 ). Adolescent cannabis users also exhibited diminished ability to disengage motivational circuitry during non-rewarding events in the monetary incentive delay task despite normal performance as evidenced by heightened striatal activity in cannabis users compared to non-users, which could drive risk-seeking behavior even in the face of negative outcomes (Jager et al., 2013 ). Additionally, adolescents with CUD exhibited greater accuracy across trials in the monetary delay task, and greater functional global connectivity across networks that included mesocorticolimbic nodes during monetary reward anticipation (Nestor et al., 2019 ). The group also showed enhanced integration, defined as higher information exchange between regions and a greater number of connections to the nearest nodes, alluding to neural refinement deficiencies. Superior performance may be mediated by higher motivation as there were no group differences in performance and global connectivity within different trials; however, an earlier study showed reduced motivation in adolescent heavy cannabis users, which may instead indicate lack of motivation at greater consumption levels, potentially failing to seek treatment (Lane et al., 2005 ). In another study, adolescent cannabis users exhibited an enhanced neural response to both wins and losses, the latter suggesting greater sensitivity during negative feedback (Acheson et al., 2015 ). In contrast to results from Nestor et al. ( 2019 ), Acheson et al. ( 2015 ) showed that despite seeing no differences in global connectivity, analyses of the individual paths revealed that adolescent cannabis users differed in connectivity from controls in one-third of the total paths analyzed in response to losses, but no individual path differences were observed during wins. Although these results confirm differences in sensitivity to negative feedback observed in previous studies, it differentially highlights the importance of assessing connectivity within individual networks when investigating alterations in reward circuitry. Both behavioral and neural findings indicate that adolescent cannabis use may increase the risk for future substance use and associated behaviors; however, further research is needed to assess the effects of vaping and different cannabis use behaviors (e.g., solitary vs. social) on this relationship.

In 2017, an estimated 3.1% of adolescents aged 12–17 had misused opioids in the past year ( Figure 1A ; Substance Use and Mental Health Services Administration, 2018). While these numbers are lower than the prevalence for other substances, the alarming trends of problematic opioid use in North America, the high mortality associated with opioid use, and their exclusion from previous reviews on this topic necessitate further attention. Opioids produce their effects by modulating the excitability of neurons via mu, kappa, and delta-opioid receptors as well as nociception receptors. Little is known about opioid receptor development and the consequences of opioid use during adolescence; however, the endogenous opioid system has been observed to change throughout adolescent development, highlighting the necessity for future research during this vulnerable window (Thorpe et al., 2020 ). Due to fewer findings, this section is considerably narrower in its scope compared to the previously reviewed substances ( Supplementary Table S4 ).

Very little clinical work has been conducted on the long-term effects of opioids on memory and cognition. Given that adolescent opioid use is rarely unaccompanied by other substance use, it is difficult to attribute any effects to opioids on their own. One study found that opioid-dependent adolescents had significantly impaired working memory, but was unable to determine if these deficits were substance-induced or pre-existing before use (Vo et al., 2014 ). However, the opioid-using group had similar levels of cannabis use as a cannabis-only using group in the same study and the working memory deficits seen were comparable to those of cannabis-only users. Future studies looking into the effects of long-term prescription opioid use in adolescence on cognition are warranted. This would allow for the study of opioids in populations that do not use other substances and give insight into the neurocognitive effects of illicit opioids without the confound of other drugs.

Studies conducted on the effects of opioids relating to cognition and psychopathology have shown higher rates of comorbid psychiatric disorders such as MDD, substance use disorder, ADHD, antisocial personality disorder, borderline personality disorder, and post-traumatic stress disorder compared to non-users (Mills et al., 2004 ; Subramaniam and Stitzer, 2009 ; Subramaniam et al., 2009 ; Edlund et al., 2015 ). Though retrospective, adolescents with MDD and non-medical prescription opioid use often reported MDD to predate opioid use, suggesting MDD to be a risk factor for future opioid abuse (Edlund et al., 2015 ). In a cross-sectional study of 14- to 18-year-olds, Subramaniam and Stitzer ( 2009 ) found that 83% of adolescents with opioid use disorders had a co-occurring psychiatric disorder. Thus, opioid use and several psychopathologies appear to be related but, unfortunately, the directional relationship between opioids and their comorbidities is not known, highlighting the need for future longitudinal studies.

As discussed in previous sections, many substances are associated with an increased risk of future substance use. Opioids are likely not an exception to this trend, and it is thus alarming that they are both regularly prescribed to adolescents and often available in lower doses in over-the-counter products such as acetaminophen and cough syrups (Van Hout and Norman, 2016 ). Indeed, one study showed that students in grade 12 who had ever used prescription opioids were 33% more likely to misuse opioids by the age of 23, independent of their cannabis, cigarette, and alcohol use (Miech et al., 2015 ). Additionally, adolescents that misuse prescription opioids were more likely to initiate heroin use, with a younger age of initiation of non-medical prescription opioid use being strongly associated with the subsequent development of opioid use disorder (Cerdá et al., 2015 ; Schepis and Hakes, 2017 ). Given the potential for prescription opioid use to increase susceptibility to opioid misuse, it is important that health professionals carefully weigh the benefits and potential detriments that opioids might have on adolescent neurodevelopment when deciding on treatment options.

The co-use of substances is common among adolescents. The National Longitudinal Study of Adolescent to Adult Health found that nearly one in five adolescents report using cigarettes, alcohol, and cannabis, either individually or in combination before the age of 16 (Moss et al., 2014 ). For clarity, we define co-use as either concurrent, in which multiple substances are used on different occasions, or simultaneous, in which substances are used on the same occasion. During adolescence, it was more common to have used cigarettes, alcohol, and cannabis concurrently than it was to have only used one of the substances individually. The survey also reported the rates of using alcohol and nicotine as 22%; cannabis and nicotine as 21.6%; and alcohol and cannabis as 34.1%. Nearly all research on adolescent substance use (as well as most reviews on the topic) has focused on individual use, but using multiple substances is more common than individual use. This underscores the need for research into the combined effects of substances on adolescent neurodevelopment. Furthermore, the neural correlates of co-use are especially understudied, highlighting the need for future research in this area. The studies to date investigating the effects of co-use are summarized in Supplementary Table S5 .

Alcohol and Nicotine

Currently, human studies on the neurobiological changes associated with combined alcohol and nicotine use in adolescence do not exist and the same is true of the effects on cognition. However, significantly increased risk for psychopathology and future substance use has been observed.

A study looking at substance use and psychiatric comorbidity in subjects aged 13–15 found that regular alcohol and nicotine use had an additive risk for psychiatric disorders, with especially high risk for depressive disorder (Boys et al., 2003 ). A 2016 study found that alcohol and cigarette consumption increased physical aggression in adolescents aged 14–16 (Matuszka et al., 2016 ). This increase was significantly greater than that observed in non-concurrent users, showing greater effects in combination than those of the individual substances.

Nicotine and alcohol also have additive effects on the risk for future substance use in that concurrent use predicts a greater risk of future substance abuse. A U.S. national survey on alcohol users aged 12–20 found that subjects with a past-year smoking status drank more alcohol on average and had a higher risk for AUD than those that drank equal amounts without smoking (Grucza and Bierut, 2006 ). These results were the strongest in younger participants. In line with these findings, a longitudinal study found similar results, showing that by age 15, alcohol users that smoked tobacco consumed more alcohol and cannabis (Schmid et al., 2007 ).

Cannabis and Nicotine

As with combined alcohol and nicotine use, no studies addressing the effects of combined cannabis and nicotine during adolescence on cognition exist. However, some evidence points to increased risk of psychiatric disorders and increased substance use following combined cannabis and nicotine consumption.

A cross-sectional study looking at combined substance use and psychiatric morbidity in adolescents aged 13–15 found that regular cannabis and nicotine use had an additive risk for psychiatric disorders (Boys et al., 2003 ). This risk was especially high for the development of depressive disorders and was increased further with the addition of regular alcohol consumption. Longitudinal studies on the effects of combined substance use on psychiatric morbidity are warranted to understand the directionality of this relationship.

The combined use of cannabis and nicotine has also been associated with increased substance use. In a cross-sectional study of cigarette smoking 13–17-year-olds, the frequency of cannabis use was associated with increased measures of nicotine addiction (Rubinstein et al., 2014 ). A cross-sectional fMRI study by Karoly et al. ( 2015 ) found that adolescents that used tobacco alone had decreased BOLD response in the nucleus accumbens during a monetary incentive delay task compared to non-using peers. However, these differences were not seen in those using both tobacco and cannabis. Cannabis may be counteracting the effects of tobacco on the nucleus accumbens, possibly explaining why the frequency of cannabis use is associated with increased measures of nicotine addiction in these populations; however, results from longitudinal studies investigating this relationship are required before any hypotheses can be made with confidence.

Cannabis and Alcohol

The effect of cannabis and alcohol co-use on cognition seems to largely depend on the cognitive behavior being measured. In a longitudinal population-based analysis of grade 7 students, Morin et al. ( 2019 ) found that among co-users, cannabis, but not alcohol, was associated with short-term neurotoxic effects on working memory and inhibitory control as well as long-term effects on perceptual reasoning and delayed memory recall. In another study, hangover symptoms among adolescent heavy drinkers were associated with worse verbal learning and memory but these deficits were not seen in adolescents with similar alcohol consumption and heavy cannabis use (Mahmood et al., 2010 ). The finding that cannabis may provide some neuroprotective effects against heavy alcohol use is also supported by some imaging studies. Alcohol and cannabis appear to have opposing effects on cortical thickness; among co-users, lifetime cannabis use is associated with decreased cortical thickness, while lifetime alcohol use is associated with increased cortical thickness (Jacobus et al., 2014 , 2015 ). Co-users have also shown differential white matter changes associated with cognition, suggesting a possible neuroadaptation resulting in additive and subtractive responses to substance use (Bava et al., 2010 ). Other studies have also found these subtractive effects, with alcohol alone affecting white matter integrity, but not both alcohol and cannabis; this further suggests possible neuroprotective effects of cannabis when combined with alcohol (Jacobus et al., 2009 ; Bava et al., 2013 ; Infante et al., 2018 ). However, a longitudinal study that compared users at baseline to their three-year follow-up found similar decreases in white matter integrity for both alcohol and co-users (Jacobus et al., 2013a ).

In some psychosocial and cognitive domains, co-use appears to have additive deficits. Co-users are more likely to drive intoxicated (Shillington and Clapp, 2001 ; Terry-McElrath et al., 2014 ) and have legal problems (Shillington and Clapp, 2001 ; Green et al., 2016 ) than those that use each substance individually, suggesting that co-use may play a role in processes, such as inhibitory control (Galambos et al., 2005 ). A study by Winward et al. ( 2014 ) also found that adolescent users of both substances performed worse on a working memory task. Some neuroimaging studies support these results. A study that compared white matter integrity pre- and post-substance use initiation found that initiation of combined alcohol and cannabis use was associated with decreased white matter integrity in most tracts, including the corpus callosum, corticospinal tract, occipital fasciculus, forceps major, internal capsule, and corona radiata, while the initiation of alcohol-only was not linked to changes in white matter integrity (Jacobus et al., 2013b ). Interestingly, in most regions at the baseline time point, youth who would later initiate both alcohol and cannabis use demonstrated FA greater than or equal to youth that initiated alcohol use only. This pre-existing increased white matter integrity could explain the supposed neuroprotective effects of cannabis suggested in other studies (Jacobus et al., 2009 ; Bava et al., 2013 ). A later study by the same group also found that alcohol-only initiators and controls have greater cortical thickness before initiation compared to those that initiated both cannabis and alcohol, further suggesting that some neurophysiological differences in these groups precede substance use (Jacobus et al., 2016 ). Functional MRI studies in co-users have shown dysfunction in frontal and temporal regions, and a decoupled association between hippocampal symmetry and verbal learning (Schweinsburg et al., 2005 , 2011 ; Medina et al., 2007b ). An fMRI study found decreased BOLD response in the thalamus, insula, and striatum versus non-users when taking risks (Claus et al., 2018 ). A cross-sectional DTI study by Bava et al. ( 2009 ) showed altered frontoparietal networks and fiber projections within circuits responsible for the modulation of complex sensory, motor, and cognitive processing, namely in fibers of the postcentral gyrus, splenium of the corpus callosum, inferior frontal region, and left superior longitudinal fasciculus. With some studies suggesting cannabis to be neuroprotective, some findings appearing to be the result of an individual substance, and others suggesting co-use to have additive deficits, it is difficult to make any clear conclusions. These conflicting findings are likely the result of significant methodological differences and the potential for different use cases to result in distinct findings (e.g., binge drinking vs. heavy drinking vs. AUD or simultaneous use vs. concurrent use). Thus, further studies are required to make sense of the complicated relationship between alcohol and cannabis co-use during adolescence.

Compared to alcohol or cannabis use alone, adolescent use of both substances is associated with an increased likelihood of a depressive disorder (Boys et al., 2003 ). This relationship is also supported by DTI studies; adolescent co-use was associated with decreased FA in the inferior frontal region and left superior longitudinal fasciculus, regions that are similarly altered in adolescent depression (Bava et al., 2009 ; Cullen et al., 2010 ).

Co-use of cannabis and alcohol appears to potentiate future substance use. Co-users consumed more illicit drugs (Magill et al., 2009 ; Green et al., 2016 ; Hayaki et al., 2016 ; Patrick et al., 2018 ) than those that used alcohol only. There is also evidence that the simultaneous use of alcohol and cannabis together have greater effects on risk for future substance use-related problems than concurrent use (Brière et al., 2011 ). Similarly, simultaneous users show increased use of illicit drugs compared with those who concurrently use both substances (Patrick et al., 2018 ). Unfortunately, without longitudinal studies following adolescents before substance use initiation, it is difficult to infer the directionality of these relationships; therefore, interpretations of these results should be cautiously done.

Limitations

Studies investigating drug-associated alterations to adolescent neurodevelopment have several limitations. Foremost, it is necessary to highlight the difficulty of recruiting younger participants due to issues surrounding parental consent, which may hinder researchers’ ability to match key variables between users and non-user controls, such as age, use of other substances, and underlying psychiatric comorbidities. Emerging evidence also underscores the importance of matching study participants based on genetic variation, as genetic variation in a variety of genes have been associated with increased risk for substance use and associated behaviors, and mediate the effects of adolescent substance use (Hines et al., 2015 ; Patriquin et al., 2015 ). Controlling for the use of other substances is also of importance as the contribution of multiple drugs to the observed neurobiological and behavioral differences are difficult to disentangle. Also, to control for the acute effects of substance use, a criterion of abstinence is put in place in many studies; however, withdrawal symptoms may confound the results of studies that employ such a design. Abstinence may also be self-reported by participants in place of objective measures, such as urine analysis, making it difficult to isolate acute drug effects from those that are long-lasting. Furthermore, this review is limited in its scope to the potential effects of nicotine, alcohol, and cannabis given their use prevalence during adolescence and, in the case of opioids, the emerging nature of the opioid epidemic. However, adolescents are known to consume a wide variety of other, albeit relatively under-investigated, drugs such as cocaine, ecstasy, and inhalants (Johnston et al., 2020 ).

Furthermore, the cross-sectional design of many studies reviewed here limits conclusions on causal directions as there is a possibility that observed neuroimaging and behavioral differences predate the onset of substance use. While we addressed studies that highlighted neurobiological or cognitive factors antedate to substance use here, studies that did not account for underlying between-subject differences may contribute to discrepant findings. In the absence of controlled trials, longitudinal studies are more useful in inferring directional relationships between drug use and neurobiological consequences, especially when baseline measurements are carried out before the onset of substance use. Therefore, more longitudinal analyses, especially studies that are concerned with structural and functional differences within the brain, are needed.

Future Directions

Future prospective longitudinal studies (e.g., the on-going Adolescent Brain Cognitive Development Study of the National Institute on Drug Abuse) looking at the markers of neurobiological function (e.g., brain imaging) before the appearance of substance use could help uncover the mechanistic underpinnings of the long-term consequences of substance use that have been reviewed here. Importantly, as studies indicate compounding detrimental effects of adolescent and prenatal drug exposure on neurological and cognitive outcomes (Jacobsen et al., 2007b , c ), not all studies outlined here control for prenatal drug exposure. Future studies would benefit from investigating the impacts of drug exposure at multiple developmental points and how this compares with adolescent-exclusive use.

The neurobiological and cognitive consequences of vaping should also be the target of future studies, as the persisting effects of adolescents using electronic drug delivery devices relative to traditional (i.e., combustible) delivery methods are largely unknown outside of future drug use susceptibility. To date, only one neuroimaging study investigating young adult e-cigarette experimenters and those at risk to try e-cigarettes exists in the literature (Garrison et al., 2018 ), though the mean age of participants in this study was above our 19-year-old cutoff age. Another study (Chen et al., 2018 ) using a group of participants with a mean age within our cutoff also investigated neural activity, however, it was in in response to e-cigarette advertisements, and the participants in this study were selected based on combustible smoking status rather than e-cigarette use. Even fewer studies have been conducted on the outcomes of cannabis vaping during adolescence despite increasing trends of vaping cannabis, as well as edible use, both of which have been associated with heavier cannabis use (Patrick et al., 2020 ). Neurobiological investigations of cannabis and nicotine vaping susceptibility and potential for harm, especially surrounding the transition to combustibles, in this at-risk population, must, therefore, become a priority for future studies.

Moreover, through the reverse translation of findings from clinical populations, the causal underpinnings of the consequences of adolescent substance use can be uncovered. Related to the emerging trends such as the increases in vaping, the availability of animal models of self-administration using electronic devices, combined with pre-clinical neuroimaging methods, will help establish the direct causal consequences of adolescent vaping (Hines et al., 2015 ; Freels et al., 2020 ). Lastly, while our review did not address specific therapeutic attempts to reverse the effects of adolescent drug use, future studies can begin to target these changes toward the development of strategies that help to reduce or prevent some of the deleterious effects of adolescent substance exposure, especially if these interventions can be targeted for use in adolescence.

Despite the overall recent downward trends in adolescent substance use the prevalence of adolescent substance use remains a significant public health concern, largely due to the consequences of this use and the especially vulnerable window of neurodevelopment during this period. In this review, we highlighted the neurobiological and behavioral changes that arise from adolescent nicotine, alcohol, cannabis, and opioid use or their combination. Specifically, adolescent drug exposure may contribute to increased risk for the development of cognitive deficits, psychopathology, or subsequent substance use disorders that may be related to the structural and functional changes in the brain. Investigating mechanisms underlying these alterations may provide novel avenues for the development of therapeutics that target these mechanisms to prevent and reduce the harm associated with substance use in adolescence.

Author Contributions

SH drafted the cannabis and alcohol literature review and the “Limitations” section. Besides, SH was responsible for consolidating the review, formatting Supplementary Tables S1–S5 , and the formatting of the review. HT drafted the nicotine literature review, the “Future Directions” section, and Figure 1 , as well as helped format Supplementary Tables S1–S5 . JF drafted the opioid and the co-use literature review and the “Introduction” section. RM contributed to the literature review and Figure 1 . JK formulated the idea for the review and guided the research and writing process. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

Funding. This work was supported by a Discovery Grant from the Natural Sciences and Engineering Research Council award (RGPIN-2019-05121) to JK.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnhum.2020.00298/full#supplementary-material .

• Abdel-Baki A., Ouellet-Plamondon C., Salvat E., Grar K., Potvin S. (2017). Symptomatic and functional outcomes of substance use disorder persistence 2 years after admission to a first-episode psychosis program . Psychiatry Res. 247 , 113–119. 10.1016/j.psychres.2016.11.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Abdullaev Y., Posner M. I., Nunnally R., Dishion T. J. (2010). Functional MRI evidence for inefficient attentional control in adolescent chronic cannabis abuse . Behav. Brain Res. 215 , 45–57. 10.1016/j.bbr.2010.06.023 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Acheson A., Ray K. L., Hines C. S., Li K., Dawes M. A., Mathias C. W., et al.. (2015). Functional activation and effective connectivity differences in adolescent marijuana users performing a simulated gambling task . J. Addict. 2015 :783106. 10.1155/2015/783106 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Addington J., Case N., Saleem M. M., Auther A. M., Cornblatt B. A., Cadenhead K. S. (2014). Substance use in clinical high risk for psychosis: a review of the literature . Early Interv. Psychiatry 8 , 104–112. 10.1111/eip.12100 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ahmadi A., Pearlson G. D., Meda S. A., Dager A., Potenza M. N., Rosen R., et al.. (2013). Influence of alcohol use on neural response to Go/No-Go task in college drinkers . Neuropsychopharmacology 38 , 2197–2208. 10.1038/npp.2013.119 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Akkermans S. E. A., Van Rooij D., Rommelse N., Hartman C. A., Hoekstra P. J., Franke B., et al.. (2017). Effect of tobacco smoking on frontal cortical thickness development: a longitudinal study in a mixed cohort of adhd-affected and -unaffected youth . Eur. Neuropsychopharmacol. 27 , 1022–1031. 10.1016/j.euroneuro.2017.07.007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Albajara Sáenz A., Villemonteix T., Massat I. (2019). Structural and functional neuroimaging in attention-deficit/hyperactivity disorder . Dev. Med. Child Neurol. 61 , 399–405. 10.1111/dmcn.14050 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Albers A. B., Biener L. (2002). The role of smoking and rebelliousness in the development of depressive symptoms among a cohort of Massachusetts adolescents . Prev. Med. 34 , 625–631. 10.1006/pmed.2002.1029 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Albertella L., Le Pelley M. E., Copeland J. (2017). Cannabis use in early adolescence is associated with higher negative schizotypy in females . Eur. Psychiatry 45 , 235–241. 10.1016/j.eurpsy.2017.07.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Aloi J., Blair K. S., Crum K. I., Meffert H., White S. F., Tyler P. M., et al.. (2018). Adolescents show differential dysfunctions related to Alcohol and Cannabis Use Disorder severity in emotion and executive attention neuro-circuitries . Neuroimage Clin. 19 , 782–792. 10.1016/j.nicl.2018.06.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Aloi J., Meffert H., White S. F., Blair K. S., Hwang S., Tyler P. M., et al.. (2019). Differential dysfunctions related to alcohol and cannabis use disorder symptoms in reward and error-processing neuro-circuitries in adolescents . Dev. Cogn. Neurosci. 36 :100618. 10.1016/j.dcn.2019.100618 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Arseneault L., Cannon M., Poulton R., Murray R., Caspi A., Moffitt T. E. (2002). Cannabis use in adolescence and risk for adult psychosis: longitudinal prospective study . BMJ 325 , 1212–1213. 10.1136/bmj.325.7374.1212 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ashtari M., Avants B., Cyckowski L., Cervellione K. L., Roofeh D., Cook P., et al.. (2011). Medial temporal structures and memory functions in adolescents with heavy cannabis use . J. Psychiatr. Res. 45 , 1055–1066. 10.1016/j.jpsychires.2011.01.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ashtari M., Cervellione K., Cottone J., Ardekani B. A., Sevy S., Kumra S. (2009). Diffusion abnormalities in adolescents and young adults with a history of heavy cannabis use . J. Psychiatr. Res. 43 , 189–204. 10.1016/j.jpsychires.2009.01.008 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ashtari M., Cottone J., Ardekani B. A., Cervellione K., Szeszko P. R., Wu J., et al.. (2007). Disruption of white matter integrity in the inferior longitudinal fasciculus in adolescents with schizophrenia as revealed by fiber tractography . Arch. Gen. Psychiatry 64 , 1270–1280. 10.1001/archpsyc.64.11.1270 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Assari S., Mistry R., Caldwell C. H., Zimmerman M. A. (2018). Marijuana use and depressive symptoms; gender differences in African American adolescents . Front. Psychol. 9 :2135. 10.3389/fpsyg.2018.02135 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Audrain-Mcgovern J., Rodriguez D., Kassel J. D. (2009). Adolescent smoking and depression: evidence for self-medication and peer smoking mediation . Addiction 104 , 1743–1756. 10.1111/j.1360-0443.2009.02617.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Azagba S., Baskerville N. B., Foley K. (2017). Susceptibility to cigarette smoking among middle and high school e-cigarette users in Canada . Prev. Med. 103 , 14–19. 10.1016/j.ypmed.2017.07.017 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Banerjee N. (2014). Neurotransmitters in alcoholism: a review of neurobiological and genetic studies . Indian J. Hum. Genet. 20 , 20–31. 10.4103/0971-6866.132750 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barrington-Trimis J. L., Kong G., Leventhal A. M., Liu F., Mayer M., Boley Cruz T., et al.. (2018). E-cigarette use and subsequent smoking frequency among adolescents . Pediatrics 142 :e20180486. 10.1542/peds.2018-0486 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barrington-Trimis J. L., Urman R., Berhane K., Unger J. B., Cruz T. B., Pentz M. A., et al.. (2016). E-cigarettes and future cigarette use . Pediatrics 138 :e20160379. 10.1542/peds.2016-0379 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Batty G. D., Deary I. J., Macintyre S. (2007). Childhood IQ in relation to risk factors for premature mortality in middle-aged persons: the aberdeen children of the 1950s study . J. Epidemiol. Community Health 61 , 241–247. 10.1136/jech.2006.048215 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bava S., Frank L. R., Mcqueeny T., Schweinsburg B. C., Schweinsburg A. D., Tapert S. F. (2009). Altered white matter microstructure in adolescent substance users . Psychiatry Res. 173 , 228–237. 10.1016/j.pscychresns.2009.04.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bava S., Jacobus J., Mahmood O., Yang T. T., Tapert S. F. (2010). Neurocognitive correlates of white matter quality in adolescent substance users . Brain Cogn. 72 , 347–354. 10.1016/j.bandc.2009.10.012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bava S., Jacobus J., Thayer R. E., Tapert S. F. (2013). Longitudinal changes in white matter integrity among adolescent substance users . Alcohol. Clin. Exp. Res. 37 , E181–E189. 10.1111/j.1530-0277.2012.01920.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beal S. J., Negriff S., Dorn L. D., Pabst S., Schulenberg J. (2014). Longitudinal associations between smoking and depressive symptoms among adolescent girls . Prev. Sci. 15 , 506–515. 10.1007/s11121-013-0402-x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Becker M. P., Collins P. F., Lim K. O., Muetzel R. L., Luciana M. (2015). Longitudinal changes in white matter microstructure after heavy cannabis use . Dev. Cogn. Neurosci. 16 , 23–35. 10.1016/j.dcn.2015.10.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Behan B., Connolly C. G., Datwani S., Doucet M., Ivanovic J., Morioka R., et al.. (2014). Response inhibition and elevated parietal-cerebellar correlations in chronic adolescent cannabis users . Neuropharmacology 84 , 131–137. 10.1016/j.neuropharm.2013.05.027 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Benowitz N. L., Nardone N., Jain S., Dempsey D. A., Addo N., St Helen G., et al.. (2018). Comparison of urine 4-(methylnitrosamino)-1–(3)Pyridyl-1-butanol and cotinine for assessment of active and passive smoke exposure in urban adolescents . Cancer Epidemiol. Biomarkers Prev. 27 , 254–261. 10.1158/1055-9965.EPI-17-0671 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beres A. M. (2017). Time is of the essence: a review of electroencephalography (EEG) and event-related brain potentials (ERPs) in language research . Appl. Psychophysiol. Biofeedback 42 , 247–255. 10.1007/s10484-017-9371-3 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Berry K. M., Fetterman J. L., Benjamin E. J., Bhatnagar A., Barrington-Trimis J. L., Leventhal A. M., et al.. (2019). Association of electronic cigarette use with subsequent initiation of tobacco cigarettes in US youths . JAMA Netw. Open 2 :e187794. 10.1001/jamanetworkopen.2018.7794 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bi Y., Yuan K., Guan Y., Cheng J., Zhang Y., Li Y., et al.. (2017). Altered resting state functional connectivity of anterior insula in young smokers . Brain Imaging Behav. 11 , 155–165. 10.1007/s11682-016-9511-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blanco C., Florez-Salamanca L., Secades-Villa R., Wang S., Hasin D. S. (2018). Predictors of initiation of nicotine, alcohol, cannabis and cocaine use: results of the national epidemiologic survey on alcohol and related conditions (NESARC) . Am. J. Addict. 27 , 477–484. 10.1111/ajad.12764 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blumenthal H., Leen-Feldner E. W., Frala J. L., Badour C. L., Ham L. S. (2010). Social anxiety and motives for alcohol use among adolescents . Psychol. Addict. Behav. 24 , 529–534. 10.1037/a0019794 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boden J. M., Fergusson D. M., Horwood L. J. (2010). Cigarette smoking and depression: tests of causal linkages using a longitudinal birth cohort . Br. J. Psychiatry 196 , 440–446. 10.1192/bjp.bp.109.065912 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bouziane C., Caan M. W. A., Tamminga H. G. H., Schrantee A., Bottelier M. A., De Ruiter M. B., et al.. (2018). ADHD and maturation of brain white matter: a DTI study in medication naive children and adults . Neuroimage Clin. 17 , 53–59. 10.1016/j.nicl.2017.09.026 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boykan R., Messina C. R., Chateau G., Eliscu A., Tolentino J., Goniewicz M. L. (2019). Self-reported use of Tobacco, E-cigarettes and marijuana versus urinary biomarkers . Pediatrics 143 :e20183531. 10.1542/peds.2018-3531 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boys A., Farrell M., Taylor C., Marsden J., Goodman R., Brugha T., et al.. (2003). Psychiatric morbidity and substance use in young people aged 13–15 years: results from the Child and Adolescent Survey of Mental Health . Br. J. Psychiatry 182 , 509–517. 10.1192/bjp.182.6.509 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brière F. N., Fallu J. S., Descheneaux A., Janosz M. (2011). Predictors and consequences of simultaneous alcohol and cannabis use in adolescents . Addict. Behav. 36 , 785–788. 10.1016/j.addbeh.2011.02.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brook D. W., Brook J. S., Zhang C., Cohen P., Whiteman M. (2002). Drug use and the risk of major depressive disorder, alcohol dependence and substance use disorders . Arch. Gen. Psychiatry 59 , 1039–1044. 10.1001/archpsyc.59.11.1039 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brook J. S., Schuster E., Zhang C. (2004). Cigarette smoking and depressive symptoms: a longitudinal study of adolescents and young adults . Psychol. Rep. 95 , 159–166. 10.2466/pr0.95.5.159-166 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brown R. A., Lewinsohn P. M., Seeley J. R., Wagner E. F. (1996). Cigarette smoking, major depression and other psychiatric disorders among adolescents . J. Am. Acad. Child Adolesc. Psychiatry 35 , 1602–1610. 10.1097/00004583-199612000-00011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brown S. A., Tapert S. F., Granholm E., Delis D. C. (2000). Neurocognitive functioning of adolescents: effects of protracted alcohol use . Alcohol. Clin. Exp. Res. 24 , 164–171. 10.1111/j.1530-0277.2000.tb04586.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brumback T., Squeglia L. M., Jacobus J., Pulido C., Tapert S. F., Brown S. A. (2015). Adolescent heavy drinkers’ amplified brain responses to alcohol cues decrease over one month of abstinence . Addict. Behav. 46 , 45–52. 10.1016/j.addbeh.2015.03.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brumback T. Y., Worley M., Nguyen-Louie T. T., Squeglia L. M., Jacobus J., Tapert S. F. (2016). Neural predictors of alcohol use and psychopathology symptoms in adolescents . Dev. Psychopathol. 28 , 1209–1216. 10.1017/s0954579416000766 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Buchmann A. F., Blomeyer D., Jennen-Steinmetz C., Schmidt M. H., Esser G., Banaschewski T., et al.. (2013). Early smoking onset may promise initial pleasurable sensations and later addiction . Addict. Biol. 18 , 947–954. 10.1111/j.1369-1600.2011.00377.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Caldwell L. C., Schweinsburg A. D., Nagel B. J., Barlett V. C., Brown S. A., Tapert S. F. (2005). Gender and adolescent alcohol use disorders on BOLD (blood oxygen level dependent) response to spatial working memory . Alcohol Alcohol. 40 , 194–200. 10.1093/alcalc/agh134 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Camchong J., Lim K. O., Kumra S. (2017). Adverse effects of cannabis on adolescent brain development: a longitudinal study . Cereb. Cortex 27 , 1922–1930. 10.1093/cercor/bhw015 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carbia C., Cadaveira F., Caamano-Isorna F., Rodriguez-Holguin S., Corral M. (2017a). Binge drinking during adolescence and young adulthood is associated with deficits in verbal episodic memory . PLoS One 12 :e0171393. 10.1371/journal.pone.0171393 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Carbia C., Cadaveira F., Lopez-Caneda E., Caamano-Isorna F., Rodriguez Holguin S., Corral M. (2017b). Working memory over a six-year period in young binge drinkers . Alcohol 61 , 17–23. 10.1016/j.alcohol.2017.01.013 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cardenas V. A., Greenstein D., Fouche J. P., Ferrett H., Cuzen N., Stein D. J., et al.. (2013). Not lesser but Greater fractional anisotropy in adolescents with alcohol use disorders . Neuroimage Clin. 2 , 804–809. 10.1016/j.nicl.2013.06.002 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Caris L., Wagner F. A., Rios-Bedoya C. F., Anthony J. C. (2009). Opportunities to use drugs and stages of drug involvement outside the United States: evidence from the Republic of Chile . Drug Alcohol Depend. 102 , 30–34. 10.1016/j.drugalcdep.2008.12.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cassidy R. N., Meisel M. K., Diguiseppi G., Balestrieri S., Barnett N. P. (2018). Initiation of vaporizing cannabis: individual and social network predictors in a longitudinal study of young adults . Drug Alcohol Depend. 188 , 334–340. 10.1016/j.drugalcdep.2018.04.014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Castellanos-Ryan N., Pingault J. B., Parent S., Vitaro F., Tremblay R. E., Seguin J. R. (2017). Adolescent cannabis use, change in neurocognitive function and high-school graduation: a longitudinal study from early adolescence to young adulthood . Dev. Psychopathol. 29 , 1253–1266. 10.1017/s0954579416001280 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cerdá M., Santaella J., Marshall B. D., Kim J. H., Martins S. S. (2015). Nonmedical prescription opioid use in childhood and early adolescence predicts transitions to heroin use in young adulthood: a national study . J. Pediatr. 167 , 605–612; e601–e602. 10.1016/j.jpeds.2015.04.071 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chaarani B., Kan K. J., Mackey S., Spechler P. A., Potter A., Orr C., et al.. (2019). Low smoking exposure, the adolescent brain and the modulating role of CHRNA5 polymorphisms . Biol. Psychiatry Cogn. Neurosci. Neuroimaging 4 , 672–679. 10.1016/j.bpsc.2019.02.006 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chandrasekar R. (2013). Alcohol and NMDA receptor: current research and future direction . Front. Mol. Neurosci. 6 :14. 10.3389/fnmol.2013.00014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cheetham A., Allen N. B., Whittle S., Simmons J. G., Yucel M., Lubman D. I. (2012). Orbitofrontal volumes in early adolescence predict initiation of cannabis use: a 4-year longitudinal and prospective study . Biol. Psychiatry 71 , 684–692. 10.1016/j.biopsych.2011.10.029 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cheetham A., Allen N. B., Whittle S., Simmons J., Yucel M., Lubman D. I. (2014). Volumetric differences in the anterior cingulate cortex prospectively predict alcohol-related problems in adolescence . Psychopharmacology 231 , 1731–1742. 10.1007/s00213-014-3483-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chen Y., Fowler C. H., Papa V. B., Lepping R. J., Brucks M. G., Fox A. T., et al.. (2018). Adolescents’ behavioral and neural responses to e-cigarette advertising . Addict. Biol. 23 , 761–771. 10.1111/adb.12510 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Choi W. S., Patten C. A., Gillin J. C., Kaplan R. M., Pierce J. P. (1997). Cigarette smoking predicts development of depressive symptoms among U.S. adolescents . Ann. Behav. Med. 19 , 42–50. 10.1007/BF02883426 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Choudhury S., Blakemore S. J., Charman T. (2006). Social cognitive development during adolescence . Soc. Cogn. Affect. Neurosci. 1 , 165–174. 10.1093/scan/nsl024 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Churchwell J. C., Lopez-Larson M., Yurgelun-Todd D. A. (2010). Altered frontal cortical volume and decision making in adolescent cannabis users . Front. Psychol. 1 :225. 10.3389/fpsyg.2010.00225 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Clark C., Haines M. M., Head J., Klineberg E., Arephin M., Viner R., et al.. (2007). Psychological symptoms and physical health and health behaviours in adolescents: a prospective 2-year study in East London . Addiction 102 , 126–135. 10.1111/j.1360-0443.2006.01621.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Claus E. D., Feldstein Ewing S. W., Magnan R. E., Montanaro E., Hutchison K. E., Bryan A. D. (2018). Neural mechanisms of risky decision making in adolescents reporting frequent alcohol and/or marijuana use . Brain Imaging Behav. 12 , 564–576. 10.1007/s11682-017-9723-x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Colder C. R., Mehta P., Balanda K., Campbell R. T., Mayhew K. P., Stanton W. R., et al.. (2001). Identifying trajectories of adolescent smoking: an application of latent growth mixture modeling . Health Psychol. 20 , 127–135. 10.1037/0278-6133.20.2.127 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Colder C. R., Shyhalla K., Frndak S., Read J. P., Lengua L. J., Hawk L. W., Jr., et al.. (2017). The prospective association between internalizing symptoms and adolescent alcohol involvement and the moderating role of age and externalizing symptoms . Alcohol. Clin. Exp. Res. 41 , 2185–2196. 10.1111/acer.13512 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Corley J., Gow A. J., Starr J. M., Deary I. J. (2012). Smoking, childhood IQ and cognitive function in old age . J. Psychosom. Res. 73 , 132–138. 10.1016/j.jpsychores.2012.03.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Creswell K. G., Chung T., Clark D. B., Martin C. S. (2015). Solitary cannabis use in adolescence as a correlate and predictor of cannabis problems . Drug Alcohol Depend. 156 , 120–125. 10.1016/j.drugalcdep.2015.08.027 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cservenka A. (2016). Neurobiological phenotypes associated with a family history of alcoholism . Drug Alcohol Depend. 158 , 8–21. 10.1016/j.drugalcdep.2015.10.021 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cservenka A., Gillespie A. J., Michael P. G., Nagel B. J. (2015). Family history density of alcoholism relates to left nucleus accumbens volume in adolescent girls . J. Stud. Alcohol Drugs 76 , 47–56. 10.15288/jsad.2015.76.47 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cullen K. R., Klimes-Dougan B., Muetzel R., Mueller B. A., Camchongg J., Houri A., et al.. (2010). Altered white matter microstructure in adolescencts with major depression: a preliminary study . J. Am. Acad. Child Adolesc. Psychiatry. 49 . 10.1097/00004583-201002000-00011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dager A. D., Anderson B. M., Rosen R., Khadka S., Sawyer B., Jiantonio-Kelly R. E., et al.. (2014). Functional magnetic resonance imaging (fMRI) response to alcohol pictures predicts subsequent transition to heavy drinking in college students . Addiction 109 , 585–595. 10.1111/add.12437 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dai H., Catley D., Richter K. P., Goggin K., Ellerbeck E. F. (2018). Electronic cigarettes and future marijuana use: a longitudinal study . Pediatrics 141 :e20173787. 10.1542/peds.2017-3787 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dao J. M., McQuown S. C., Loughlin S. E., Belluzzi J. D., Leslie F. M. (2011). Nicotine alters limbic function in adolescent rat by a 5-HT1A receptor mechanism . Neuropsychopharmacology 36 , 1319–1331. 10.1038/npp.2011.8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• De Bellis M. D., Clark D. B., Beers S. R., Soloff P. H., Boring A. M., Hall J., et al.. (2000). Hippocampal volume in adolescent-onset alcohol use disorders . Am. J. Psychiatry 157 , 737–744. 10.1176/appi.ajp.157.5.737 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• De Bellis M. D., Narasimhan A., Thatcher D. L., Keshavan M. S., Soloff P., Clark D. B. (2005). Prefrontal cortex, thalamus and cerebellar volumes in adolescents and young adults with adolescent-onset alcohol use disorders and comorbid mental disorders . Alcohol. Clin. Exp. Res. 29 , 1590–1600. 10.1097/01.alc.0000179368.87886.76 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• De Bellis M. D., Wang L., Bergman S. R., Yaxley R. H., Hooper S. R., Huettel S. A. (2013). Neural mechanisms of risky decision-making and reward response in adolescent onset cannabis use disorder . Drug Alcohol Depend. 133 , 134–145. 10.1016/j.drugalcdep.2013.05.020 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Degenhardt L., Coffey C., Carlin J. B., Swift W., Moore E., Patton G. C. (2010). Outcomes of occasional cannabis use in adolescence: 10-year follow-up study in Victoria, Australia . Br. J. Psychiatry 196 , 290–295. 10.1192/bjp.bp.108.056952 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dierker L., Swendsen J., Rose J., He J., Merikangas K., Tobacco Etiology Research Network (Tern) (2012). Transitions to regular smoking and nicotine dependence in the Adolescent National Comorbidity Survey (NCS-A) . Ann. Behav. Med. 43 , 394–401. 10.1007/s12160-011-9330-9 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• DiFranza J. R., Rigotti N. A., Mcneill A. D., Ockene J. K., Savageau J. A., St Cyr D., et al.. (2000). Initial symptoms of nicotine dependence in adolescents . Tob. Control 9 , 313–319. 10.1136/tc.9.3.313 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• DiFranza J. R., Savageau J. A., Fletcher K., Pbert L., O’Loughlin J., Mcneill A. D., et al.. (2007). Susceptibility to nicotine dependence: the development and assessment of nicotine dependence in youth 2 study . Pediatrics 120 , e974–e983. 10.1542/peds.2007-0027 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• DiFranza J. R., Savageau J. A., Rigotti N. A., Fletcher K., Ockene J. K., Mcneill A. D., et al.. (2002). Development of symptoms of tobacco dependence in youths: 30 month follow up data from the DANDY study . Tob. Control 11 , 228–235. 10.1136/tc.11.3.228 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dinn W. M., Aycicegi A., Harris C. L. (2004). Cigarette smoking in a student sample: neurocognitive and clinical correlates . Addict. Behav. 29 , 107–126. 10.1016/j.addbeh.2003.07.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dumontheil I. (2016). Adolescent brain development . Curr. Opin. Behav. Sci. 10 , 39–44. Available online at: 10.1016/j.cobeha.2016.04.012 [ CrossRef ] [ Google Scholar ]
• Duncan B., Rees D. I. (2005). Effect of smoking on depressive symptomatology: a reexamination of data from the National Longitudinal Study of Adolescent Health . Am. J. Epidemiol. 162 , 461–470. 10.1093/aje/kwi219 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Duperrouzel J., Hawes S. W., Lopez-Quintero C., Pacheco-Colon I., Comer J., Gonzalez R. (2018). The association between adolescent cannabis use and anxiety: a parallel process analysis . Addict. Behav. 78 , 107–113. 10.1016/j.addbeh.2017.11.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Duperrouzel J. C., Hawes S. W., Lopez-Quintero C., Pacheco-Colon I., Coxe S., Hayes T., et al.. (2019). Adolescent cannabis use and its associations with decision-making and episodic memory: preliminary results from a longitudinal study . Neuropsychology 33 , 701–710. 10.1037/neu0000538 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Edlund M. J., Forman-Hoffman V. L., Winder C. R., Heller D. C., Kroutil L. A., Lipari R. N., et al.. (2015). Opioid abuse and depression in adolescents: results from the national survey on drug use and health . Drug Alcohol Depend. 152 , 131–138. 10.1016/j.drugalcdep.2015.04.010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Edwards A. C., Heron J., Dick D. M., Hickman M., Lewis G., Macleod J., et al. (2014). Adolescent alcohol use is positively associated with later depression in a population-based U.K. cohort . J. Stud. Alcohol Drugs 75 , 758–765. 10.15288/jsad.2014.75.758 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Elsey J. W., Crowley M. J., Mencl W. E., Lacadie C. M., Mayes L. C., Potenza M. N. (2016). Relationships between impulsivity, anxiety and risk-taking and the neural correlates of attention in adolescents . Dev. Neuropsychol. 41 , 38–58. 10.1080/87565641.2016.1167212 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Enstad F., Evans-Whipp T., Kjeldsen A., Toumbourou J. W., Von Soest T. (2019). Predicting hazardous drinking in late adolescence/young adulthood from early and excessive adolescent drinking-a longitudinal cross-national study of Norwegian and Australian adolescents . BMC Public Health 19 :790. 10.1186/s12889-019-7099-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Epstein K. A., Kumra S. (2015). Altered cortical maturation in adolescent cannabis users with and without schizophrenia . Schizophr. Res. 162 , 143–152. 10.1016/j.schres.2014.11.029 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Everett S. A., Warren C. W., Sharp D., Kann L., Husten C. G., Crossett L. S. (1999). Initiation of cigarette smoking and subsequent smoking behavior among U.S. high school students . Prev. Med. 29 , 327–333. 10.1006/pmed.1999.0560 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fasteau M., Mackay D., Smith D. J., Meyer T. D. (2017). Is adolescent alcohol use associated with self-reported hypomanic symptoms in adulthood? Findings from a prospective birth cohort . Psychiatry Res. 255 , 232–237. 10.1016/j.psychres.2017.05.034 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fedota J. R., Ding X., Matous A. L., Salmeron B. J., Mckenna M. R., Gu H., et al.. (2018). Nicotine abstinence influences the calculation of salience in discrete insular circuits . Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3 , 150–159. 10.1016/j.bpsc.2017.09.010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Field M., Christiansen P., Cole J., Goudie A. (2007). Delay discounting and the alcohol Stroop in heavy drinking adolescents . Addiction 102 , 579–586. 10.1111/j.1360-0443.2007.01743.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fiorentini A., Volonteri L. S., Dragogna F., Rovera C., Maffini M., Mauri M. C., et al.. (2011). Substance-induced psychoses: a critical review of the literature . Curr. Drug. Abuse Rev. 4 , 228–240. 10.2174/1874473711104040228 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fontes M. A., Bolla K. I., Cunha P. J., Almeida P. P., Jungerman F., Laranjeira R. R., et al.. (2011). Cannabis use before age 15 and subsequent executive functioning . Br. J. Psychiatry 198 , 442–447. 10.1192/bjp.bp.110.077479 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Freels T. G., Baxter-Potter L. N., Lugo J. M., Glodosky N. C., Wright H. R., Baglot S. L., et al.. (2020). Vaporized cannabis extracts have reinforcing properties and support conditioned drug-seeking behavior in rats . J. Neurosci. 40 , 1897–1908. 10.1523/JNEUROSCI.2416-19.2020 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• French L., Gray C., Leonard G., Perron M., Pike G. B., Richer L., et al.. (2015). Early cannabis use, polygenic risk score for schizophrenia and brain maturation in adolescence . JAMA Psychiatry 72 , 1002–1011. 10.1001/jamapsychiatry.2015.1131 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gage S. H., Hickman M., Heron J., Munafo M. R., Lewis G., Macleod J., et al.. (2015). Associations of cannabis and cigarette use with depression and anxiety at age 18: findings from the avon longitudinal study of parents and children . PLoS One 10 :e0122896. 10.1371/journal.pone.0122896 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Galambos N., Leadbeater B., Barker E. (2004). Gender differences in and risk factors for depression in adolescence: a 4-year longitudinal study . Int. J. Behav. Dev. 28 , 16–25. 10.1080/01650250344000235 [ CrossRef ] [ Google Scholar ]
• Galambos N. L., Macdonald S. W., Naphtali C., Cohen A. L., De Frias C. M. (2005). Cognitive performance differentiates selected aspects of psychosocial maturity in adolescence . Dev. Neuropsychol. 28 , 473–492. 10.1207/s15326942dn2801_2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Galván A., Poldrack R. A., Baker C. M., Mcglennen K. M., London E. D. (2011). Neural correlates of response inhibition and cigarette smoking in late adolescence . Neuropsychopharmacology 36 , 970–978. 10.1038/npp.2010.235 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Garrison K. A., O’Malley S. S., Gueorguieva R., Krishnan-Sarin S. (2018). A fMRI study on the impact of advertising for flavored e-cigarettes on susceptible young adults . Drug Alcohol Depend. 186 , 233–241. 10.1016/j.drugalcdep.2018.01.026 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Glass K., Flory K. (2010). Why does ADHD confer risk for cigarette smoking? A review of psychosocial mechanisms . Clin. Child Fam. Psychol. Rev. 13 , 291–313. 10.1007/s10567-010-0070-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gobbi G., Atkin T., Zytynski T., Wang S., Askari S., Boruff J., et al.. (2019). Association of cannabis use in adolescence and risk of depression, anxiety and suicidality in young adulthood: a systematic review and meta-analysis . JAMA Psychiatry 76 , 426–434. 10.1001/jamapsychiatry.2018.4500 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gogliettino A. R., Potenza M. N., Yip S. W. (2016). White matter development and tobacco smoking in young adults: a systematic review with recommendations for future research . Drug Alcohol Depend. 162 , 26–33. 10.1016/j.drugalcdep.2016.02.015 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gogos A., Skokou M., Ferentinou E., Gourzis P. (2019). Nicotine consumption during the prodromal phase of schizophrenia-a review of the literature . Neuropsychiatr. Dis. Treat. 15 , 2943–2958. 10.2147/NDT.s210199 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldenson N. I., Leventhal A. M., Stone M. D., Mcconnell R. S., Barrington-Trimis J. L. (2017). Associations of electronic cigarette nicotine concentration with subsequent cigarette smoking and vaping levels in adolescents . JAMA Pediatr. 171 , 1192–1199. 10.1001/jamapediatrics.2017.3209 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goldstein R. Z., Volkow N. D. (2002). Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex . Am. J. Psychiatry 159 , 1642–1652. 10.1176/appi.ajp.159.10.1642 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goniewicz M. L., Boykan R., Messina C. R., Eliscu A., Tolentino J. (2019). High exposure to nicotine among adolescents who use Juul and other vape pod systems (‘Pods’) . Tob. Control 28 , 676–677. 10.1136/tobaccocontrol-2018-054565 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goodman E., Capitman J. (2000). Depressive symptoms and cigarette smoking among teens . Pediatrics 106 , 748–755. 10.1542/peds.106.4.748 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Green K. M., Musci R. J., Johnson R. M., Matson P. A., Reboussin B. A., Ialongo N. S. (2016). Outcomes associated with adolescent marijuana and alcohol use among urban young adults: a prospective study . Addict. Behav. 53 , 155–160. 10.1016/j.addbeh.2015.10.014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Groenman A. P., Oosterlaan J., Rommelse N., Franke B., Roeyers H., Oades R. D., et al.. (2013). Substance use disorders in adolescents with attention deficit hyperactivity disorder: a 4-year follow-up study . Addiction 108 , 1503–1511. 10.1111/add.12188 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grucza R. A., Bierut L. J. (2006). Cigarette smoking and the risk for alcohol use disorders among adolescent drinkers . Alcohol. Clin. Exp. Res. 30 , 2046–2054. 10.1111/j.1530-0277.2006.00255.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hanna R. C., Shalvoy A., Cullum C. M., Ivleva E. I., Keshavan M., Pearlson G., et al.. (2016). Cognitive function in individuals with psychosis: moderation by adolescent cannabis use . Schizophr. Bull. 42 , 1496–1503. 10.1093/schbul/sbw030 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hanson K. L., Cummins K., Tapert S. F., Brown S. A. (2011). Changes in neuropsychological functioning over 10 years following adolescent substance abuse treatment . Psychol. Addict. Behav. 25 , 127–142. 10.1037/a0022350 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hanson K. L., Winward J. L., Schweinsburg A. D., Medina K. L., Brown S. A., Tapert S. F. (2010). Longitudinal study of cognition among adolescent marijuana users over three weeks of abstinence . Addict. Behav. 35 , 970–976. 10.1016/j.addbeh.2010.06.012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Harvey M. A., Sellman J. D., Porter R. J., Frampton C. M. (2007). The relationship between non-acute adolescent cannabis use and cognition . Drug Alcohol Rev. 26 , 309–319. 10.1080/09595230701247772 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hayaki J., Anderson B. J., Stein M. D. (2016). Dual cannabis and alcohol use disorders in young adults: problems magnified . Subst. Abus. 37 , 579–583. 10.1080/08897077.2016.1176613 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hengartner M. P., Angst J., Ajdacic-Gross V., Rössler W. (2020). Cannabis use during adolescence and the occurrence of depression, suicidality and anxiety disorder across adulthood: findings from a longitudinal cohort study over 30 years . J. Affect. Disord. 272 , 98–103. 10.1016/j.jad.2020.03.126 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hiemstra M., Nelemans S. A., Branje S., Van Eijk K. R., Hottenga J. J., Vinkers C. H., et al.. (2018). Genetic vulnerability to schizophrenia is associated with cannabis use patterns during adolescence . Drug Alcohol Depend. 190 , 143–150. 10.1016/j.drugalcdep.2018.05.024 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hines L. A., Morley K. I., Mackie C., Lynskey M. (2015). Genetic and environmental interplay in adolescent substance use disorders . Curr. Addict. Rep. 2 , 122–129. 10.1007/s40429-015-0049-8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hu M. C., Griesler P., Schaffran C., Kandel D. (2011). Risk and protective factors for nicotine dependence in adolescence . J. Child Psychol. Psychiatry 52 , 1063–1072. 10.1111/j.1469-7610.2010.02362.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ilomäki R., Riala K., Hakko H., Lappalainen J., Ollinen T., Rasanen P., et al.. (2008). Temporal association of onset of daily smoking with adolescent substance use and psychiatric morbidity . Eur. Psychiatry 23 , 85–91. 10.1016/j.eurpsy.2007.10.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Infante M. A., Courtney K. E., Castro N., Squeglia L. M., Jacobus J. (2018). Adolescent Brain surface area Pre- and post-cannabis and alcohol initiation . J. Stud. Alcohol Drugs 79 , 835–843. 10.15288/jsad.2018.79.835 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Infante M. A., Nguyen-Louie T. T., Worley M., Courtney K. E., Coronado C., Jacobus J. (2020). Neuropsychological trajectories associated with adolescent alcohol and cannabis use: a prospective 14-year study . J. Int. Neuropsychol. Soc. 26 , 480–491. 10.1017/S1355617719001395 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jackson A., Green B., Erythropel H. C., Kong G., Cavallo D. A., Eid T., et al.. (2020). Influence of menthol and green apple e-liquids containing different nicotine concentrations among youth e-cigarette users . Exp. Clin. Psychopharmacol. 10.1037/pha0000368 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jackson N. J., Isen J. D., Khoddam R., Irons D., Tuvblad C., Iacono W. G., et al.. (2016). Impact of adolescent marijuana use on intelligence: results from two longitudinal twin studies . Proc. Natl. Acad. Sci. U S A 113 , E500–E508. 10.1073/pnas.1516648113 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobsen L. K., Krystal J. H., Mencl W. E., Westerveld M., Frost S. J., Pugh K. R. (2005). Effects of smoking and smoking abstinence on cognition in adolescent tobacco smokers . Biol. Psychiatry 57 , 56–66. 10.1016/j.biopsych.2004.10.022 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobsen L. K., Mencl W. E., Constable R. T., Westerveld M., Pugh K. R. (2007a). Impact of smoking abstinence on working memory neurocircuitry in adolescent daily tobacco smokers . Psychopharmacology 193 , 557–566. 10.1007/s00213-007-0797-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobsen L. K., Picciotto M. R., Heath C. J., Frost S. J., Tsou K. A., Dwan R. A., et al.. (2007b). Prenatal and adolescent exposure to tobacco smoke modulates the development of white matter microstructure . J. Neurosci. 27 , 13491–13498. 10.1523/JNEUROSCI.2402-07.2007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobsen L. K., Slotkin T. A., Mencl W. E., Frost S. J., Pugh K. R. (2007c). Gender-specific effects of prenatal and adolescent exposure to tobacco smoke on auditory and visual attention . Neuropsychopharmacology 32 , 2453–2464. 10.1038/sj.npp.1301398 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobsen L. K., Mencl W. E., Westerveld M., Pugh K. R. (2004). Impact of cannabis use on brain function in adolescents . Ann. N Y Acad. Sci. 1021 , 384–390. 10.1196/annals.1308.053 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobus J., Castro N., Squeglia L. M., Meloy M. J., Brumback T., Huestis M. A., et al.. (2016). Adolescent cortical thickness pre- and post marijuana and alcohol initiation . Neurotoxicol. Teratol. 57 , 20–29. 10.1016/j.ntt.2016.09.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobus J., Mcqueeny T., Bava S., Schweinsburg B. C., Frank L. R., Yang T. T., et al.. (2009). White matter integrity in adolescents with histories of marijuana use and binge drinking . Neurotoxicol. Teratol. 31 , 349–355. 10.1016/j.ntt.2009.07.006 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobus J., Squeglia L. M., Bava S., Tapert S. F. (2013a). White matter characterization of adolescent binge drinking with and without co-occurring marijuana use: a 3-year investigation . Psychiatry Res. 214 , 374–381. 10.1016/j.pscychresns.2013.07.014 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobus J., Squeglia L. M., Infante M. A., Bava S., Tapert S. F. (2013b). White matter integrity pre- and post marijuana and alcohol initiation in adolescence . Brain Sci. 3 , 396–414. 10.3390/brainsci3010396 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobus J., Squeglia L. M., Meruelo A. D., Castro N., Brumback T., Giedd J. N., et al.. (2015). Cortical thickness in adolescent marijuana and alcohol users: a three-year prospective study from adolescence to young adulthood . Dev. Cogn. Neurosci. 16 , 101–109. 10.1016/j.dcn.2015.04.006 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jacobus J., Squeglia L. M., Sorg S. F., Nguyen-Louie T. T., Tapert S. F. (2014). Cortical thickness and neurocognition in adolescent marijuana and alcohol users following 28 days of monitored abstinence . J. Stud. Alcohol Drugs 75 , 729–743. 10.15288/jsad.2014.75.729 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jager G., Block R. I., Luijten M., Ramsey N. F. (2010). Cannabis use and memory brain function in adolescent boys: a cross-sectional multicenter functional magnetic resonance imaging study . J. Am. Acad. Child Adolesc. Psychiatry 49 , 561–572, 572.e1–572.e3. 10.1016/j.jaac.2010.02.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jager G., Block R. I., Luijten M., Ramsey N. F. (2013). Tentative evidence for striatal hyperactivity in adolescent cannabis-using boys: a cross-sectional multicenter fMRI study . J. Psychoactive Drugs 45 , 156–167. 10.1080/02791072.2013.785837 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jamal M., Does A. J., Penninx B. W., Cuijpers P. (2011). Age at smoking onset and the onset of depression and anxiety disorders . Nicotine Tob. Res. 13 , 809–819. 10.1093/ntr/ntr077 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jaworska N., MacQueen G. (2015). Adolescence as a unique developmental period . J. Psychiatry Neurosci. 40 , 291–293. 10.1503/jpn.150268 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jin L. Z., Rangan A., Mehlsen J., Andersen L. B., Larsen S. C., Heitmann B. L. (2017). Association between use of cannabis in adolescence and weight change into midlife . PLoS One 12 :e0168897. 10.1371/journal.pone.0168897 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Johnson J. A., Zatorre R. J. (2006). Neural substrates for dividing and focusing attention between simultaneous auditory and visual events . NeuroImage 31 , 1673–1681. 10.1016/j.neuroimage.2006.02.026 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Johnston L. D., Miech R. A., O’Malley P. M., Bachman J. G., Shulenberg J. E., Patrick M. E. (2020). Monitoring the Future National Survey Results on Drug Use 1975–2019: Overview, Key Findings on Adolescent Drug Use. Ann Arbor: Institute for Social Research. [ Google Scholar ]
• Jones S. A., Cservenka A., Nagel B. J. (2016). Binge drinking impacts dorsal striatal response during decision making in adolescents . NeuroImage 129 , 378–388. 10.1016/j.neuroimage.2016.01.044 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jones H. J., Gage S. H., Heron J., Hickman M., Lewis G., Munafo M. R., et al.. (2018). Association of combined patterns of tobacco and cannabis use in adolescence with psychotic experiences . JAMA Psychiatry 75 , 240–246. 10.1001/jamapsychiatry.2017.4271 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jones S. A., Steele J. S., Nagel B. J. (2017). Binge drinking and family history of alcoholism are associated with an altered developmental trajectory of impulsive choice across adolescence . Addiction 112 , 1184–1192. 10.1111/add.13823 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jun J., Kim S. H., Wu L. (2019). Tobacco risk information and comparative risk assessment of E-cigarettes Vs. cigarettes: application of the reinforcing spirals model . J. Health Commun. 24 , 422–431. 10.1080/10810730.2019.1630526 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jurk S., Mennigen E., Goschke T., Smolka M. N. (2018). Low-level alcohol consumption during adolescence and its impact on cognitive control development . Addict. Biol. 23 , 313–326. 10.1111/adb.12467 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kaasbøll C., Hagen R., Gråwe R. W. (2018). Population-based associations among cannabis use, anxiety and depression in norwegian adolescents . J. Child Adol. Subst. Abus. 27 , 238–243. 10.1080/1067828x.2018.1462281 [ CrossRef ] [ Google Scholar ]
• Kaplow J. B., Curran P. J., Angold A., Costello E. J. (2001). The prospective relation between dimensions of anxiety and the initiation of adolescent alcohol use . J. Clin. Child Psychol. 30 , 316–326. 10.1207/s15374424jccp3003_4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Karoly H. C., Bryan A. D., Weiland B. J., Mayer A., Dodd A., Feldstein Ewing S. W. (2015). Does incentive-elicited nucleus accumbens activation differ by substance of abuse? An examination with adolescents . Dev. Cogn. Neurosci. 16 , 5–15. 10.1016/j.dcn.2015.05.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Katz G., Kunyvsky Y., Hornik-Lurie T., Raskin S., Abramowitz M. Z. (2016). Cannabis and alcohol abuse among first psychotic episode inpatients . Isr. J. Psychiatry Relat. Sci. 53 , 10–15. [ PubMed ] [ Google Scholar ]
• Khokhar J. Y., Dwiel L. L., Henricks A. M., Doucette W. T., Green A. I. (2018). The link between schizophrenia and substance use disorder: a unifying hypothesis . Schizophr. Res. 194 , 78–85. 10.1016/j.schres.2017.04.016 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Khurana A., Romer D., Betancourt L. M., Brodsky N. L., Giannetta J. M., Hurt H. (2013). Working memory ability predicts trajectories of early alcohol use in adolescents: the mediational role of impulsivity . Addiction 108 , 506–515. 10.1111/add.12001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Klein H., Sterk C. E., Elifson K. W. (2013). Initial smoking sxperiences and current smoking behaviors and perceptions among current smokers . J. Addict. 2013 :491797. 10.1155/2013/491797 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kuhn S., Mascharek A., Banaschewski T., Bodke A., Bromberg U., Buchel C., et al.. (2019). Predicting development of adolescent drinking behaviour from whole brain structure at 14 years of age . Elife 8 :e44056. 10.7554/eLife.44056 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kumra S., Robinson P., Tambyraja R., Jensen D., Schimunek C., Houri A., et al.. (2012). Parietal lobe volume deficits in adolescents with schizophrenia and adolescents with cannabis use disorders . J. Am. Acad. Child Adolesc. Psychiatry 51 , 171–180. 10.1016/j.jaac.2011.11.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lai C. H., Wu Y. T. (2014). Alterations in white matter micro-integrity of the superior longitudinal fasciculus and anterior thalamic radiation of young adult patients with depression . Psychol. Med. 44 , 2825–2832. 10.1017/s0033291714000440 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lane S. D., Cherek D. R., Pietras C. J., Steinberg J. L. (2005). Performance of heavy marijuana-smoking adolescents on a laboratory measure of motivation . Addict. Behav. 30 , 815–828. 10.1016/j.addbeh.2004.08.026 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lane S. D., Cherek D. R., Tcheremissine O. V., Steinberg J. L., Sharon J. L. (2007). Response perseveration and adaptation in heavy marijuana-smoking adolescents . Addict. Behav. 32 , 977–990. 10.1016/j.addbeh.2006.07.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lanza S. T., Vasilenko S. A. (2015). New methods shed light on age of onset as a risk factor for nicotine dependence . Addict. Behav. 50 , 161–164. 10.1016/j.addbeh.2015.06.024 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lee J. H., Lim Y., Wiederhold B. K., Graham S. J. (2005). A functional magnetic resonance imaging (FMRI) study of cue-induced smoking craving in virtual environments . Appl. Psychophysiol. Biofeedback 30 , 195–204. 10.1007/s10484-005-6377-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Leventhal A. M., Strong D. R., Kirkpatrick M. G., Unger J. B., Sussman S., Riggs N. R., et al.. (2015). Association of electronic cigarette use with initiation of combustible tobacco product smoking in early adolescence . JAMA 314 , 700–707. 10.1001/jama.2015.8950 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lewinsohn P. M., Rohde P., Brown R. A. (1999). Level of current and past adolescent cigarette smoking as predictors of future substance use disorders in young adulthood . Addiction 94 , 913–921. 10.1046/j.1360-0443.1999.94691313.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Li Y., Yuan K., Cai C., Feng D., Yin J., Bi Y., et al.. (2015). Reduced frontal cortical thickness and increased caudate volume within fronto-striatal circuits in young adult smokers . Drug Alcohol Depend. 151 , 211–219. 10.1016/j.drugalcdep.2015.03.023 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Li Y., Yuan K., Guan Y., Cheng J., Bi Y., Shi S., et al.. (2017). The implication of salience network abnormalities in young male adult smokers . Brain Imaging Behav. 11 , 943–953. 10.1007/s11682-016-9568-8 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lichenstein S. D., Musselman S., Shaw D. S., Sitnick S., Forbes E. E. (2017). Nucleus accumbens functional connectivity at age 20 is associated with trajectory of adolescent cannabis use and predicts psychosocial functioning in young adulthood . Addiction 112 , 1961–1970. 10.1111/add.13882 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lisdahl K. M., Sher K. J., Conway K. P., Gonzalez R., Feldstein Ewing S. W., Nixon S. J., et al.. (2018). Adolescent brain cognitive development (ABCD) study: overview of substance use assessment methods . Dev. Cogn. Neurosci. 32 , 80–96. 10.1016/j.dcn.2018.02.007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lisdahl K. M., Thayer R., Squeglia L. M., Mcqueeny T. M., Tapert S. F. (2013). Recent binge drinking predicts smaller cerebellar volumes in adolescents . Psychiatry Res. 211 , 17–23. 10.1016/j.pscychresns.2012.07.009 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lubman D. I., Cheetham A., Yücel M. (2015). Cannabis and adolescent brain development . Pharmacol. Ther. 148 , 1–16. 10.1016/j.pharmthera.2014.11.009 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Luciana M., Collins P. F., Muetzel R. L., Lim K. O. (2013). Effects of alcohol use initiation on brain structure in typically developing adolescents . Am. J. Drug Alcohol Abuse 39 , 345–355. 10.3109/00952990.2013.837057 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Magill M., Barnett N. P., Apodaca T. R., Rohsenow D. J., Monti P. M. (2009). The role of marijuana use in brief motivational intervention with young adult drinkers treated in an emergency department . J. Stud. Alcohol Drugs 70 , 409–413. 10.15288/jsad.2009.70.409 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mahedy L., Field M., Gage S., Hammerton G., Heron J., Hickman M., et al.. (2018). Alcohol use in adolescence and later working memory: findings from a large population-based birth cohort . Alcohol Alcohol. 53 :501. 10.1093/alcalc/agy005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mahmood O. M., Jacobus J., Bava S., Scarlett A., Tapert S. F. (2010). Learning and memory performances in adolescent users of alcohol and marijuana: interactive effects . J. Stud. Alcohol Drugs 71 , 885–894. 10.15288/jsad.2010.71.885 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mallard T. T., Ashenhurst J. R., Harden K. P., Fromme K. (2018). GABRA2, alcohol and illicit drug use: an event-level model of genetic risk for polysubstance use . J. Abnorm. Psychol. 127 , 190–201. 10.1037/abn0000333 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mashhoon Y., Betts J., Farmer S. L., Lukas S. E. (2018). Early onset cigarette smokers exhibit greater P300 reactivity to smoking-related stimuli and report greater craving . Brain Res. 1687 , 173–184. 10.1016/j.brainres.2018.02.037 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mason W. A., Kosterman R., Haggerty K. P., Hawkins J. D., Redmond C., Spoth R. L., et al.. (2008). Dimensions of adolescent alcohol involvement as predictors of young-adult major depression . J. Stud. Alcohol Drugs 69 , 275–285. 10.15288/jsad.2008.69.275 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Matuszka B., Bácskai E., Czobor P., Gerevich J. (2016). Physical aggression and concurrent alcohol and tobacco use among adolescents . Int. J. Ment. Health Addict. 15 , 90–99. 10.1007/s11469-015-9630-6 [ CrossRef ] [ Google Scholar ]
• McGrath J. J., Alati R., Clavarino A., Williams G. M., Bor W., Najman J. M., et al.. (2016). Age at first tobacco use and risk of subsequent psychosis-related outcomes: a birth cohort study . Aust. N Z J. Psychiatry 50 , 577–583. 10.1177/0004867415587341 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• McNeill A. D., West R. J., Jarvis M., Jackson P., Bryant A. (1986). Cigarette withdrawal symptoms in adolescent smokers . Psychopharmacology 90 , 533–536. 10.1007/bf00174074 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• McQueeny T., Padula C. B., Price J., Medina K. L., Logan P., Tapert S. F. (2011). Gender effects on amygdala morphometry in adolescent marijuana users . Behav. Brain Res. 224 , 128–134. 10.1016/j.bbr.2011.05.031 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• McQueeny T., Schweinsburg B. C., Schweinsburg A. D., Jacobus J., Bava S., Frank L. R., et al.. (2009). Altered white matter integrity in adolescent binge drinkers . Alcohol. Clin. Exp. Res. 33 , 1278–1285. 10.1111/j.1530-0277.2009.00953.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Meda S. A., Dager A. D., Hawkins K. A., Tennen H., Raskin S., Wood R. M., et al.. (2017). Heavy drinking in college students is associated with accelerated gray matter volumetric decline over a 2 year period . Front. Behav. Neurosci. 11 :176. 10.3389/fnbeh.2017.00176 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Medina K. L., Hanson K. L., Schweinsburg A. D., Cohen-Zion M., Nagel B. J., Tapert S. F. (2007a). Neuropsychological functioning in adolescent marijuana users: subtle deficits detectable after a month of abstinence . J. Int. Neuropsychol. Soc. 13 , 807–820. 10.1017/s1355617707071032 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Medina K. L., Schweinsburg A. D., Cohen-Zion M., Nagel B. J., Tapert S. F. (2007b). Effects of alcohol and combined marijuana and alcohol use during adolescence on hippocampal volume and asymmetry . Neurotoxicol. Teratol. 29 , 141–152. 10.1016/j.ntt.2006.10.010 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Medina K. L., McQueeny T., Nagel B. J., Hanson K. L., Schweinsburg A. D., Tapert S. F. (2008). Prefrontal cortex volumes in adolescents with alcohol use disorders: unique gender effects . Alcohol. Clin. Exp. Res. 32 , 386–394. 10.1111/j.1530-0277.2007.00602.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Medina K. L., McQueeny T., Nagel B. J., Hanson K. L., Yang T. T., Tapert S. F. (2009). Prefrontal cortex morphometry in abstinent adolescent marijuana users: subtle gender effects . Addict. Biol. 14 , 457–468. 10.1111/j.1369-1600.2009.00166.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Medina K. L., Nagel B. J., Tapert S. F. (2010). Abnormal cerebellar morphometry in abstinent adolescent marijuana users . Psychiatry Res. 182 , 152–159. 10.1016/j.pscychresns.2009.12.004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Meier M. H., Caspi A., Ambler A., Harrington H., Houts R., Keefe R. S., et al.. (2012). Persistent cannabis users show neuropsychological decline from childhood to midlife . Proc. Natl. Acad. Sci. U S A 109 , E2657–E2664. 10.1073/pnas.1206820109 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Meier M. H., Caspi A., Danese A., Fisher H. L., Houts R., Arseneault L., et al.. (2018). Associations between adolescent cannabis use and neuropsychological decline: a longitudinal co-twin control study . Addiction 113 , 257–265. 10.1111/add.13946 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Meier M. H., Schriber R. A., Beardslee J., Hanson J., Pardini D. (2019). Associations between adolescent cannabis use frequency and adult brain structure: a prospective study of boys followed to adulthood . Drug Alcohol Depend. 202 , 191–199. 10.1016/j.drugalcdep.2019.05.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Melaugh McAteer A., Curran D., Hanna D. (2015). Alcohol attention bias in adolescent social drinkers: an eye tracking study . Psychopharmacology 232 , 3183–3191. 10.1007/s00213-015-3969-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Miech R., Johnston L., O’Malley P. M., Keyes K. M., Heard K. (2015). Prescription opioids in adolescence and future opioid misuse . Pediatrics 136 , e1169–e1177. 10.1542/peds.2015-1364 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Miech R. A., Patrick M. E., O’Malley P. M., Johnson L. D. (2017). E-cigarette use as a predictor of cigarette smoking: results from a 1-year follow-up of a national sample of 12th grade students . Tob. Control 26 , e106–e111. 10.1136/tobaccocontrol-2016-053291 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mills K. L., Teesson M., Darke S., Ross J., Lynskey M. (2004). Young people with heroin dependence: findings from the Australian Treatment Outcome Study (ATOS) . J. Subst. Abuse Treat. 27 , 67–73. 10.1016/j.jsat.2004.05.001 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mokrysz C., Landy R., Gage S. H., Munafo M. R., Roiser J. P., Curran H. V. (2016). Are IQ and educational outcomes in teenagers related to their cannabis use? A prospective cohort study . J. Psychopharmacol. 30 , 159–168. 10.1177/0269881115622241 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moon S. S., Mo B. C., Basham R. (2010). Adolescent depression and future smoking behavior: a prospective study . Child Adolesc. Soc. Work J. 27 , 405–422. 10.1007/s10560-010-0212-y [ CrossRef ] [ Google Scholar ]
• Morales A. M., Jones S. A., Ehlers A., Lavine J. B., Nagel B. J. (2018). Ventral striatal response during decision making involving risk and reward is associated with future binge drinking in adolescents . Neuropsychopharmacology 43 , 1884–1890. 10.1038/s41386-018-0087-8 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morin J.-F. G., Afzali M. H., Bourque J., Stewart S. H., Séguin J. R., O’Leary-Barrett M., et al.. (2019). A population-based analysis of the relationship between substance use and adolescent cognitive development . Am. J. Psychiatry 176 , 98–106. 10.1176/appi.ajp.2018.18020202 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morrell H. E., Cohen L. M., McChargue D. E. (2010). Depression vulnerability predicts cigarette smoking among college students: gender and negative reinforcement expectancies as contributing factors . Addict. Behav. 35 , 607–611. 10.1016/j.addbeh.2010.02.011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moss H. B., Chen C. M., Yi H. Y. (2014). Early adolescent patterns of alcohol, cigarettes and marijuana polysubstance use and young adult substance use outcomes in a nationally representative sample . Drug Alcohol Depend. 136 , 51–62. 10.1016/j.drugalcdep.2013.12.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Munafò M. R., Hitsman B., Rende R., Metcalfe C., Niaura R. (2008). Effects of progression to cigarette smoking on depressed mood in adolescents: evidence from the national longitudinal study of adolescent health . Addiction 103 , 162–171. 10.1111/j.1360-0443.2007.02052.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mustonen A., Ahokas T., Nordström T., Murray G. K., Mäki P., Jääskeläinen E., et al.. (2018). Smokin’ hot: adolescent smoking and the risk of psychosis . Acta Psychiatr. Scand. 138 , 5–14. 10.1111/acps.12863 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Myles N., Newall H., Compton M. T., Curtis J., Nielssen O., Large M. (2012a). The age at onset of psychosis and tobacco use: a systematic meta-analysis . Soc. Psychiatry Psychiatr. Epidemiol. 47 , 1243–1250. 10.1007/s00127-011-0431-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Myles N., Newall H. D., Curtis J., Nielssen O., Shiers D., Large M. (2012b). Tobacco use before, at, and after first-episode psychosis: a systematic meta-analysis . J. Clin. Psychiatry 73 , 468–475. 10.4088/jcp.11r07222 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nagel B. J., Schweinsburg A. D., Phan V., Tapert S. F. (2005). Reduced hippocampal volume among adolescents with alcohol use disorders without psychiatric comorbidity . Psychiatry Res. 139 , 181–190. 10.1016/j.pscychresns.2005.05.008 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health (2016). E-Cigarette Use Among Youth and Young Adults: A Report of the Surgeon General. Atlanta, GA: National Center for Chronic Disease Prevention and Health Promotion (Us) Office on Smoking and Health. [ PubMed ] [ Google Scholar ]
• Needham B. L. (2007). Gender differences in trajectories of depressive symptomatology and substance use during the transition from adolescence to young adulthood . Soc. Sci. Med. 65 , 1166–1179. 10.1016/j.socscimed.2007.04.037 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nestor L. J., Behan B., Suckling J., Garavan H. (2019). Cannabis-dependent adolescents show differences in global reward-associated network topology: a functional connectomics approach . Addict. Biol. 25 :e12752. 10.1111/adb.12752 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nguyen-Louie T. T., Matt G. E., Jacobus J., Li I., Cota C., Castro N., et al.. (2017). Earlier alcohol use onset predicts poorer neuropsychological functioning in young adults . Alcohol. Clin. Exp. Res. 41 , 2082–2092. 10.1111/acer.13503 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nguyen-Louie T. T., Tracas A., Squeglia L. M., Matt G. E., Eberson-Shumate S., Tapert S. F. (2016). Learning and memory in adolescent moderate, binge, and extreme-binge drinkers . Alcohol. Clin. Exp. Res. 40 , 1895–1904. 10.1111/acer.13160 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Noorbakhsh S., Afzali M. H., Boers E., Conrod P. J. (2020). Cognitive function impairments linked to alcohol and cannabis use during adolescence: a study of gender differences . Front. Hum. Neurosci. 14 :95. 10.3389/fnhum.2020.00095 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Norman A. L., Pulido C., Squeglia L. M., Spadoni A. D., Paulus M. P., Tapert S. F. (2011). Neural activation during inhibition predicts initiation of substance use in adolescence . Drug Alcohol Depend. 119 , 216–223. 10.1016/j.drugalcdep.2011.06.019 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• O’Dell L. E., Bruijnzeel A. W., Ghozland S., Markou A., Koob G. F. (2004). Nicotine withdrawal in adolescent and adult rats . Ann. N Y Acad. Sci. 1021 , 167–174. 10.1196/annals.1308.022 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Orr C., Spechler P., Cao Z., Albaugh M., Chaarani B., Mackey S., et al.. (2019). Grey matter volume differences associated with extremely low levels of cannabis use in adolescence . J. Neurosci. 39 , 1817–1827. 10.1523/JNEUROSCI.3375-17.2018 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Padula C. B., Schweinsburg A. D., Tapert S. F. (2007). Spatial working memory performance and fMRI activation interaction in abstinent adolescent marijuana users . Psychol. Addict. Behav. 21 , 478–487. 10.1037/0893-164x.21.4.478 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pampati S., Buu A., Hu Y. H., Mendes De Leon C. F., Lin H. C. (2018). Effects of alcohol and cigarette use on the initiation, reinitiation and persistence of cannabis use from adolescence to emerging adulthood . Addict. Behav. 79 , 144–150. 10.1016/j.addbeh.2017.12.019 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Parada M., Corral M., Mota N., Crego A., Rodriguez Holguin S., Cadaveira F. (2012). Executive functioning and alcohol binge drinking in university students . Addict. Behav. 37 , 167–172. 10.1016/j.addbeh.2011.09.015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Parker M. A., Villanti A. C., Quisenberry A. J., Stanton C. A., Doogan N. J., Redner R., et al.. (2018). Tobacco product harm perceptions and new use . Pediatrics 142 :e20181505. 10.1542/peds.2018-1505 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Parrish K. H., Atherton O. E., Quintana A., Conger R. D., Robins R. W. (2016). Reciprocal relations between internalizing symptoms and frequency of alcohol use: findings from a longitudinal study of Mexican-origin youth . Psychol. Addict. Behav. 30 , 203–208. 10.1037/adb0000138 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patrick M. E., Kloska D. D., Terry-Mcelrath Y. M., Lee C. M., O’Malley P. M., Johnston L. D. (2018). Patterns of simultaneous and concurrent alcohol and marijuana use among adolescents . Am. J. Drug Alcohol Abuse 44 , 441–451. 10.1080/00952990.2017.1402335 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patrick M. E., Miech R. A., Kloska D. D., Wagner A. C., Johnston L. D. (2020). Trends in marijuana vaping and edible consumption From 2015 to 2018 among adolescents in the US . JAMA Pediatr. 6 :e200175. 10.1001/jamapediatrics.2020.0175 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patriquin M. A., Bauer I. E., Soares J. C., Graham D. P., Nielsen D. A. (2015). Addiction pharmacogenetics: a systematic review of the genetic variation of the dopaminergic system . Psychiatr. Genet. 25 , 181–193. 10.1097/ypg.0000000000000095 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pertwee R. G. (1997). Pharmacology of cannabinoid CB1 and CB2 receptors . Pharmacol. Ther. 74 , 129–180. 10.1016/s0163-7258(97)82001-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Peters S., Peper J. S., Van Duijvenvoorde A. C. K., Braams B. R., Crone E. A. (2017). Amygdala-orbitofrontal connectivity predicts alcohol use two years later: a longitudinal neuroimaging study on alcohol use in adolescence . Dev. Sci. 20 :e12448. 10.1111/desc.12448 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pfefferbaum A., Kwon D., Brumback T., Thompson W. K., Cummins K., Tapert S. F., et al.. (2018). Altered brain developmental trajectories in adolescents after initiating drinking . Am. J. Psychiatry 175 , 370–380. 10.1176/appi.ajp.2017.17040469 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pitsillou E., Bresnehan S. M., Kagarakis E. A., Wijoyo S. J., Liang J., Hung A., et al.. (2020). The cellular and molecular basis of major depressive disorder: towards a unified model for understanding clinical depression . Mol. Biol. Rep. 47 , 753–770. 10.1007/s11033-019-05129-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pope H. G., Jr., Gruber A. J., Hudson J. I., Cohane G., Huestis M. A., Yurgelun-Todd D. (2003). Early-onset cannabis use and cognitive deficits: what is the nature of the association? Drug Alcohol Depend. 69 , 303–310. 10.1016/s0376-8716(02)00334-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Raud L., Westerhausen R., Dooley N., Huster R. J. (2020). Differences in unity: the go/no-go and stop signal tasks rely on different mechanisms . NeuroImage 210 :116582. 10.1016/j.neuroimage.2020.116582 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Renard J., Krebs M. O., Le Pen G., Jay T. M. (2014). Long-term consequences of adolescent cannabinoid exposure in adult psychopathology . Front. Neurosci. 8 :361. 10.3389/fnins.2014.00361 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rendu F., Peoc’h K., Berlin I., Thomas D., Launay J. M. (2011). Smoking related diseases: the central role of monoamine oxidase . Int. J. Environ. Res. Public Health 8 , 136–147. 10.3390/ijerph8010136 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Repetto P. B., Caldwell C. H., Zimmerman M. A. (2005). A longitudinal study of the relationship between depressive symptoms and cigarette use among African American adolescents . Health Psychol. 24 , 209–219. 10.1037/0278-6133.24.2.209 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Riggs N. R., Chou C. P., Li C., Pentz M. A. (2007). Adolescent to emerging adulthood smoking trajectories: when do smoking trajectories diverge and do they predict early adulthood nicotine dependence? Nicotine Tob. Res. 9 , 1147–1154. 10.1080/14622200701648359 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rodriguez D., Moss H. B., Audrain-Mcgovern J. (2005). Developmental heterogeneity in adolescent depressive symptoms: associations with smoking behavior . Psychosom. Med. 67 , 200–210. 10.1097/01.psy.0000156929.83810.01 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rojas N. L., Killen J. D., Haydel K. F., Robinson T. N. (1998). Nicotine dependence among adolescent smokers . Arch. Pediatr. Adolesc. Med. 152 , 151–156. 10.1001/archpedi.152.2.151 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Romer D. (2010). Adolescent risk taking, impulsivity, and brain development: implications for prevention . Dev. Psychobiol. 52 , 263–276. 10.1002/dev.20442 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ruan H., Zhou Y., Luo Q., Robert G. H., Desrivieres S., Quinlan E. B., et al.. (2019). Adolescent binge drinking disrupts normal trajectories of brain functional organization and personality maturation . Neuroimage Clin. 22 :101804. 10.1016/j.nicl.2019.101804 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rubinstein M. L., Luks T. L., Dryden W. Y., Rait M. A., Simpson G. V. (2011a). Adolescent smokers show decreased brain responses to pleasurable food images compared with nonsmokers . Nicotine Tob. Res. 13 , 751–755. 10.1093/ntr/ntr046 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rubinstein M. L., Luks T. L., Moscicki A. B., Dryden W., Rait M. A., Simpson G. V. (2011b). Smoking-related cue-induced brain activation in adolescent light smokers . J. Adolesc Health 48 , 7–12. 10.1016/j.jadohealth.2010.09.016 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rubinstein M. L., Rait M. A., Prochaska J. J. (2014). Frequent marijuana use is associated with greater nicotine addiction in adolescent smokers . Drug Alcohol Depend. 141 , 159–162. 10.1016/j.drugalcdep.2014.05.015 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Russo S. J., Nestler E. J. (2013). The brain reward circuitry in mood disorders . Nat. Rev. Neurosci. 14 , 609–625. 10.1038/nrn3381 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schepis T. S., Hakes J. K. (2017). Age of initiation, psychopathology, and other substance use are associated with time to use disorder diagnosis in persons using opioids nonmedically . Subst. Abus. 38 , 407–413. 10.1080/08897077.2017.1356791 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schleider J. L., Ye F., Wang F., Hipwell A. E., Chung T., Sartor C. E. (2019). Longitudinal reciprocal associations between anxiety, depression, and alcohol use in adolescent girls . Alcohol. Clin. Exp. Res. 43 , 98–107. 10.1111/acer.13913 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schmid B., Hohm E., Blomeyer D., Zimmermann U. S., Schmidt M. H., Esser G., et al.. (2007). Concurrent alcohol and tobacco use during early adolescence characterizes a group at risk . Alcohol Alcohol. 42 , 219–225. 10.1093/alcalc/agm024 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schneider M. (2013). Adolescence as a vulnerable period to alter rodent behavior . Cell Tissue Res. 354 , 99–106. 10.1007/s00441-013-1581-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Scholes-Balog K. E., Hemphill S. A., Evans-Whipp T. J., Toumbourou J. W., Patton G. C. (2016). Developmental trajectories of adolescent cannabis use and their relationship to young adult social and behavioural adjustment: a longitudinal study of Australian youth . Addict. Behav. 53 , 11–18. 10.1016/j.addbeh.2015.09.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schweinsburg A. D., McQueeny T., Nagel B. J., Eyler L. T., Tapert S. F. (2010). A preliminary study of functional magnetic resonance imaging response during verbal encoding among adolescent binge drinkers . Alcohol 44 , 111–117. 10.1016/j.alcohol.2009.09.032 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schweinsburg A. D., Schweinsburg B. C., Cheung E. H., Brown G. G., Brown S. A., Tapert S. F. (2005). fMRI response to spatial working memory in adolescents with comorbid marijuana and alcohol use disorders . Drug Alcohol Depend. 79 , 201–210. 10.1016/j.drugalcdep.2005.01.009 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schweinsburg A. D., Schweinsburg B. C., Nagel B. J., Eyler L. T., Tapert S. F. (2011). Neural correlates of verbal learning in adolescent alcohol and marijuana users . Addiction 106 , 564–573. 10.1111/j.1360-0443.2010.03197.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Scott J. C., Rosen A. F. G., Moore T. M., Roalf D. R., Satterthwaite T. D., Calkins M. E., et al.. (2019). Cannabis use in youth is associated with limited alterations in brain structure . Neuropsychopharmacology 44 , 1362–1369. 10.1038/s41386-019-0347-2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Scott J. C., Wolf D. H., Calkins M. E., Bach E. C., Weidner J., Ruparel K., et al.. (2017). Cognitive functioning of adolescent and young adult cannabis users in the philadelphia neurodevelopmental cohort . Psychol. Addict. Behav. 31 , 423–434. 10.1037/adb0000268 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Selemon L. D., Zecevic N. (2015). Schizophrenia: a tale of two critical periods for prefrontal cortical development . Transl. Psychiatry 5 :e623. 10.1038/tp.2015.115 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shahzade C., Chun J., Delisi L. E., Manschreck T. C. (2018). Patterns in adolescent cannabis use predict the onset and symptom structure of schizophrenia-spectrum disorder . Schizophr. Res. 197 , 539–543. 10.1016/j.schres.2018.01.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shillington A. M., Clapp J. D. (2001). Substance use problems reported by college students: combined marijuana and alcohol use versus alcohol-only use . Subst. Use Misuse 36 , 663–672. 10.1081/ja-100103566 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Slomp F. M., Bara T. S., Picharski G. L., Cordeiro M. L. (2019). Association of cigarette smoking with anxiety, depression, and suicidal ideation among brazilian adolescents . Neuropsychiatr. Dis. Treat. 15 , 2799–2808. 10.2147/ndt.s217069 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Solowij N., Jones K. A., Rozman M. E., Davis S. M., Ciarrochi J., Heaven P. C., et al.. (2012). Reflection impulsivity in adolescent cannabis users: a comparison with alcohol-using and non-substance-using adolescents . Psychopharmacology 219 , 575–586. 10.1007/s00213-011-2486-y [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Soneji S., Barrington-Trimis J. L., Wills T. A., Leventhal A. M., Unger J. B., Gibson L. A., et al.. (2017). Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: a systematic review and meta-analysis . JAMA Pediatr. 171 , 788–797. 10.1001/jamapediatrics.2017.1488 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spear L. P. (2000). The adolescent brain and age-related behavioral manifestations . Neurosci. Biobehav. Rev. 24 , 417–463. 10.1016/s0149-7634(00)00014-2 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spear L. P. (2014). Adolescents and alcohol: acute sensitivities, enhanced intake and later consequences . Neurotoxicol. Teratol. 41 , 51–59. 10.1016/j.ntt.2013.11.006 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spechler P. A., Orr C. A., Chaarani B., Kan K. J., Mackey S., Morton A., et al.. (2015). Cannabis use in early adolescence: evidence of amygdala hypersensitivity to signals of threat . Dev. Cogn. Neurosci. 16 , 63–70. 10.1016/j.dcn.2015.08.007 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spindle T. R., Hiler M. M., Cooke M. E., Eissenberg T., Kendler K. S., Dick D. M. (2017). Electronic cigarette use and uptake of cigarette smoking: a longitudinal examination of U.S. college students . Addict. Behav. 67 , 66–72. 10.1016/j.addbeh.2016.12.009 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squeglia L. M., Jacobus J., Tapert S. F. (2009a). The influence of substance use on adolescent brain development . Clin. EEG Neurosci. 40 , 31–38. 10.1177/155005940904000110 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squeglia L. M., Spadoni A. D., Infante M. A., Myers M. G., Tapert S. F. (2009b). Initiating moderate to heavy alcohol use predicts changes in neuropsychological functioning for adolescent girls and boys . Psychol. Addict. Behav. 23 , 715–722. 10.1037/a0016516 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squeglia L. M., Pulido C., Wetherill R. R., Jacobus J., Brown G. G., Tapert S. F. (2012a). Brain response to working memory over three years of adolescence: influence of initiating heavy drinking . J. Stud. Alcohol Drugs 73 , 749–760. 10.15288/jsad.2012.73.749 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squeglia L. M., Sorg S. F., Schweinsburg A. D., Wetherill R. R., Pulido C., Tapert S. F. (2012b). Binge drinking differentially affects adolescent male and female brain morphometry . Psychopharmacology 220 , 529–539. 10.1007/s00213-011-2500-4 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squeglia L. M., Rinker D. A., Bartsch H., Castro N., Chung Y., Dale A. M., et al.. (2014). Brain volume reductions in adolescent heavy drinkers . Dev. Cogn. Neurosci. 9 , 117–125. 10.1016/j.dcn.2014.02.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squeglia L. M., Schweinsburg A. D., Pulido C., Tapert S. F. (2011). Adolescent binge drinking linked to abnormal spatial working memory brain activation: differential gender effects . Alcohol. Clin. Exp. Res. 35 , 1831–1841. 10.1111/j.1530-0277.2011.01527.x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Squeglia L. M., Tapert S. F., Sullivan E. V., Jacobus J., Meloy M. J., Rohlfing T., et al.. (2015). Brain development in heavy-drinking adolescents . Am. J. Psychiatry 172 , 531–542. 10.1176/appi.ajp.2015.14101249 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stanton W. R. (1995). DSM-III-R tobacco dependence and quitting during late adolescence . Addict. Behav. 20 , 595–603. 10.1016/0306-4603(95)00019-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stein J. A., Newcomb M. D., Bentler P. M. (1996). Initiation and maintenance of tobacco smoking: changing personality correlates in adolescence and young adulthood . J. Appl. Soc. Psychol. 26 , 160–187. 10.1111/j.1559-1816.1996.tb01844.x [ CrossRef ] [ Google Scholar ]
• Strong C., Juon H. S., Ensminger M. E. (2014). Long-term effects of adolescent smoking on depression and socioeconomic status in adulthood in an urban African American cohort . J. Urban Health 91 , 526–540. 10.1007/s11524-013-9849-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Subramaniam G. A., Stitzer M. A. (2009). Clinical characteristics of treatment-seeking prescription opioid vs. heroin-using adolescents with opioid use disorder . Drug Alcohol Depend. 101 , 13–19. 10.1016/j.drugalcdep.2008.10.015 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Subramaniam G. A., Stitzer M. L., Woody G., Fishman M. J., Kolodner K. (2009). Clinical characteristics of treatment-seeking adolescents with opioid versus cannabis/alcohol use disorders . Drug Alcohol Depend. 99 , 141–149. 10.1016/j.drugalcdep.2008.07.016 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Subramaniam P., Rogowska J., Dimuzio J., Lopez-Larson M., Mcglade E., Yurgelun-Todd D. (2018). Orbitofrontal connectivity is associated with depression and anxiety in marijuana-using adolescents . J. Affect. Disord. 239 , 234–241. 10.1016/j.jad.2018.07.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Substance Abuse and Mental Health Services Administration (2018). Key Substance Use and Mental Health Indicators in the United States: Results from the 2017 National Survey on Drug Use and Health [Online]. Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. Available online at: https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHFFR2017/NSDUHFFR2017.htm . Accessed 2019. [ Google Scholar ]
• Substance Abuse and Mental Health Services Administration (2019). First-Episode Psychosis and Co-Occurring Substance Use Disorders. Rockville, MD: Substance Abuse and Mental Health Services Administration. [ Google Scholar ]
• Swartz J. R., Weissman D. G., Ferrer E., Beard S. J., Fassbender C., Robins R. W., et al.. (2020). Reward-related brain activity prospectively predicts increases in alcohol use in adolescents . J. Am. Acad. Child Adolesc. Psychiatry 59 , 391–400. 10.1016/j.jaac.2019.05.022 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Swift W., Coffey C., Degenhardt L., Carlin J. B., Romaniuk H., Patton G. C. (2012). Cannabis and progression to other substance use in young adults: findings from a 13-year prospective population-based study . J. Epidemiol. Community Health 66 :e26. 10.1136/jech.2010.129056 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Taioli E., Wynder E. L. (1991). Effect of the age at which smoking begins on frequency of smoking in adulthood . N. Engl. J. Med. 325 , 968–969. 10.1056/nejm199109263251318 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tapert S. F., Cheung E. H., Brown G. G., Frank L. R., Paulus M. P., Schweinsburg A. D., et al.. (2003). Neural response to alcohol stimuli in adolescents with alcohol use disorder . Arch. Gen. Psychiatry 60 , 727–735. 10.1001/archpsyc.60.7.727 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tapert S. F., Schweinsburg A. D., Barlett V. C., Brown S. A., Frank L. R., Brown G. G., et al.. (2004). Blood oxygen level dependent response and spatial working memory in adolescents with alcohol use disorders . Alcohol. Clin. Exp. Res. 28 , 1577–1586. 10.1097/01.alc.0000141812.81234.a6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tapert S. F., Schweinsburg A. D., Drummond S. P., Paulus M. P., Brown S. A., Yang T. T., et al.. (2007). Functional MRI of inhibitory processing in abstinent adolescent marijuana users . Psychopharmacology 194 , 173–183. 10.1007/s00213-007-0823-y [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Taylor M., Collin S. M., Munafo M. R., Macleod J., Hickman M., Heron J. (2017). Patterns of cannabis use during adolescence and their association with harmful substance use behaviour: findings from a UK birth cohort . J. Epidemiol. Community Health 71 , 764–770. 10.1136/jech-2016-208503 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tercyak K. P., Lerman C., Audrain J. (2002). Association of attention-deficit/hyperactivity disorder symptoms with levels of cigarette smoking in a community sample of adolescents . J. Am. Acad. Child Adolesc. Psychiatry 41 , 799–805. 10.1097/00004583-200207000-00011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Terry-McElrath Y. M., O’Malley P. M., Johnston L. D. (2014). Alcohol and marijuana use patterns associated with unsafe driving among U.S. high school seniors: high use frequency, concurrent use, and simultaneous use . J. Stud. Alcohol Drugs 75 , 378–389. 10.15288/jsad.2014.75.378 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tervo-Clemmens B., Simmonds D., Calabro F. J., Day N. L., Richardson G. A., Luna B. (2018). Adolescent cannabis use and brain systems supporting adult working memory encoding, maintenance, and retrieval . NeuroImage 169 , 496–509. 10.1016/j.neuroimage.2017.12.041 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thorpe H. H. A., Hamidullah S., Jenkins B. W., Khokhar J. Y. (2020). Adolescent neurodevelopment and substance use: receptor expression and behavioral consequences . Pharmacol. Ther. 206 :107431. 10.1016/j.pharmthera.2019.107431 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Toftdahl N. G., Nordentoft M., Hjorthoj C. (2016). Prevalence of substance use disorders in psychiatric patients: a nationwide Danish population-based study . Soc. Psychiatry Psychiatr. Epidemiol. 51 , 129–140. 10.1007/s00127-015-1104-4 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Treur J. L., Willemsen G., Bartels M., Geels L. M., Van Beek J. H., Huppertz C., et al.. (2015). Smoking during adolescence as a risk factor for attention problems . Biol. Psychiatry 78 , 656–663. 10.1016/j.biopsych.2014.06.019 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• US Department of Health and Human Services (2016). E-Cigarette Use Among Youth and Young Adults: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services. [ Google Scholar ]
• Van Amsterdam J., Van Der Velde B., Schulte M., Van Den Brink W. (2018). Causal factors of increased smoking in ADHD: a systematic review . Subst. Use Misuse 53 , 432–445. 10.1080/10826084.2017.1334066 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Van Ewijk H., Groenman A. P., Zwiers M. P., Heslenfeld D. J., Faraone S. V., Hartman C. A., et al.. (2015). Smoking and the developing brain: altered white matter microstructure in attention-deficit/hyperactivity disorder and healthy controls . Hum. Brain Mapp. 36 , 1180–1189. 10.1002/hbm.22695 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• van Ewijk H., Heslenfeld D. J., Zwiers M. P., Buitelaar J. K., Oosterlaan J. (2012). Diffusion tensor imaging in attention deficit/hyperactivity disorder: a systematic review and meta-analysis . Neurosci. Biobehav. Rev. 36 , 1093–1106. 10.1016/j.neubiorev.2012.01.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Van Hout M. C., Norman I. (2016). Misuse of non-prescription codeine containing products: recommendations for detection and reduction of risk in community pharmacies . Int. J. Drug Policy 27 , 17–22. 10.1016/j.drugpo.2015.09.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Viner R. M., Taylor B. (2007). Adult outcomes of binge drinking in adolescence: findings from a UK national birth cohort . J. Epidemiol. Community Health 61 , 902–907. 10.1136/jech.2005.038117 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vo H. T., Schacht R., Mintzer M., Fishman M. (2014). Working memory impairment in cannabis- and opioid-dependent adolescents . Subst. Abus. 35 , 387–390. 10.1080/08897077.2014.954027 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vogel E. A., Cho J., McConnell R. S., Barrington-Trimis J. L., Leventhal A. M. (2020). Prevalence of electronic cigarette dependence among youth and its association with future use . JAMA Netw. Open 3 :e1921513. 10.1001/jamanetworkopen.2019.21513 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vogel E. A., Prochaska J. J., Ramo D. E., Andres J., Rubinstein M. L. (2019). Adolescents’ E-cigarette use: increases in frequency, dependence, and nicotine exposure over 12 months . J. Adolesc Health 64 , 770–775. 10.1016/j.jadohealth.2019.02.019 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Volkow N. D., Morales M. (2015). The brain on drugs: from reward to addiction . Cell 162 , 712–725. 10.1016/j.cell.2015.07.046 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wagener T. L., Floyd E. L., Stepanov I., Driskill L. M., Frank S. G., Meier E., et al.. (2017). Have combustible cigarettes met their match? The nicotine delivery profiles and harmful constituent exposures of second-generation and third-generation electronic cigarette users . Tob. Control 26 , e23–e28. 10.1136/tobaccocontrol-2016-053041 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wang M. Q., Fitzhugh E. C., Turner L., Fu Q., Westerfield R. C. (1996). Association of depressive symptoms and school adolescents’ smoking: a cross-lagged analysis . Psychol. Rep. 79 , 127–130. 10.2466/pr0.1996.79.1.127 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weiland B. J., Thayer R. E., Depue B. E., Sabbineni A., Bryan A. D., Hutchison K. E. (2015). Daily marijuana use is not associated with brain morphometric measures in adolescents or adults . J. Neurosci. 35 , 1505–1512. 10.1523/JNEUROSCI.2946-14.2015 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weiser M., Reichenberg A., Grotto I., Yasvitzky R., Rabinowitz J., Lubin G., et al.. (2004). Higher rates of cigarette smoking in male adolescents before the onset of schizophrenia: a historical-prospective cohort study . Am. J. Psychiatry 161 , 1219–1223. 10.1176/appi.ajp.161.7.1219 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weiser M., Zarka S., Werbeloff N., Kravitz E., Lubin G. (2010). Cognitive test scores in male adolescent cigarette smokers compared to non-smokers: a population-based study . Addiction 105 , 358–363. 10.1111/j.1360-0443.2009.02740.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wetherill R. R., Squeglia L. M., Yang T. T., Tapert S. F. (2013). A longitudinal examination of adolescent response inhibition: neural differences before and after the initiation of heavy drinking . Psychopharmacology 230 , 663–671. 10.1007/s00213-013-3198-2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wills T. A., Knight R., Sargent J. D., Gibbons F. X., Pagano I., Williams R. J. (2017). Longitudinal study of E-cigarette use and onset of cigarette smoking among high school students in Hawaii . Tob. Control 26 , 34–39. 10.1136/tobaccocontrol-2015-052705 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wilson W., Mathew R., Turkington T., Hawk T., Coleman R. E., Provenzale J. (2000). Brain morphological changes and early marijuana use: a magnetic resonance and positron emission tomography study . J. Addict. Dis. 19 , 1–22. 10.1300/j069v19n01_01 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Windle M., Windle R. C. (2001). Depressive symptoms and cigarette smoking among middle adolescents: prospective associations and intrapersonal and interpersonal influences . J. Consult. Clin. Psychol. 69 , 215–226. 10.1037/0022-006x.69.2.215 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Winward J. L., Hanson K. L., Tapert S. F., Brown S. A. (2014). Heavy alcohol use, marijuana use and concomitant use by adolescents are associated with unique and shared cognitive decrements . J. Int. Neuropsychol. Soc. 20 , 784–795. 10.1017/s1355617714000666 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Womack S. R., Shaw D. S., Weaver C. M., Forbes E. E. (2016). Bidirectional associations between cannabis use and depressive symptoms from adolescence through early adulthood among at-risk young men . J. Stud. Alcohol Drugs 77 , 287–297. 10.15288/jsad.2016.77.287 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wraw C., Der G., Gale C. R., Deary I. J. (2018). Intelligence in youth and health behaviours in middle age . Intelligence 69 , 71–86. 10.1016/j.intell.2018.04.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wu L. T., Anthony J. C. (1999). Tobacco smoking and depressed mood in late childhood and early adolescence . Am. J. Public Health 89 , 1837–1840. 10.2105/ajph.89.12.1837 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yin J., Yuan K., Feng D., Cheng J., Li Y., Cai C., et al.. (2016). Inhibition control impairments in adolescent smokers: electrophysiological evidence from a Go/NoGo study . Brain Imaging Behav. 10 , 497–505. 10.1007/s11682-015-9418-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yousaf T., Dervenoulas G., Politis M. (2018). Advances in MRI methodology . Int. Rev. Neurobiol. 141 , 31–76. 10.1016/bs.irn.2018.08.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yu D., Yuan K., Zhang B., Liu J., Dong M., Jin C., et al.. (2016). White matter integrity in young smokers: a tract-based spatial statistics study . Addict. Biol. 21 , 679–687. 10.1111/adb.12237 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zahr N. M., Pfefferbaum A. (2017). Alcohol’s effects on the brain: neuroimaging results in humans and animal models . Alcohol Res. 38 , 183–206. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zammit S., Allebeck P., Dalman C., Lundberg I., Hemmingsson T., Lewis G. (2003). Investigating the association between cigarette smoking and schizophrenia in a cohort study . Am. J. Psychiatry 160 , 2216–2221. 10.1176/appi.ajp.160.12.2216 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhang R., Geng X., Lee T. M. C. (2017). Large-scale functional neural network correlates of response inhibition: an fMRI meta-analysis . Brain Struct. Funct. 222 , 3973–3990. 10.1007/s00429-017-1443-x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

National Drug & Alcohol Facts Week Educates Youth Around the World

Since being launched by NIDA scientists in 2010, National Drug and Alcohol Facts Week (NDAFW) seeks to inspire dialogue with and among youth about drug use and addiction during one week each March; this year, it took place March 18–24. During this week, a variety of events and activities bring together scientists, educators, community organizations, health care providers, and students. The aim of NDAFW is to educate youth and young adults about what we have learned from science about addiction in a fun and age-appropriate manner, thereby helping to stop the spread of misinformation about drug and alcohol use. This knowledge empowers youth and young adults to make informed decisions about drug and alcohol use and their mental health.

Countries outside of the United States also have been participating in NDAFW for years. In 2024, 50 organizations from 21 countries, three U.S. territories, and one British territory participated. Participants were located on six continents, including Asia (India, Pakistan, Philippines, and Qatar), Africa (Burundi, Ghana, Kenya, Nigeria, Sierra Leone, South Africa, and Zambia), Europe (Italy, Serbia, Ukraine, and United Kingdom), South America (Brazil, Colombia, Cuba, and Guatemala), North America (Canada, Cayman Islands, Guam, Puerto Rico, Virgin Islands), and Australia. 

Organizers of these events range from governmental entities, such as the Drug and Food Control Organization in India and the National Drug Council in the Cayman Islands, and educational institutions, such as the University of Turin in Italy, to health care and other regional and local organizations, such as the Mathare Community Anti-Drugs Coalition in Kenya or the Harm Reduction Zambia Network in Zambia. Overall, they had signed up to organize 259 different events; for example, the Cuban Instituto Nacional de Higiene, Epidemiología y Microbiología held 14 events, the Prime Youths & Women Empowerment Initiative in Nigeria planned 20 events, and the National Community Ambassadors in Kenya registered an amazing 47 events!

These events covered a wide range of projects and activities. The Guam Behavioral Health & Wellness Center Prevention and Training Branch developed and implemented educational opioid overdose prevention and vaping/nicotine cessation digital banners and social media posts. In South Africa, Solution Base: Social Crime Prevention Service, in partnership with other organizations such as the South African Police Service, invited ex-prisoners and drug users to be motivational speakers visiting several schools in the area to explain the risk and danger of substance use disorder. The Nigerian organization Chen Teen and Youth Development Initiative focused on mental health issues through a school outreach program entitled “Addressing Overwhelm,” which seeks to educate students on understanding overwhelm, the various degrees of it, and steps to addressing it.

If you missed participating in NDAFW 2024, consider signing up for its observance in 2025. NIDA materials and step-by-step guidelines are available to help international drug abuse professionals plan, promote, and host NDAFW events. International organizers who want to plan NDAFW events for 2025 should register soon to obtain materials in time for the event. 

For more information, see NIDA’s NDAFW online guide .

• OAY COVID-19 RESPONSES

5 Ways to Empower the Youth Against Drug Abuse

Published by admin on June 27, 2023 June 27, 2023

In the shadows of society, a perilous menace silently thrives, gripping the lives of individuals, families, and entire communities.

This menace is drug abuse , a destructive force that rips apart dreams, erodes health, and corrodes the very fabric of society. But what lies beneath the surface of this seemingly invisible enemy? What fuels its relentless spread, leaving countless lives in ruins?

Drug abuse, simply put, is the harmful and excessive consumption of substances that alter the mind and body.

These substances, both legal and illegal, possess the power to induce euphoria, numb pain, or offer temporary escape from the burdens of reality.

However, when abused, they unleash a sinister cycle of addiction, leading individuals down a treacherous path where their lives become tangled in the web of illicit trafficking.

The detrimental impact of drug abuse and illicit trafficking on young individuals cannot be underestimated, making it crucial to prioritize their mental health and overall well-being.

Empowering Youth for a Drug-Free Future

Youth development plays a pivotal role in shaping the future of societies worldwide. However, the rise in drug abuse and illicit trafficking has emerged as a major setback, impeding the growth and potential of young individuals.

Addressing this issue requires a multifaceted approach, focusing on prevention, education, and support systems.

Prevention through Education

Education remains a cornerstone in the fight against drug abuse and illicit trafficking. Empowering young people with knowledge about the risks and consequences associated with substance abuse equips them to make informed decisions.

Schools and educational institutions should integrate comprehensive drug education programs that emphasize the physical, psychological, and social ramifications of drug abuse.

Creating Support Systems

Building robust support systems is essential to ensuring that young individuals facing drug-related challenges receive the help they need.

Governments, NGOs, and communities must collaborate to establish accessible and confidential helplines,counseling services, and rehabilitation programs.

By creating a safe space for open dialogue and support, we can encourage young people to seek assistance and embark on a journey of recovery.

Promoting Mental Health

Mental health is a vital aspect of youth development, and addressing it is crucial in combating drug abuse and illicit trafficking. Investing in mental health services, raising awareness about mental well-being, and reducing stigma are essential steps.

By fostering resilience, coping mechanisms, and emotional intelligence, we empower young individuals to navigate the complexities of life without resorting to substances.

Engaging Youth as Agents of Change

Young people possess immense potential to drive change in their communities. Engaging them in anti-drug campaigns, peer support networks, and advocacy initiatives allows them to become active participants in the fight against drug abuse and illicit trafficking.

By amplifying their voices and providing platforms for expression, we can harness their energy, creativity, and passion to make a lasting impact.

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Rome Free Academy presents its plan to tackle youth drug use

This story was done in collaboration with the People's Perception (P3) Project and students from Rome Free Academy.

Historically speaking, it’s not a bad time to be the lungs of a teenager. Regular use of tobacco and drugs among high school students has been on a downward trend. 

Notable declines were first seen during the Covid-19 pandemic. In 2023 , 29 percent of high school seniors reported using marijuana in the previous year – down 37 percent from 2017. 

Rome Free Academy (RFA) Principal Brian LeBaron has devoted his career to helping students choose “healthy pathways.”

He recently sat down with the Observer-Dispatch, along with RFA Health Education teacher Reanna Zappavigna and Rome Police Chief Kevin James, to discuss changing patterns and the reasons behind shifting drug-use trends.

'Treat and control'

Despite recent legalization of marijuana in New York, and the rising popularity of Delta-8 disposable weed pens, LeBaron clarified the most common drug spotted at RFA is nicotine. 

“Schools across the country are dealing with the same problem,” LaBaron acknowledged. “Especially with new, enticing flavors being released; it doesn’t make the temptation any easier.” 

To “treat and control” LeBaron said RFA pushes a dynamic health curriculum, invites guest speakers, and offers services through Connected Community Schools – a teacher-led alliance that began at RFA in 2017 and has grown to encompass a network of 53 other schools. 

“We have six counselors, two social workers, and four assistant principals that work hand-in-hand,” LeBaron said. “On Friday’s we gather for support staff meetings where we all bring up the names of kids that are struggling. We refer each student to a Connected Community Schools worker and delineate a path forward.” 

LeBaron referred to teachers as RFA’s “frontline workers” since they are often the first to spot altered behavior. He said staff is encouraged to call parents if they notice anything as little as increased lethargy or general distractibility to sharp changes in grade point average. 

In LeBaron’s experience, the triggers that prompt teen drug use are stress and curiosity.

He explained how relationship-building is key in reprogramming unhealthy decision processes. He noted that the goal is to get to the root of the problem and pursue remediation so students can “focus on their academics without distraction.” 

“It’s not so much about fixing the problem but rather creating avenues to provide students the support they need,”LeBaron said.  “Kids will try to be secretive, and there are consequences that come with that, but when they feel trusted to confide in us they often do. That’s the goal, to allow a safe space where students feel prompted to resort to honesty.” 

7 challenges

At RFA, if a student is caught under the influence they are offered admittance to the ‘seven challenges’ program, a remedial pathway that can be taken as an alternative to suspension. 

“We want our kids to stay in school and on track with their studies,” LaBaron said.

He described that RFA regularly conducts drug searches. When protocol has been breached – possession, sale, or use of drugs – depending on the severity different consequences are administered. 

“We look into everything,” continued LeBaron. “Even if we just hear rumors about a student we’ll follow up and conduct an investigation. Oftentimes if a student is in the wrong they tend to fess up and we deal with things from there.” 

'Everything is figure-out-able'

Last fall, on Nov. 21, Michael DeLeon addressed the middle and high school students at RFA. After rejecting a former life of addiction he committed to helping teens across the U.S. helping to “steer straight.” 

According to LeBaron, the presentation focused on the JUUL fixation, the susceptibility of young brains to the stimulant, and the trifecta gateway into experimenting with alcohol and marijuana. 

“He left a box for kids to leave their vapes in, no questions asked,” Lebaron recalled. “One by one after the lecture the kids handed them in."

When asked the importance of a drug-free environment Lebaron said “because these kids are our future.”

“If we're not here to offer them guidance then we're not doing our job right,” he added. “Sometimes it’s more about being a parent, a mentor, a philosopher, a counselor, or a therapist. At RFA we believe in taking a holistic approach; we wear many hats. Our responsibility is to remind students that their future's so bright they need sunglasses.” 

Smiling, LeBaron pointed to the quote on his desk: “Everything is figure-out-able and I’d say, looking at the progress we’ve made, we’re doing a great job.” 

Regional trends

Drug issues vary region by region. But, the national fentanyl issue has trickled into the city of Rome, Police Chief Kevin James confirmed. 

“Teen drug use may be on the decline; it's still pervasive in our community and something to keep an eye on,” he added. 

Lately, the police department has been seeing an increase in overdoses involving fentanyl mixed and methamphetamine or marijuana.

“You have no idea what you’re getting sold these days,” James emphasized. 

Officers receive training on how to identify dealers and drug tactics, as well as what to do in the case of accidental exposure. They are also taught how to address the ongoing crisis with local youth. 

“When the kids are younger we educate parents on the dangers of accidental ingestion,” James said. “Once they’re teens, we dive in deeper about the danger of drugs and how to navigate peer-pressure.” 

RFA and the Rome Police Department work together. 

The school handles Code of Conduct violations while the police handle criminal law enforcement, James said. He agreed with LeBaron that there is no “one-size-fits-all” approach; depending on the age of the offender, consequences diverge.

“It’s paramount for students' cognitive development to stay clean from drugs,” James highlighted. “In the past we’ve dropped the ball with spreading information on substance abuse. That’s our fault. But moving forward that won't be the case. We’re already commissioned more of our officers to be part of the D.A.R.E. program at RFA.” 

D.A.R.E. to dive in deeper

As RFA students are required to take health in eighth grade then again sophomore through senior year of high school.

Zappavigna outlined how health curriculum has changed over the years. 

State mandates push educators to teach about “mental health, alcohol, drugs, tobacco abuse and the prevention and detection of certain cancers," she said. Adding that curriculum must also integrate the National Health Standards "skills-based health approach."

“Simply put, we are asked to teach the foundational skills needed to make decisions,” said Zappavigna. “We encourage students to make choices based on their own personal values, morals, and beliefs with a firm understanding on how each action makes an impact.” 

Zappavigna referenced the most recent National Youth Risk Behavior (NYRB)  survey where 36.2 percent of high school students admitted to having used a vaping product, 22.7 percent to having had one drink of alcohol within 30 days of completing the survey, and 27.8 percent to having tried marijuana at least once before. 

“The 'just say no' approach was part of the old D.A.R.E. program,” Zappavigna said. “But what worked in the 80’s doesn't work today. Now we teach a new set of comprehensive concepts. We analyze external influences – such as social media, pop culture, and peer pressure – so students can identify threats  and work them effectively.” 

Looking forward

Lately, students have come to heath class with questions about the opioid crisis and fentanyl, Zappavigna said. And still, parents tend to ask more about alcohol consumption than drug use, she highlighted.

“These kids are confused,”Zappavigna claimed. “They see Narcan commercial campaigns on television and now the product is even sold at local pharmacies. But who is talking about these topics with them outside of school?”. 

To help “bridge the gap,” Zappavigna said she hopes to partner up the Oneida County’s Health Department in the future to “ implement more youth substance programming for families.” 

As stated by Zappavigna, ‌in the long term, addiction prevention requires social change. ‌‌But first, we need to keep ‌‌our young people alive, which means having honest conversations.

California Invests More Than $50 Million in Youth Substance Abuse Prevention

Published: Apr 10, 2024

WHAT YOU NEED TO KNOW: California is awarding new grants to fund  the “Elevate Youth California” campaign – a statewide program dedicated to supporting youth mental health and preventing substance abuse among kids and teens.

SACRAMENTO — Today, Governor Gavin Newsom announced the California Department of Health Care Services (DHCS) is awarding over $51 million to 75 community-based and tribal organizations, utilizing Prop 64 funding, to further support youth mental health and expand the state’s substance abuse prevention programs. Efforts like these help inform young Californians about the dangers of drugs, how to prevent substance abuse, and cope with adversity and trauma.

“As a father, I know that kids today are under more stress than ever. California is committed to providing the mental health support that children need and deserve — and tools to help them cope with adversity.” Governor Gavin Newsom

“Kids are under tremendous stress and looking for ways to cope. As parents, the Governor and I are committed to strategic investments like these that support young people’s physical and mental health, ensuring they have the resources they need to understand and prevent substance use disorder.” First Partner Jennifer Siebel Newsom

HELPING CALIFORNIANS: The Elevate Youth Campaign (EYC) provides three-year grants to youth-focused community-based and tribal organizations that:

• Implement youth development, peer support, and mentoring programs that are evidence-based and help kids heal and recover from trauma, cope with adversity, and thrive.
• Empower youth to get involved in their communities.
• Prioritize harm reduction and public health solutions that address and prevent substance use disorder.

KEY NUMBERS: Since 2019, DHCS’ EYC program has engaged 6,793 new diverse stakeholders over five grant cycles:

• providing services to 68,539 youth;
• holding 41,185 prevention program events with 296,435 participants;
• convening 259 listening sessions.

BIGGER PICTURE: Programs and investments like these are all part of Governor Newsom and First Partner Siebel Newsom’s Mental Health Movement , which takes a whole-body approach to helping people get the support and care they need – focusing on:

• Treatment and Housing for Those Who Need it Most;
• Increasing Access to Mental Health Services;
• Building our Health Care Workforce;
• Supporting and Serving our Kids.

As part of this effort, the Newsom Administration launched the Children and Youth Behavioral Health Initiative and the Master Plan to Tackle the Fentanyl & Opioid Crisis . To learn more about California’s response to the opioid crisis, visit www.opioids.ca.gov .

MORE DETAILS ON TODAY’S INVESTMENT