case study on clinical depression

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Patient Case Presentation

Figure 1.  Blue and silver stethoscope (Pixabay, N.D.)

Ms. S.W. is a 48-year-old white female who presented to an outpatient community mental health agency for evaluation of depressive symptoms. Over the past eight weeks she has experienced sad mood every day, which she describes as a feeling of hopelessness and emptiness. She also noticed other changes about herself, including decreased appetite, insomnia, fatigue, and poor ability to concentrate. The things that used to bring Ms. S.W. joy, such as gardening and listening to podcasts, are no longer bringing her the same happiness they used to. She became especially concerned as within the past two weeks she also started experiencing feelings of worthlessness, the perception that she is a burden to others, and fleeting thoughts of death/suicide.

Ms. S.W. acknowledges that she has numerous stressors in her life. She reports that her daughter’s grades have been steadily declining over the past two semesters and she is unsure if her daughter will be attending college anymore. Her relationship with her son is somewhat strained as she and his father are not on good terms and her son feels Ms. S.W. is at fault for this. She feels her career has been unfulfilling and though she’d like to go back to school, this isn’t possible given the family’s tight finances/the patient raising a family on a single income.

Ms. S.W. has experienced symptoms of depression previously, but she does not think the symptoms have ever been as severe as they are currently. She has taken antidepressants in the past and was generally adherent to them, but she believes that therapy was more helpful than the medications. She denies ever having history of manic or hypomanic episodes. She has been unable to connect to a mental health agency in several years due to lack of time and feeling that she could manage the symptoms on her own. She now feels that this is her last option and is looking for ongoing outpatient mental health treatment.

Past Medical History

• Hypertension, diagnosed at age 41

Past Surgical History

• Wisdom teeth extraction, age 22

Pertinent Family History

• Mother with history of Major Depressive Disorder, treated with antidepressants
• Maternal grandmother with history of Major Depressive Disorder, Generalized Anxiety Disorder
• Brother with history of suicide attempt and subsequent inpatient psychiatric hospitalization,
• Brother with history of Alcohol Use Disorder
• Father died from lung cancer (2012)

Pertinent Social History

• Works full-time as an enrollment specialist for Columbus City Schools since 2006
• Has two children, a daughter age 17 and a son age 14
• Divorced in 2015, currently single
• History of some emotional abuse and neglect from mother during childhood, otherwise denies history of trauma, including physical and sexual abuse
• Smoking 1/2 PPD of cigarettes
• Occasional alcohol use (approximately 1-2 glasses of wine 1-2 times weekly; patient had not had any alcohol consumption for the past year until two weeks ago)

Mental Health Case Study: Understanding Depression through a Real-life Example

Imagine feeling an unrelenting heaviness weighing down on your chest. Every breath becomes a struggle as a cloud of sadness engulfs your every thought. Your energy levels plummet, leaving you physically and emotionally drained. This is the reality for millions of people worldwide who suffer from depression, a complex and debilitating mental health condition.

Understanding depression is crucial in order to provide effective support and treatment for those affected. While textbooks and research papers provide valuable insights, sometimes the best way to truly comprehend the depths of this condition is through real-life case studies. These stories bring depression to life, shedding light on its impact on individuals and society as a whole.

In this article, we will delve into the world of mental health case studies, using a real-life example to explore the intricacies of depression. We will examine the symptoms, prevalence, and consequences of this all-encompassing condition. Furthermore, we will discuss the significance of case studies in mental health research, including their ability to provide detailed information about individual experiences and contribute to the development of treatment strategies.

Through an in-depth analysis of a selected case study, we will gain insight into the journey of an individual facing depression. We will explore their background, symptoms, and initial diagnosis. Additionally, we will examine the various treatment options available and assess the effectiveness of the chosen approach.

By delving into this real-life example, we will not only gain a better understanding of depression as a mental health condition, but we will also uncover valuable lessons that can aid in the treatment and support of those who are affected. So, let us embark on this enlightening journey, using the power of case studies to bring understanding and empathy to those who need it most.

Understanding Depression

Depression is a complex and multifaceted mental health condition that affects millions of people worldwide. To comprehend the impact of depression, it is essential to explore its defining characteristics, prevalence, and consequences on individuals and society as a whole.

Defining depression and its symptoms

Depression is more than just feeling sad or experiencing a low mood. It is a serious mental health disorder characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. Individuals with depression often experience a range of symptoms that can significantly impact their daily lives. These symptoms include:

1. Persistent feelings of sadness or emptiness. 2. Fatigue and decreased energy levels. 3. Significant changes in appetite and weight. 4. Difficulty concentrating or making decisions. 5. Insomnia or excessive sleep. 6. feelings of guilt, worthlessness, or hopelessness. 7. Loss of interest or pleasure in activities.

Exploring the prevalence of depression worldwide

Depression knows no boundaries and affects individuals from all walks of life. According to the World Health Organization (WHO), an estimated 264 million people globally suffer from depression. This makes depression one of the most common mental health conditions worldwide. Additionally, the WHO highlights that depression is more prevalent among females than males.

The impact of depression is not limited to individuals alone. It also has significant social and economic consequences. Depression can lead to impaired productivity, increased healthcare costs, and strain on relationships, contributing to a significant burden on families, communities, and society at large.

The impact of depression on individuals and society

Depression can have a profound and debilitating impact on individuals’ lives, affecting their physical, emotional, and social well-being. The persistent sadness and loss of interest can lead to difficulties in maintaining relationships, pursuing education or careers, and engaging in daily activities. Furthermore, depression increases the risk of developing other mental health conditions, such as anxiety disorders or substance abuse.

On a societal level, depression poses numerous challenges. The economic burden of depression is significant, with costs associated with treatment, reduced productivity, and premature death. Moreover, the social stigma surrounding mental health can impede individuals from seeking help and accessing appropriate support systems.

Understanding the prevalence and consequences of depression is crucial for policymakers, healthcare professionals, and individuals alike. By recognizing the significant impact depression has on individuals and society, appropriate resources and interventions can be developed to mitigate its effects and improve the overall well-being of those affected.

The Significance of Case Studies in Mental Health Research

Case studies play a vital role in mental health research, providing valuable insights into individual experiences and contributing to the development of effective treatment strategies. Let us explore why case studies are considered invaluable in understanding and addressing mental health conditions.

Why case studies are valuable in mental health research

Case studies offer a unique opportunity to examine mental health conditions within the real-life context of individuals. Unlike large-scale studies that focus on statistical data, case studies provide a detailed examination of specific cases, allowing researchers to delve into the complexities of a particular condition or treatment approach. This micro-level analysis helps researchers gain a deeper understanding of the nuances and intricacies involved.

The role of case studies in providing detailed information about individual experiences

Through case studies, researchers can capture rich narratives and delve into the lived experiences of individuals facing mental health challenges. These stories help to humanize the condition and provide valuable insights that go beyond a list of symptoms or diagnostic criteria. By understanding the unique experiences, thoughts, and emotions of individuals, researchers can develop a more comprehensive understanding of mental health conditions and tailor interventions accordingly.

How case studies contribute to the development of treatment strategies

Case studies form a vital foundation for the development of effective treatment strategies. By examining a specific case in detail, researchers can identify patterns, factors influencing treatment outcomes, and areas where intervention may be particularly effective. Moreover, case studies foster an iterative approach to treatment development—an ongoing cycle of using data and experience to refine and improve interventions.

By examining multiple case studies, researchers can identify common themes and trends, leading to the development of evidence-based guidelines and best practices. This allows healthcare professionals to provide more targeted and personalized support to individuals facing mental health conditions.

Furthermore, case studies can shed light on potential limitations or challenges in existing treatment approaches. By thoroughly analyzing different cases, researchers can identify gaps in current treatments and focus on areas that require further exploration and innovation.

In summary, case studies are a vital component of mental health research, offering detailed insights into the lived experiences of individuals with mental health conditions. They provide a rich understanding of the complexities of these conditions and contribute to the development of effective treatment strategies. By leveraging the power of case studies, researchers can move closer to improving the lives of individuals facing mental health challenges.

Examining a Real-life Case Study of Depression

In order to gain a deeper understanding of depression, let us now turn our attention to a real-life case study. By exploring the journey of an individual navigating through depression, we can gain valuable insights into the complexities and challenges associated with this mental health condition.

Introduction to the selected case study

In this case study, we will focus on Jane, a 32-year-old woman who has been struggling with depression for the past two years. Jane’s case offers a compelling narrative that highlights the various aspects of depression, including its onset, symptoms, and the treatment journey.

Background information on the individual facing depression

Before the onset of depression, Jane led a fulfilling and successful life. She had a promising career, a supportive network of friends and family, and engaged in hobbies that brought her joy. However, a series of life stressors, including a demanding job, a breakup, and the loss of a loved one, began to take a toll on her mental well-being.

Jane’s background highlights a common phenomenon – depression can affect individuals from all walks of life, irrespective of their socio-economic status, age, or external circumstances. It serves as a reminder that no one is immune to mental health challenges.

Presentation of symptoms and initial diagnosis

Jane began noticing a shift in her mood, characterized by persistent feelings of sadness and a lack of interest in activities she once enjoyed. She experienced disruptions in her sleep patterns, appetite changes, and a general sense of hopelessness. Recognizing the severity of her symptoms, Jane sought help from a mental health professional who diagnosed her with major depressive disorder.

Jane’s case exemplifies the varied and complex symptoms associated with depression. While individuals may exhibit overlapping symptoms, the intensity and manifestation of those symptoms can vary greatly, underscoring the importance of personalized and tailored treatment approaches.

By examining this real-life case study of depression, we can gain an empathetic understanding of the challenges faced by individuals experiencing this mental health condition. Through Jane’s journey, we will uncover the treatment options available for depression and analyze the effectiveness of the chosen approach. The case study will allow us to explore the nuances of depression and provide valuable insights into the treatment landscape for this prevalent mental health condition.

The Treatment Journey

When it comes to treating depression, there are various options available, ranging from therapy to medication. In this section, we will provide an overview of the treatment options for depression and analyze the treatment plan implemented in the real-life case study.

Overview of the treatment options available for depression

Treatment for depression typically involves a combination of approaches tailored to the individual’s needs. The two primary treatment modalities for depression are psychotherapy (talk therapy) and medication. Psychotherapy aims to help individuals explore their thoughts, emotions, and behaviors, while medication can help alleviate symptoms by restoring chemical imbalances in the brain.

Common forms of psychotherapy used in the treatment of depression include cognitive-behavioral therapy (CBT), interpersonal therapy (IPT), and psychodynamic therapy. These therapeutic approaches focus on addressing negative thought patterns, improving relationship dynamics, and gaining insight into underlying psychological factors contributing to depression.

In cases where medication is utilized, selective serotonin reuptake inhibitors (SSRIs) are commonly prescribed. These medications help rebalance serotonin levels in the brain, which are often disrupted in individuals with depression. Other classes of antidepressant medications, such as serotonin-norepinephrine reuptake inhibitors (SNRIs) or tricyclic antidepressants (TCAs), may be considered in specific cases.

Exploring the treatment plan implemented in the case study

In Jane’s case, a comprehensive treatment plan was developed with the intention of addressing her specific needs and symptoms. Recognizing the severity of her depression, Jane’s healthcare team recommended a combination of talk therapy and medication.

Jane began attending weekly sessions of cognitive-behavioral therapy (CBT) with a licensed therapist. This form of therapy aimed to help Jane identify and challenge negative thought patterns, develop coping strategies, and cultivate more adaptive behaviors. The therapeutic relationship provided Jane with a safe space to explore and process her emotions, ultimately helping her regain a sense of control over her life.

In conjunction with therapy, Jane’s healthcare provider prescribed an SSRI medication to assist in managing her symptoms. The medication was carefully selected based on Jane’s specific symptoms and medical history, and regular follow-up appointments were scheduled to monitor her response to the medication and adjust the dosage if necessary.

Analyzing the effectiveness of the treatment approach

The effectiveness of treatment for depression varies from person to person, and it often requires a period of trial and adjustment to find the most suitable intervention. In Jane’s case, the combination of cognitive-behavioral therapy and medication proved to be beneficial. Over time, she reported a reduction in her depressive symptoms, an improvement in her overall mood, and increased ability to engage in activities she once enjoyed.

It is important to note that the treatment journey for depression is not always linear, and setbacks and challenges may occur along the way. Each individual responds differently to treatment, and adjustments might be necessary to optimize outcomes. Continuous communication between the individual and their healthcare team is crucial to addressing any concerns, monitoring progress, and adapting the treatment plan as needed.

By analyzing the treatment approach in the real-life case study, we gain insights into the various treatment options available for depression and how they can be tailored to meet individual needs. The combination of psychotherapy and medication offers a holistic approach, addressing both psychological and biological aspects of depression.

The Outcome and Lessons Learned

After undergoing treatment for depression, it is essential to assess the outcome and draw valuable lessons from the case study. In this section, we will discuss the progress made by the individual in the case study, examine the challenges faced during the treatment process, and identify key lessons learned.

Discussing the progress made by the individual in the case study

Throughout the treatment process, Jane experienced significant progress in managing her depression. She reported a reduction in depressive symptoms, improved mood, and a renewed sense of hope and purpose in her life. Jane’s active participation in therapy, combined with the appropriate use of medication, played a crucial role in her progress.

Furthermore, Jane’s support network of family and friends played a significant role in her recovery. Their understanding, empathy, and support provided a solid foundation for her journey towards improved mental well-being. This highlights the importance of social support in the treatment and management of depression.

Examining the challenges faced during the treatment process

Despite the progress made, Jane faced several challenges during her treatment journey. Adhering to the treatment plan consistently proved to be difficult at times, as she encountered setbacks and moments of self-doubt. Additionally, managing the side effects of the medication required careful monitoring and adjustments to find the right balance.

Moreover, the stigma associated with mental health continued to be a challenge for Jane. Overcoming societal misconceptions and seeking help required courage and resilience. The case study underscores the need for increased awareness, education, and advocacy to address the stigma surrounding mental health conditions.

Identifying the key lessons learned from the case study

The case study offers valuable lessons that can inform the treatment and support of individuals with depression:

1. Holistic Approach: The combination of psychotherapy and medication proved to be effective in addressing the psychological and biological aspects of depression. This highlights the need for a holistic and personalized treatment approach.

2. Importance of Support: Having a strong support system can significantly impact an individual’s ability to navigate through depression. Family, friends, and healthcare professionals play a vital role in providing empathy, understanding, and encouragement.

3. Individualized Treatment: Depression manifests differently in each individual, emphasizing the importance of tailoring treatment plans to meet individual needs. Personalized interventions are more likely to lead to positive outcomes.

4. Overcoming Stigma: Addressing the stigma associated with mental health conditions is crucial for individuals to seek timely help and access the support they need. Educating society about mental health is essential to create a more supportive and inclusive environment.

By drawing lessons from this real-life case study, we gain insights that can improve the understanding and treatment of depression. Recognizing the progress made, understanding the challenges faced, and implementing the lessons learned can contribute to more effective interventions and support systems for individuals facing depression.In conclusion, this article has explored the significance of mental health case studies in understanding and addressing depression, focusing on a real-life example. By delving into case studies, we gain a deeper appreciation for the complexities of depression and the profound impact it has on individuals and society.

Through our examination of the selected case study, we have learned valuable lessons about the nature of depression and its treatment. We have seen how the combination of psychotherapy and medication can provide a holistic approach, addressing both psychological and biological factors. Furthermore, the importance of social support and the role of a strong network in an individual’s recovery journey cannot be overstated.

Additionally, we have identified challenges faced during the treatment process, such as adherence to the treatment plan and managing medication side effects. These challenges highlight the need for ongoing monitoring, adjustments, and open communication between individuals and their healthcare providers.

The case study has also emphasized the impact of stigma on individuals seeking help for depression. Addressing societal misconceptions and promoting mental health awareness is essential to create a more supportive environment for those affected by depression and other mental health conditions.

Overall, this article reinforces the significance of case studies in advancing our understanding of mental health conditions and developing effective treatment strategies. Through real-life examples, we gain a more comprehensive and empathetic perspective on depression, enabling us to provide better support and care for individuals facing this mental health challenge.

As we conclude, it is crucial to emphasize the importance of continued research and exploration of mental health case studies. The more we learn from individual experiences, the better equipped we become to address the diverse needs of those affected by mental health conditions. By fostering a culture of understanding, support, and advocacy, we can strive towards a future where individuals with depression receive the care and compassion they deserve.

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4 Treatment of Depression

• Published: February 2013
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Chapter 4 covers the treatment of depression, and discusses popular myths regarding depression, its frequency, characteristics and diagnosis, and includes case studies, assessment, case conceptualization, intervention development and course of treatment, problems that may arise in therapy, ethical considerations, common mistakes in the course of treatment, relapse prevention, and cultural factors.

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Nih research matters.

April 23, 2024

Research in Context: Treating depression

Finding better approaches.

While effective treatments for major depression are available, there is still room for improvement. This special Research in Context feature explores the development of more effective ways to treat depression, including personalized treatment approaches and both old and new drugs.

Everyone has a bad day sometimes. People experience various types of stress in the course of everyday life. These stressors can cause sadness, anxiety, hopelessness, frustration, or guilt. You may not enjoy the activities you usually do. These feelings tend to be only temporary. Once circumstances change, and the source of stress goes away, your mood usually improves. But sometimes, these feelings don’t go away. When these feelings stick around for at least two weeks and interfere with your daily activities, it’s called major depression, or clinical depression.

In 2021, 8.3% of U.S. adults experienced major depression. That’s about 21 million people. Among adolescents, the prevalence was much greater—more than 20%. Major depression can bring decreased energy, difficulty thinking straight, sleep problems, loss of appetite, and even physical pain. People with major depression may become unable to meet their responsibilities at work or home. Depression can also lead people to use alcohol or drugs or engage in high-risk activities. In the most extreme cases, depression can drive people to self-harm or even suicide.

The good news is that effective treatments are available. But current treatments have limitations. That’s why NIH-funded researchers have been working to develop more effective ways to treat depression. These include finding ways to predict whether certain treatments will help a given patient. They're also trying to develop more effective drugs or, in some cases, find new uses for existing drugs.

Finding the right treatments

The most common treatments for depression include psychotherapy, medications, or a combination. Mild depression may be treated with psychotherapy. Moderate to severe depression often requires the addition of medication.

Several types of psychotherapy have been shown to help relieve depression symptoms. For example, cognitive behavioral therapy helps people to recognize harmful ways of thinking and teaches them how to change these. Some researchers are working to develop new therapies to enhance people’s positive emotions. But good psychotherapy can be hard to access due to the cost, scheduling difficulties, or lack of available providers. The recent growth of telehealth services for mental health has improved access in some cases.

There are many antidepressant drugs on the market. Different drugs will work best on different patients. But it can be challenging to predict which drugs will work for a given patient. And it can take anywhere from 6 to 12 weeks to know whether a drug is working. Finding an effective drug can involve a long period of trial and error, with no guarantee of results.

If depression doesn’t improve with psychotherapy or medications, brain stimulation therapies could be used. Electroconvulsive therapy, or ECT, uses electrodes to send electric current into the brain. A newer technique, transcranial magnetic stimulation (TMS), stimulates the brain using magnetic fields. These treatments must be administered by specially trained health professionals.

“A lot of patients, they kind of muddle along, treatment after treatment, with little idea whether something’s going to work,” says psychiatric researcher Dr. Amit Etkin.

One reason it’s difficult to know which antidepressant medications will work is that there are likely different biological mechanisms that can cause depression. Two people with similar symptoms may both be diagnosed with depression, but the causes of their symptoms could be different. As NIH depression researcher Dr. Carlos Zarate explains, “we believe that there’s not one depression, but hundreds of depressions.”

Depression may be due to many factors. Genetics can put certain people at risk for depression. Stressful situations, physical health conditions, and medications may contribute. And depression can also be part of a more complicated mental disorder, such as bipolar disorder. All of these can affect which treatment would be best to use.

Etkin has been developing methods to distinguish patients with different types of depression based on measurable biological features, or biomarkers. The idea is that different types of patients would respond differently to various treatments. Etkin calls this approach “precision psychiatry.”

One such type of biomarker is electrical activity in the brain. A technique called electroencephalography, or EEG, measures electrical activity using electrodes placed on the scalp. When Etkin was at Stanford University, he led a research team that developed a machine-learning algorithm to predict treatment response based on EEG signals. The team applied the algorithm to data from a clinical trial of the antidepressant sertraline (Zoloft) involving more than 300 people.

EEG data for the participants were collected at the outset. Participants were then randomly assigned to take either sertraline or an inactive placebo for eight weeks. The team found a specific set of signals that predicted the participants’ responses to sertraline. The same neural “signature” also predicted which patients with depression responded to medication in a separate group.

Etkin’s team also examined this neural signature in a set of patients who were treated with TMS and psychotherapy. People who were predicted to respond less to sertraline had a greater response to the TMS/psychotherapy combination.

Etkin continues to develop methods for personalized depression treatment through his company, Alto Neuroscience. He notes that EEG has the advantage of being low-cost and accessible; data can even be collected in a patient’s home. That’s important for being able to get personalized treatments to the large number of people they could help. He’s also working on developing antidepressant drugs targeted to specific EEG profiles. Candidate drugs are in clinical trials now.

“It’s not like a pie-in-the-sky future thing, 20-30 years from now,” Etkin explains. “This is something that could be in people's hands within the next five years.”

New tricks for old drugs

While some researchers focus on matching patients with their optimal treatments, others aim to find treatments that can work for many different patients. It turns out that some drugs we’ve known about for decades might be very effective antidepressants, but we didn’t recognize their antidepressant properties until recently.

One such drug is ketamine. Ketamine has been used as an anesthetic for more than 50 years. Around the turn of this century, researchers started to discover its potential as an antidepressant. Zarate and others have found that, unlike traditional antidepressants that can take weeks to take effect, ketamine can improve depression in as little as one day. And a single dose can have an effect for a week or more. In 2019, the FDA approved a form of ketamine for treating depression that is resistant to other treatments.

But ketamine has drawbacks of its own. It’s a dissociative drug, meaning that it can make people feel disconnected from their body and environment. It also has the potential for addiction and misuse. For these reasons, it’s a controlled substance and can only be administered in a doctor’s office or clinic.

Another class of drugs being studied as possible antidepressants are psychedelics. These include lysergic acid diethylamide (LSD) and psilocybin, the active ingredient in magic mushrooms. These drugs can temporarily alter a person’s mood, thoughts, and perceptions of reality. Some have historically been used for religious rituals, but they are also used recreationally.

In clinical studies, psychedelics are typically administered in combination with psychotherapy. This includes several preparatory sessions with a therapist in the weeks before getting the drug, and several sessions in the weeks following to help people process their experiences. The drugs are administered in a controlled setting.

Dr. Stephen Ross, co-director of the New York University Langone Health Center for Psychedelic Medicine, describes a typical session: “It takes place in a living room-like setting. The person is prepared, and they state their intention. They take the drug, they lie supine, they put on eye shades and preselected music, and two therapists monitor them.” Sessions last for as long as the acute effects of the drug last, which is typically several hours. This is a healthcare-intensive intervention given the time and personnel needed.

In 2016, Ross led a clinical trial examining whether psilocybin-assisted therapy could reduce depression and anxiety in people with cancer. According to Ross, as many as 40% of people with cancer have clinically significant anxiety and depression. The study showed that a single psilocybin session led to substantial reductions in anxiety and depression compared with a placebo. These reductions were evident as soon as one day after psilocybin administration. Six months later, 60-80% of participants still had reduced depression and anxiety.

Psychedelic drugs frequently trigger mystical experiences in the people who take them. “People can feel a sense…that their consciousness is part of a greater consciousness or that all energy is one,” Ross explains. “People can have an experience that for them feels more ‘real’ than regular reality. They can feel transported to a different dimension of reality.”

About three out of four participants in Ross’s study said it was among the most meaningful experiences of their lives. And the degree of mystical experience correlated with the drug’s therapeutic effect. A long-term follow-up study found that the effects of the treatment continued more than four years later.

If these results seem too good to be true, Ross is quick to point out that it was a small study, with only 29 participants, although similar studies from other groups have yielded similar results. Psychedelics haven’t yet been shown to be effective in a large, controlled clinical trial. Ross is now conducting a trial with 200 people to see if the results of his earlier study pan out in this larger group. For now, though, psychedelics remain experimental drugs—approved for testing, but not for routine medical use.

Unlike ketamine, psychedelics aren’t considered addictive. But they, too, carry risks, which certain conditions may increase. Psychedelics can cause cardiovascular complications. They can cause psychosis in people who are predisposed to it. In uncontrolled settings, they have the risk of causing anxiety, confusion, and paranoia—a so-called “bad trip”—that can lead the person taking the drug to harm themself or others. This is why psychedelic-assisted therapy takes place in such tightly controlled settings. That increases the cost and complexity of the therapy, which may prevent many people from having access to it.

Better, safer drugs

Despite the promise of ketamine or psychedelics, their drawbacks have led some researchers to look for drugs that work like them but with fewer side effects.

Depression is thought to be caused by the loss of connections between nerve cells, or neurons, in certain regions of the brain. Ketamine and psychedelics both promote the brain’s ability to repair these connections, a quality called plasticity. If we could understand how these drugs encourage plasticity, we might be able to design drugs that can do so without the side effects.

Dr. David Olson at the University of California, Davis studies how psychedelics work at the cellular and molecular levels. The drugs appear to promote plasticity by binding to a receptor in cells called the 5-hydroxytryptamine 2A receptor (5-HT2AR). But many other compounds also bind 5-HT2AR without promoting plasticity. In a recent NIH-funded study, Olson showed that 5-HT2AR can be found both inside and on the surface of the cell. Only compounds that bound to the receptor inside the cells promoted plasticity. This suggests that a drug has to be able to get into the cell to promote plasticity.

Moreover, not all drugs that bind 5-HT2AR have psychedelic effects. Olson’s team has developed a molecular sensor, called psychLight, that can identify which compounds that bind 5-HT2AR have psychedelic effects. Using psychLight, they identified compounds that are not psychedelic but still have rapid and long-lasting antidepressant effects in animal models. He’s founded a company, Delix Therapeutics, to further develop drugs that promote plasticity.

Meanwhile, Zarate and his colleagues have been investigating a compound related to ketamine called hydroxynorketamine (HNK). Ketamine is converted to HNK in the body, and this process appears to be required for ketamine’s antidepressant effects. Administering HNK directly produced antidepressant-like effects in mice. At the same time, it did not cause the dissociative side effects and addiction caused by ketamine. Zarate’s team has already completed phase I trials of HNK in people showing that it’s safe. Phase II trials to find out whether it’s effective are scheduled to begin soon.  

“What [ketamine and psychedelics] are doing for the field is they’re helping us realize that it is possible to move toward a repair model versus a symptom mitigation model,” Olson says. Unlike existing antidepressants, which just relieve the symptoms of depression, these drugs appear to fix the underlying causes. That’s likely why they work faster and produce longer-lasting effects. This research is bringing us closer to having safer antidepressants that only need to be taken once in a while, instead of every day.

—by Brian Doctrow, Ph.D.

Related Links

• How Psychedelic Drugs May Help with Depression
• Biosensor Advances Drug Discovery
• Neural Signature Predicts Antidepressant Response
• How Ketamine Relieves Symptoms of Depression
• Protein Structure Reveals How LSD Affects the Brain
• Predicting The Usefulness of Antidepressants
• Depression Screening and Treatment in Adults
• Serotonin Transporter Structure Revealed
• Placebo Effect in Depression Treatment
• When Sadness Lingers: Understanding and Treating Depression
• Psychedelic and Dissociative Drugs

References:  An electroencephalographic signature predicts antidepressant response in major depression.  Wu W, Zhang Y, Jiang J, Lucas MV, Fonzo GA, Rolle CE, Cooper C, Chin-Fatt C, Krepel N, Cornelssen CA, Wright R, Toll RT, Trivedi HM, Monuszko K, Caudle TL, Sarhadi K, Jha MK, Trombello JM, Deckersbach T, Adams P, McGrath PJ, Weissman MM, Fava M, Pizzagalli DA, Arns M, Trivedi MH, Etkin A.  Nat Biotechnol.  2020 Feb 10. doi: 10.1038/s41587-019-0397-3. Epub 2020 Feb 10. PMID: 32042166. Rapid and sustained symptom reduction following psilocybin treatment for anxiety and depression in patients with life-threatening cancer: a randomized controlled trial. Ross S, Bossis A, Guss J, Agin-Liebes G, Malone T, Cohen B, Mennenga SE, Belser A, Kalliontzi K, Babb J, Su Z, Corby P, Schmidt BL. J Psychopharmacol . 2016 Dec;30(12):1165-1180. doi: 10.1177/0269881116675512. PMID: 27909164. Long-term follow-up of psilocybin-assisted psychotherapy for psychiatric and existential distress in patients with life-threatening cancer. Agin-Liebes GI, Malone T, Yalch MM, Mennenga SE, Ponté KL, Guss J, Bossis AP, Grigsby J, Fischer S, Ross S. J Psychopharmacol . 2020 Feb;34(2):155-166. doi: 10.1177/0269881119897615. Epub 2020 Jan 9. PMID: 31916890. Psychedelics promote neuroplasticity through the activation of intracellular 5-HT2A receptors.  Vargas MV, Dunlap LE, Dong C, Carter SJ, Tombari RJ, Jami SA, Cameron LP, Patel SD, Hennessey JJ, Saeger HN, McCorvy JD, Gray JA, Tian L, Olson DE.  Science . 2023 Feb 17;379(6633):700-706. doi: 10.1126/science.adf0435. Epub 2023 Feb 16. PMID: 36795823. Psychedelic-inspired drug discovery using an engineered biosensor.  Dong C, Ly C, Dunlap LE, Vargas MV, Sun J, Hwang IW, Azinfar A, Oh WC, Wetsel WC, Olson DE, Tian L.  Cell . 2021 Apr 8: S0092-8674(21)00374-3. doi: 10.1016/j.cell.2021.03.043. Epub 2021 Apr 28. PMID: 33915107. NMDAR inhibition-independent antidepressant actions of ketamine metabolites. Zanos P, Moaddel R, Morris PJ, Georgiou P, Fischell J, Elmer GI, Alkondon M, Yuan P, Pribut HJ, Singh NS, Dossou KS, Fang Y, Huang XP, Mayo CL, Wainer IW, Albuquerque EX, Thompson SM, Thomas CJ, Zarate CA Jr, Gould TD. Nature . 2016 May 26;533(7604):481-6. doi: 10.1038/nature17998. Epub 2016 May 4. PMID: 27144355.

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• Published: 22 April 2024

Impact of COVID-19 pandemic on depression incidence and healthcare service use among patients with depression: an interrupted time-series analysis from a 9-year population-based study

• Vivien Kin Yi Chan 1   na1 ,
• Yi Chai 1 , 2   na1 ,
• Sandra Sau Man Chan 3 ,
• Hao Luo 4 ,
• Mark Jit 5 , 7 ,
• Martin Knapp 4 , 6 ,
• David Makram Bishai 7 ,
• Michael Yuxuan Ni 7 , 8 , 9 ,
• Ian Chi Kei Wong 1 , 10 , 11 , 13 &
• Xue Li   ORCID: orcid.org/0000-0003-4836-7808 1 , 10 , 12 , 13  

BMC Medicine volume  22 , Article number:  169 ( 2024 ) Cite this article

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Most studies on the impact of the COVID-19 pandemic on depression burden focused on the earlier pandemic phase specific to lockdowns, but the longer-term impact of the pandemic is less well-studied. In this population-based cohort study, we examined the short-term and long-term impacts of COVID-19 on depression incidence and healthcare service use among patients with depression.

Using the territory-wide electronic medical records in Hong Kong, we identified all patients aged ≥ 10 years with new diagnoses of depression from 2014 to 2022. We performed an interrupted time-series (ITS) analysis to examine changes in incidence of medically attended depression before and during the pandemic. We then divided all patients into nine cohorts based on year of depression incidence and studied their initial and ongoing service use patterns until the end of 2022. We applied generalized linear modeling to compare the rates of healthcare service use in the year of diagnosis between patients newly diagnosed before and during the pandemic. A separate ITS analysis explored the pandemic impact on the ongoing service use among prevalent patients with depression.

We found an immediate increase in depression incidence (RR = 1.21, 95% CI: 1.10–1.33, p  < 0.001) in the population after the pandemic began with non-significant slope change, suggesting a sustained effect until the end of 2022. Subgroup analysis showed that the increases in incidence were significant among adults and the older population, but not adolescents. Depression patients newly diagnosed during the pandemic used 11% fewer resources than the pre-pandemic patients in the first diagnosis year. Pre-existing depression patients also had an immediate decrease of 16% in overall all-cause service use since the pandemic, with a positive slope change indicating a gradual rebound over a 3-year period.

Conclusions

During the pandemic, service provision for depression was suboptimal in the face of increased demand generated by the increasing depression incidence during the COVID-19 pandemic. Our findings indicate the need to improve mental health resource planning preparedness for future public health crises.

Peer Review reports

The COVID-19 pandemic that began in 2020 has resulted in an unprecedented public health crisis, with 771 million confirmed cases and over 6 million deaths across the globe as of September 2023 [ 1 ]. To curb the spread and reduce the mortality of SARS-CoV-2 infections, governments worldwide enacted stringent measures to contain its spread, including social mobility restrictions, mask-wearing, massive screenings, and lockdowns. Despite their effectiveness in limiting viral spread, these measures may have created a macro-environment of fear, social exclusion of individuals who contracted the virus, and reduced community cohesion [ 2 , 3 , 4 ]. The pandemic and the ensuing measures also led to economic disruption and created financial hardship for millions of families [ 4 , 5 ]. The combined pandemic stresses may have exacerbated the risk factors for mental health conditions including depression. Among patients with pre-existing depression, the government effort re-prioritized for outbreak control may have also led to disrupted non-emergency services and unmet care need in mental health [ 6 ].

A meta-analysis estimated an additional 53 million cases of depression and a 27.6% increase in its global prevalence in 2020 due to COVID-19-related illnesses and reduced mobility [ 7 ], which affected individuals across age groups [ 8 , 9 , 10 ]. In Hong Kong, a survey showed a consistent mental health crisis with a two-fold increase in depression symptoms and a 28.3% rise in the stress level even during the well-managed small-scale outbreaks [ 11 ]. Conversely, other studies reported that the pandemic reduced the risk of depression and self-harm because of the emotional security provided by timely government intervention, but these findings were confounded by increased barriers to seek medical help [ 12 , 13 , 14 ]. In the emergency phase of the pandemic, it was reported that lockdowns significantly reduced healthcare service use for both outpatient and inpatient services [ 15 , 16 , 17 ]. Studies also found an elevated risk of depression relapse and use of antidepressants [ 18 , 19 ].

Literature exploring pandemic impact on depression has mostly focused on the earlier phase of the pandemic (2020–2021) when short-term lockdown orders were in place. There are fewer studies and more mixed results for the post-emergency phase. Hong Kong followed the “dynamic zero-COVID policy” of China with strict border control, contact tracing, and quarantine before cases spread until the end of 2022 and so recorded a low number of SARS-CoV-2 cases for most of the time before a major Omicron outbreak [ 20 ]. It did not experience full lockdown, although stringent infection control and social measures were deployed for an entire 3-year-long period. This context thus enables us to evaluate the longer-term pandemic impact apart from a focus on lockdowns. In the late pandemic period, it is also useful to understand any potential decline in depression incidence and rebound in health service utilization. Using interrupted time series (ITS) analysis with a cohort study, we examined the changes in depression incidence and healthcare service use due to the pandemic, aiming to measure both the short-term (immediately after pandemic onset) and long-term (3 years since the outbreak) impacts on the burden of depression. We aimed to facilitate better preparedness in mental health resource planning for future public health crises.

Data source

We analyzed the Clinical Data Analysis and Reporting System (CDARS), the territory-wide routine electronic medical record (EMR) developed by the Hospital Authority, which manages all public healthcare services in Hong Kong and provides publicly funded healthcare services to all eligible residents (> 7.6 million). CDARS covers real-time anonymized patient-level data, including demographics, deaths, attendances, and all-cause diagnoses coded based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM), since 1993 across outpatient, inpatient, and emergency settings for research and auditing purposes in the public sector. The quality and accuracy of CDARS have been demonstrated in population-based studies on COVID-19 [ 21 , 22 ] and depression [ 23 , 24 ]. In Hong Kong, the public healthcare is heavily subsidized at a highly affordable price, while the private sector is financed mainly by non-compulsory medical insurance and out-of-pocket payments. The Hospital Authority thus manages 76% of chronic medical conditions including mental health illnesses despite a dual-track public and private system [ 25 ].

Study design and participants

This study consisted of both a quasi-experimental design with ITS analyses and a population-based retrospective study. We first identified all patients who received new clinical diagnoses of major depressive disorder or dysthymia (ICD-9-CM codes: 296.2, 300.4, 311) between January 2014 and December 2022. Patients aged below 10 were excluded to avoid confusion with maternal depression in the coding system. We performed an ITS analysis to evaluate changes in medically attended depression incidence during 36 quarters of data observations. The data cut point was the first quarter of 2020, leaving 24 quarters as pre-cut points and 12 quarters as post-cut points. ITS analysis is a valuable tool to assess the impact of population-level interventions or major macro-environmental changes and widely used in various health policy assessments [ 26 ]. Since patients who received incident diagnoses in different years could have different disease durations and care needs, we divided all patients into nine “incident cohorts” (2014 to 2022 cohorts) based on year of depression incidence. All patients were followed up until the end of 2022 for their service use patterns across outpatient, inpatient, and emergency settings.

An exploratory trend analysis showed that use of healthcare resources was the greatest at the beginning of the disease course before stabilizing. Recognizing this feature, we separately investigated the pandemic impact on the (1) initial and (2) ongoing healthcare service use. Respectively, we compared the rates of healthcare service use during the first calendar year following diagnosis, which potentially represents the most care-demanding phase, among patients newly diagnosed during the pandemic (2020 to 2022, the exposure groups) with those diagnosed before the pandemic (2014 to 2019, the reference groups) using a generalized linear model. To study the ongoing resource utilization among the relatively stable prevalent patients, defined as having a disease duration for at least 3 years by the start of the pandemic (i.e., represented by all patients in the 2014–2016 cohorts), we conducted another ITS analysis to compare their rates of service use before and during the pandemic until the end of 2022. The data points before the third calendar year of diagnosis were excluded in the analysis. The linkage between the three parts of analyses is illustrated in Additional file 1 : Figure S1.

Exposure and outcomes of interest

Our study defined the exposure as the macro-environment with the implementation of containment measures in response to the pandemic. Based on the COVID-19 Stringency Index by the Oxford COVID-19 Government Response Tracker, the Hong Kong government introduced relevant policies since January 2020 and announced the lifting of most mandates by December 2022 [ 27 ]. With quarterly data, we operationally defined the exposure period starting from the first quarter of 2020 until December 2022 (the intervention period). The reference period (the pre-pandemic period) was between the first quarter of 2014 and the last quarter of 2019.

The first outcome of interest was quarterly incidence of medically attended depression, defined as the number of patients who received depression diagnosis in the current quarter but without history of depression divided by the local eligible population, with age standardization using 5-year age bands based on the 2021 mid-year population. The second outcome was quarterly or yearly rates of attendance episodes or bed-days by incident cohort and service setting, defined by the total visit episodes or bed-days in the current period divided by the number of patients with depression whose observation period (from their first diagnosis to death or end of study) fell within the same period. We further stratified the outpatient attendance into “all-cause” (all outpatient services) and “psychiatric-related” (psychiatric specialist clinic, day hospital, and community nursing) use. Stratified data were unavailable in the inpatient and emergency settings.

Statistical analysis

In the ITS analyses, we applied segmented quasi-Poisson regression models since the data showed signs of overdispersion [ 28 ]. We included a continuous time variable in quarters, a binary indicator for the pandemic period (the exposure period) to represent level change (immediate effect) and the interaction of the two to measure slope change (gradual effect) [ 29 ], offsetting the logarithm of the local population or patients with depression. We adjusted the quarters of the data points to account for seasonality. Residual plots, autocorrelation function, and partial autocorrelation function suggested very little evidence of autocorrelation [ 28 , 30 ]. We then used Newey-West method to obtain robust standard errors and address autocorrelation up to the largest lag [ 31 , 32 ]. In the comparison of the initial healthcare service use between patients newly diagnosed during and before the pandemic, we fitted the rates of service use in the year of diagnosis between cohorts using a generalized linear model with negative binomial log link function. The model adjusted for a binary indicator of whether the diagnosis year occurred before or during the pandemic (the exposure period) and offset the logarithm of incident patients with depression in each cohort. In all analyses, we excluded data points related to major local social movements in 2014 and 2019 to address confounding due to changes in socio-political environment [ 33 , 34 , 35 ].

Subgroup and sensitivity analyses

In the ITS analysis to evaluate changes in depression incidence, we further stratified the analysis into three age groups: adolescents (10–24), adults (25–64), and the older population (65 +) to explore whether these population subgroups were differentially susceptible to a new depression diagnosis as a result of the pandemic.

During the first quarter of 2022, there was an unprecedented abrupt increase of SARS-CoV-2 cases due to the Omicron variant, marking the start of “fifth-wave outbreak” in Hong Kong [ 20 ]. In contrast to the earlier waves of smaller-scale outbreaks (below 13,000 cumulative cases before 2022), the public healthcare services were overwhelmed at the beginning of this wave, which possibly strained diagnostic capacity and caused the number of depression diagnoses to be lower than usual. We therefore performed sensitivity analyses for the ITS analyses for depression incidence and healthcare service use by adjusting a variable indicating the relevant quarter to validate the results. In addition, since outpatient service reception may be subject to long waiting time, we conducted an additional sensitivity analysis with a 6-month lag for the pandemic period by adding a binary indicator for the transition period and re-defining the pandemic to start from the third quarter of 2020. Lastly, we also performed sensitivity analyses for the pandemic impact on ongoing healthcare resource utilization by changing the defined disease duration of 3 years as stable patients into 2 years.

All data were analyzed using R version 4.0.3 and cross-validated by two investigators.

Over the 9-year study period, we identified 85,111 patients with new depression diagnosis, who generated 4,433,558 attendance or admission episodes across all diagnosis settings and 1,327,424 inpatient bed-days. For these patients, the mean age was 48.6 (SD:19.8) with 71.6% being female. Detailed demographic characteristics of the patients diagnosed in each year are summarized in Additional file 2 : Table S1.

Incidence of medically attended depression

Figure  1 illustrates the trends of the observed and model-implied quarterly incidence of medically attended depression between 2014 and 2022. The average quarterly incidence rates were 3.44 and 3.59 per 10,000 population before and during the pandemic (Additional file 2 : Table S2), respectively. After adjusting for major social movements, ITS analysis showed a small but marginally significant decline in the population incidence in the pre-pandemic period (risk ratio, RR = 0.995, 95% CI: 0.99–1.00, p  = 0.042). Since the pandemic, however, there was a significant immediate increase in incidence indicated by level change (RR = 1.21, 95% CI: 1.10–1.33, p  < 0.001), with a non-significant slope change (Fig.  1 A).

Interrupted time series analysis plot of pandemic impact on depression incidence

Stratifying by age groups, ITS analysis showed a slow but significant decline in incidence in the pre-pandemic period among adults and the older population (RR = 0.99, 95% CI: 0.99–0.99) but a significant increase over the time among adolescents (RR = 1.04, 95% CI: 1.04–1.05) before the pandemic. Since the pandemic, we found significant level increases indicating immediate effects of the pandemic among adults (RR = 1.19, 95% CI: 1.09–1.29) and the older population (RR = 1.33, 95% CI: 1.29–1.38, all p  < 0.001), but not adolescents. The slope changes remained non-significant in all subgroups (Fig.  1 B–D).

In the sensitivity analysis which accounted for the fifth-wave outbreak, we found a similar level change (RR = 1.20, 95% CI: 1.10–1.32, p  < 0.001) as the main analysis, with a significant but slowly declining pre-pandemic trend (RR = 0.995, 95% CI: 0.990–0.999, p  = 0.039). Using a 6-month transition window showed a consistent level change (RR = 1.28, 95% CI: 1.22–1.34, p  < 0.001) and pre-pandemic trend (RR = 0.995, 95% CI: 0.994–0.996, p  < 0.001). The slope changes in both sensitivity analyses remained non-significant.

Healthcare service use

In each incident cohort, the patterns followed the natural disease history such that the greatest service demand consistently occurred within the first 2 years of a depression diagnosis, followed by gradual decline subsequently (Fig.  2 ). During the pandemic, service utilization appeared to decrease further across all diagnosis settings except for inpatient bed-days. All counts and rates of healthcare service use are listed in Additional file 2 : Tables S3–S12.

Trend of healthcare resource utilization from 2014 to 2022

Pandemic impact on initial healthcare service use

Table 1 details the rates of healthcare service use in the year of diagnosis stratified by incident cohort and the regression results across diagnosis settings. Annual rates of overall all-cause visits per patient in the year of diagnosis were 10.5 to 10.8 episodes among patients diagnosed between 2015 and 2018, in contrast to 9.0 to 10.2 episodes among those diagnosed between 2020 and 2022. Adjusting for major social movements, the negative binomial model showed that the pandemic was associated with significantly reduced utilization in inpatient bed-days (RR = 0.78, 95% CI: 0.70–0.85), outpatient all-cause visits (RR = 0.89, 95% CI: 0.85–0.93), outpatient psychiatric visits (RR = 0.82, 95% CI: 0.76–0.88), and overall all-cause visits (RR = 0.89, 95% CI: 0.85–0.94, all p  < 0.001). Being diagnosed during the pandemic was not significantly associated with changes in rates of emergency and inpatient admission episodes.

Pandemic impact on ongoing healthcare service use

For the combined 2014–2016 cohorts, the mean rate of overall all-cause visits counting from their third year of diagnosis was 3.38 episodes per patient in the pre-pandemic period, which dropped to 2.25 episodes per patient in the pandemic period. Adjusting for social movements, the ITS analysis showed significant decreases in the original trends of ongoing service use in all diagnosis settings (RRs ranged from 0.96 to 0.99, all p  < 0.01) before the pandemic (Table  2 and Fig.  3 ). When the pandemic began, there were immediate decreases in service use indicated by significant level changes in inpatient admission episodes (RR = 0.91, 95% CI: 0.83–0.99, p  = 0.024), inpatient bed-days (RR = 0.87, 95% CI: 0.78–0.96, p  = 0.017), outpatient all-cause visits (RR = 0.83, 95% CI: 0.76–0.91, p  < 0.001), outpatient psychiatric visits (RR = 0.77, 95% CI: 0.74–0.83, p  < 0.001), and overall all-cause visits (RR = 0.84, 95% CI: 0.76–0.92, p  < 0.001), but not emergency visits. Regarding gradual effects, there were significant but small slope changes during the pandemic across all diagnosis settings except inpatient bed-days, with RRs ranging from 1.02 to 1.03, indicating a gradual rebound over time (Table  2 and Fig.  3 ).

Impact of the pandemic on the ongoing healthcare resource utilization among the 2014–2016 cohorts

In the sensitivity analyses accounting for the fifth-wave outbreak and changing definition of disease duration prior to the pandemic, effect sizes were largely consistent with those in the main analysis (Additional file 2 : Tables S13–S14).

Using a 9-year population-based study with a quasi-experimental design, we present the immediate and long-term impacts of 3 years of the pandemic on depression burden. We found a 21% immediate increase in incidence of medically attended depression, with 19% and 33% increases among adults and the older population during the 3-year period. There was no significant slope change during the pandemic, indicating a sustained effect towards the end of 2022. Though the pandemic did not affect incidence among adolescents, the incidence had been rising significantly in this subgroup over time even before the pandemic. Despite the increasing overall incidence, patients newly diagnosed during the pandemic used 11% fewer resources in their year of diagnosis than the pre-pandemic patients. Patients with pre-existing depression also had an immediate decrease by 16% in overall all-cause visits, with a positive slope change which suggests a gradual rebound over 3 years.

Rising incidence of medically attended depression

Our findings are largely consistent with the previous literature that has reported an increased prevalence of depressive mood during the pandemic [ 7 , 8 , 9 , 10 , 11 ]. However, the results from EMR-based studies that focused on clinically confirmed incident diagnoses were mixed. A cohort study based on the UK Biobank reported a 2.0- to 3.1-fold increase in new diagnoses of depressive or anxiety disorders compared to the pre-pandemic period, especially in the first 6 months of the pandemic [ 36 ]. Another Israeli time-series analysis observed a 36% increase in depression incidence among youth [ 37 ]. Conversely, population-based time-series and cohort studies in the UK found a 28% to 43% decline in recorded depression incidence with a gradual return towards pre-pandemic rates [ 38 , 39 ]. One explanation for such discrepancies is service disruption during lockdowns that led to under-diagnoses in primary care systems. Alternatively, the nature of social measures may have contributed to the trends differently. Costa-Font et al. highlighted that a “preventive lockdown” when there was low mortality appeared to increase depressive symptoms, but it was the opposite when lockdowns were in a high-mortality context [ 40 ]. This echoes with our findings in Hong Kong, where control measures were mostly preventive following the “dynamic zero-COVID” approach while maintaining low case load most of the time.

In our subgroup analysis, we found that adults and the older population were prone to developing depression due to the pandemic, but adolescents were not. However, prior studies tend to report consistent risks across age groups: adults were likely to suffer from job insecurity and increased caregiver responsibilities, older adults were susceptible to prolonged isolation, fear of illness, and grief of losing the loved ones, while adolescents faced school closures, reduced peer interactions, and outdoor activities [ 37 , 41 , 42 , 43 , 44 ]. Between 2014 and 2019, we found the incidence among Hong Kong adolescents was already increasing, with rates doubling within 5 years and overtaking the incidence among adults and the older population. This pre-existing rising trend might explain why the pandemic, despite being an additional risk factor, did not have as comparable impact as in other age groups due to a potential diminishing marginal effect. The earlier rise in adolescent depression may have stemmed from existing contextual forces including social unrest and other unknown stressors [ 35 ]. Our findings suggest that resources for depression care among adults and the older population are needed to prepare for future pandemic threats. However, policymakers should be aware of the worrying mental health situation in adolescents. As the rising incidence was minimally linked to the pandemic in this subgroup, it implies that the mental health crisis could persist in the future regardless of the pandemic. Further investigation is needed to confirm the stressors behind the recent trend and ways to reverse the deterioration in adolescent mental health.

Declining use of healthcare resources

Given the increased demand for depression care during the pandemic, evaluating the pattern in healthcare service use in this critical period is important to identify potential unmet care needs, optimize strategies of service provision, and strengthen the preparedness for future pandemics. Despite the rising incidence, we found that the pandemic substantially reduced the use of inpatient and outpatient services among both newly diagnosed and pre-existing patients. This is consistent with the previous studies in South Africa, South Korea, the United States, and the UK, which estimated 15% to 51% reductions in healthcare resource utilization depending on diagnosis settings [ 15 , 17 , 45 , 46 ]. Most of them were conducted during the early phase of the pandemic with a focus on lockdowns. This may explain the generally greater decline in service use compared with our observations for Hong Kong. Among the pre-existing patients, the reductions in service use were unlikely to represent an immediate improvement in depression outcomes but rather the limited capacity of the system even without mobility restriction to access. This also affected the care delivery for the rising number of new patients during the pandemic, who need the greatest care in the first years of diagnosis but accessed less care than historical controls. The findings therefore revealed a suboptimal service provision in response to the extra care demand generated by the pandemic.

In our study, the service types most impacted by the pandemic were the inpatient bed-days for newly diagnosed patients and outpatient psychiatric visits for pre-existing patients. This is consistent with the observation that most inpatient care occurred at the beginning of the disease course, while outpatient follow-ups became more common as patients stabilized. During the pandemic, however, inpatient resources were reserved for outbreak control, leaving the new patients with inadequate service access. Among pre-existing patients, reluctance to visit clinics owing to fear of getting infected may have discouraged them from attending regular appointments [ 47 ]. Video consultations for SARS-CoV-2 infected cases have been initiated since July 2022, which led to 214,900 consultations for quarantined patients [ 48 ]. “Tele-psychiatry” in the post-pandemic era is worth investigation for its feasibility and effectiveness in extending continuity of care, as it enables follow-ups after hospital discharge and ensures ongoing patient access even without physical attendance.

Strengths and limitations

One of the major strengths of our study is the use of territory-wide longitudinal data with a large sample size, which allowed a quasi-experimental study design. This enabled us to investigate the population-level impact of the pandemic and validate prior findings from smaller community-based studies. The context of Hong Kong also enabled us to study the longer-term impact of the entire pandemic apart from a focus on lockdowns. When studying healthcare service use, our study differed from previous studies by separating patients into nine incident cohorts before analyzing their rates of service use during the follow-up. This allowed us to differentiate the pandemic impact more clearly on new and pre-existing patients, unlike most of the previous studies.

There are also limitations to our study. Firstly, patients’ decision to seek treatment mediates whether their condition is recorded. Systematic differences between age groups in the propensity to seek treatment during different periods rather than differences in the underlying population-level burden may have driven the trends before and after 2020. Secondly, we were unable to stratify the patterns of service use into all-cause and psychiatric-related use in the emergency and inpatient settings since such information was not available in the raw data. Thirdly, though the public sector provides the majority of local healthcare services, patients may have sought help from private doctors especially when the public healthcare system was overwhelmed at the start of the fifth-wave outbreak, possibly leading to underestimated incidence and healthcare service use. Patients who were diagnosed in private clinics before seeking care in the public sector may also be labeled as incident cases later than actual diagnosis date. We therefore performed sensitivity analyses but found no change in the conclusion. Lastly, the findings represent the mixed overall effect of the pandemic macro-environment, but the current time-series study was unable to disentangle the effects of specific contributing factors.

Using ITS analyses from a 9-year cohort study, we found a persistent increase in incidence of medically attended depression over the pandemic period in the overall population, adults, and the older population. However, patients newly diagnosed during the pandemic used fewer resources in their first year of diagnosis than pre-pandemic patients. Pre-existing patients also had immediate decreases in healthcare service use following the pandemic in all diagnosis settings, with a gradual rebound over 3 years. Our findings highlight the need to improve the preparedness in mental health resource planning for future public health crises.

Availability of data and materials

We are unable to directly share the data used in this study since the data custodian, the Hong Kong Hospital Authority who manages the Clinical Data Analysis and Reporting System (CDARS), has not given permission. However, CDARS data can be accessed via the Hospital Authority Data Sharing Portal for research purpose. The relevant information can be found online ( https://www3.ha.org.hk/data ).

Abbreviations

Clinical Data Analysis and Reporting System

• Electronic medical records

Interrupted time-series

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Acknowledgements

We thank Ms. Qiwen Fang and Ms. Yin Zhang for assistance in data retrieval; Dr. Deliang Yang and Ms. Jin Lee for statistical advice and support; we also thank Ms. Lisa Lam for English proofreading.

The study was jointly supported by the Collaborative Research Fund (ACESO, C7154-20GF), the Research Impact Fund (SCAN-2030, R7007-22) granted by the Research Grant Council, University Grants Committee, and the Health and Medical Research Fund (COVID19F04; COVID19F11) granted by the Health Bureau, The Government of the Hong Kong Special Administrative Region, and the Laboratory of Data Discovery for Health (D 2 4H) funded by the Innovation and Technology Commission for data during the pandemic, modeling depression burden between 2014 and 2022. The funders had no active role in the design and conduct of the work and in the analysis, interpretation, and preparation of study reports.

Author information

Vivien Kin Yi Chan and Yi Chai are co-first authors with equal contribution.

Authors and Affiliations

Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Vivien Kin Yi Chan, Yi Chai, Ian Chi Kei Wong & Xue Li

The Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong SAR, China

Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China

Sandra Sau Man Chan

Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, China

Hao Luo & Martin Knapp

Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK

Care Policy and Evaluation Centre, Department of Health Policy, London School of Economics and Political Science, London, UK

Martin Knapp

School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Mark Jit, David Makram Bishai & Michael Yuxuan Ni

The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China

Michael Yuxuan Ni

Urban Systems Institute, The University of Hong Kong, Hong Kong SAR, China

Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China

Ian Chi Kei Wong & Xue Li

School of Pharmacy, Aston University, London, UK

Ian Chi Kei Wong

Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

Advanced Data Analytics for Medical Science (ADAMS) Limited, Hong Kong SAR, China

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X Li and ICK Wong conceived the study idea and study design. VKY Chan and Y Chai gathered the data and performed data analyses. All authors provided clinical, statistical, and epidemiological advice and interpreted the results. VKY Chan and X Li wrote and revised the drafts with all authors’ critical comments and revisions. All authors agree to be accountable for all aspects of the work. X Li and ICK Wong obtained the funding and supervised the study conduct. The corresponding authors confirm that all co-authors meet authorship criteria. All authors read and approved the final manuscript.

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Vivien Kin Yi Chan: @vivienkinyichan

Martin Knapp: @knappem

Hao Luo: @HaoLuo429

Ian Chi Kei Wong: @Ian_HKU

Xue Li: @snowly12191

HKU Pharmacy: @HkuPharm

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Correspondence to Ian Chi Kei Wong or Xue Li .

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This study received ethics approval from the Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong Western Cluster (UW 20-218). Informed consent has been waived as the study utilized de-identified data.

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Competing interests

X Li received research grants from the Hong Kong Health and Medical Research Fund (HMRF, HMRF Fellowship Scheme, HKSAR), Research Grants Council Early Career Scheme (RGC/ECS, HKSAR), Janssen, and Pfizer; internal funding from the University of Hong Kong; and consultancy fees from Merck Sharp & Dohme and Pfizer; she is also a non-executive director of Advanced Data Analytics for Medical Science (ADAMS) Limited Hong Kong; all are unrelated to this work. ICK Wong received research funding outside the submitted work from Amgen, Bristol-Myers Squibb, Pfizer, Janssen, Bayer, GSK, Novartis, Takeda, the Hong Kong Research Grants Council, the Hong Kong Health and Medical Research Fund, National Institute for Health Research in England, European Commission, National Health and Medical Research Council in Australia, and the European Union’s Seventh Framework Programme for research and technological development. He has also received consulting fees from IQVIA, the WHO, and expert testimony for Appeal Court in Hong Kong over the past 3 years. He is an advisory member of Pharmacy and Poisons Board, Expert Committee on Clinical Events Assessment Following COVID-19 Immunization, and the Advisory Panel on COVID-19 Vaccines of the Hong Kong Government. He is also a non-executive director of Jacobson Medical Hong Kong, Advanced Data Analytics for Medical Science (ADAMS) Limited, and OCUS Innovation Limited (Hong Kong, Ireland, and UK), and the founder and a director of Therakind Limited (UK). Other authors declared no competing interests related to this study.

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Supplementary Information

Additional file 1: figure s1..

Study schema to illustrate the linkage between analyses.

Additional file 2: Table S1.

Age and sex distribution of patients newly diagnosed with depression between 2014 and 2022. Table S2. Quarterly age-standardized incidence and counts of patients newly diagnosed with depression between 2014 to 2022. Table S3. Quarterly counts of accident & emergency visit among incident cohorts between 2014 and 2022. Table S4. Quarterly counts of inpatient admission among incident cohorts between 2014 and 2022. Table S5. Quarterly counts of inpatient stay among incident cohorts between 2014 and 2022. Table S6. Quarterly counts of outpatient all-cause visit among incident cohorts between 2014 and 2022. Table S7. Quarterly counts of outpatient psychiatric-related visit among incident cohorts between 2014 and 2022. Table S8. Quarterly rates of accident & emergency visit among incident cohorts between 2014 and 2022. Table S9. Quarterly rates of inpatient admission among incident cohorts between 2014 and 2022. Table S10. Quarterly rates of inpatient stay among incident cohorts between 2014 and 2022. Table S11. Quarterly rates of outpatient all-cause visit among incident cohorts between 2014 and 2022. Table S12. Quarterly rates of outpatient psychiatric-related visit among incident cohorts between 2014 and 2022. Table S13. Sensitivity analysis results of ITS analysis of pandemic impact on the ongoing healthcare resource utilization among the 2014-2016 cohorts by adjusting for the fifth-wave outbreak. Table S14. Sensitivity analysis results of ITS analysis of pandemic impact on the ongoing healthcare resource utilization among the 2014-2017 cohorts (changing the defined disease duration prior to the pandemic from 3 years to 2 years).

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Chan, V.K.Y., Chai, Y., Chan, S.S.M. et al. Impact of COVID-19 pandemic on depression incidence and healthcare service use among patients with depression: an interrupted time-series analysis from a 9-year population-based study. BMC Med 22 , 169 (2024). https://doi.org/10.1186/s12916-024-03386-z

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Cognitive profile in multiple sclerosis and post-COVID condition: a comparative study using a unified taxonomy

• Cristina Delgado-Alonso 1 ,
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• Central nervous system infections
• Multiple sclerosis

Post-COVID condition (PCC) and multiple sclerosis (MS) share some clinical and demographic features, including cognitive symptoms and fatigue. Some pathophysiological mechanisms well-known in MS, such as autoimmunity, neuroinflammation and myelin damage, have also been implicated in PCC. In this study, we aimed to compare the cognitive phenotypes of two large cohorts of patients with PCC and MS, and to evaluate the relationship between fatigue and cognitive performance. Cross-sectional study including 218 patients with PCC and 218 with MS matched by age, sex, and years of education. Patients were evaluated with a comprehensive neuropsychological protocol and were categorized according to the International Classification of Cognitive Disorders system. Fatigue and depression were also assessed. Cognitive profiles of PCC and MS largely overlapped, with a greater impairment in episodic memory in MS, but with small effect sizes. The most salient deficits in both disorders were in attention and processing speed. The severity of fatigue was greater in patients with PCC. Still, the correlations between fatigue severity and neuropsychological tests were more prominent in the case of MS. There were no differences in the severity of depression among groups. Our study found similar cognitive profiles in PCC and MS. Fatigue was more severe in PCC, but was more associated with cognitive performance in MS. Further comparative studies addressing the mechanisms related to cognitive dysfunction and fatigue may be of interest to advance the knowledge of these disorders and develop new therapies.

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Introduction.

Cognitive dysfunction and fatigue are commonly reported after the acute phase of SARS-CoV-2 infection and have been emphasized as the most frequent symptoms by the World Health Organization in the post-COVID condition or Long-COVID (PCC) 1 . Several studies have confirmed the presence of objective cognitive deficits in neuropsychological assessments 2 , 3 . PCC occurs mainly in middle and working age, and women are predominant 4 , 5 , 6 , 7 . Cognitive deficits are more prominent in attention and processing speed, episodic memory and executive function and have been linked to structural and functional brain changes in neuroimaging studies 8 , 9 , 10 , 11 , 12 , 13 . A longitudinal study showed greater reductions in cortical thickness and brain volumes in patients after COVID-19 than in healthy controls compared with neuroimaging acquired before the pandemic 14 . A recent study has also associated fatigue in PCC with structural imaging changes in the thalamus and basal ganglia 15 .

Similarly, most patients with Multiple Sclerosis (MS) also report fatigue and cognitive deficits. Cognitive deficits are especially focused on attention and processing speed impairment, followed by executive function and episodic memory. MS is a recognized autoimmune disorder, and cognitive deficits have been linked to cortical and subcortical structural and functional brain damage 16 , 17 .

Although the pathophysiology of PCC and the neurological symptoms of PCC is still unknown, several studies suggest mechanisms of neuroinflammation, autoimmune disorders, myelin dysregulation, and reactivation of another virus (such as Epstein-Barr infection) 18 , 19 . Although in a different clinical course and extent, these mechanisms are at least partially shared with MS. Besides, the role of Epstein-Barr virus or other viral infections in the development of MS and/or disease activity is supported by some studies 20 . Overall, this suggests an interest in evaluating the similarities and differences in the cognitive profiles of patients with PCC and MS. Comparative studies may also be useful to contextualize the cognitive deficits found in PCC, which have important socioeconomic consequences 21 . However, to our knowledge, there are no studies comparing cognitive dysfunction associated with PCC and MS. In addition, the relationship between fatigue and cognitive performance is still unclear. Previous studies in MS have found that cognitive tests assessing vigilance and alertness are more related to fatigue, which could be caused by shared mechanisms associated with brain atrophy and neurochemical dysfunction 22 . In PCC, fatigue has also correlated with some attentional tests 23 , 24 . Thus, this study aimed to compare the cognitive phenotypes of two large cohorts of patients with PCC and MS that were examined with the same neuropsychological protocol. We also aimed to evaluate the relationships between fatigue, and cognitive performance in the two cohorts. We also compared the frequency of depression, which is a relevant factor in both MS and PCC 25 , 26 , 27 .

Study design and participants

We conducted a cross-sectional investigation including patients with PCC and MS involved in previous research studies evaluating the cognitive characteristics of these disorders 28 , 29 . Patients were recruited from specific clinical programs dedicated to diagnosing and treating individuals with PCS and MS, where comprehensive neuropsychological assessment were integrated into the clinical protocol. The research protocol was approved by the Ethics Committee of our center (Comité de Ética de la Investigación con Medicamentos del Hospital Clínico San Carlos). Written informed consent was obtained from all participants.

Patients with PCC met the following criteria: (a) Diagnosis of COVID-19 confirmed by RT-PCR; (b) cognitive complaints or fatigue in close relationship with SARS-CoV-2 infection; (c) WHO criteria for Post-COVID-19 condition 1 . Exclusion criteria were as follows: (a) any cognitive complaint before COVID-19; (b) any medical, systemic, neurological or developmental comorbidity potentially linked to cognitive dysfunction; (c) history of alcohol or drug abuse; (d) neuropsychiatric disorders not attributable to PCC; (e) any sensory or motor disorder potentially biasing assessments.

Patients with MS met the following criteria: (a) diagnosis of MS according to the 2010 McDonald criteria 30 ; (2) age between 18 and 80 years. Exclusion criteria were as follows: (a) a relapse within the previous two months or active treatment with corticosteroids; (b) any other medical, systemic, neurological or developmental comorbidity potentially causing cognitive impairment; (c) history of alcohol or drug abuse; (d) neuropsychiatric disorders not attributable to MS; (e) sensory or motor disorder biasing assessments.

From an initial sample of 240 patients with PCC (mean age 48.42 ± 10.84 years, 77.9% of women, mean time since the acute infection of 17.48 ± 8.43 months), and 298 patients with MS (mean age 48.09 ± 9.84 years, 69.8% of women, mean duration of disease of 15.87 ± 7.85 years), a matched sample of 436 participants (218 per group) was obtained. The main clinical and demographic characteristics of each group and the vaccination status are presented in Table 1 . Time of SARS-CoV-2 infection (month and year) leading to PCC and time of assessments is shown in Supplementary Fig.  1 .

Neuropsychological and behavioral assessments

Patients were evaluated with a comprehensive neuropsychological protocol that is mainly based on the cognitive tests included in the Neuronorma battery. This was a set of neuropsychological tests co-normed in our country in older and young people 31 , 32 and has been validated in several diseases 33 . Previous works by our group implemented this battery to describe the cognitive profile in patients with MS and recently in PCC 28 , 29 . This battery was administered by trained neuropsychologists. The following tests were shared in the assessment of patients with PCC and MS and were included in the present study: forward and backward digit span, Corsi block-tapping test, Symbol Digit Modalities Test, Boston Naming Test (BNT), Judgment Line Orientation (JLO), Rey-Osterrieth Complex Figure (ROCF) (copy and recall at 3, 30 min, and recognition), Free and Cued Selective Reminding Test (FCSRT) (total free recall, total recall, delayed free recall, and delayed total recall), verbal fluencies (animals and words beginning with “ p ” and “m” in 1 min each one), Stroop Color-Word Interference Test.

Furthermore, patients were evaluated with the Modified Fatigue Impact Scale (MFIS) 34 . MFIS contains 21 items related to cognitive, physical, and psychosocial dimensions of fatigue, which are scored using a Likert-type scale. The assessment evaluates the impact of fatigue in the past 4 weeks. Additionally, depression was assessed with the Beck Depression Inventory-II 32 . Following previous literature, we used a cut-off of ≥ 38 to delineate fatigue and ≥ 19 to define moderate to severe depression 34 , 35 .

Statistical analysis

Statistical analysis was conducted using IBM(R) SPSS v26.0, JASP v0.16.1 and R software 36 . Figures were prepared using the ggplot2 package (v3.4.1). Using MedCalc 20.218, patients with MS and PCC were matched 1:1 according to sex, age (± 3 years), and years of education (± 3 years). The two independent samples t-test was used to compare the two groups. Effect sizes were estimated with Cohen’s d, and were classified as small (d = 0.2–0.49), moderate (d = 0.5–0.79), and large (d ≥ 0.8).

We calculated the percentage of impairment of each test according to the normative data correcting by age, years of education, and sex when needed. Normative data are based on a multicenter study conducted in Spain before the pandemic 31 , 32 . In addition, we used the criteria proposed by IC-CoDiMS and IC-CoDi-COVID groups to describe the cognitive phenotypes in patients with MS and PCC, respectively 37 , 38 . In this taxonomy, initially developed for epilepsy as IC-CoDE 39 , 40 , a domain is considered impaired when two tests within the same domain fall below the cutoff. For this study, we used -1 S.D as the cutoff to define impairment, according to the findings of the previous studies in both MS and PCC using these criteria 37 , 38 . Five cognitive domains are considered: attention/processing speed, executive function, language, visuospatial, and episodic memory. Then, according to the number of domains impaired, the patients are classified as: cognitively intact, single-domain impairment, bi-domain impairment, or multi-domain impairment (≥ 3 impaired domains). The tests specified in Table 2 were used to describe each cognitive domain. The chi-squared test was used to compare cognitive phenotypes between MS and PCC.

Pearson’s coefficient was used to estimate the correlations between fatigue and neuropsychological tests in PCC and MS. Coefficients < 0.40 were interpreted as a weak correlation, 0.40–0.69 as moderate, and > 0.69 as strong. Fisher r-to-z transformation was calculated to compare between correlation coefficients.

A p -value < 0.05 was considered statistically significant. Due to the number of cognitive tests in the neuropsychological protocol, we also specified those contrasts statistically significant after False-Discovery Rate (FDR) correction in the comparison of cognitive performance between PCC and MS.

Ethical approval

This study was approved by the Ethics and Research Committee from our centre and was performed according to the Declaration of Helsinki and its later amendments.

Comparison between PCC and MS

Patients with MS showed lower raw scores compared to PCC in Corsi forward and backwards, FCSRT (total free recall), ROCF memory at 3 and 30 min, and semantic verbal fluency. Conversely, PCC showed greater fatigue severity measured with MFIS. There were no statistically significant differences in the other neuropsychological tests and depressive symptoms. Effect size was moderate for fatigue, and low for the other significant neuropsychological tests. All results are shown in Table 3 .

Cognitive phenotypes

There were no statistically significant differences in the cognitive phenotypes (χ 2 3.014, p  = 0.389). Specifically, 127 (58.25%) of PCC patients were regarded as cognitively intact, and 91 (41.74%) as cognitively impaired, 43 (19.72%) showed single-domain, 27 (12.38%) bi-domain, and 21 (9.63%) generalized impairment. Patients with MS were classified as cognitively intact in 112 cases (51.37%), and cognitively impaired in 106 (48.62%). Of those with impairment, 44 (20.18%) showed single-domain, 38 (17.43%) bi-domain, and 24 (11.00%) generalized impairment (Fig.  1 ).

Circle chart representing the cognitive phenotypes in PCC and MS.

Regarding the specific cognitive domains, 63 (28.89%) of PCC and 81 (37.15%) of MS showed impairment in attention/processing speed (χ 2  = 3.36, p  = 0.067); 24 (11.00%) and 37 (16.97%) in episodic memory (χ 2  = 3.22, p  = 0.073); 41 (18.8%) and 46 (21.10%) in executive function (χ 2  = 0.359, p  = 0.549); 14 (6.42%) and 16 (7.33%) in visuospatial function (χ 2  = 0.143, p  = 0.705); and 22 (10.09%) and 26 (11.92%) in language (χ 2  = 0.375, p  = 0.541). The frequency of impairment of each individual test is shown in Supplementary Fig.  2 . Patients with MS showed higher frequency of impairment in Stroop trial 1 (χ 2  = 6.29, p  = 0.012), semantic fluency (χ 2  = 9.86, p  = 0.002), letter fluency (χ 2  = 9.42, p  = 0.002), ROCF at 3 and 30 min (χ 2  = 6.12, p  = 0.013 and χ 2  = 13.28, p  < 0.001, respectively), and FCSRT total free recall (χ 2  = 7.20, p  = 0.007). The other tests, including SDMT, showed no statistically significant differences ( p  > 0.05).

Comparison of cognitive profiles within the groups with cognitive impairment

We also compared those patients meeting the criteria for cognitive impairment with PCC and MS. Patients with MS showed lower scores in ROCF memory at 3 min and 30 min and semantic fluency in age- and education-adjusted scaled scores (Supplementary Table 1 ). Patients with PCC showed greater severity of fatigue (59.95 ± 14.98 vs 54.47 ± 20.89, t = 2.13, p  = 0.034). As depicted in Fig.  2 , the represented cognitive profile showed a more prominent impairment in those tests associated with attention and information processing speed.

Violin plots representing the adjusted scaled scores (mean 10, standard deviation 3) in each cognitive test in patients with PCC (green) and MS (red) classified as cognitively impaired. The dots represent the mean of each group. DSF digit span forward, DSB digit span backward, CF Corsi forward, CB Corsi backward, SDMT symbol digit modalities test, FCSRT (free and cued selective reminding test, fr1 free recall 1, tfr total free recall, tr total recall, dfr delayed free recall, dtr delayed total recall, ROCF3 Rey-Osterrieth complex figure (memory at 3 min), ROCF30 Rey-Osterrieth complex figure (memory at 30 min), ROCFr Rey-Osterrieth complex figure (memory recognition), ROCFc Rey-Osterrieth complex figure (copy accuracy), ROCFct Rey-Osterrieth complex figure (copy time), JLO judgment line orientation, BNT Boston naming test, SF semantic fluency (animals), LF letter fluency (words beginning with “ p ”).

Correlations between fatigue and neuropsychological tests

All correlations are shown in Fig.  3 . In PCC, MFIS (total score) showed weak correlations with SDMT, FCSRT (total free recall and total recall), Stroop (parts 1, 2, and 3) and semantic and letter fluency. In MS, MFIS (total score) showed moderate correlations with SDMT, FCSRT (free delayed recall and total delayed recall), Stroop test (parts 1 and 2); and weak correlations were found with almost all the other tests.

Heatmap showing correlations between MFIS (total score) and neuropsychological tests scores in PCC and MS. The size and direction of the correlation are shown in the right vertical label. DSF digit span forward, DSB digit span backward, CF Corsi forward, CB Corsi backward, SDMT symbol digit modalities test, FCSRT (free and cued selective reminding test), fr1 free recall 1, tfr total free recall, tr total recall, dfr delayed free recall, dtr delayed total recall, ROCF3 Rey-Osterrieth complex figure (memory at 3 min), ROCF30 Rey-Osterrieth complex figure (memory at 30 min), ROCFr Rey-Osterrieth complex figure (memory recognition), ROCFc Rey-Osterrieth complex figure (copy accuracy), ROCFct Rey-Osterrieth complex figure (copy time), JLO judgment line orientation, BNT Boston naming test, SF semantic fluency (animals), LF letter fluency (words beginning with “ p ”).

Patients with MS showed higher correlations than PCC in the following tests: digit span forward (z = 3.02, p  = 0.002), digit span backward (r = 3.53, p  < 0.001), Corsi forward (Z = 2.45, p  = 0.014), SDMT (z = 2.82, p  = 0.004), Boston Naming Test (z = 2.81, p  = 0.005), FCSRT recall trial 1 (Z = 2.15, p  = 0.015), FCSRT total free recall (Z = 2.38, p  = 0.017), FCSRT total recall (z = 3.63, p  < 0.001), FCSRT delayed free recall (Z = 4.94, p  < 0.001), FCSRT delayed total recall (Z = 3.93, p  < 0.001), ROCF (memory at 3 min) (Z = 2.26, p  = 0.011), ROCF (memory at 30 min) (z = 2.1, p  = 0.035), semantic fluency (Z = 2.03, p  = 0.04), letter fluency (M-words) (z = 2.64, p  = 0.008), letter fluency (R-words) (z = 3.02, p  = 0.002), Judgment Line Orientation (Z = 3.26, p  = 0.001). There were no statistically significant differences ( p  > 0.05) in the comparison of correlation coefficients in Stroop (trials 1, 2, and 3), Corsi backward, ROCF (copy accuracy and time), ROCF (memory recognition), and letter fluency ( P -words).

In this study, we examined the existence of differences in cognitive characteristics between PCC and MS, and the relationship between cognitive function and fatigue. We used two large cohorts of patients that were evaluated with a common neuropsychological protocol. Our study found a significant overlap in cognitive profile between both diseases. Importantly, attention and processing speed were the most pronounced deficits in both disorders, which is consistent with previous studies 2 , 37 , 41 , 42 . Few differences were found in episodic memory tests, which were more impaired in the group of patients with MS than PCC. Similarly, semantic fluency was also more impaired, which could also be linked to the greater impairment of episodic memory 43 . However, effect sizes for these tests were small, confirming that MS and PCC present a very similar cognitive profile.

We applied a novel approach using an international classification of cognitive disorders that is being implemented across several disease groups 37 , 38 , 40 , 44 . This classification system is based on a five-domain cognitive model (attention/processing speed, executive function, episodic memory, visuospatial function, and language) and provides a working definition of impairment to identify cognitive phenotypes and improve cognitive diagnostics. This taxonomy has found reproducible findings across several independent cohorts examined with different neuropsychological batteries in epilepsy 40 , multiple sclerosis 37 and PCC 38 . It has also shown favorable cross-cultural properties in diverse settings 36 . Our study also supports the use of this taxonomy as a valid method for comparative research across disorders.

By comparing both disorders, the similarities in the cognitive characteristics and the severity of deficits contribute to contextualizing the cognitive dysfunction in PCC. In this regard, our findings suggest that cognitive deficits in PCC are almost as pronounced and prevalent as in MS, and fatigue is even more severe, supporting the mounting evidence that fatigue and cognitive dysfunction are associated with occupational issues and socioeconomic consequences 45 , 46 .

The severity and frequency of fatigue was greater in patients with PCC. Interestingly, correlations between MFIS total score (evaluating fatigue impact in the last 4 weeks) and neuropsychological tests were larger in the case of MS. However, the cognitive tests that showed stronger correlations with MFIS were similar in both disorders (e.g., Stroop). This may suggest common mechanisms and neural underpinnings in fatigue and cognitive dysfunction in both disorders, as has been recently described 9 . This opens the way to test new therapies for fatigue based on their association with functional brain changes, such as non-invasive brain stimulation, which have shown positive effects in two clinical trials 47 , 48 . However, at the same time, the lower correlation with neuropsychological tests and the greater severity of fatigue in PCC suggest the existence of other mechanisms (probably not dependent on the central nervous system and including systemic processes such as immune mechanisms, mitochondrial dysfunction or muscle abnormalities) involved in the pathophysiology of fatigue in PCC 24 , 49 , 50 . In contrast, fatigue in MS would be more dependent on central mechanisms.

Another interesting result is the lack of significant differences in the severity and frequency of depressive symptoms. Although neuropsychiatric symptoms have been especially emphasized in PCC, most studies did not include a control group 51 . The prevalence of depression is higher in MS than in the general population, and has been associated with several factors, including genetic and immunological factors, brain changes, and psychosocial factors 52 . Similarly, in PCC, proinflammatory factors and psychosocial factors have been hypothesized, but clear evidence about the pathophysiology of depression is still lacking.

Our study has some limitations. First, although the protocol includes several tests of the main cognitive domains, we acknowledge the possibility of differences between groups if other specific tests are used. In this regard, a more thorough analysis of attention and executive function subdomains may be of interest to further characterize the cognitive mechanisms impaired in each disorder. In this study, we selected only those tests shared by both cohorts to avoid potential differences in the frequency of impairment to the length of the battery or the number of neuropsychological tests and scores. Second, fatigue was only assessed with MFIS, which mainly evaluates the impact of fatigue in daily living. More comprehensive questionnaires may be of interest to evaluate potential differences in the clinical characteristics of fatigue across disorders. Additionally, it could also be of interest to evaluate the feeling of fatigue on the same day of the examination because MFIS considers the fatigue severity in the 4 weeks before the assessment. Third, our study is performed in a single center. However, demographic characteristics and degree of impairment in both PCC and MS are consistent with previous studies of the literature, suggesting that both cohorts are representative of these disorders. In this regard, the most important proportion were infected during the first waves (especially the first in March 2020) and before vaccines were available. Furthermore, we must acknowledge the possibility of selection bias, particularly concerning MS, where individuals with more pronounced motor and cognitive impairments may be less inclined to undergo extensive neuropsychological evaluations. Nevertheless, our study was conducted within a framework where comprehensive neuropsychological assessments are standard practice for both MS and PCS. Additionally, the demographic characteristics of our participants closely resemble those of other large-scale studies published in the field 53 , 54 .

In conclusion, our study finds similar cognitive profiles in PCC and MS, which are mainly characterized by attention and processing speed deficits. Fatigue was more severe in PCC, but the relationship between fatigue and cognitive function was greater in the case of MS. Further comparative studies addressing the mechanisms associated with cognitive dysfunction and fatigue may be of interest to advance the knowledge of these disorders and develop new therapies.

Data availability

The datasets generated and analyzed are available from the corresponding author on reasonable request.

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Acknowledgements

The authors acknowledge all the participants in this study, and specifically the association of patients with long-COVID “Asociación Madrileña de Covid Persistente (AMACOP)” for their support.

This research has received funding from the Nominative Grant FIBHCSC 2020 COVID-19 (Department of Health, Community of Madrid) and Fundación para el Conocimiento Madri + d (Healthstart plus program, Community of Madrid, REACT-EU funds) through project G63-HEALTHSTARPLUS-HSP4. Jordi A Matias-Guiu is supported by Instituto de Salud Carlos III through the project INT20/00079 and INT23/00017 (co-funded by European Regional Development Fund “A way to make Europe”). María Valles-Salgado is supported by the Instituto de Salud Carlos III through a predoctoral contract (FI20/000145) (co-funded by European Regional Development Fund “A way to make Europe”). Maria Diez-Cirarda is funded by a Sara Borrell postdoctoral fellowship from the Instituto de Salud Carlos III (CD22/00043) (co-funded by European Regional Development Fund “A way to make Europe”). Silvia Mas-Oliver is supported by Fundación para el Conocimiento madri + d through project G63-HEALTHSTARPLUS-HSP4.

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Cristina Delgado-Alonso, Alfonso Delgado-Alvarez, María Díez-Cirarda, Silvia Oliver-Mas, Constanza Cuevas, Paloma Montero-Escribano, María José Gil-Moreno, Juan Ignacio López-Carbonero, Jorge Matias-Guiu & Jordi A. Matias-Guiu

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Conceptualization: C.D.A., J.M.G., J.A.M.G. Methodology: C.D.A., M.D.C., B.P.H., J.A.M.G. Data curation: all. Investigation: all. Formal analysis: C.D.A., A.M.R.L., J.A.M.G. Project administration: J.M.G., J.A.M.G. Writing original draft: C.D.A., J.A.M.G. Writing – review & editing: A.D.A., M.D.C., J.M.G., B.P.H.

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Delgado-Alonso, C., Delgado-Alvarez, A., Díez-Cirarda, M. et al. Cognitive profile in multiple sclerosis and post-COVID condition: a comparative study using a unified taxonomy. Sci Rep 14 , 9806 (2024). https://doi.org/10.1038/s41598-024-60368-0

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• Assessing quality of direct-to-consumer telemedicine in China: a cross-sectional study using unannounced standardised patients
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• Zhen Zeng 1 ,
• Dong (Roman) Xu 2 ,
• Yiyuan Cai 3 ,
• http://orcid.org/0000-0002-7943-4041 Wenjie Gong 1 , 4 , 5
• 1 HER Team and Department of Maternal and Child Health, Xiangya School of Public Health , Central South University , Changsha , China
• 2 SMU Institute for Global Health (SIGHT), School of Health Management and Dermatology Hospital , Southern Medical University (SMU) , Guangzhou , China
• 3 Department of Epidemiology and Health Statistics, School of Public Health , Guizhou Medical University , Guiyang , China
• 4 Institute of Applied Health Research , University of Birmingham , Birmingham B15 2TT , UK
• 5 Department of Psychiatry , University of Rochester , Rochester , New York , USA
• Correspondence to Professor Wenjie Gong, HER Team and Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, Changsha, Hunan, 410078, China; gongwenjie{at}csu.edu.cn

Direct-to-onsumer telemedicine (DTCT) has become popular as an alternative to traditional care. However, uncertainties about the potential risks associated with the lack of comprehensive quality evaluation could influence its long-term development. This study aimed to assess the quality of care provided by DTCT platforms in China using unannounced standardised patients (USP) between July 2021 and January 2022. The study assessed consultation services on both hospital and enterprise-sponsored platforms using the Institute of Medicine quality framework. It employed 10 USP cases, covering conditions such as diabetes, asthma, common cold, gastritis, angina, low back pain, child diarrhoea, child dermatitis, stress urinary incontinence and postpartum depression. Descriptive and regression analyses were employed to examine platform characteristics and compare quality across platform types. The results showed that of 170 USP visits across 107 different telemedicine platforms, enterprise-sponsored platforms achieved a 100% success in access, while hospital-sponsored platforms had a success rate of only 47.5% (56/118). Analysis highlighted a low overall correct diagnosis rate of 45% and inadequate adherence to clinical guidelines across all platforms. Notably, enterprise-sponsored platforms outperformed in accessibility, response time and case management compared with hospital-sponsored platforms. This study highlights the suboptimal quality of DTCT platforms in China, particularly for hospital-sponsored platforms. To further enhance DTCT services, future studies should compare DTCT and in-person care, aiming to identify gaps and potential risks associated with using DTCT as alternatives or supplements to traditional care. The potential of future development in enhancing DTCT services may involve exploring the integration of hospital resources with the technology and market capabilities of enterprise-sponsored platforms.

• Health services research
• Quality measurement
• Performance measures
• Patient-centred care

https://doi.org/10.1136/bmjqs-2024-017072

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Introduction

Direct-to-consumer telemedicine (DTCT) signifies a revolutionary global healthcare delivery model. It involves patients independently initiating medical services remotely, engaging directly with healthcare providers through text messaging or video/phone calls. By bypassing traditional intermediaries like referral clinicians or facilitators, DTCT empowers patients to access medical care swiftly and efficiently. 1 In China, DTCT is burgeoning, boasting over 1700 registered platforms, 2 each serving as an individual website or application for DTCT services. These platforms are categorised into two main types: hospital sponsored and enterprise sponsored. Hospital-sponsored platforms, associated with single physical hospitals, primarily use in-house medical staff and offer streamlined functions due to resource limitations. In contrast, enterprise-sponsored platforms, supported by larger corporations, provide access to a wider network of licensed physicians and offer a diverse range of functions with sophisticated user interfaces. 3 Despite enhanced accessibility and convenience compared with in-person care, DTCT faces quality challenges, such as communication difficulties and antibiotic misuse. 4 5 However, research on DTCT quality remains limited, especially in the context of China. To address this gap, our study employs unannounced standardised patients (USPs)—individuals trained and validated to portray specific medical conditions in a consistent and standardised manner 6 7 —to assess DTCT quality in China across different platform types based on the Institute of Medicine (IOM) quality framework.

This cross-sectional study examined both types of DTCT platforms that offered Chinese language services. The study was conducted between July 2021 and January 2022. Prior informed consent was waived due to minimal risk, and all analyses were performed on fully deidentified aggregated data. 8

To assess consultation service quality, we employed 10 different USP cases, each representing a specific medical condition (details in online supplemental eMethods and online supplemental example of a USP case ). Following thorough training and assessment, we selected 15 qualified USPs from the initial pool of 25 candidates. Each case was assigned at least one USP. As USPs’ initial requests for physician consultations could be denied for various reasons, mirroring real-world scenarios, each case made consultation requests until they completed at least five consultations on each platform type. The USPs captured screenshots and recorded the consultation process, including any failed attempts and reasons for failure. We evaluated the access success rate and assessed consultation quality using the IOM quality framework 9 ( table 1 ).

Supplemental material

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Quality outcome indicators

We conducted descriptive and regression analyses to evaluate platform characteristics and quality outcomes. Regression analyses compared quality differences using ordinary least squares and logistic regressions, with hospital-sponsored platforms as the benchmark and controlling for patient case fixed effects. Adjusted differences and 95% CIs were reported, adjusted for platform-level clustering. Statistical significance was set at α=0.05. Stata SE (V.16.0) was used for all analyses.

Our study involved 170 visits, with 52 visits on 10 enterprise-sponsored platforms and 118 visits on 97 hospital-sponsored platforms (summarised in online supplemental eTable 1 ). The overall access rate was 63.5% (108/170). Enterprise-sponsored platforms achieved a 100% access rate (52/52), significantly higher than hospital-sponsored ones (56/118, 47.5%) (p<0.001). Common reasons for unsuccessful consultations included incomplete functions, like platforms claiming to offer DTCT services but lacking an accessible feature for initiating online consultations with physicians (25/62, 40.3%), and no response (13/62, 21.0%).

Of 108 successful consultations, 49 consultations (45%) received a correct diagnosis, while adherence to published guidelines was low for consultation (15%) and management decisions (31%). On average, physicians took 3 hours and 47 min to respond, with the total interaction time spanning 12 hours and 19 min. After controlling for disease case fixed effects and adjusting SEs for clustering at platform level, enterprise-sponsored platforms had higher rates of completed management decisions, shorter response times and higher costs ( table 2 ).

Comparison on main quality outcomes between platform types for successful consultations

In this study, we rigorously evaluated the quality of care across two types of DTCT platforms in China using USPs. With 108 successful visits out of 170 attempts, we found a diagnostic accuracy of approximately 45%, along with a decline in completion rates for recommended management decisions to 31%. This may be linked to the limited inquiries during USP encounters, with clinicians asking only about 15% of recommended consultation questions per visit. Besides traditional quality metrics, the timeliness of DTCT services is concerning, with a response time of 3 hours and 47 min and an overall interaction time of 12 hours and 19 min. In China, DTCT predominantly operates asynchronously, leading to these extended durations. Despite offering a more flexible alternative to in-person counselling by eliminating the need for travel, prolonged waiting times may still impact user satisfaction and the perceived effectiveness of DTCT platforms. Future research should prioritise enhancing the timeliness of DTCT services to ensure prompt access and timely interactions. The patient-centredness score averaging 2.4, indicating a medium to low level of patient-centred care, 10 is potentially influenced by less satisfactory outcomes discussed earlier in terms of effectiveness, safety and timeliness. These findings raise concerns about how effective DTCT services are. Further evaluation, including a direct comparison with in-person care, is needed for a clearer understanding of their quality. This can guide improvement measures, especially when DTCT services act as alternatives or supplements to traditional in-person care.

Notably, enterprise-sponsored platforms achieved 100% access success, surpassing hospital-sponsored ones at 47.5%. They exhibited superior performance in response times and completion rates for management decisions. Despite recent growth and policy support, 3 hospital-sponsored platforms seem to be in early developmental stages, potentially limiting medical resource accessibility. These findings challenge a marketing survey suggesting a preference for hospital-sponsored platforms, 11 emphasising higher access denial risks and less timely responses for consumers on these platforms.

This study was limited by the use of a uniform USP for each case. Using standardised scenarios with different USPs potentially allows for a comprehensive assessment of equity. However, due to constraints imposed by scripted scenarios in our study, this aspect was not explored.

Our study highlights the suboptimal quality of DTCT in China, specifically disparities between hospital-sponsored and enterprise-sponsored platforms. These findings likely echo broader challenges and principles inherent in DTCT globally. As DTCT gains momentum after COVID-19, future research becomes critical to effectively address these issues.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by Xiangya School of Public Health (IRB No XYGW-2021-37). Prior informed consent was waived due to minimal risk and no individually identifiable information on physicians.

Acknowledgments

We sincerely thank Xiaohui Wang, Yaolong Chen, Yun Lu, Xiaojing Fan, Zhongliang Zhou, Jay Pan, and Chengxiang Tang for their unwavering leadership in development and management the SP cases. We also apperciate Lu Liu, Chunping Li, and Huanyu Hu for their their diligent efforts as project assistants, as well as all the standardized patients and study coordinators for their hard work.

• Elliott T ,
• iiMedia Report
• Resneck JS ,
• Steuer M , et al
• Gidengil CA , et al
• Peabody JW ,
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• Colliver JA ,
• Rhodes KV ,
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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

Contributors WG conceived the study. ZZ coordinated the daily implementation of this study under the supervision of DRX, YC and WG. ZZ carried out data analysis and composed the initial manuscript draft, receiving guidance from WG and DRX. All authors contributed to critical review of the manuscript and approved the final draft.

Funding This study was funded by China Medical Board (20-368), Swiss Agency for Development and Cooperation (81067392) and the National Natural Science Foundation of China (82273643).

Competing interests None declared.

Provenance and peer review Not commissioned; internally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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