research on obesity rates

When did obesity increase? How do rates vary across the world? What is the health impact?

By Hannah Ritchie and Max Roser

This page was first published in August 2017 and last revised in January 2024.

Obesity is a major risk factor for a range of diseases, including heart disease, stroke, diabetes, and various types of cancer.

It is most commonly measured using the body mass index (BMI) scale.

On this page, you will find global data and research on obesity — its prevalence, drivers, health consequences, and trends over time.

See all interactive charts on Obesity ↓

Related topics:

Food Supply

How had the availability of food changed over time? How does food supply vary across the world today?

Hunger and Undernourishment

How does undernourishment vary across the world? How has it changed over time?

Micronutrient Deficiency

Food is not only a source of energy and protein, but also micronutrients — vitamins and minerals — which are essential to good health. Who is most affected by the "hidden hunger" of micronutrient deficiency?

Other research and writing on obesity on Our World in Data:

What is obesity and how is it measured?

What is obesity, obesity is commonly measured using the body mass index (bmi) scale.

Obesity can be measured in different ways, but the most common is the Body Mass Index (BMI) scale, which is calculated based on a person’s height and weight.

BMI is defined as their weight in kilograms divided by the square of their height in meters (kg/m 2 ). 1

BMI values are used to define whether an individual is considered to be underweight, healthy, overweight, or obese.

In adults, the WHO defines these categories using the cut-off points: an individual with a BMI between 25 and 30 is considered "overweight", while a BMI greater than 30 is defined as "obese". 2 However, different cut-off points are used for other groups, such as children and pregnant women.

Read more in our article:

Obesity is a leading risk factor for poor health outcomes. How is obesity defined and measured?

Obesity is one of the leading risk factors for early death

Obesity is responsible for millions of premature deaths each year.

Obesity is one of the world's largest health problems — one that has shifted from being a problem in rich countries to a health challenge around the world.

The Global Burden of Disease is a major global study on the causes and risk factors for death and disease. 3 The authors of the study have estimated the annual number of deaths attributed to a wide range of risk factors, as you can see below.

Obesity — defined as having a high body mass index — is a risk factor for several of the world's leading causes of death, including heart disease, stroke, diabetes, and various types of cancer. 4

As you can see, it’s estimated that around 5 million people died prematurely in 2019 as a result of obesity, which makes it one of the leading causes of death worldwide.

How do researchers estimate the death toll caused by each risk factor, whether it’s smoking, obesity, or air pollution?

Risk factors are important to understand because they can help us identify how to save lives. How do researchers estimate their impact?

The global distribution of health impacts from obesity

What share of global deaths are the result of obesity.

Globally, it’s estimated that almost 10% in 2019 resulted from the consequences of obesity — this was almost double the share in 1990.

This share varies significantly across the world. In the map here we see the share of deaths attributed to obesity across countries.

Across many middle-income countries — such as in Eastern Europe, Central Asia, North Africa, and Latin America — more than 15% of deaths were attributed to obesity in 2019.

This results from both a high prevalence of obesity, as well as poorer overall health and healthcare systems compared to high-income countries with similarly high levels of obesity.

In 2019, in most high-income countries, the share of deaths attributed to obesity was in the range of 8 to 10%. In many middle-income countries, this share was almost twice as high.

In contrast, across several low-income countries — especially across Sub-Saharan Africa — it’s estimated that obesity accounts for under 5% of deaths.

There is a large difference in death rates from obesity across the world

Death rates from obesity can also help us understand differences in the impact of obesity between countries and over time.

In the map here you can see differences in death rates from obesity across the world, per 100,000 people in the population.

Death rates tend to be higher in Eastern Europe, Central Asia, North Africa, and Latin America.

In contrast, they tend to be much lower in Western Europe, Australia, and East Asia.

When we look at the relationship between death rates and the prevalence of obesity we find a positive one: death rates tend to be higher in countries where more people are obese.

But what we also notice is that for a given prevalence of obesity, death rates can vary by a factor of four. While around a quarter of people in Russia and Norway are obese, death rates in Russia are much higher.

It's not only the prevalence of obesity that plays a role but also other factors — such as underlying health, other confounding risk factors (such as alcohol , drugs , smoking , and other lifestyle factors), and healthcare systems.

What share of adults are obese?

Obesity varies widely worldwide and has become more common.

In the chart here, we see the share of adults (aged 18 years and older) who are obese across regions. These estimates are based on survey data and statistical modeling by the World Health Organization (WHO).

Overall we see a pattern roughly in line with prosperity: the prevalence of obesity tends to be higher in richer countries across Europe, North America, and Oceania. Obesity rates tend to be much lower across South Asia and Sub-Saharan Africa.

More than a third of adults in the United States were obese in 2016. In countries such as India and Nigeria, that share was far lower.

The chart also shows the share of adults who are obese has grown over time.

Obesity in men vs. women

See the data in our interactive visualization

What share of adults are overweight?

Globally, it’s estimated that around two-fifths of adults were overweight or obese in 2016. 4

The classification of "overweight" is also defined based on the body-mass index — it refers to BMI values between 25 and 30.

In the map here we see the share of adults who are overweight or obese across countries.

As you can see, the share of people who are overweight tends to be higher in richer countries and lower in poorer countries.

In many high-income countries such as the United States, it’s estimated that over 60% of adults are overweight or obese.

In contrast, across South Asia and Sub-Saharan Africa, it’s estimated that around one in five adults are overweight or obese.

Share of women who are overweight or obese

Body mass index (bmi), mean bmi in adult women.

In the map, here we see the distribution of average (mean) BMI in adult women across the world.

In 2016, the global average (mean) BMI in women was around 25, which is the cut-off for overweight. The average BMI tends to be higher in North and South America and North Africa, and lower in sub-Saharan Africa and Asia.

Mean BMI in adult men

In the map here we see the distribution of mean BMI for adult men across the world.

In 2016, the global average (mean) BMI in men was estimated to be around 25, which is the cut-off for overweight. The average BMI tends to be higher in North and South America and North Africa, and lower in sub-Saharan Africa and Asia.

Childhood obesity

Share of children that are overweight.

Obesity and overweight in children are also measured based on body mass index (BMI).

However, BMI scores are interpreted differently for children and adolescents.

Weight categories are defined by comparing weight to the WHO Growth Standards — a child is defined as overweight if their weight-for-height is more than two standard deviations from the median of the WHO Child Growth Standards. 4

What are the drivers of obesity?

At a basic level, weight gain — eventually leading to being overweight or obesity — is determined by a balance of energy. 5

When we consume more energy — typically measured in calories — than the energy expended to maintain life and carry out daily activities, we gain weight. This is called an “energy surplus”. When we consume less energy than we expend, we lose weight — this is an “energy deficit”.

This means there are two potential drivers of the increase in obesity rates in recent decades — either we eat more (an increase in calorie intake) or we expend less energy in our daily life through lower activity levels. Both elements are likely to play a role in the rise in obesity.

To tackle obesity, interventions that address both energy intake and expenditure are important. 6

Daily supply of calories

Over the past century — but particularly over the past 50 years — the supply of calories has increased across the world.

In the 1960s, the global average supply of calories (that is, the availability of calories for people to eat) was around 2,200 kcal per person per day. By 2013, this had increased to 2800kcal.

Across most countries, energy consumption has therefore increased. Without an increase in energy expenditure, weight gain and obesity tend to rise.

In the chart here, we see the relationship between the share of men who are overweight or obese versus the daily average supply of kilocalories per person.

Overall there is a strong positive relationship: countries with higher rates of overweight tend to have a higher supply of calories.

If you press "play" on the interactive timeline you can see how this has changed for each country over time. Countries tend to move upwards and to the right: the supply of calories has increased as obesity rates have increased.

Is BMI an appropriate measure of weight-related health?

The merits of using BMI as an indicator of body fat and obesity are contested.

A key contention to the use of BMI indicators is that it is a measure of body mass/weight rather than providing a direct measure of body fat.

Whilst physicians continue to use BMI as a general indicator of weight-related health risks, there are some cases where its use should be considered more carefully, which are listed below, and physicians are recommended to evaluate BMI results carefully on an individual basis. 7

• Muscle mass can increase body weight; this means athletes or individuals with a high muscle mass percentage can be deemed overweight on the BMI scale, even if they have a low or healthy body fat percentage;
• Muscle and bone density tends to decline as we get older; this means that an older individual may have a higher percentage body fat than a younger individual with the same BMI;
• Women tend to have a higher body fat percentage than men for a given BMI.

Interactive charts on Obesity

For example, an adult who weighs 70kg and whose height is 1.75m will have a BMI of 22.9. This is calculated as 70kg / 1.75 2 = 70 / 3.06 = 22.9

World Health Organization. BMI Classification.  Global Database on Body Mass Index . Available online .

The latest study can be found at the website of the Lancet here: TheLancet.com/GBD

The 2017 study was published as GBD 2017 Risk Factor Collaborators — "Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017" and is online here .

WHO (2018) — Fact sheet — Obesity and overweight. Updated February 2018. Online here .

Hall, K. D., Heymsfield, S. B., Kemnitz, J. W., Klein, S., Schoeller, D. A., & Speakman, J. R. (2012). Energy balance and its components: implications for body weight regulation.  The American Journal of Clinical Nutrition ,  95 (4), 989-994. Available online .

Hill, J. O., Wyatt, H. R., & Peters, J. C. (2012). Energy balance and obesity .  Circulation ,  126 (1), 126-132.

Centers for Disease Control and Prevention. Body Mass Index: Considerations for Practitioners. Available online .

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• Review Article
• Published: 27 February 2019

Obesity: global epidemiology and pathogenesis

• Matthias Blüher 1  

Nature Reviews Endocrinology volume  15 ,  pages 288–298 ( 2019 ) Cite this article

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The prevalence of obesity has increased worldwide in the past ~50 years, reaching pandemic levels. Obesity represents a major health challenge because it substantially increases the risk of diseases such as type 2 diabetes mellitus, fatty liver disease, hypertension, myocardial infarction, stroke, dementia, osteoarthritis, obstructive sleep apnoea and several cancers, thereby contributing to a decline in both quality of life and life expectancy. Obesity is also associated with unemployment, social disadvantages and reduced socio-economic productivity, thus increasingly creating an economic burden. Thus far, obesity prevention and treatment strategies — both at the individual and population level — have not been successful in the long term. Lifestyle and behavioural interventions aimed at reducing calorie intake and increasing energy expenditure have limited effectiveness because complex and persistent hormonal, metabolic and neurochemical adaptations defend against weight loss and promote weight regain. Reducing the obesity burden requires approaches that combine individual interventions with changes in the environment and society. Therefore, a better understanding of the remarkable regional differences in obesity prevalence and trends might help to identify societal causes of obesity and provide guidance on which are the most promising intervention strategies.

Obesity prevalence has increased in pandemic dimensions over the past 50 years.

Obesity is a disease that can cause premature disability and death by increasing the risk of cardiometabolic diseases, osteoarthritis, dementia, depression and some types of cancers.

Obesity prevention and treatments frequently fail in the long term (for example, behavioural interventions aiming at reducing energy intake and increasing energy expenditure) or are not available or suitable (bariatric surgery) for the majority of people affected.

Although obesity prevalence increased in every single country in the world, regional differences exist in both obesity prevalence and trends; understanding the drivers of these regional differences might help to provide guidance for the most promising intervention strategies.

Changes in the global food system together with increased sedentary behaviour seem to be the main drivers of the obesity pandemic.

The major challenge is to translate our knowledge of the main causes of increased obesity prevalence into effective actions; such actions might include policy changes that facilitate individual choices for foods that have reduced fat, sugar and salt content.

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Obesity Trends

Tracking the Global Epidemic

Once just a problem of wealthy nations, obesity now impacts countries at all economic levels, bringing with it a wave of ill health and lost productivity.

• Worldwide the rate of obesity has nearly doubled since 1980, with just over 200 million adult men and just under 300 million adult women with obesity. ( 1 )
• Obesity rates have been steadily rising in children, too: In 2010, 43 million preschool children had overweight or obesity, a 60 percent increase since 1990. ( 2 )
• A 2014 study of overweight and obesity in children and adults from 1980-2013 found that worldwide, the proportion of adults with overweight or obesity increased, and prevalence also increased substantially in children and adolescents in both developed and developing countries. ( 3 )
• These jumps in child and adult obesity rates show no sign of stopping without dedicated efforts to combat the epidemic.

Of all high income countries, the United States has the highest rates of overweight and obesity, with fully a third of the population obese-a rate projected to rise to around 50 percent by 2030. ( 4 ) As with most health issues, the burden of obesity isn’t felt equally across all parts of society. The poor have higher rates than those with higher income. Those with less education have higher rates than those with more education. And certain minority groups-especially African-American and Hispanic women-have much higher rates than other groups.

Health Risks : An overview of obesity-related diseases and conditions

Beyond North America, the regions of Europe, South and Central America, Western Pacific, and parts of Africa and Asia also have elevated obesity rates, with only a handful of areas with low and consistent levels of obesity.

• For many low and middle income countries already struggling in the world economy, obesity takes a particularly high toll-sapping productivity, increasing illness in sole wage earners, and further stretching health systems already burdened with persistent problems of infectious disease and even starvation and under nutrition.

The worldwide spread of obesity and resulting increase in rates of chronic disease and other serious conditions threatens health systems, economies, and individual lives. Bringing the problem back under control will take multifaceted country-level as well as global-level efforts, and they cannot begin in earnest soon enough.

1. Finucane MM, Stevens GA, Cowan MJ, et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants.

4. de Onis M, Blossner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr . 2010;92:1257-64.

3. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, Mullany EC, Biryukov S, Abbafati C, Abera SF, Abraham JP, Abu-Rmeileh NM, Achoki T, AlBuhairan FS, Alemu ZA, Alfonso R, Ali MK, Ali R, Guzman NA, Ammar W, Anwari P, Banerjee A, Barquera S, Basu S, Bennett DA, Bhutta Z, Blore J, Cabral N, Nonato IC, Chang JC, Chowdhury R, Courville KJ, Criqui MH, Cundiff DK, Dabhadkar KC, Dandona L, Davis A, Dayama A, Dharmaratne SD, Ding EL, Durrani AM, Esteghamati A, Farzadfar F, Fay DF, Feigin VL, Flaxman A, Forouzanfar MH, Goto A, Green MA, Gupta R, Hafezi-Nejad N, Hankey GJ, Harewood HC, Havmoeller R, Hay S, Hernandez L, Husseini A, Idrisov BT, Ikeda N, Islami F, Jahangir E, Jassal SK, Jee SH, Jeffreys M, Jonas JB, Kabagambe EK, Khalifa SE, Kengne AP, Khader YS, Khang YH, Kim D, Kimokoti RW, Kinge JM, Kokubo Y, Kosen S, Kwan G, Lai T, Leinsalu M, Li Y, Liang X, Liu S, Logroscino G, Lotufo PA, Lu Y, Ma J, Mainoo NK, Mensah GA, Merriman TR, Mokdad AH, Moschandreas J, Naghavi M, Naheed A, Nand D, Narayan KM, Nelson EL, Neuhouser ML, Nisar MI, Ohkubo T, Oti SO, Pedroza A, Prabhakaran D, Roy N, Sampson U, Seo H, Sepanlou SG, Shibuya K, Shiri R, Shiue I, Singh GM, Singh JA, Skirbekk V, Stapelberg NJ, Sturua L, Sykes BL, Tobias M1, Tran BX, Trasande L, Toyoshima H, van de Vijver S, Vasankari TJ, Veerman JL, Velasquez-Melendez G, Vlassov VV, Vollset SE, Vos T, Wang C, Wang X, Weiderpass E, Werdecker A, Wright JL, Yang YC, Yatsuya H, Yoon J, Yoon SJ, Zhao Y, Zhou M, Zhu S, Lopez AD, Murray CJ, Gakidou E. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet . 2014 384 (9945):766-81.

4. Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet . 2011;378:815-25.

Rare Genes Can Raise Odds for Obesity 6-Fold

By Dennis Thompson HealthDay Reporter

THURSDAY, April 4, 2024 (HealthDay News) -- Two newly discovered genetic variations can have a powerful effect on a person’s risk for obesity, a new report says.

Variants in the gene BSN, also known as Bassoon, can increase risk of obesity as much as sixfold, researchers report April 4 in the journal Nature Genetics .

These variants affect about 1 in every 6,500 adults, researchers said.

Variants of the APBA1 gene also are associated with increased obesity risk, results show.

U.S. Cities With the Most Homelessness

“We have identified two genes with variants that have the most profound impact on obesity risk at a population level we’ve ever seen,” said researcher Giles Yeo , a professor with the Medical Research Council’s Metabolic Diseases Unit at Cambridge University.

Previous genetic variants associated with obesity have been linked to the brain pathways normally associated with appetite regulation, known as the leptin-melanocortin pathway, researchers said.

Interestingly, neither the BSN nor the APBA1 gene are known to be involved in that brain pathway, researchers said.

Instead, prior studies have found that these genes play a role in the transmission of signals between brain cells -- suggesting that age-related brain declines might affect appetite control.

Further, neither gene is associated with childhood obesity risk, researchers said.

For the study, researchers used data from the UK Biobank genetic research project to perform genetic sequencing of body mass index in more than a half-million people.

They found that the BSN gene variants also had other health effects, such as increasing risk of type 2 diabetes and non-alcoholic fatty liver disease.

“The genetic variants we identify in BSN confer some of the largest effects on obesity, type 2 diabetes and fatty liver disease observed to date and highlight a new biological mechanism regulating appetite control,” researcher John Perry , a professor at Cambridge, said in a university news release.

More information

The Harvard T.H. Chan School of Public Health has more on obesity and genetics .

SOURCE: University of Cambridge, news release, April 4, 2024

Copyright © 2024 HealthDay . All rights reserved.

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Racial and Ethnic Disparities in Adult Obesity in the United States: CDC’s Tracking to Inform State and Local Action

ESSAY — Volume 16 — April 11, 2019

Ruth Petersen, MD, MPH 1 ; Liping Pan, MD, MPH 1 ; Heidi M. Blanck, PhD 1 ( View author affiliations )

Suggested citation for this article: Petersen R, Pan L, Blanck HM. Racial and Ethnic Disparities in Adult Obesity in the United States: CDC’s Tracking to Inform State and Local Action. Prev Chronic Dis 2019;16:180579. DOI: http://dx.doi.org/10.5888/pcd16.180579 external icon .

PEER REVIEWED

What Causes These Disparities?

What is dnpao doing to address these disparities, what’s next, acknowledgments, author information.

The Centers for Disease Control and Prevention (CDC) plays a key role in tracking data on the burden of obesity and its related racial and ethnic disparities to provide information that can highlight areas where state and local actions are most needed. Until further innovations allow for measured data on height and weight to be available for all states, self-reported data are the best source for understanding where the burden of obesity is highest among different populations. This understanding is critical given that the prevalence of obesity is increasing among adults in the United States (1). As such, obesity continues to put a strain on overall health status, health care costs, productivity, and the capacity for deployment and readiness of military personnel. Adults with obesity often have multiple-organ system complications from the condition and, as a result, are more at risk for heart disease, stroke, type 2 diabetes, and multiple types of cancers (2). The estimated annual medical cost of obesity in the United States was $147 billion in 2008 (3). Compared with spending for someone of normal weight, medical spending for a person with obesity was $1,429 higher (42% higher) per year (3). Adult obesity decreases productivity, and the cost of lost productivity is between $3.4 and $6.4 billion per year (4). Adult obesity also increases the risk of workplace injuries (2). Obesity among young adults limits the eligibility for many to serve in our military, given the weight standards for recruitment that nearly 1 in 4 young adults are not able to meet (5).

Among many other factors, the risk of adult obesity is greater among adults who had obesity as children, and racial and ethnic disparities exist by the age of 2 (6). If nothing else is done in the United States beyond what is being done now, simulated growth trajectories that model today’s children show that over half (59% of today’s toddlers and 57% of children aged 2 to 19) will have obesity at age 35 (7). Early feeding patterns, including how babies are fed and how caregivers use food in response to an infant’s mood, affect acute growth, future eating patterns, and the risk of obesity (8). Similarly, family and caregiver modeling of healthy behaviors, food offerings, and active playtime, as well as characteristics of neighborhoods such as walkability and traffic volume, may affect children’s nutrition and physical activity habits (9,10).

As sectors come together to reduce the obesity epidemic, we are aware how challenging success will be due to factors such as 1) the contributing risk factors of genetic and biological attributes; 2) individual behaviors (parenting styles, dietary patterns, physical activity levels, medication use, sleep, stress management); and 3) community and societal factors that influence individual, family, and collective access to healthy, affordable foods and beverages; access to safe and convenient places for physical activity; and exposure to the marketing of unhealthy products (2).

By using self-reported data of height and weight from the Behavioral Risk Factor Surveillance System, CDC’s Division of Nutrition, Physical Activity, and Obesity (DNPAO) has published state-specific obesity maps since 1999. Obesity is defined as a body mass index (a person’s weight in kilograms divided by the square of height in meters) of 30.0 or higher. These maps have shown the growing epidemic that has affected our nation from coast to coast. Although the data collection methods changed in 2011, which somewhat limits our ability to assess trends, the 2017 data continue to show that obesity prevalence among adults remains high across the country (Figure 1). The state-specific prevalence ranges from a low of 22.6% in Colorado to a high of 38.1% in West Virginia (11).

Figure 1. Prevalence of self-reported obesity among US adults, by state and territory, Behavioral Risk Factor Surveillance System (BRFSS), 2017. Obesity was defined as a body mass index of 30 or higher based on self-reported weight in kilograms divided by the square of the height in meters. Prevalence estimates reflect changes in BRFSS methods that started in 2011. These estimates should not be compared to prevalence estimates before 2011. No area had a prevalence of <20%, and all had sufficient data to determine prevalence. [A tabular version of this figure is also available.]

For the past 4 years, CDC has published more detailed state and territorial maps that combine 3 years of data to create stable estimates of self-reported adult obesity by race/ethnicity. These maps help demonstrate the geographic and racial/ethnic disparities in obesity burden. Although the previously released overall state-specific maps demonstrate where obesity may be influencing health, health care costs, well-being, and productivity across states and regions, the racial and ethnic maps for 2015 through 2017 illustrate that the negative effects are disproportionately burdensome for particular populations. Combined data for 2015 through 2017 allowed for assessment by major racial/ethnic categories and found that non-Hispanic black adults had the highest prevalence of obesity (38.4%) overall, followed by Hispanic adults (32.6%) and non-Hispanic white adults (28.6%). To identify areas of highest burden, we used a cut point of 35%. We chose this cut point because it was a somewhat natural breaking point in the data and roughly reflected areas with the highest burden. By using this cut point, we found that overall, 31 states and the District of Columbia had an obesity prevalence of 35% or higher among non-Hispanic black adults; 8 states had an obesity prevalence of 35% or higher among Hispanic adults; and only 1 state had an obesity prevalence of 35% or higher among non-Hispanic white adults (Figure 2).

Figure 2. Prevalence of self-reported obesity among non-Hispanic white, non-Hispanic black, and Hispanic adults, by state and territory, Behavioral Risk Factor Surveillance System, 2015–2017. Obesity was defined as a body mass index of 30 or higher based on self-reported weight in kilograms divided by the square of the height in meters. Prevalence estimates reflect changes in BRFSS methods that started in 2011. These estimates should not be compared to prevalence estimates before 2011. Areas are indicated as having insufficient data if they had a sample size of less than 50 or a relative standard error (dividing the standard error by the prevalence) of 30% or more. [A tabular version of this figure is also available.]

Although the exact causes of these differences in obesity are not all known, they likely in part reflect differences in social and economic advantage related to race or ethnicity (12). This concept aligns with other, more general statements about health disparities explaining that disparities are “closely linked with social, economic, and/or environmental disadvantage” and show the effect where groups of people “have systematically experienced greater social and/or economic obstacles to health . . . based on their racial or ethnic group” (13). Underlying risks that may help explain disparities in obesity prevalence among non-Hispanic black and the Hispanic populations could include lower high school graduation rates, higher rates of unemployment, higher levels of food insecurity, greater access to poor quality foods, less access to convenient places for physical activity, targeted marketing of unhealthy foods, and poor access to health care or referrals to convenient community organizations that aid family-management or self-management resources (14–17).

From a large number of high-quality applicants, in 2018 DNPAO competitively funded 16 state health departments (or a similar entity), 15 land grant colleges and universities, and 31 community-focused grantees to work over the course of 5 years with multiple sectors and coalitions to prioritize and implement best practices to increase healthy eating and active living to prevent obesity and other chronic diseases. With technical assistance from DNPAO public health specialists and subject matter experts, grantees use a menu of evidence-based strategies and performance metrics to develop their implementation plan, work plan, and evaluation process. To obtain the largest public health impact from limited resources, grantees are asked to focus their work on populations that have the greatest disparities and needs. Strategies for DNPAO grantees include establishing healthy nutrition standards in settings such as workplaces, hospitals, early care and education (ECE), after-school and recreational programs, and faith-based organizations; working with food vendors, distributors, and producers to increase procurement and sales of healthier foods; improving programs and systems at the state and local level to increase access to healthier food; and implementing community planning and transportation plans that support safe and accessible physical activity by connecting sidewalks, paths, bike routes, public transit with homes, ECE, schools, parks and recreation centers, and other everyday destinations.

As an example of reaching vulnerable individuals, state health department grantees may focus obesity prevention efforts at a state level by targeting early obesity risk through system changes in the ECE setting through state licensing, state subsidy, or state quality rating systems. States may pair these efforts with promoting the use of food reimbursement programs for meals that meet minimum nutritional standards among centers serving low-income children. In addition, state health departments may work to set a standard for implementation of food service guidelines so other government entities, work sites, park and recreation centers, and hospitals can follow that example and obtain the needed technical assistance for spreading implementation. State health department grantees may also work across sectors (such as the transportation and community planners) to improve environmental supports for physical activity through the implementation of master plans and land-use interventions. These efforts to increase access to safe and convenient places for physical activity are generally targeted to geographical areas with the highest burden of obesity and chronic disease. Such efforts can include connecting neighborhoods with sidewalks, paths, bike routes, and public transit that lead to local schools, parks and recreation centers, and local businesses.

DNPAO manages 2 additional public health practice programs that have had success in reducing the risk factors for obesity in populations with the greatest disparities. These programs include the Racial and Ethnic Approaches to Community Health (REACH) program and the High Obesity Program (HOP). The REACH program focuses on improving health for racial and ethnic groups with the highest disease burden. Obesity reduction among the black population is often a key goal for REACH recipients. For example, from 2008 through 2012, 14 REACH grantees implemented strategies to address disparities in obesity among black populations. These strategies included expanding healthy food choices in grocery stores, creating neighborhood farmers markets, implementing Complete Street policies, and improving walkability and safety of neighborhood streets. The prevalence of obesity decreased about 1 percentage point in these REACH communities, but not in the comparison populations during the same time (18).

Land grant universities in states where counties have more than a 40% prevalence of adult obesity are eligible to apply for HOP. These grantees work in predominantly rural areas where residents may have less access to healthy foods and fewer opportunities to be physically active, which may increase their risk of obesity (19–21). HOP grantees use the same menu of DNPAO evidence-based strategies to improve nutrition and physical activity to reduce obesity and other chronic diseases; however, they might tailor their implementation plan given the rural nature of their target population with the highest risk of obesity. Examples include work at the Texas AgriLife Extension (Texas A&M University), which established a farmers market at a local community center to help increase access to fresh produce. Since the creation of this market, more than 800 community members purchased over 12,000 pounds of fresh fruits and vegetables. Another example is the work of the extension staff in Ouachita County (University of Arkansas) at a low-income housing complex to improve access to physical activity for residents with limited mobility. They identified a walking path and developed signs to indicate how many laps equaled a half-mile. Eighty-four percent of residents now walk regularly and use the path at least 1 or 2 times a week (22).

Implementing approaches that take into account racial and ethnic disparities is critical to addressing the high burden of obesity and its many negative consequences. Although a population-based approach is needed to increase availability and access to healthy foods and beverages and safe and convenient places for physical activity for all Americans, targeted approaches are needed to address the risks that drive the disparities. Such an approach will mean taking into account food insecurity, safe drinking water, and cultural nutrition and physical activity patterns as well as environmental and policy contexts that influence the risk. Efforts may need to include more attention to upstream determinants of health or attributes of the communities where the populations with the highest burden live. The findings linking neighborhood features to one’s health status illustrate how a community can influence risk of many chronic health conditions, including obesity. For example, a study of neighborhoods in 3 US metropolitan regions (San Diego, Seattle, and Baltimore) from 2009 to 2010 assessed pedestrian environment features for walkability factors (eg, density). The study found that “across all three regions, low-income neighborhoods and neighborhoods with a high proportion of racial/ethnic minorities had poorer aesthetics and social elements (eg, graffiti, broken windows, litter) than neighborhoods with higher median income or fewer racial/ethnic minorities” (20). Likewise, if marketing of unhealthy products and/or fast-food establishments are unequally distributed across a community or are clustered near schools, communities may consider addressing this issue paired with improving healthy offerings (16,23,24). For individuals from the groups with the largest disparities, it is also important to focus attention on enhancing access to and reimbursement for quality health care services for growth assessment and obesity screening, and for persons with obesity and disease risk, appropriate referral to evidence-based healthy weight or prediabetes management programs and other treatment modalities (25,26).

In isolation, DNPAO resources, equivalent to $0.31 investment per American per year, will not be able to prevent obesity among at-risk Americans nor reduce the racial and ethnic disparities in the national burden of obesity. In addition to public health, many partners are needed, including policy makers, state and local organizations, business and community leaders, ECE, schools, industry, federal agencies, health care systems and providers, payers, faith-based organizations, community planners, food growers and distributors, families, and individuals. Using combined approaches, these partners should strive to best improve the ability to prevent obesity and its consequences for those with the burden. Such multisector partnerships can create positive changes at the community level to promote healthy eating and active living in areas where individuals may be at risk for obesity because of where they live and work. These focus areas could include making it easier for families with children to buy healthy, affordable foods and beverages near their homes; helping to provide access to safe, free drinking water in places such as community parks, recreation areas, child care centers, and schools; helping local schools open up gyms, playgrounds, and sports fields during nonschool hours so more children can safely play; increasing the number of safe and accessible sidewalks and bike paths to schools, parks and everyday destinations; and helping schools and ECE providers use best practices for improving nutrition and increasing physical activity. Demonstrated success in these approaches would be reductions in the disparities in upstream indicators (ie, improved community and behavioral determinants of health) and reductions in the obesity burden that is evident in CDC’s childhood obesity data and the maps above.

DNPAO is committed to supporting efforts to reduce racial and ethnic disparities in obesity by continuing to share what is working through partners and grantees, to develop tools that aid community engagement and the implementation of evidenced-based interventions, and to track obesity and its risk factors. Each sector and organization has a role to play in being part of the solution. To reduce the current disparities that exist in the burden of obesity, all parts of society need to relentlessly and intentionally work to address the causes of these disparities to help give all a fair chance at health.

No financial support was received for this work. The findings and conclusions of this report are those of the authors and do not necessarily reflect the official position of CDC.

Corresponding Author: Ruth Petersen, MD, MPH, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Hwy, MS S107-5, Atlanta, GA 30341-3717. Telephone: 770-488-6001. Email: [email protected] .

Author Affiliations: 1 National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.

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• University of Connecticut Rudd Center for Food Policy and Obesity. Increasing disparities in unhealthy food advertising targeted to Hispanic and black youth. http://uconnruddcenter.org/files/Pdfs/TargetedMarketingReport2019.pdf. Accessed January 19, 2019.
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The opinions expressed by authors contributing to this journal do not necessarily reflect the opinions of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors’ affiliated institutions.

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• New Research in April: Col...

New Research in April: Colorectal Cancer, Kidney Health, OR Supply Costs, and More

March 15, 2024

CHICAGO: The April issue of the Journal of the American College of Surgeons (JACS) , which includes research presented at the Southern Surgical Association 135th Annual Meeting, features new research on topics ranging from colorectal cancer and social vulnerability to operating room supply costs, the rise in school shootings since 1970, and the impact of permitless open carry laws on suicide rates, among others.

Read highlights from the issue below. The full issue is available on the JACS website .

Social Vulnerability Index and Survivorship after Colorectal Cancer Resection

Researchers analyzed whether data from the Social Vulnerability Index (SVI) can help predict complications and survival rates for colorectal cancer (CRC) patients. A high SVI rating was independently associated with major perioperative complications and survival rates after resection of 872 CRC patients. Findings indicate the SVI may be a useful measure to determine CRC patients who may benefit from outreach interventions.

DOI: 10.1097/XCS.0000000000000961

Health Inequities in Likelihood and Time to Renal Recovery after Living Kidney Donation: Implications for Black American Kidney Health

There exists a lack of live kidney donation studies examining health inequities in renal recovery post-donation. Researchers retrospectively analyzed 100,121 living kidney donors reported to the Scientific Registry of Transplant Recipients between 1999-2021. Findings revealed:

• Black living kidney donors, especially young Black males, were less likely to recover kidney function
• Time to renal recovery for Black patients was significantly longer than their White counterparts
• Black living donors appear to have the greatest future risk of end-stage kidney disease

There is a need for enhanced living kidney donor follow-up, authors note.

DOI: 10.1097/XCS.0000000000000970

Decreased Operating Room Supply Costs and Increased Value of Care after Implementing a Sustainable Quality Intervention

Operating room costs are the second most expensive element of surgical care. To reduce costs, researchers implemented a sustainable quality improvement intervention using automated electronic health record data to analyze operating room supply cost data with patient and case characteristics and outcomes. Results show:

• A decrease in operating room supply costs
• A decrease in incidence of cases with out-of-control costs
• No difference in duration of surgery or patient outcomes
• An increase in the value of care

DOI: 10.1097/XCS.0000000000000972

Patients With Obesity and Kidney Failure May Be Newly Eligible for Kidney Transplants

A collaborative study between bariatric and transplant teams has created a viable pathway for patients with obesity who also have end-stage renal disease to become eligible for kidney transplants through weight loss surgery. Postoperative outcomes indicate significant improvements in BMI, hypertension, and diabetes management, enhancing patients’ overall health and transplant viability.

Read the press release

Study Reveals the Impact of Behavioral Health Disorders on Cancer Surgery Outcomes

One in 15 cancer patients in the Medicare system have at least one behavioral health disorder (BHD). BHDs, which include substance abuse, eating disorders, and sleep disorders, are linked to worse surgical outcomes and higher health care costs in cancer patients. Patients with BHDs are less likely to undergo surgical resection and have higher odds of postoperative complications.

Study Quantifies Dramatic Rise in School Shootings and Related Fatalities Since 1970

In the 53 years leading up to May 2022, the number of school shootings annually increased more than 12 times. The likelihood of children being school shooting victims has increased more than fourfold, and the rate of death from school shootings has risen more than sixfold. The incidents studied involved 3,083 victims, including 2,033 children ages 5-17 years, and 1,050 adults ages 18-74 years.

Permitless Open Carry Laws May Lead to More Firearm-Related Suicides

Suicide by firearm rates increased 18% in nine years in states that began allowing firearm owners to openly carry a firearm without a permit. U.S. rates of firearm-related suicide rose from 21,175 in 2013 to 26,328 in 2021, an increase that may be related to more permissive open carry laws.

Journalists should contact [email protected] to receive a full copy of any of these studies or to set up an interview with a researcher.

About the American College of Surgeons

The American College of Surgeons is a scientific and educational organization of surgeons that was founded in 1913 to raise the standards of surgical practice and improve the quality of care for all surgical patients. The College is dedicated to the ethical and competent practice of surgery. Its achievements have significantly influenced the course of scientific surgery in America and have established it as an important advocate for all surgical patients. The College has approximately 90,000 members and is the largest organization of surgeons in the world. "FACS" designates that a surgeon is a Fellow of the American College of Surgeons.

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The Epidemiology of Obesity: A Big Picture

The epidemic of overweight and obesity presents a major challenge to chronic disease prevention and health across the life course around the world. Fueled by economic growth, industrialization, mechanized transport, urbanization, an increasingly sedentary lifestyle, and a nutritional transition to processed foods and high calorie diets over the last 30 years, many countries have witnessed the prevalence of obesity in its citizens double, and even quadruple. Rising prevalence of childhood obesity, in particular, forebodes a staggering burden of disease in individuals and healthcare systems in the decades to come. A complex, multifactorial disease, with genetic, behavioral, socioeconomic, and environmental origins, obesity raises risk of debilitating morbidity and mortality. Relying primarily on epidemiologic evidence published within the last decade, this non-exhaustive review discusses the extent of the obesity epidemic, its risk factors—known and novel—, sequelae, and economic impact across the globe.

1. Introduction

Obesity is a complex, multifactorial, and largely preventable disease ( 1 ), affecting, along with overweight, over a third of the world’s population today ( 2 , 3 ). If secular trends continue, by 2030 an estimated 38% of the world’s adult population will be overweight and another 20% will be obese ( 4 ). In the USA, the most dire projections based on earlier secular trends point to over 85% of adults being overweight or obese by 2030 ( 5 ). While growth trends in overall obesity in most developed countries seem to have leveled off ( 2 ), morbid obesity in many of these countries continues to climb, including among children. In addition, obesity prevalence in developing countries continues to trend upwards toward US levels.

Obesity is typically defined quite simply as excess body weight for height, but this simple definition belies an etiologically complex phenotype primarily associated with excess adiposity, or body fatness, that can manifest metabolically and not just in terms of body size ( 6 ). Obesity greatly increases risk of chronic disease morbidity—namely disability, depression, type 2 diabetes, cardiovascular disease, certain cancers—and mortality. Childhood obesity results in the same conditions, with premature onset, or with greater likelihood in adulthood ( 6 ). Thus, the economic and psychosocial costs of obesity alone, as well as when coupled with these comorbidities and sequealae, are striking.

In this article, we outline the prevalence and trends of obesity, then review the myriad risk factors to which a preventive eye must be turned, and finally present the costs of obesity in terms of its morbidity, mortality, and economic burden.

2. Classification of Body Weight in Adults

The current most widely used criteria for classifying obesity is the body mass index (BMI; body weight in kilograms, divided by height in meters squared, Table 1 ), which ranges from underweight or wasting (<18.5 kg/m 2 ) to severe or morbid obesity (≥40 kg/m 2 ). In both clinical and research settings, waist circumference, a measure of abdominal adiposity, has become an increasingly important and discriminating measure of overweight/obesity ( 7 ). Abdominal adiposity is thought to be primarily visceral, metabolically active fat surrounding the organs, and is associated with metabolic dysregulation, predisposing individuals to cardiovascular disease and related conditions ( 8 ). Per internationally used guidelines of metabolic syndrome—a cluster of dysmetabolic conditions that predispose individuals to cardiovascular disease of which abdominal adiposity is one component—a waist circumference resulting in increased cardiovascular risk is defined as ≥94 cm in European men, and ≥80 cm in European women, with different cut points recommended in other races and ethnicities (e.g., ≥90 and ≥80 cm in men and women, respectively, in South Asians, Chinese, and Japanese) ( 8 , 9 ).

Common Classifications of Body Weight in Adults and Children

Abbreviations used: BMI, body mass index; IOTF, International Obesity Task Force; SD, standard deviation; WHO, World Health Organization; WH weight-for-height; Z, z score.

3. Classification of Body Weight in Children

In children, body weight classifications ( Table 1 ) differ from those of adults because body composition varies greatly as a child develops, and further varies between boys and girls primarily owing to differences in sexual development and maturation. The World Health Organization (WHO) Child Growth Standards are the most widely currently used classification system of weight and height status for children from birth to 5 years old, based on data from children in six regions across the globe born and raised in optimal conditions ( 10 ). In 2007, the WHO published updated growth references combining the 1977 National Center for Health Statistics (NCHS)/WHO growth reference and the 2006 WHO Child Growth Standards to create the most recent BMI-for-age references for individuals aged 5–19 years ( 11 ). Thus, the latest WHO guidelines are designed to represent relatively seamless standards and references from birth all the way into late adolescence/early adulthood.

In the USA, the Centers for Disease Control and Prevention (CDC) currently use the 2000 CDC growth references based on 1963–1994 US children’s data, to determine age- and sex-specific BMI percentiles for children aged 2–19 years ( 12 ). Overweight is defined in US children as age- and sex-specific BMI ≥85th and <95th percentile, while obesity is ≥95th percentile ( 13 ). Cut points for severe obesity in childhood have been proposed in recognition of the alarming growing prevalence of this extreme condition, defined as the 99th BMI percentile ( 13 ) or 120% of the 95th percentile ( 14 ). For US children <2 years old, the CDC currently uses the 2006 WHO Child Growth Standards, described above ( 15 ).

4. Prevalence and Trends

4.1. adult obesity—us and europe.

The first indications that obesity was taking on epidemic proportions originated in the USA and Europe. With few restrictions on access to or availability of food, the prevalence of overweight and obesity in the USA climbed virtually unmitigated over the last 50 years. Today, those who are overweight (BMI 25–<30 kg/m 2 ) or obese (BMI ≥30 kg/m 2 ) in the USA eclipse two-fold the numbers of those who are normal weight ( 16 ). In US adults, 1960–1994 trends showed that while levels of overweight hovered at approximately 31% over the time period, in contrast, age-adjusted obesity jumped from 13 to 23%, bringing the crude prevalence of overweight or obesity to 55% of the American population ( 17 ). Unfortunately, 1994 did not represent the endpoint of the upward trend, as the following decade saw adult obesity rise from 23 to 32% by 2003–2004 ( 16 ). In the last 10 years, national estimates of obesity seem to indicate that the steady upward trend of obesity in Americans has leveled off at a prevalence of about 35% ( 16 ) ( Figure 1 ), perhaps having reached some “Malthusian” obesity limit. However, certain subpopulations are faring worse than others, as 2011–2012 obesity rates in Hispanics and non-Hispanic blacks were 43 and 48%, respectively, pointing to a disproportionate burden in differing racial/ethnic and/or socioeconomic status (SES) groups. Gender also plays a role, with women being disproportionately affected by extreme obesity (classes 2–3, BMI ≥35 kg/m 2 ) than men, regardless of age or race/ethnicity ( 16 ).

Trends in age-adjusted prevalence of overweight, obesity, and extreme obesity in US adults, aged 20–74 years, 1960–2012. Trends in prevalence of overweight as BMI 25–<30 kg/m 2 (circles), and upward trends in obesity as BMI ≥30 kg/m 2 (squares), and extreme obesity as BMI ≥40 kg/m 2 (diamonds) in adult males (closed points) and females (open points). The figure is based on data from NHES I (1960–1962), NHANES I (1971–1974), NHANES II (1976–1980), NHANES III (1988–1994), and NHANES (1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012). Data derived are derived from Ogden, et al ., and Fryar, et al . ( 16 , 141 ). BMI, body mass index; NHANES, National Health and Nutrition Examination Survey; NHES, National Health Examination Survey.

Meanwhile, in Europe, longitudinal data (1992–1998 to 1998–2005) from participants in five countries involved in the European Prospective Investigation into Cancer and Nutrition (EPIC) study (Italy, the United Kingdom, the Netherlands, Germany, and Denmark), indicate that adult obesity increased modestly from 13 to 17% in that time period ( 18 ). However, were such linear trends were to continue, the overall obesity prevalence in these populations could reach 30% by 2015, paralleling US rates. A more conservative projection suggests a prevalence of just 20% obesity in these populations by 2015, if public awareness and public health measures take hold ( 18 ).

European studies including populations beyond EPIC indicate there is considerable disparity in overweight/obesity between European countries. A systematic review of national and regional surveys conducted between 1990 and 2008 points to obesity rates as low as 4.0 and 6.2% in French men and women, respectively (regional survey, 1994–1996), and as high as 30.0 and 32.0% in Czech men and women, respectively (national survey, 2002–2005) ( 19 ). Regional trends within Europe are apparent, with southern Italy and southern Spain, and Eastern European countries showing higher prevalence of obesity than countries in Western and Northern Europe ( 19 ). As in the USA, these data suggest that socioeconomic disparities and relatively recent/ongoing economic transitions are playing a considerable role in apparent differences across and within countries with respect to obesity risk.

4.2. Child Obesity—USA and Europe

US children may be faring better than their adult counterparts in some ways ( 16 ), potentially offsetting earlier dire predictions of rampant obesity by 2030 ( 5 ). In national surveys, levels of overweight in children, as in adults, seem to have leveled off (or even declined) at approximately 30% of US children aged 2–19 years ( 16 , 20 ). However, this belies a potentially disturbing long-term trend in the rising prevalence of extreme obesity (equivalent to adult class 2 obesity and higher, BMI ≥35 kg/m 2 ). Since 1999–2000, the prevalence of class 2 obesity in children (BMI ≥120% of the 95th percentile) has risen from 3.8 to 5.9% and class 3 obesity (BMI ≥140% of the 95th percentile) has doubled from 0.9 to 2.1%, the latter category jumping 30% since 2009–2010 alone ( 20 ). Again, as in their adult counterparts, certain sub-populations appear to be faring worse than others, notably Hispanic girls and Black boys, in whom overweight, obesity, and class 2 obesity have increased significantly ( 20 ).

Childhood obesity prevalence varies considerably between and within countries as well. Relatively recent estimates based on 2007–2008 data of children aged 6–9-years collected in 12 European countries as a part of the WHO European Childhood Obesity Surveillance Initiative observed overweight/obesity (BMI z score >+1 standard deviation [SD]) prevalence of 19.3–49.0% of boys and 18.4–42.5% of girls, while obesity (BMI z score >+2 SD) affected 6.0–26.6% of boys and 4.6–17.3% of girls. Researchers continued to observe the trend of north-south and west-east gradients evident in adults, with the highest levels of overweight in southern European countries ( 21 ).

4.3. Obesity Beyond North America and Europe

The data discussed above focus on the USA and European countries, many with robust national health surveillance programs. While historical data tends to be scarcer outside of these regions, an alarming picture has emerged over the last decades in low- and middle-income countries around the globe, complicated by rapidly changing socioeconomic environments. While country-specific trends are not discussed in this article, regional and national estimates of long-term changes in child (<20 years old) and adult (>20 years old) overweight and obesity have increased in nearly all countries and regions since 1980 ( Figure 2 ) ( 2 , 3 ). While the USA still may boast the largest absolute numbers of overweight and obese individuals, several other nations exceed the USA in terms of overall prevalence and, moreover, the rate of growth in certain countries is disheartening. For example, the prevalence of overweight and obesity in nationally representative Mexican adults was estimated to be 71.3% overweight/obese, with overweight at 38.8% and obesity at 32.4% ( 22 ). This prevalence represents an increase of 15% since 2000, placing this population among the most rapidly accelerating in terms of obesity prevalence over the last decade. Further, while rates of overweight remained relatively stable since 2000 at approximately 38% overall, extreme obesity (class 3, BMI ≥40 kg/m 2 ) increased by an estimated 76.5% from 2000 to 2012. These trends are also evident in countries outside of the Americas. In China, for example, between 1993 and 2009, overweight (BMI 25 to <27.5 kg/m 2 ) doubled in men (8 to 17%) and increased from 11 to 14% in women. Meanwhile, obesity (BMI ≥27.5 kg/m 2 ) nearly quadrupled in men, from 3 to 11%, and doubled in women, from 5 to 10%. Chinese children are faring as badly as their adult counterparts: overweight/obesity doubled from 6 to 13% in children aged 6–17 years over the same time period, suggesting that the obesity epidemic will continue to deepen in this country ( 23 ).

Prevalence of overweight and obesity in adults aged ≥20 years by global region, 1980–2008. From left to right, each column represents the estimated regional prevalence of overweight and obesity for 1980, 1985, 1990, 1995, 2000, 2005, and 2008. For a given region, a dark gray column indicates the lowest estimated prevalence in the trend, while the highest estimated prevalence is indicated by a black column. As is evident, the vast majority of regions demonstrate the lowest estimated prevalence of overweight and obesity in 1980, and the highest in 2008, demonstrating the global reach of obesity. The scale shows 25, 50, and 100% prevalence columns, for reference. Asterisks denotes high income. Data are sourced from Stevens, et al . ( 3 ).

5. Risk Factors for Obesity

Currently, our greatest gap in knowledge is not regarding the numbers of risk factors, nor in their independent impact on risk, but rather in how they interact with one another—their confluence—to produce today’s aptly if unfortunately named “globesity” epidemic. Obesity arises as the result of an energy imbalance between calories consumed and the calories expended, creating an energy surplus and a state of positive energy balance resulting in excess body weight. This energy imbalance is partially a result of profound social and economic changes at levels well beyond the control of any single individual. These “obesogenic” changes—economic growth, growing availability of abundant, inexpensive, and often nutrient-poor food, industrialization, mechanized transportation, urbanization—have been occurring in high-income countries since the early 20th century, and today these forces are accelerating in low- and middle-income countries. And yet, not all of us living in obesogenic environments experience the same growth in our waistlines. Hereditary factors—genetics, family history, racial/ethnic differences—and our particular socioeconomic and sociocultural milieus have been shown to affect risk of obesity ( Table 2 ) even in ostensibly similar obesogenic environments. So while body weight regulation is and should be viewed as a complex interaction between environmental, socioeconomic, and genetic factors, ultimately, personal behaviors in response to these conditions continue to play a dominant role in preventing obesity. Importantly, apart from genetics, every risk factor discussed below is modifiable .

Risk Factors, Comorbidities, and Sequelae of Obesity

5.1. Genetics of Obesity

To date, over 60 relatively common genetic markers 1 have been implicated in elevated susceptibility to obesity ( 24 , 25 ); however, the 32 most common genetic variants are thought to account for <1.5% of the overall inter-individual variation in BMI ( 24 ). When these 32 “top” genetic hits are combined into a genetic risk of obesity score, those with the highest genetic risk (i.e., carriers of over 38 risk alleles), have just a 2.7 kg/m 2 higher BMI on average than those with a low genetic risk. This translates into about a 15-lb (7-kg) weight difference between two 5’3” (160 cm) individuals with high versus low genetic risk ( 24 ). Although genetics undoubtedly play a role, this relatively small difference in BMI, coupled with the dramatic rise in obesity over the last half century in developed and developing nations alike point to obesity risk factors beyond genetics. A concomitant and rich area of research has therefore evolved investigating gene-environment interaction based on the idea that underlying genetic risk predisposes individuals to particularly adverse (or beneficial) effects of behavioral or environmental exposures such as diet and exercise, a concept scientifically popularized in, for example, the “thrifty gene” hypothesis ( 26 ). In many ways, these types of gene-environment interactions are playing out in population research: for example, a variant in FTO (rs9939609)—the strongest obesity susceptibility locus—increases odds of obesity in risk allele carriers by an estimated 23% per allele; however, this risk is modified by physical activity in adults ( 27 , 28 ) and children ( 29 ), among other factors. Nevertheless, these types of interactions have so far been investigated in relatively few genetic risk loci out of millions, and with just a handful of environmental factors, raising important questions of how to aggregate this complexity for public health and ultimately personalized medicine.

In addition, parental diet, lifestyle, and other exposures have been implicated in subsequent offspring obesity risk, including famine exposure ( 30 ), parental obesity ( 31 – 33 ), smoking ( 34 ), endocrine-disrupting and other chemicals ( 35 , 36 ), and weight gain during gestation and gestational diabetes ( 33 , 37 ). These and other studies point to lasting effects of fetal programming that via differing mechanisms, likely epigenetic, result in substantial repercussions in life course health, with implications across the socioeconomic/food availability spectrum. Careful management of diet and lifestyle in pre- and perinatal periods could exert a considerable impact on the obesity epidemic for generations to come ( 37 ).

5.2. Individual Behaviors

5.2.1. diet.

In the decades preceding the 21st century, the vast majority of research on obesity risk factors focused on individual-level, largely modifiable behaviors. The role of diet and physical activity in mitigating obesity risk and reducing prevalent obesity have received the most attention, and with good reason: 15% of deaths in 2000 in the USA were attributable to excess weight, owing to poor diet and physical inactivity ( 38 ). Caloric intake and expenditure needed for weight maintenance or healthy growth has historically taken center stage ( 39 ), and caloric restriction remains today a primary focus of most popular and clinical weight-management and weight-loss approaches.

Beyond overall caloric intake to regulate body weight, a tremendous amount of research has attempted to resolve the roles of diet quality and dietary patterns, including those specifying combinations of macronutrients ( 40 ). Evidence from clinical trials have almost universally shown that caloric restriction, regardless of dietary pattern, is associated with better weight outcomes ( 40 ). Although the metabolic nuances and relative merits of the differing dietary patterns for various comorbid conditions are still being investigated, the evidence seems to suggest that merely adhering to a diet—nearly irrespective of what type of healthy diet it is—has an impact on weight loss/control ( 41 – 43 ).

For long-term maintenance of healthy weight, evidence from observational cohorts indicate that diets that are considered “healthier” lead to better long-term weight maintenance, or at least mitigate weight gain typically associated with aging through middle age. For example, research in US health professionals pointed to averaged 4-year weight gain throughout middle age as being strongly associated with increasing intake of potato chips and potatoes, sugar-sweetened beverages, and processed and unprocessed red meats, but inversely associated with the intake of vegetables, fruits, whole grains, nuts, and yogurt ( 44 ). Specific food groups, such as sugar-sweetened beverages, have received considerable attention largely because added sugar consumption (primarily as sugar-sweetened beverages) has been rising concomitantly with prevalent obesity ( 45 ). Indeed, the weight of the evidence about the role of sugar-sweetened beverages in obesity ( 46 , 47 ) is a strong impetus for public health interventions and policies, such as limiting advertising on these beverages as in Mexico ( 48 ), attempts to limit beverage sizes permitted for sale as in New York City ( 49 ), taxation, eliminating sale in schools, etc.

5.2.2. Physical Activity, Sedentary Behaviors, and Sleep

Personal behaviors beyond diet (physical activity, sleep, sedentary and screen time, and stress) have also been independently associated with weight change and maintenance in adulthood. Combined with diet, these elements have synergistic and likely cumulative effects on an individual’s ability to maintain or obtain a healthy body weight over the life course. Recently reviewed evidence from randomized trials and observational studies support 2008 US recommendations for weight management ( 50 ), consistently showing that in general, 150–250 minutes per week of moderate intensity activity is required to prevent weight gain, or aid in weight loss when accompanied by dietary restriction ( 51 ). Activity (>250 minutes per week) is associated with weight loss and weight maintenance after weight loss ( 51 ). Leisure-time activities involving sitting, but which are not truly restful behaviors, such as getting <6 or >8 hours of sleep in adults and adolescents ( 44 , 52 – 55 ) or <10–11 hours of sleep in children ( 52 ), television viewing or screen time ( 44 , 56 , 57 ), and other leisure-time sitting ( 58 ) are also associated with weight gain.

5.3. Socioeconomic Risk Factors: Income and Education

Income has had a shifting role in obesity risk over the last century. As late as the mid-20th century, the USA and Europe could link wealth directly with obesity—the wealthier an individual, the more likely to be overweight. Over the last few decades, however, perhaps owing to the abundance of cheap and highly available food, coupled with changing sociocultural norms, this link has flipped. Today, wealth in the USA tends to be inversely correlated with obesity, and it is those who are at or below the level of poverty who appear to have the highest rates of obesity ( 59 ). Indeed, in US cities where the homeless are surveyed, the prevalence of overweight and obesity parallels that of non-homeless populations, contrary to our typical beliefs about thinness accompanying food insecurity or homelessness ( 60 , 61 ).

More broadly, across 11 Organisation for Economic Co-Operation and Development (OECD) countries, SES, whether defined by household income or occupation-based social class, showed an inverse relationship with obesity: women, in particular, had consistently higher prevalence of overweight/obesity the less affluent they were ( 62 ). In men, too, those in low income strata tended to have higher prevalence of obesity, but the gradient for overweight reversed in about half of the countries surveyed. That is, in some countries, poverty was associated with more prevalent overweight than wealth, but in others, lower income was associated with more favorable weight status. The differences between sexes in terms of income status and obesity, in particular the trend reversal in men, may be in part due to low-paying jobs typically involving more physically demanding work performed by men more than by women ( 62 ). Adding complexity to this picture is the role of education: in the 11 OECD countries discussed above, education showed a strong inverse relationship with overweight/obesity, particularly in women, who had consistently higher prevalence of overweight/obesity the less educated they were ( 62 ).

As wealth rises in low- and middle-income countries, it is expected for poverty-obesity patterns to begin more closely mimicking those of high-income countries. Evidence of this transition is already accumulating. In explorations of the role of education and wealth in women and weight status in four middle-income countries (Colombia, Peru, Jordan, and Egypt), authors observed a significant interaction between education and wealth: in women with little or no education, higher income was associated with 9–40% higher odds of obesity, while in those with higher levels of education, the association with income was either not present (Egypt, Peru) or associated with 14–16% lower odds of obesity (Jordan, Colombia) ( 63 ). This suggests that in currently transitioning economies, education may offset the apparently negative effects of increasing purchasing power in emerging obesogenic environments. However, the protective effect of education has yet to be seen in the poorer countries, such as India, Nigeria, and Benin, where both education and wealth were directly associated with increased odds of obesity ( 63 ).This is perhaps unsurprising, as obesity was relatively rare at <6.0% of women in these countries, and >50% of women had little or no education.

The glimmer of hope, then, is that in the context of a paradigm of diseases of affluence, in which the transition to wealth seem to invariably lead to higher obesity and thus greater chronic disease burden, higher education levels may yet offset some of the frightening challenges that lay before us.

5.4. Environmental 2 Risk Factors

5.4.1. the built environment.

Research on the built environment tends to focus on a few measurable characteristics of neighborhoods as they relate to weight status, while holding sociodemographic and other person-level characteristics constant. Such neighborhood characteristics range from more concrete factors (e.g., fast food restaurants, supermarkets, parks, transportation, etc.) to more variably scored factors (e.g., walkability, neighborhood healthiness). Most studies of the built environment have been cross-sectional, tending to focus on one or two characteristics; thus, findings on the relative importance or effects of given characteristics on obesity have been inconsistent ( 66 – 72 ), revealing the fundamental challenge of teasing out whether neighborhood characteristics play a causal role in weight status, or whether health-minded folks inhabit health-friendly areas to begin with (residential selection bias) ( 73 ). However, the emerging picture points to the primacy of diet-related built environments over those associated with physical activity. While presence of neighborhood physical activity or recreational spaces has been associated with increased physical activity levels or energy expenditure ( 71 , 72 ), healthy food environments, characterized by availability of produce or presence of supermarkets over convenience stores or fast food restaurants, play a potentially more important role ( 68 , 70 , 74 , 75 ).

Research on the causality of the built environment as obesity-inducing or health-promoting is critical for municipalities and public health authorities to justify potentially costly improvements to public spaces and/or zoning regulations. There is an unmet need for standardized measures, definitions, and criteria, established residential and occupational geographic radii relevant to health, and research methodologies that can take into account the complexity of something as seemingly simple as a neighborhood.

5.4.2. Environmental “Pathogens”: Viruses, Microbiomes, and Social Networks

Growing evidence from animal and human studies indicates that obesity may be attributable to infection, or that obesity itself may be a contagion. Infectious agents include viruses, the trillions of microbiota inhabiting the human gut, and, of course, obese humans as infectious agents themselves.

Although several viruses have been identified as potentially having a causal role in obesity ( 76 ), Ad-36 is among the most studied, being causally associated with adiposity in animals. Studies in diverse human populations generally support greater Ad-36 viral loads as probably causal of obesity in both children and adults ( 76 – 79 ), with links to other metabolic traits ( 77 , 79 ).

Ground-breaking research in the last decade has emerged on the role of trillions of gut bacteria—the human microbiome—in relation to obesity, energy metabolism, and carbohydrate and lipid digestion, opening promising therapeutic avenues for obesity and disease ( 80 ). Two primary phyla of bacteria differ in their proportions in lean vs. obese populations; these proportions change as obese individuals lose weight and correlate highly with the percentage of body weight lost ( 81 ). Broad and sometimes dramatic changes in microbiome populations have been catalogued following gastric bypass surgery ( 80 ), and in both the short- ( 82 , 83 ) and long-term ( 81 , 83 ) in response to changes in dietary composition ( 80 ). Research in mice indicates that increased adiposity is a transmissible trait via microbiome transplantation ( 84 ), and has prompted similar experimental fecal transplantation research in humans for the promotion of weight loss ( 85 ). In addition, other research has examined the feeding of pre- and probiotics as therapeutic modalities designed to manipulate the gut microbiome; these strategies also show promise for a range of conditions ( 85 ).

Finally, the importance of social networks—real and virtual—in obesity is a fascinating, relatively new area of research that capitalizes on known characteristics of infectious disease transmission. In a landmark 2007 study examining the spread of obesity due to social ties using 32-year prospective data from the Framingham Heart Study, Christakis and Fowler ( 86 ) showed that an individual’s chances of becoming obese increased by 57% if he or she had a friend who became obese in a given 4-year interval. This was a stronger risk ratio than that observed between pairs of adult siblings or even between spouses. Conversely, it may be possible to capitalize on the social contagion of obesity in the reverse direction, that is, in the promotion of healthy weight and behavior. Intervention studies of weight loss often include a social-relational component, although the evidence supporting any single approach or its efficacy is relatively scarce ( 87 ). In theory, a supportive network, community, or coaching relationship is supposed to improve weight loss; despite a lack of strong evidence, it is a key component of many popular commercial (e.g., Weight Watchers), trial/intervention, and online approaches.

6. Costs of Obesity: Co-Morbidities, Mortality, and Economic Burden

Obesity is associated with concomitant or increased risk of nearly every chronic condition, from diabetes, to dyslipidemia, to poor mental health. Its impacts on risk of stroke and cardiovascular disease, certain cancers, and osteoarthritis are significant.

6.1. Overall Mortality

In the year 2000 in the USA, 15% of deaths were attributable to excess weight, owing to poor diet and physical inactivity ( 38 ). Overweight/obesity in middle age shortens life expectancy by an estimated 4–7 years ( 88 ). Many long-term cohort studies, as well as three recent major syntheses of pooled data from established cohorts ( 89 – 91 ), which adequately accounted for history of smoking and chronic disease status, unequivocally show that overweight and obesity over the life course is associated with excess risk of total mortality, death from cardiovascular disease, diabetes, cancer, or accidental death ( 89 – 97 ).

Some studies suggest that excess body weight may be protective against mortality from certain chronic conditions—resulting in a so-called “obesity paradox.” However, most studies that have shown an obesity paradox, or no association between obesity and mortality, have been conducted in groups of older (>65) or elderly patients or in those with chronic conditions, or have inadequately accounted for smoking. Indeed, the role of excess adiposity in old age is unclear. While the protective effects of overweight in specific instances of diseased older populations may be real, these observations are fraught with methodological problems, especially reverse causation, and belie the limitations of generalizing excess adiposity’s supposed benefits to younger populations over the life course, not least because excess body weight leads to higher disease incidence to begin with ( 7 ).

6.2. Diabetes

Excess weight and diabetes are so tightly linked that the American Diabetes Association recommends physicians test for type 2 diabetes and assess risk of future diabetes in asymptomatic people ≥45 years old simply if they are overweight/obese, and regardless of age if they are severely obese ( 98 ). Overweight raises risk of developing type 2 diabetes by a factor of three, and obesity by a factor of seven, compared to normal weight ( 99 ). Excess weight in childhood and in young adulthood, and weight gain through early to mid-adulthood are strong risk factors for diabetes ( 100 – 102 ). While not every overweight/obese individual has diabetes, some 80% of those with diabetes are overweight/obese ( 103 ). Obesity itself raises diabetes risk even in the absence of other metabolic dysregulation (insulin resistance, poor glycemic control, hypertension, dyslipidemia). While metabolically healthy obese individuals are estimated to have half the risk of their metabolically unhealthy counterparts, they still have four times the risk of those who are normal weight and metabolically healthy ( 104 ).

6.3. Heart and Vascular Diseases

Ischemic heart disease and stroke are the leading causes of death in the USA and globally ( 105 ). Excess body weight is a well-known risk factor for heart disease and ischemic stroke, including their typical antecedents—dyslipidemia and hypertension. Recent studies have consistently shown that benign obesity appears to be a myth ( 106 – 108 ); overweight clearly adds to risk of heart disease and stroke beyond its implications for hypertension, dyslipidemia, and dysglycemia.

Given childhood obesity rates, research has lately focused on the role of obesity in early life and subsequent adulthood disease. Obesity in childhood or adolescence has been associated with twofold or higher risk of adult hypertension, coronary heart disease, and stroke ( 100 ). A recent study pooling data from four child cohorts (aged 11 years at baseline with average 23-year follow-up), observed that, compared with individuals who were normal weight in childhood and non-obese as adults, those who were normal weight or overweight but became obese as adults, or who were obese and stayed obese into adulthood, had considerably higher risk of high-risk dyslipidemia, hypertension, and higher carotid intima-media thickness. Notably, those individuals who were overweight/obese as children, but non-obese as adults, had similar risk profiles to those individuals who were never obese, indicating that the potential health effects of childhood obesity can be offset by weight loss prior to or while entering into adulthood ( 109 ).

6.4. Cancer

An estimated 6% of all cancers (4% in men, 7% in women) diagnosed in 2007 were attributable to obesity ( 110 ). Beyond being a major risk factor for diabetes, which itself is a risk factor for most cancers, obesity has long been understood to be associated with increased risk of esophageal, colon, pancreatic, postmenopausal breast, endometrial, and renal cancers ( 111 ). More recently, evidence has accumulated that overweight and/or obesity raise risk of cancers of the gallbladder ( 112 ), liver ( 113 ), ovaries (epithelial) ( 114 ), and advanced cancer of the prostate ( 115 ), as well as leukemia ( 116 ).

6.5. Trauma and Infection

A study in Pennsylvania (USA) trauma centers (2000–2009) showed that in-hospital mortality and risk of major complications of surgery were increased in obese patients as compared to non-obese patients. Severely obese patients had upwards of 30% increased risk of mortality from their trauma than non-obese patients, and double the risk of major complications. Severely obese females also had more than double the risk of developing wound complications, and quadruple the risk of developing decubitus ulcers ( 117 ). A recent meta-analysis of obesity in trauma care concluded that obesity was associated with 45% increased odds of mortality, longer stays in the intensive care unit, and higher rates of complications, and tended to associate with longer durations of mechanical ventilation and longer stays in the hospital overall, compared to non-obese patients, despite equivalent injury severity ( 118 ).

While elevated risk of chronic disease is a seemingly obvious consequence of obesity, increasing attention is being given to increased risk of infection and infectious disease in obesity, including surgical-site, intensive care unit (ICU)-acquired catheter, blood, nosocomial, urinary tract, and cellulitis and other skin infections ( 119 ), community-acquired infections, and poorer recovery outcomes owing to higher risk of influenza, pneumonia, bacteremia, and sepsis ( 119 ). Impaired immunological response may be an underlying mechanism; recent research has demonstrated lower vaccine efficacy and serological response to vaccination in the obese. For example, a recent study estimated an eightfold increase in the odds of non-responsiveness to hepatitis-B vaccination in obese versus normal-weight women ( 120 ).

The consequences of a global obesity epidemic may not merely be greater chronic and infectious disease burden for the obese, but also a greater global burden of infectious disease owing to obesity. Greater infectious disease vigilance may be required in populations with high levels of overweight/obesity, and there is a clear need for better clinical practice guidelines (e.g., use and dosage of antimicrobials, vaccines, other pharmaceuticals) for obese individuals.

6.6. Mental Health

The role of weight in mental health faces causal challenges, but what is clear is that obesity and adiposity are associated with anatomical as well as functional changes in the human brain. Studies in older populations have shown that BMI is inversely correlated with brain volume, and that obese older adults, compared to normal weight counterparts, show atrophy in the frontal lobes, anterior cingulate gyrus, hippocampus, and thalamus ( 121 ). In addition, obesity in children and adolescents (aged >9 years) has been associated with smaller orbitofrontal cortex gray matter volume, along with poorer performance in certain domains of executive function (e.g., inhibitory control) ( 122 ). Being overweight in midlife increases risk of Alzheimer's disease, vascular dementia, or any type of dementia by 35, 33, and 26%, respectively; even higher risk is observed for obesity ( 123 ). Importantly, physical activity, even among overweight individuals, may stave off poor mental functioning: moderately active or highly active adult overweight Finns did not have significantly increased risk of poor mental functioning at a 7-year follow-up compared to those who were normal weight and highly active, but inactive and overweight patients presented a nearly 40% increased risk of poor mental functioning ( 124 ). Thus, exercise may play an important mediating role in the relationship between excess body weight and age-related cognitive decline.

6.7. Economic Burden of Obesity

In the USA, recent estimates indicate that obese men are thought to incur an additional US$1,152 per year in medical spending, particularly due to hospitalizations and prescription drugs, compared to their non-obese counterparts, while obese women incur over double that of obese men, an additional US$3,613 per year in medical spending (year 2005 values). Extrapolating these costs to the national level, authors estimate some US$190 billion per year of healthcare spending, approximately 21% of US healthcare expenditures, is due to treating obesity and obesity-related conditions ( 125 ).

Total hospital costs account for a part of this: another author group studied non-bariatric, non-obstetric hospital procedures for obese patients, finding they were US$648 higher (year 2009 values) per capita than for non-obese patients. The estimated national hospital expenditures for the largest volume surgical procedures was US$160 million higher per year for obese than for their non-obese counterparts ( 126 ).

Employers bear a substantial brunt of obesity-related costs in the USA. Data from the Human Capital Management Services Research Reference Database (2001–2012) on employees and their dependents was used to compare medical, drug, sick leave, short-term disability, and workers’ compensation costs as well as absent days across three BMI strata: <27, ≥27–<30, and ≥30 kg/m 2 . Each of the costs was incrementally higher in ascending BMI categories. For example, total annual costs and total days absent in the highest vs. lowest BMI strata were US$6,313 versus US$4,258 (year 2012 values), and 7.5 versus 4.5 days. In addition, productivity was lowest in the obese group ( 127 ).

Finally, lifetime direct incremental medical costs of obesity in childhood in the USA were estimated to range from US$12,660 to US$19,630 (year 2012 values) for an obese 10-year old compared to a normal-weight 10-year old, if expected weight gain through adulthood among the normal weight child occurs ( 128 ). If normal weight children were to not continue on the typical weight gain trajectory into overweight/obesity, estimated incremental medical costs for today’s 10-year old obese child ranges between US$16,310 and US$39,080. Putting these figures into perspective, multiplying the lifetime medical cost estimate of US$19,000 by the number of obese 10-year-olds today generates a total direct medical cost of obesity of roughly US$14 billion for this 10-year old age group alone. In terms of big picture savings, the upper estimate of US$39,000 per case represents two years of public college tuition for that child ( 128 ).

In Europe, a 2008 review of 13 studies in ten Western European countries estimated the obesity-related healthcare burden had a relatively conservative upper limit of €10.4 billion annually (in Germany, in 1995 € equivalent), and ranging between <0.1 to 0.61% of each country’s gross domestic product (GDP). The review relied on study data from as early as the 1980s in the Netherlands, through 2002 in most of the remaining countries surveyed ( 129 ). A more recent review focused on 19 studies published in 2007–2010 in eight Western European countries (predominantly Germany, Denmark, and the United Kingdom). Excess health care costs of obesity or derivations of excess health care costs by comparisons of mean costs between normal weight and obese individuals in seven of the reviewed studies were between €117 and €1,873 per person (based on the € valuation given in each study year). Excess costs increased particularly due to severe obesity. Approximately 23% of medication costs and 6.9% of out-of-pocket costs were attributable to overweight or obesity. Health economic models estimated that 2.1–4.7% of total health care costs and 2.8% of total hospital costs were due to overweight and obesity. Total (direct and indirect) costs were generally unchanged from the 2008 estimate of the earlier review, accounting for 0.47–0.61% of GDP in these countries ( 130 ).

In the context of the Brazilian Unified Health System (i.e., public hospitals), estimated direct costs of diseases related to overweight/obesity in outpatient and inpatient care based on 2008–2010 data were US$2.1 billion annually (year 2010 values), 68.4% of which was attributable to hospitalizations, and the remainder due to ambulatory procedures ( 131 ). The largest costs of outpatient and inpatient care in both sexes were due to cardiovascular disease (US$747 million) followed by overweight- and obesity-related neoplasms (US$299.8 million), asthma (US$34 million), type 2 diabetes (US$3.7 million), and osteoarthritis (US$3.9 million). Authors estimated that these direct costs were a considerable underestimate of the true burden of overweight/obesity in Brazil, which would include private health care expenditures, as well as indirect costs due to lost productivity, premature death, and home care ( 131 ).

Given the predicted rise in obesity in Brazil, coronary heart disease, stroke, hypertension, cancers, osteoarthritis, and diabetes are projected to at least double by 2050, with concomitant doubling in health care costs, from US$5.8 billion in 2010 to US$10.1 billion per year—totaling US$330 billion over 40 years (year 2010 values). It is estimated that a 5% reduction in mean BMI across the population could save Brazil some US$57 billion over that time frame ( 132 ). A similar analytic approach that substituted Mexican prevalence and trends for the Brazilian ones estimated 2010 costs of obesity at US$806 million (year 2000 values), which were projected to increase to US$1.7 billion by 2050, at which point a mere 1% reduction in BMI prevalence in Mexico could save an estimated US$85 million per year ( 133 ).

Of course, none of these estimates include dollars spent on the weight-loss industry, which is estimated to be over US$60 billion dollars in 2014 in the USA alone ( 134 ), and includes non-prescription drugs and supplements, diet plans, gym memberships, workout videos, and an endless stream of money-making schemes.

7. Touching on Solutions, and Some Conclusions

Obesity is a major contributor to preventable disease and death across the globe, and poses a nearly unprecedented challenge not just to those tasked with addressing it at the public health level, or at the healthcare provider level, but to each of us as individuals, for none of us are immune. Increasing ease of life, owing to reduced physical labor and automated transportation, an increasingly sedentary lifestyle, and liberal access to calorie-dense food, driven by dramatic economic growth in many parts of the world in the last century, have turned a once rare disease of the affluent into one of the most common diseases—increasingly of the poor. That barely one in three people in the USA today are normal weight portends, quite simply, an astounding and frightening future. Significant reductions in public health and healthcare expenditures could occur around the world if we were able to stem the tide of childhood obesity trends, and if young and middle-aged overweight and obese adults lost approximately10% of their body weight, as recommended for a considerably reduced risk of debilitating chronic conditions ( 135 ).

Obesity is complex. Although its risk factors are myriad and compounding, there is an urgent need for deeper understanding of the way risk factors interact with each other, and the potential solutions to the epidemic are as multi-leveled and complex as its causes. There are calls for applying systems-level ( 136 ) and systems epidemiology ( 137 ) approaches to this and related nutrition and metabolic diseases, approaches which attempt to comprehensively address biological, behavioral, and environmental contributors to disease as well as their intricate feedback loops. Additional research on solutions to this epidemic would include, for example, examining the relative cost/benefit to individuals and populations of individual versus systemic policies and/or interventions, concurrently or independently, particularly when individuals and communities must decide between approaches given limited resources, and moreover, with the currently limited evidence in the case of broad industry, agricultural, or public health policies. For example, we could attempt to limit national production and import of sugar-sweetened beverages, tax sugar-sweetened beverages, or restrict fast food restaurant zoning. These largely political acts seem relatively inexpensive, but may have economic impacts in communities and regions beyond what we currently understand. We may push for the increasing medicalization of obesity, including developing an obesity vaccine. While such a “cure” may someday arise, the medicalization of a condition typically improves its treatment rather than its prevention, and prevention is key in the case of obesity. However, preventing and remediating obesity in children and adults—e.g., via health and wellness incorporation into curricula at every educational level from kindergarten through medical school—requires vast resources allocated to educators, as well as earlier diagnosis and treatment of overweight (education, counseling, drug treatment, etc.). Given these resource costs, perhaps greater attention should be given to pregnancy, a condition which is already highly medicalized and which may be an ideal preventive avenue for the provision of nutrition education and intensive monitoring of weight gain, to ensure that children have the most optimal start with respect to their future obesity risk. Clearly, no single approach is optimal, but with limited resources, an evidence base supporting one or more approaches or their combination is needed, as is tenacity and perhaps some audacity by local government and public health authorities in testing some of these approaches within their populations. However, an epidemic of this magnitude needs, quite simply, more resources. One of the reasons why the American Medical Association opted to declare obesity a “disease” was to give obesity the label it needs for greater allocation of resources for research, prevention, and treatment ( 1 ).

Despite the many unknowns, we can be cautiously optimistic about our ability to address the obesity epidemic. Indeed, we have relatively successfully faced similarly daunting public health challenges before: smoking, to name just one. While tobacco can loosely be thought of as a single product, and our food culture is infinitely more complex, as a case study in how to approach obesity, it provides numerous lessons in multi-level solutions to a major health threat in terms of both mitigation and prevention. We began by developing an understanding of smoking’s epidemiological impact and the healthcare costs borne by society, uncovered its biological basis, learned about and applied behavior change, and initiated and carried out vast public health, public policy, political, and economic strategies that ultimately affected whole environments as well as sociocultural norms.

It took over half a century to achieve the immense success we have with regard to smoking in the USA and still we are not yet tobacco-free ( 138 ); other parts of the world continue to wrestle with it to a greater degree. It has only been a couple decades since we first deeply appreciated that obesity was epidemic. We clearly still have a long way to go.

Key Points for Decision Makers

• In 2013, an estimated one in three adults worldwide was overweight or obese, and adult obesity exceeded 50% in several countries around the globe. While the prevalence of adult obesity in the developed world seems to have stabilized, the prevalence of obesity in children and adolescents globally, as well as adults obesity in developing countries, is still increasing. In addition, some developed countries continue to observe increasing prevalence of extreme classes of obesity.
• Overweight and obesity—defined as excess body weight for height—have genetic, behavioral, socioeconomic, and environmental origins.
• Obesity increases risk of major chronic diseases, including heart disease, diabetes, depression, and many cancers, as well as premature death.
• Estimates of annual healthcare costs attributable to obesity are US$190 billion per year in the USA, approximately 21% of US healthcare expenditures.
• Given its complexity, the obesity epidemic requires multilevel and integrated solutions, from individual intervention, to broad food policy, industry, and agriculture initiatives.

Acknowledgements

The authors declare no conflict of interest. AH is supported by an American Diabetes Association Mentor-Based Postdoctoral Fellowship award. FH is supported by NIH grants DK51158, HL60712, P30 DK46200, and U54CA155626. The authors broadly thank the researchers in this field for their consistent and tireless work in illuminating the etiology, sequelae, and solutions to this complex condition.

1 See also http://www.genome.gov/gwastudies/

2 We do not review the impact of food, agriculture, trade, and nutrition policy on obesity in the present paper, but refer interested readers to a recent review ( 64 ). Further, we do not address the body of growing evidence on the role of environmental pollutants–“obesogens”–in obesity, specifically those known as endocrine-disrupting chemicals. We refer readers to recent reviews on the topic ( 35 , 36 , 65 ).

Author Contributions

AH wrote the first draft of the paper. AH and FH contributed to writing, revised, and edited the paper. AH is the final guarantor of this work and takes full responsibility for its contents. Both authors read and approved the final manuscript.