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Socioeconomics of Obesity

Chika Vera Anekwe, MD, MPH*,1,2, Amber R. Jarrell3, Matthew J. Townsend2, Gabriela I. Gaudier4, Julia M. Hiserodt2, Fatima Cody Stanford, MD, MPH, MPA2,5

1Massachusetts General Hospital, MGH Weight Center, Department of Medicine- Division of Endocrinology-Endocrine Unit Boston, MA

2Harvard Medical School, Boston, MA

3Louisiana State University School of Medicine, New Orleans, LA

4Tufts University School of Medicine, Boston, MA

5Massachusetts General Hospital, MGH Weight Center, Department of Medicine- Division of Endocrinology-Neuroendocrine Unit, Department of Pediatrics-Division of Endocrinology Boston, MA

Abstract

Purpose of Review—The purpose of this review is to evaluate and emphasize important

findings in the recent literature regarding the socioeconomics of obesity. It is important to evaluate

trends of this global epidemic and elucidate its impact on different demographic groups and across

socioeconomic strata.

Recent Findings—Obesity rates continue to increase domestically and globally which is

associated with a concomitant rise in medical and economic costs. There are disparities in obesity

rates based on race/ethnicity, sex, gender and sexual identity, and socioeconomic status, yet these

disparities are not explained fully by health behaviors, socioeconomic position or cumulative

stress alone – community and societal environmental factors have a significant role in the obesity

epidemic.

Summary—Socioeconomic factors contribute to obesity on an individual and community level,

and any viable approach to sustainably addressing the obesity epidemic must take these factors

into account.

Keywords

Obesity; socioeconomic status; body mass index; race; gender; minority

Terms of use and reuse: academic research for non-commercial purposes, see here for full terms. http://www.springer.com/gb/open- access/authors-rights/aam-terms-v1 *Corresponding Author: Chika Vera Anekwe, MD, MPH, Massachusetts General Hospital Weight Center, 50 Staniford Street, Suite 430, Boston, MA 02114, Tel: 617-726-4400, Fax: 617-724-6565, canekwe@mgh.harvard.edu.

Conflict of Interest Chika Vera Anekwe, Amber R. Jarrell, Matthew J. Townsend, Gabriela I. Gaudier and Julia M. Hiserodt declare that they have no conflict of interest. Fatima Cody Stanford serves on the advisory board of Novo Nordisk

Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.

Ethics Committee Approval: This viewpoint was considered IRB exempt.

HHS Public Access Author manuscript Curr Obes Rep. Author manuscript; available in PMC 2021 September 01.

Published in final edited form as: Curr Obes Rep. 2020 September ; 9(3): 272–279. doi:10.1007/s13679-020-00398-7.

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Introduction

Obesity is a chronic disease with significant medical, social and economic consequences

both domestically and globally. Obesity is determined by an individual’s body mass index

(BMI), defined as weight in kilograms divided by height in meters squared. For adults, the

overweight BMI range is 25 kg/m2 to less than 30 kg/m2, and the obesity BMI range is 30

kg/m2 and higher [1]. The prevalence of obesity in the United States is high and continues to

increase [2]. In 2011–2012 16.9% of children and 34.9% of adults were affected by obesity

[3]; in 2015–2016, prevalence rose to18.5% in children and in 2017–2018 prevalence

reached 42.4% in adults [4]. Predictive modeling suggests the prevalence of obesity in U.S.

adults will be 48.9% by the year 2030 [5]. This epidemic has the greatest prevalence among

socially disadvantaged groups and under-represented persons (ethnic/racial minorities,

women, and individuals from lower socioeconomic backgrounds) [2].

Obesity affects different racial and ethnic groups at various rates. According to the National

Center for Health Statistics in 2017–2018, black women had the greatest prevalence of

obesity at 56.9% among U.S. adults. The prevalence of obesity among Hispanic women was

43.7%. The prevalence of obesity among men was quite similar among black and white

males at 41.1% and 44.7%, respectively. Non-Hispanic Asian men and women had the

lowest prevalence of obesity compared to any other race/ethnicity [4].

Sexual minority populations, such as lesbian, gay, or bisexual groups, are at risk of obesity

due to consequences of homophobia, prejudice, and increased levels of stress [6, 7]. Lesbian

and bisexual females have an increased prevalence of obesity when compared with straight

females. However, gay males have a lower prevalence of obesity when compared to straight

males [6, 7]. Gender minority populations include transgender and individuals who do not

conform to societal norms of sexual orientation, gender identity, and/or gender expression.

Gender nonconforming birth-assigned females were found to have higher BMIs than gender

conforming females, while gender nonconforming birth-assigned males had a lower BMI

than their conforming counterparts [7]. Outpatient data of adolescents between the ages of

14–25 diagnosed with gender dysphoria or taking gender affirming hormones in 2008–2014

were analyzed retrospectively. Affirmed males (female to male) experienced a significant

increase in BMI (presumably due to exogenous testosterone), while affirmed females (male

to female) did not experience any significant weight change throughout their course of

hormone administration [8].

Obesity in children (classified as ages 2–11) and adolescents (ages 12–21) is based on age

and sex-specific percentiles using the CDC’s BMI-for-age growth charts [9, 10]. The

overweight category ranges from 85th to less than the 95th percentiles. Obesity is defined as

at or above the 95th percentile [11]. Like adults, the prevalence of obesity in youth varies by

race/ethnicity and gender. In 2011 – 2014, increased prevalence of obesity was seen in

Hispanic children and adolescents when compared to white, black, or Asian youths. The

lowest prevalence of obesity was among Asian children and adolescents [12]. Adolescents

with overweight have a 70% chance of becoming an adult with overweight or obesity [9].

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Socioeconomic status (SES) is an important factor associated with obesity. SES can be

determined using variables such as education, income, and occupation, with education

considered to be the most stable variable over time [2]. A study of data collected in the

National Health and Nutrition Examination Survey (NHANES) from 1971–1974, 1976–

1980, 1988–1994, and 1999–2000 of adults between the ages of 20–60 identified trends in

obesity rates among SES levels. Education levels were classified as low (less than high

school, 9th grade or below), medium (high school, 10th to 12th grade), and high (college or

higher). Over the past 3 decades, the prevalence of obesity increased among low SES

groups, while increasing significantly among high SES groups, thus leading to a reduction in

disparities in obesity rates across different SES groups [2]. This trend was consistent across

ethnic/racial and gender categories. Although low SES is an established risk factor for

obesity [13], its impact may be mediated in part by psychosocial stress [14]. A study

examining the effects of neighborhood poverty and psychosocial stress on central adiposity

demonstrated that people living in neighborhoods with increased poverty and unfair

treatment were at an increased risk of central adiposity [15]. These findings indicate that

increased stress exposure among blacks and US born Hispanics as compared with whites

and non US born Hispanics, may play a role in disparities in obesity rates among these

group [14, 15].

Consideration of Obesity as a Disadvantaged Status

Obesity may be viewed as a form of socioeconomic disadvantage. Multiple longitudinal

studies have demonstrated that childhood and/or adolescent obesity is associated with

persistent or widened socio-economic disadvantage in adulthood [16, 17]. In addition,

midlife obesity is associated with multiple indicators of socio-economic disadvantage in

midlife and later adulthood, as measured by both subjective and objective measures [18].

Individuals with obesity encounter bias in multiple settings, with reported rates of weight

discrimination approaching those due to gender and race/ethnicity [19–22]. In the

workplace, employees with obesity are perceived as having lower supervisory potential,

lower self-discipline, and worse personal hygiene; as less likely to be seen as suitable for

public-facing sales positions; and as having lower promotion prospects compared to

average-weight peers [19]. Data from the National Longitudinal Survey of Youth (NLSY)

obtained annually from 1979 through 1994 and biennially from 1994 through 1998, suggest

the existence of a wage penalty for obesity, after controlling for sociodemographic,

economic, and health variables, ranging from 0.7–3.4% for men and 2.3–6.1% for women

[23]. These wage penalties appear stronger for women than men as a whole, and may

accumulate over time [24].

Weight stigma also pervades medical practice. Physicians may perceive patients with higher

BMI to be less adherent to medications, even after adjusting for patient demographic factors,

patient-reported adherence, literacy level, and blood pressure control [25]. Weight stigma

has also been observed among nurses, medical students, psychologists, and dietitians [21].

These biases may affect medical practice and health care utilization. Patients with obesity

are less likely to complete recommended cancer screenings and more likely to avoid care

[21]. It has also been suggested that the activation of pathophysiologic stress pathways

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triggered by weight stigma may act as an intermediary between obesity and poor health

outcomes [26]. Indeed, as a consequences of external weight bias, those with obesity

develop internalized weight bias which leads to both physical and mental health impairment

[27, 28]. In these several ways, obesity may drive disadvantage in ways not captured by

classical measures of socioeconomic status.

In considering obesity as a disadvantaged status, we must also address the overlapping

concepts of food insecurity and the built environment. 11.8% of U.S. households were food

insecure at some time in 2017 [29]. Persistent household food insecurity, in particular when

present without hunger, has been associated with a 22% increased odds of childhood obesity

versus children who are food secure throughout childhood [30]. Adults with food insecurity

similarly have a higher prevalence of obesity compared to adults who are food secure [31].

While studies reveal differing associations by race/ethnicity, age, and gender, the most

consistently reported association between food insecurity and obesity is among women [31,

32]. The built environment, including housing, transportation, workspaces, and recreational

infrastructure, has a strong influence on obesogenic status. The presence of a neighborhood

conducive to physical activity or active commuting [33] and healthy food environments, as

characterized by a lower percentage of limited-service restaurants [33] or convenience stores

[34], higher density of supermarkets [35], and perceived availability of healthy foods [36]

have each been associated with lower BMIs.

Socioeconomically disadvantaged groups face unique obesity treatment challenges. In

addition to having increased baseline risk and burden of obesity, black, Hispanic, and low-

income individuals are underrepresented in existing treatment literature; the few published

trials have demonstrated drawbacks such as high attrition rates and weight loss outcomes

lower than expected [37, 38]. Harvey and Ogden suggest several potential solutions

including material incentives and telecommunications technologies via Internet or mobile

phone to reduce barriers to accessing treatment, such as transportation, costs, and childcare

concerns [37]. One promising 24-month randomized effectiveness trial employed a

behavioral weight loss approach including skills training, community health educators, and

eHealth tools for self-monitoring progress and real-time feedback, demonstrating 6-month

weight losses sustained at study conclusion (24 months) in a population with 71.2% black

and 13.1% Hispanic individuals, as well as 32.9% who did not complete high school [39].

While such trials offer some potential solutions, more studies are needed to better meet the

disproportionate burden of obesity among disadvantaged groups.

Ethnic/Racial Burden of Obesity

Racial and ethnic disparities are apparent not only in obesity rates but also in the rates of co-

morbidities associated with obesity such as hypertension, diabetes, and arthritis [40]. Over a

lifetime, cardiovascular conditions are increased in patients with obesity, particularly in

black and Hispanic-Americans. Metabolic syndrome, made up of a constellation of

metabolic abnormalities such as hypertriglyceridemia and low high density lipoprotein

(HDL) cholesterol, is increased in patients with obesity, specifically Mexican-Americans

followed by black women, black men and finally Caucasians [41]. Similar ethnic/racial

trends are seen in the rates of diabetes; however, this association persists after controlling for

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BMI, indicating that BMI alone does not account for the ethnic/racial disparities. These

trends are different among those with hypertension – Mexican-Americans have the lowest

rates while blacks have the highest. In terms of mortality, whites with obesity have a higher

risk of death than blacks [42, 43], though the risk of stroke and coronary heart disease are

increased in the latter [40].

There are several mechanisms that may explain the disparities in obesity rates between

majority and minority populations. The role of health behaviors is one mechanism. Racial

and ethnic minorities have been shown to generally consume fewer fruits and vegetables and

engage in less physical activity than whites [14, 44]. Poor dietary quality is highly correlated

with food insecurity, more commonly seen in Hispanics, blacks and low SES populations

[45]. Another factor that may lead to increased food intake and insulin resistance is

psychosocial stress, which is found at a higher proportion in racial/ethnic minorities [14].

There is also evidence to support that decreased sleep duration can lead to increased body

mass [46]. Reduced sleep duration is associated with longer work hours, low SES, and lower

education, all of which occur more often in racial/ethnic minorities, specifically in black

men; however the overall association between sleep and BMI can vary by race/ethnicity,

gender and age [45].

Interplay of Gender and Obesity

Dietary intake is the predominant factor affecting gender differences in obesity. Women tend

to eat more calorie dense foods even though they report the desire to eat healthier more than

men [47]. Alternatively, men experience weight gain in response to increased alcohol intake

compared to women, though this may vary based on type and frequency of alcohol

consumed [47]. An individual’s social network may affect obesity, more in men than

women. Males with friends with obesity increase their personal risk of obesity. Additionally,

some developed countries consider men with obesity as having higher social status while

attributing thin body habitus in women as more desirable. Female obesity (but not male

obesity) can be trended based on the economic status of the country, with most countries

showing a positive correlation between SES status and obesity in women [47, 48]. Changes

in labor needs and unemployment rates have dramatically reduced physical activity levels

for both men and women [47]. Moreover, regional cultural beliefs have led to disparities in

female weight such as restricted public physical activity for women in some Arab countries

and emphasis on excess weight as a sign of fertility in some Asian and African countries

[47]. Education seems to play a role in obesity rates predominantly in women, for whom

increased education is highly correlated to less risk for obesity in many countries [49].

Socioeconomic status affects the relationship between gender and obesity in myriad ways. In

males, lower income jobs often include manual labor or more physical work than many jobs

women at the same SES level may obtain [13, 47]. The prevalence of obesity in men tends to

be comparable across SES classes as defined by education level, though it is still greatly

affected by race/ethnicity. For example, highly educated white men had a lower risk of

obesity in comparison to highly educated black men [2]. However when income or

occupation are considered, men at the lowest SES levels have nearly double the risk of

obesity [49]. Women in the U.S. have a much clearer trend across SES levels than men with

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significant increases in rates of obesity as SES status decreases [50]. A long-term

consequence of obesity in lower SES women is a higher risk of their children developing

obesity [49].

Indeed, the predominant risk factor for obesity in children is parental or familial obesity

regardless of gender, though particular parental behaviors may lead to weight gain in female

adolescents more than males [51]. Obesity trends vary based on ethnicity within all age

groups in a similar pattern as adult trends, but more variation is seen in female youths than

males [51]. As children transition into their teenage years, males develop more fat-free mass

leading to a higher total energy expenditure compared to females. Conversely, menstruation

can lead to cravings in young females, particularly for fat and carbohydrate rich foods.

Additionally, physical activity is an important factor affecting obesity in children. Females

generally tend to engage in less physical activity than males of the same age, but sedentary

behavior regardless of gender is a contributing influence on obesity [51].

Economic Impact of Obesity

Medical costs of obesity and obesity-related diseases

Obesity is a risk factor for multiple chronic diseases and accounts for a significant portion of

costs associated with these diseases. The relative risk of developing many chronic diseases is

increased in those with overweight and obesity, including end-stage renal disease, congestive

heart failure, and many cancers such as breast, endometrial and gallbladder cancer [52].

Type 2 diabetes is increased in those with obesity by a factor of 7 [13, 53], while 6% of

cancers diagnosed in 2007 were attributed to obesity [13, 54]. There is also an increased risk

of infection and major complications during hospitalizations in individuals with obesity [13,

55]. For those with obesity, there is a twofold increase in the risk of hypertension [56], and

an almost 3.5-fold increase for diabetes and twofold increase for diabetes and

hyperlipidemia, respectively [57, 58].

The economic burden of these chronic conditions has been estimated by many groups and

ranges widely. Waters and Graf did an extensive review of obesity-related diseases, and

analyzed the costs of these diseases that can be directly attributed to obesity by calculating

the population attributable risk, or the percent of cases that can be directly attributed to

obesity, and multiplying those percentages by the total healthcare costs for each chronic

disease in 2016. The direct medical costs of overweight and obesity was determined to be

$480.7 billion for 2016 alone [52]. Hruby and Hu estimated that for men and women with

obesity, respectively, there was an additional $1,152 per year and $3,613 per year in medical

spending, with a total of $190 billion nationally going to the treatment of obesity and

obesity-related diseases [13].

Work absenteeism

The economic costs of obesity reach outside of healthcare as well. Multiple studies have

found positive and statistically significant correlations between obesity and absenteeism,

likely related to the increased risk of chronic disease as described above. Kleinman and

colleagues analyzed four categories of absenteeism, including total absence days, sick-leave

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days, disability days, and workers’ compensation days, and found that employees with

obesity (BMI ≥ 30) had 1.43 more sick days and 3.08 more total absences, had a 70%

increase in short-term disability days, and had a 281% difference in workers’ compensation

days compared to employees with BMI < 27 kg/m2 [59]. Other studies report increased

absence rates ranging from 25% to almost 200% more absence days in employees with

obesity compared to those without obesity [57, 59]. These increased absences from the

workforce account for significant economic costs, with one study estimating that losses from

absenteeism range from $3.38 billion to $6.38 billion nationally per year, and another study

estimating the total cost to be as high as $11.2 billion per year [57, 60].

Lower work productivity

Studies have shown that even when present at work, employees with obesity have decreased

productivity compared to those with standard weight (defined here as BMI between 18.5 –

25 kg/m2)[57]. It is unclear what the cause of this decreased productivity is, but it is

hypothesized to be either a result of the increased medical comorbidities found in those with

obesity and/or factors that may contribute to having both obesity and decreased productivity

in an individual [57].

This decrease in productivity comes at a cost to employers. A systematic review by Goettler

and colleagues found that the cost from decreased productivity of individuals with obesity

ranged between $11 and $4175 per individual per year [61]. Another study by Ricci and

Chee analyzed the amount of lost productive time between individuals with standard weight,

overweight, and obesity, and found that although there were no differences in productivity

costs between employees with standard weight and overweight, those with obesity had an

excess cost of $11.7 billion per year in lost productivity [62].

Premature death

Premature death also contributes to the economic costs of obesity. Obesity is a significant

risk factor for the development of chronic diseases such as cardiovascular disease, diabetes,

and cancer, and these increased comorbidities have led to a decreased life expectancy for

those with obesity. One study estimates that obesity contributed to a total of 4 to 7 years lost

per individual, while others have reported between 6 and 14 years of life lost [13, 63, 64].

The monetary cost of these years lost is currently unreported, and more work still needs to

be done to determine the economic impact of this decreased life expectancy.

Health insurance and disability insurance costs

Although less studied than other economic costs of obesity, it is thought that there are

increased costs from the public health insurance structure in relation to obesity. As described

above, obesity accrues large excess medical costs via increased chronic conditions and these

increased medical costs are shared among the larger population within our public health

insurance structure, rather than burdening the one individual. One study has argued that this

public sharing of medical costs transfers the cost away from those with obesity, removing

monetary incentive for those with obesity to improve their weight status and overall health

[57]. The estimated cost of this externality is about $150 per individual [57].

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Transportation costs

Transportation costs have also increased with the rising incidence of obesity. As weight

increases, so does the amount of fuel required to transport an individual any given distance.

In the airline industry between 1990 and 2000, increases in weight required an additional

350 million gallons of jet fuel per year, accounting for an additional $275 million in jet fuel

costs in the year 2000 alone [65]. For non-commercial highway vehicles, increases in

average weight since the 1960s have accounted for an additional 1 billion gallons of fuel

used per year, which accounts for about $2.7 billion a year [57, 66, 67].

Educational attainment

Educational attainment has been studied as another potential area of economic impact of

obesity. The relationship between educational attainment and obesity is currently debated,

although studies have begun to point to an overall negative relationship between educational

attainment and excess weight [57, 68–70]. A study conducted by Hagman and colleagues

compared Swedish adolescents with obesity to a matched cohort with standard weight and

analyzed the percentage of students who completed at least 12 years of school. They found

that 55.4% of adolescents with obesity completed at least 12 years of school, compared to

76.2% of adolescents with standard weight status [69]. French and colleagues found that

girls with obesity were less likely to attain a Bachelor’s degree and were also less likely to

earn more than $50,000 annually, although they did not find a significant difference in boys

with obesity, while Black and colleagues found that boys with obesity had significantly

lower scores on national math and literacy assessments, with no difference for girls when

sociodemographic factors were taken into account [68]. The economic impact of this

decrease in educational attainment has yet to be quantified.

Quality of Life (QoL)

QoL is another important measure of the impact of obesity and is likely related to many of

the above factors, including medical comorbidities, absenteeism, productivity, and

educational attainment. Individuals with obesity have been shown to have poorer QoL in

multiple categories compared to standard weight individuals, with physical QoL measures

more closely associated with BMI than mental/psychological measures [71, 72]. These

effects were consistent across gender and age groups. Importantly, certain aspects of QoL

have been shown to improve with weight loss, especially in individuals who undergo

bariatric surgery, likely because of the increased percentage of weight lost with surgical

interventions compared to other interventions [72].

Global economic impact

The rising economic impact of obesity is a global phenomenon. The worldwide prevalence

of overweight and obesity increased from 921 million to 2.1 billion people from 1980 to

2013, while total population rose from 4.4 billion to 7.2 billion during the same time period;

this represents an increased prevalence of 27.5% for adults and 47.1% for children [73]. A

2014 McKinsey Global Institute analysis estimated the global economic cost of obesity to be

approximately $2.0 trillion or 2.8% of global gross domestic product (GDP), close to the

total economic impact from tobacco or armed conflict [74]. Within these staggering

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aggregate statistics, there is significant variation by country, age, and sex. For example,

higher rates of overweight and obesity exist among women than men in developing

countries, contrasted with higher rates among men than women in developed countries [73].

Gaps remain in our understanding of the true global economic impact of obesity. Systematic

reviews of the costs of obesity based solely on English-language studies inevitably leave

low- and middle-income countries underrepresented in the literature [75, 76]. Additionally,

the available literature displays significant heterogeneity in methods, such as the inclusion of

direct versus indirect costs, variability in which comorbidities of obesity are considered, and

how future economic costs are discounted [75].

Conclusion and Future Directions

The growing national and global impact of obesity is undeniable. Data projections show that

by the year 2030 the prevalence of adult obesity and severe obesity in the United States will

rise to 48.9% and 24.2%, respectively, and severe obesity will be the most common BMI

category nationwide among women, black adults and low-income adults [5]. Current global

trends predict 38% of the world’s adults will have overweight while 20% will be affected

with obesity by 2030 [13, 77]. Excess body weight is estimated to affect two billion people

worldwide as of 2015, and accounts for approximately four million deaths (which represents

5% of all global deaths) and 120 million disability-adjusted life years [74, 78, 79]. The

global economic impact of obesity is estimated to be approximately $2.0 trillion [74, 79].

Clearly obesity poses substantial health and economic burdens on society and is a major

public health challenge of national and global importance.

The sequalae of obesity include health-related, social, psychological and economic

consequences on both the individual and societal levels [13]. Contributors to obesity, or

obesogenic factors, include industrialization, mechanized transportation, urbanization,

technology, and an increasingly abundant supply of inexpensive energy-dense food [13, 74].

Heritable factors such as genetics, family history, race, ethnicity, as well as socioeconomic

and sociocultural environment also interact in order to determine an individual’s

susceptibility to obesity [13]. Our collective approach to treating obesity must be as multi-

faceted as its etiology. Similarly broad approaches and strategies have been, and continue to

be employed to face other daunting public health challenges such as smoking, alcohol use

and armed violence [13, 74].

The impact of various obesity treatment interventions has been analyzed, with findings

suggesting that key areas for promoting behavioral change include informing, enabling,

motivation and influencing individuals to change their routine and habits [74]. Informing the

public is key to approaching any public health concern, with the effectiveness of the

information depending on how, when, where and to whom it is delivered. Enabling choice is

critical for behavioral change, particularly if poor choices are made more difficult to access.

Motivation to change habits through personal goals and incentives can play a role, although

the impact in this area can vary. Finally, influencing behavior via choice architecture,

priming and social norms has been shown to have a powerful effect [74]. Within this

framework, some specific interventions that have been proposed include increasing active

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transport, increasing healthcare payor incentives, improving access to and affordability of

healthy foods, limiting availability and media marketing of high-calorie foods, improving

food labeling, reformulating food products to be more healthful, and increasing access to

weight management programs and bariatric surgery [33, 74]. These approaches can be

implemented at the level of government, school systems, employers, health-care systems,

restaurants, food retailers, manufacturers and foodservice providers [74, 78].

Obesity is known to arise as a result of positive energy balance – that is, an excess of

calories consumed in relation to calories expended, which gives rise to the storage of excess

energy as body fat [13]. While on the surface, the implication may be that individual

behavioral change can reverse the balance, personal behavior occurs largely in response to

complex environmental, socioeconomic and genetic factors [2, 13]. Thus, there is a great

need for population-based, community-level, and environmental approaches for the

prevention and management of obesity [2, 33]. Ideally, these approaches should target

multiple population-level risk factors and be tailored to the specific gender-based,

socioeconomic, and geographic needs of vulnerable target populations [13, 33, 74, 78, 80].

Funding Information:

National Institutes of Health and Massachusetts General Hospital Executive Committee on Research (ECOR)(FCS), National Institutes of Health NIDDK P30 DK040561 (FCS) and L30 DK118710 (FCS)

References

1. CDC, Defining Adult Overweight and Obesity CDC, Editor. 2017.

2. Zhang Q and Wang Y, Trends in the Association between Obesity and Socioeconomic Status in U.S. Adults: 1971 to 2000. 2004 12(10): p. 1622–1632.

3. Ogden C, et al., Prevalence of Childhood and Adult Obesity in the United States, 2011–2012. JAMA, 2014 311(8): p. 806–814. [PubMed: 24570244]

4. Hales C, et al., Prevalence of Obesity Among Adults: United States, 2017–2018. 2020: NCHS Data Brief. p. 1–8.

5. Ward ZJ, et al., Projected U.S. State-Level Prevalence of Adult Obesity and Severe Obesity. N Engl J Med, 2019 381: p. 2440–2450. [PubMed: 31851800]

6. Azagba S, Shan L, and Latham K, Overweight and Obesity among Sexual Minority Adults in the United States. Int J Environ Res Public Health, 2019 16(10): p. 1828.

7. Grammer AC, et al., Overweight and obesity in sexual and gender minority adolescents: A systematic review. Obesity Reivews, 2019 20(10).

8. Jarin J, et al., Cross-Sex Hormones and Metabolic Parameters in Adolescents with Gender Dysphoria. Pediatrics, 2017 139(5).

9. MacKay AP and Duran C, Adolescent Health in the United States, CDC, Editor. 2007, National Center for Health Statistics.

10. Hardin AP and Hackell JM, Age Limit of Pediatrics. Pediatrics, 2017 140(3): p. e20172151. [PubMed: 28827380]

11. CDC, Defining Childhood Obesity, CDC, Editor. 2018.

12. Ogden CL, et al., Trends in Obesity Prevalence Among Children and Adolescents in the United States, 1988–1994 Through 2013–2014. JAMA, 2016 315(21): p. 2292–2299. [PubMed: 27272581]

13. Hruby A and Hu FB, The Epidemiology of Obesity: A Big Picture. Pharmacoeconomics, 2015 33(7): p. 673–689. [PubMed: 25471927]

Anekwe et al. Page 10

Curr Obes Rep. Author manuscript; available in PMC 2021 September 01.

A uthor M

anuscript A

uthor M anuscript

A uthor M

anuscript A

uthor M anuscript

14. Cuevas AG, et al., Assessing the Role of Health Behaviors, Socioeconomic Status, and Cumulative Stress for Racial/Ethnic Disparities in Obesity. Obesity, 2020 28(1): p. 161–170. [PubMed: 31858741]

15. Kwarteng JL, et al., Independent Effects of Neighborhood Poverty and Psychosocial Stress on Obesity Over Time. J Urban Health, 2017 94: p. 791–802. [PubMed: 28895036]

16. Clarke PJ, et al., Midlife health and socioeconomic consequences of persistent overweight across early adulthood: findings from a national survey of American adults (1986–2008). Am J Epidemiol, 2010 172(5): p. 540–8. [PubMed: 20610468]

17. Gortmaker SL, et al., Social and economic consequences of overweight in adolescence and young adulthood. N Engl J Med, 1993 329(14): p. 1008–12. [PubMed: 8366901]

18. Hiilamo A, et al., Obesity and socioeconomic disadvantage in midlife female public sector employees: a cohort study. BMC Public Health, 2017 17.

19. Puhl R and Brownell KD, Bias, discrimination, and obesity. Obesity Research, 2001 9(12): p. 788– 805. [PubMed: 11743063]

20. Puhl RM, Andreyeva T, and Brownell KD, Perceptions of weight discrimination: prevalence and comparison to race and gender discrimination in America. International Journal of Obesity, 2008 32: p. 992–1000. [PubMed: 18317471]

21. Puhl RM and Heuer CA, The Stigma of Obesity: A Review and Update. Obesity, 2009 17(5): p. 941–64. [PubMed: 19165161]

22. Phelan SM, et al., Impact of weight bias and stigma on quality of care and outcomes for patients with obesity. Obes Rev, 2015 16(4): p. 319–26. [PubMed: 25752756]

23. Baum CL 2nd and Ford WF, The wage effects of obesity: a longitudinal study. Health Econ, 2004 13(9): p. 885–99. [PubMed: 15362180]

24. Pinkston JC, The dynamic effects of obesity on the wages of young workers. Econ Hum Biol, 2017 27(Pt A): p. 154–166. [PubMed: 28649037]

25. Huizinga MM, et al., Disparity in Physician Perception of Patients’ Adherence to Medications by Obesity Status. Obesity, 2010 18(10): p. 1932–1937. [PubMed: 20186132]

26. Muennig P, The body politic: the relationship between stigma and obesity-associated disease. BMC Public Health, 2008 8: p. 128. [PubMed: 18426601]

27. Latner JD, Durso LE, and Mond JM, Health and health-related quality of life among treatment- seeking overweight and obese adults: associations with internalized weight bias. J Eat Disord, 2013 1: p. 3. [PubMed: 24764526]

28. Rubino F, et al., Joint international consensus statement for ending stigma of obesity. Nat Med, 2020 26(4): p. 485–497. [PubMed: 32127716]

29. Coleman-Jensen A, et al., Statistical supplement to household food security in the United States in 2017. 2018, US Department of Agriculture Economic Research Service: Washington, DC.

30. Metallinos-Katsaras E, Must A, and Gorman K, A Longitudinal Study of Food Insecurity on Obesity in Preschool Children. 2012 112(12): p. 1949–1958.

31. Pan L, et al., Food Insecurity Is Associated with Obesity among US Adults in 12 States. J Acad Nutr Diet, 2012 112(9): p. 1403–1409. [PubMed: 22939441]

32. Franklin B, et al., Exploring mediators of food insecurity and obesity: a review of recent literature. J Community Health, 2012 37(1): p. 253–64. [PubMed: 21644024]

33. Rummo PE, et al., Impact of Changes in the Food, Built, and Socioeconomic Environment on BMI in US Counties, BRFSS 2003‐2012. Obesity, 2020 28(1): p. 31–39. [PubMed: 31858733]

34. Morland K, Diez Roux AV, and Wing S, Supermarkets, other food stores, and obesity: the atherosclerosis risk in communities study. Am J Prev Med, 2006 30(4): p. 333–9. [PubMed: 16530621]

35. Boone-Heinonen J, et al., The neighborhood energy balance equation: does neighborhood food retail environment + physical activity environment = obesity? The CARDIA study. PLoS One, 2013 8(12): p. e85141. [PubMed: 24386458]

36. Auchincloss AH, et al., Neighborhood health-promoting resources and obesity risk (the multi- ethnic study of atherosclerosis). Obesity (Silver Spring), 2013 21(3): p. 621–8. [PubMed: 23592671]

Anekwe et al. Page 11

Curr Obes Rep. Author manuscript; available in PMC 2021 September 01.

A uthor M

anuscript A

uthor M anuscript

A uthor M

anuscript A

uthor M anuscript

37. Harvey JR and Ogden D, Obesity Treatment in Disadvantaged Population Groups: Where Do We Stand and What Can We Do? Preventive Medicine, 2014 86: p. 71–75.

38. Osei-Assibey G, et al., Dietary and lifestyle interventions for weight management in adults from minority ethnic/non-White groups: a systematic review. Obes Rev, 2010 11(11): p. 769–76. [PubMed: 20059708]

39. Bennett GG, et al., Obesity treatment for socioeconomically disadvantaged patients in primary care practice. Arch Intern Med, 2012 172(7): p. 565–574. [PubMed: 22412073]

40. Cossrow N and Falkner B, Race/Ethnic Issues in Obesity and Obesity-Related Comorbidities. The Journal of Clinical Endocrinology & Metabolism, 2004 89(6): p. 2590–2594. [PubMed: 15181028]

41. Ford ES, Giles WH, and Dietz WH, Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAMA, 2002 287(3): p. 356–9. [PubMed: 11790215]

42. Calle EE, et al., Body-mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med, 1999 341(15): p. 1097–105. [PubMed: 10511607]

43. Fontaine KR, et al., Years of life lost due to obesity. JAMA, 2003 289(2): p. 187–93. [PubMed: 12517229]

44. August KJ and Sorkin DH, Racial/ethnic disparities in exercise and dietary behaviors of middle- aged and older adults. J Gen Intern Med, 2011 26(3): p. 245–50. [PubMed: 20865342]

45. Krueger PM and Reither EN, Mind the gap: race/ethnic and socioeconomic disparities in obesity. 2015 11(95).

46. Beccuti G and Pannain S, Sleep and obesity. Curr Opin Clin Nutr Metab Care, 2011 14(4): p. 402– 12. [PubMed: 21659802]

47. Kanter R and Caballero B, Global Gender Disparities in Obesity: A Review. Advances in Nutrition, 2012 3(4): p. 491–498. [PubMed: 22797984]

48. Jones-Smith JC, et al., Cross-national comparisons of time trends in overweight inequality by socioeconomic status among women using repeated cross-sectional surveys from 37 developing countries, 1989–2007. Am J Epidemiol, 2011 173(6): p. 667–75. [PubMed: 21300855]

49. Devaux M and Sassi F, Social inequalities in obesity and overweight in 11 OECD countries European Journal of Public Health, 2013 23(3): p. 464–469. [PubMed: 21646363]

50. Ogden CL, et al., Obesity and Socioeconomic Status in Adults: United States, 2005–2008, in NCHS Data Brief, Services U.S.D.o.H.a.H., Editor. 2010.

51. Sweeting H, Gendered dimensions of obesity in childhood and adolescence. Nutrition Journal, 2008 7.7

52. Waters H and Graf M, America’s Obesity Crisis: The Health and Economic Costs of Excess Weight. 2018, Milken Institute.

53. Abdullah A, et al., The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract, 2010 89(3): p. 309–19. [PubMed: 20493574]

54. Polednak AP, Estimating the number of U.S. incident cancers attributable to obesity and the impact on temporal trends in incidence rates for obesity-related cancers. Cancer Detect Prev, 2008 32(3): p. 190–9. [PubMed: 18790577]

55. Glance LG, et al., Impact of obesity on mortality and complications in trauma patients. Ann Surg, 2014 259(3): p. 576–81. [PubMed: 24263314]

56. Thompson D, et al., Lifetime health and economic consequences of obesity. Arch Intern Med, 1999 159(18): p. 2177–83. [PubMed: 10527295]

57. Hammond RA and Levine R, The economic impact of obesity in the United States. Diabetes Metab Syndr Obes. 2010 3.3

58. Mokdad AH, et al., Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA, 2003 289(1): p. 76–9. [PubMed: 12503980]

59. Kleinman N, et al., Cohort analysis assessing medical and nonmedical cost associated with obesity in the workplace. Journal of occupational and environmental medicine, 2014 56(2): p. 161–170. [PubMed: 24451611]

Anekwe et al. Page 12

Curr Obes Rep. Author manuscript; available in PMC 2021 September 01.

A uthor M

anuscript A

uthor M anuscript

A uthor M

anuscript A

uthor M anuscript

60. Asay GRB, et al., Absenteeism and Employer Costs Associated With Chronic Diseases and Health Risk Factors in the US Workforce. Preventing chronic disease, 2016 13: p. E141–E141. [PubMed: 27710764]

61. Goettler A, Grosse A, and Sonntag D, Productivity loss due to overweight and obesity: a systematic review of indirect costs. BMJ open, 2017 7(10): p. e014632–e014632.

62. Ricci JA and Chee E, Lost productive time associated with excess weight in the U.S. workforce. Journal of occupational and environmental medicine, 2005 47(12): p. 1227–1234. [PubMed: 16340703]

63. Fontaine KR, et al., Years of Life Lost Due to Obesity. JAMA, 2003 289(2): p. 187–193. [PubMed: 12517229]

64. Peeters A, et al., Obesity in Adulthood and Its Consequences for Life Expectancy: A Life-Table Analysis. Annals of Internal Medicine, 2003 138(1): p. 24–32. [PubMed: 12513041]

65. Dannenberg AL, Burton DC, and Jackson RJ, Economic and environmental costs of obesity: the impact on airlines. American journal of preventive medicine, 2004 27(3): p. 264–264. [PubMed: 15450642]

66. Jacobson SH and King DM, Measuring the potential for automobile fuel savings in the US: The impact of obesity. Transportation Research Part D, 2009 14(1): p. 6–13.

67. Jacobson SH and McLay LA, The Economic Impact of Obesity on Automobile Fuel Consumption. The Engineering Economist, 2006 51(4): p. 307–323.

68. Black N, Johnston DW, and Peeters A, Childhood Obesity and Cognitive Achievement. Health economics, 2015 24(9): p. 1082–1100. [PubMed: 26123250]

69. Hagman E, et al., Childhood Obesity, Obesity Treatment Outcome, and Achieved Education: A Prospective Cohort Study. The Journal of adolescent health : official publication of the Society for Adolescent Medicine, 2017 61(4): p. 508–513. [PubMed: 28693958]

70. French SA, et al., Obesity in Adolescence Predicts Lower Educational Attainment and Income in Adulthood: The Project EAT Longitudinal Study. Obesity (Silver Spring, Md), 2018 26(9): p. 1467–1473.

71. Apovian CM, The Clinical and Economic Consequences of Obesity Am J Manag Care, 2013 19(10 Suppl): p. s219–28.

72. Kolotkin RL and Andersen JR, A systematic review of reviews: exploring the relationship between obesity, weight loss and health-related quality of life. Clinical obesity, 2017 7(5): p. 273–289. [PubMed: 28695722]

73. Ng M, et al., Global, regional and national prevalence of overweight and obesity in children and adults 1980–2013: A systematic analysis. The Lancet, 2014 384(9945): p. 766–781.

74. Dobbs R, et al., Overcoming obesity: An initial economic analysis. 2014, McKinsey Global Institute.

75. Tremmel M, et al., Economic Burden of Obesity: A Systematic Literature Review. Int J Environ Res Public Health, 2017 14(4).

76. Withrow D and Alter DA, The economic burden of obesity worldwide: a systematic review of the direct costs of obesity. Obes Rev, 2011 12(2): p. 131–41. [PubMed: 20122135]

77. Kelly T, et al., Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond), 2008 32(9): p. 1431–7. [PubMed: 18607383]

78. Lyn R, Heath E, and Dubhashi J, Global Implementation of Obesity Prevention Policies: a Review of Progress, Politics, and the Path Forward. Curr Obes Rep, 2019 8(4): p. 504–516. [PubMed: 31673982]

79. Afshin A, et al., Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med, 2017 377(1): p. 13–27. [PubMed: 28604169]

80. Ghosh A, Charlton KE, and Batterham MJ, Socioeconomic disadvantage and its implications for population health planning of obesity and overweight, using cross-sectional data from general practices from a regional catchment in Australia. BMJ, 2016 6(5).

Anekwe et al. Page 13

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