ABSTRACT

The evidence for social and environmental factors that contribute to obesity are often underappreciated. Obesity prevalence is significantly associated with sex, racial ethnic identity, and socioeconomic status, which creates complex relationships between each of these characteristics. Food availability remains an important factor associated with obesity that relates to differences in prevalence seen across geographical areas and higher rates of obesity within low socioeconomic status individuals. Proliferation of high calorie, energy dense food options that are or perceived as more affordable combined with reductions in occupational and transportation related physical activity can contribute to a sustained positive energy balance. Additionally, environments experiencing deprivation, disorder, or high crime have been shown to be associated with higher odds of obesity, which may appear more frequently in low social status individuals. Both objective and subjective measures of social status and inequality are associated with increased energy intake and decreased energy expenditure, which could place individuals of low social status at greater risk for obesity development. Given the complexity of this multifactorial disease, effective obesity care requires knowledge of these complex relationships and an integration between the health systems and surrounding community. Resources for practicing clinicians regarding methods of screening for social and environmental factors in clinical care are provided in addition to information on a program that has been widely dispersed and made accessible to those who may be the most at risk. For complete coverage of all related areas of Endocrinology, please visit our on-line FREE web-text, WWW.ENDOTEXT.ORG.

INTRODUCTION

Many medical providers appreciate the significant social and environmental determinants of obesity but are unsure how to address them. Others consider these factors outside of their control and scope of practice, and are thus hesitant to even broach the topic with their patients. Finally, many medical providers still attribute obesity to causes within a person’s control, such as dietary choices, amount of exercise, or willpower, (1, 2) which perpetuates a stigma that accompanies this disease. Specifically, the prevailing stigma is that those who suffer from obesity represent a population who lack the willingness to change their poor lifestyle habits or harbor a character flaw that, at its extreme, infers immoral behaviors (e.g., gluttony). In reality, obesity is a multifactorial disease (3) that is caused by a combination of biological, genetic, social, environmental, and behavioral determinants. In order to address this gap in the understanding of the social and environmental determinants of obesity and improve the care of patients with obesity, this chapter will review the evidence for the social and environmental determinants of obesity development. The specific areas to be covered include social identity, social status, societal trends, and influences of the built, industrial, and social environments, all factors that are closely associated with the prevalence or incidence of obesity or that impact efforts to prevent and treat this disease. Resources for the busy clinician that will support implemental changes in one’s practice to improve the care and management of patients with obesity, as well as evidenced-based opportunities for advocacy in the community, will be included in the final section.

This chapter is divided into three primary sections based on the progression of thought and evidence surrounding the social and environmental determinants of obesity: individual characteristics, environmental characteristics, and social hierarchy influences. Individual characteristics are those that are attributed to the individual with obesity such as their sex, age, race, ethnicity, and socioeconomic status (SES). Environmental characteristics surround the individual, including the physical spaces where people live, work, and play, as well as sociocultural norms. The social hierarchy refers to social status or social rank of individuals within larger society or a local community.

INDIVIDUAL CHARACTERISTICS

The prevalence of obesity varies according to key individual characteristics such as age, sex, race and ethnicity, and SES. The prevalence of obesity increases cross-sectionally across the lifespan: from 13.9%, in early childhood (2-5 years old) to 18.4% in childhood (6-11 years old), 20.6% in adolescence (12-19 years old), 35.7%, in young adulthood (20-39 years old), 42.8% in adulthood (40-59 years old), and 41.0% in older adulthood (≥60 years old) (4). As of 2016, the prevalence of adult obesity in women in the United States was 41.1% and in men was 37.9% (4). In the decade between 2007-2008 and 2015-2016, obesity significantly increased only in women (4), suggesting a sex-specific vulnerability to expression of this disease. Additionally, when race and ethnicity are considered, significant interactions between race and sex emerge. Non-Hispanic black, non-Hispanic Asian, and Hispanic women all have significantly higher prevalence of obesity than men with the same racial ethnic identity (5). In men and women, non-Hispanic Asians have significantly lower prevalence of obesity compared to all other major races and ethnicities in the United States (Note: not adjusted for ethnic specific cut points for Asians), and Non-Hispanic blacks and Hispanics have significantly higher prevalence of obesity compared to Non-Hispanic whites (5). It is not fully clear why differences in obesity prevalence by race and ethnicity are present, but some evidence points to differences in genetic backgrounds that affect body composition and fat distribution (6, 7), and to differences in cultural body image standards (8). Additionally, in the United States, race and ethnicity are confounded with SES, which is one of the most potent indicators of overall health in the United States (9).

A significantly greater proportion of underrepresented racial ethnic minorities are considered low SES compared to non-Hispanic Asians and non-Hispanic whites in the United States. Socioeconomic status is a composite measure that can be represented by measures of income, educational attainment, or occupational status. In the 2017 Census, 21.2% of non-Hispanic blacks and 18.3% of Hispanics lived below the poverty level compared to 8.7% of non-Hispanic whites and 10% of non-Hispanic Asians (10). Non-Hispanic Asians (53.9%) and non-Hispanic whites (36.2%) are more likely to earn a bachelor’s degree than non-Hispanic blacks (22.5%) and Hispanics (15.5%) (11). In terms of health, low SES in childhood is associated with adult development of cardiovascular risk factors and a 20% increase in the odds of having central obesity (as defined by a waist circumference >102 cm for men or > 88 cm for women) (12). In adult women, obesity prevalence increases with decreasing income and educational attainment; however, in non-Hispanic black women, obesity prevalence differs by education gradients but not by income gradients (13). Conversely, non-Hispanic black men have a higher prevalence of obesity in the highest income group, but all the men’s racial ethnic groups showed similar relationships between obesity rates and education gradients as women (13). Higher SES is also associated with healthy lifestyle behaviors that are often the first line of prevention or treatment for obesity. On the other hand, low SES is associated with less leisure time physical activity (14) and consumption of energy-dense diets that are nutrient poor (15); however, SES is not the only factor that influences these behaviors. Further exploration of how SES affects resources and the ability to practice healthy behaviors is expounded upon in the next section.

ENVIRONMENTAL CHARACTERISTICS

SOCIAL HIERARCHY

Animal research consistently shows that animals of subordinate status experience adverse physiological and behavioral changes compared to their high status counterparts: higher levels of cortisol (primates) (55), elevated blood pressure (rats, rabbits, baboons, macaques) (56), elevated heart rate (primates) (56), accumulation of visceral fat (rats) (57), increased ad-libitum energy-dense food consumption (macaques, rats) (57, 58), cardiovascular disease (mice) (59), and shortened lifespan (mice) (59). This implies that social standing, regardless of species, has physiological implications and could be contributing to obesity development and poor health. The findings from animal models thus serve as the basis for parallel outcomes reported in humans of low social status.

Social status can be measured objectively or subjectively. Objective measures typically include socioeconomic status (SES) variables, such as income, education, or occupation, which were discussed as individual level factors at the beginning of this chapter. Social status can also be represented by manifestations of status differentials, including inequality between groups or measurable differences in the ability for someone to obtain basic life necessities, such as food security. High levels of absolute income/wealth may be related to health not only through better material conditions, but also through social position. However, in an analysis of two nationally representative British panel studies, ranked position of income/wealth, not absolute income/wealth, predicted adverse health outcomes such as obesity, presence of chronic disease, and poor ratings of physical functioning and pain (60). In a worldwide study of physical activity, countries with large activity inequality predicted obesity better than the total volume of physical activity within the country (61). Activity inequality is identified by calculating a Gini coefficient for population step count data from each country, 0 = complete equality, 1= complete inequality. Individuals in the top five countries for physical activity inequality (Saudi Arabia, USA, Egypt, Canada, Australia) were 196% more likely to have obesity than individuals from more equal societies that did not have large disparities in step counts across the population. Gender differences account for 43% of the inequality observed, however, this effect was mitigated in societies that rated higher in walkability (61). Inequality can also drive calorie consumption. Individuals who are experimentally induced to view themselves as poor in reference to others exhibited increased calorie intake (62). Additionally, individuals who believed they were poorer or wealthier than an interaction partner exhibited higher levels of anxiety in regards to that difference in status that, in turn, led to increased calorie consumption (62).

Food insecurity affects approximately 11.8 percent of families in the United States and has been linked to obesity and diabetes. Food insecurity occurs when “the intake of one or more members of a household is reduced and eating patterns are disrupted (sometimes resulting in hunger) because of insufficient money and other resources for food” (63). In women, food insecurity status predicts overweight/obese status differentially across racial ethnic groups. Non-Hispanic white women who are food insecure are 41% more likely to have overweight or obesity whereas Hispanic women who are food insecure are 29% more likely to have overweight and obesity (64). Among non-Hispanic black women and men, food insecurity did not predict overweight or obesity status (64). A population-based study in Canada revealed that persons in food insecure households had double the risk of developing type 2 diabetes compared to persons in food secure households, even after controlling for age, gender, income, race, physical activity, smoking status, alcohol consumption, diet quality, and BMI (65). Reduced food availability is theorized to initiate compensatory biological mechanisms that boost caloric intake, decrease resting metabolic rate, and increase storage of adipose tissue as a protective mechanism for survival (66). Research in youth has provided evidence for a moderating effect of food insecurity on the relationship between income and subjective social status (67). This means that low income is more strongly associated with low subjective social status when the household is also food insecure.

Subjective measures of social status (SSS) are typically measured by asking individuals to place themselves on 10-rung ladders based on where they perceive their rank within society and the community. Experimental evidence demonstrates a relationship between feelings of low social status and increased calorie intake. Cornil and Chandon showed that hometowns of National Football League teams consumed more calories after a team loss than hometowns of winning teams or of hometowns where teams didn’t play (68). Manipulations of social status in an experimental setting show that acute eating behavior post experimental manipulation consists of higher calorie food choices and higher total calorie intake in the low status group (69). Additionally, individuals randomized to a low social status condition, had increased levels of ghrelin, a hormone that stimulates appetite, as compared to the high social status condition, suggesting a physiological hunger response to low perceived social status (70). Studies of physical activity and SSS show that low SSS is associated with significantly lower levels of moderate to vigorous physical activity (71, 72), which could contribute to a lower overall energy expenditure. Closely related to SSS are other perceptive representations of status differentials, such as perceived discrimination, which is associated with increased weight and BMI in women (73) and increased abdominal adiposity in non-Hispanic whites (74).

Researchers have integrated individual and environmental factors into design and development of interventions to improve weight outcomes or weight-related behaviors (healthy eating, physical activity); however, not all of them are successful. For example, a study among low-income women with children in rural Mexico randomly assigned families to cash or in-kind transfers (food baskets) and found that women in the food basket and cash groups actually gained weight compared to women in the control group (75). This study and others that show weight gain occurring in spite of access to resources or poverty relief imply accounting for individual and environmental factors alone may not paint a complete picture of obesity development. Granted, it is important to consider that systemic environmental changes, such as placement of sidewalks or fruits and vegetables in a corner store, may not be adequately captured in a short time frame typical of academic studies. However, the small or nonexistent changes observed when resources are supplied warrants further investigation into deeper realms of social hierarchical constructs, as well as continued study of individual and environmental factors to improve treatment and prevention of obesity.

CLINICAL IMPLICATIONS AND CONCLUSIONS

Given the extent of the information on individual, environmental, and social hierarchy constraints on obesity development, it is important to understand how these can merge with clinical care. It is evident that there is no one simple solution and effective care requires knowledge of these complex relationships and an integration between the health system and the surrounding community. For example, based on the knowledge that the social determinants of health can influence diabetes and its comorbidities, the American Diabetes Association recommends in its clinical guidelines that providers “assess the social context… and apply that information to treatment decisions” (76). In conjunction with recognition of the impact of social and environmental determinants on multiple chronic diseases, some researchers propose that “community vital signs” be integrated into the electronic health record (EHR) (77) and some community health centers have begun pilot testing a social determinants questionnaire in their HER (78). Knowledge provided by these “vital signs” and social determinants could help providers make appropriate lifestyle-tailored recommendations for the patient.

Discussing context surrounding food in a patient’s life can provide insight into the realistic expectations for a patient’s diet. Food insecurity can be identified with a short two question screener (79) and implementation in clinics has shown that screening improves clinician awareness of food insecurity, helping to better understand the lengths to which it affects patient treatment (80). Positive responses from physicians after pilot testing that incorporates screening into clinical practice mitigates concerns that discussions about food security would be stigmatizing to the patient (80). Patients who identify as food insecure can be referred to local food banks or community programs that will connect patients with resources at a federal and community level.

Patients that are finding it difficult to follow lifestyle modification recommendations to lose weight to prevent diabetes development may benefit from the Diabetes Prevention Program. The Diabetes Prevention Program is a lifestyle program focused on weight loss through dietary change and increased physical activity. While the overall weight loss was modest (~4% after 4 years), participants lowered their chances of developing diabetes by 58% during long-term follow-up (81). This program has been adapted for implementation and dissemination purposes and now the CDC’s National Diabetes Prevention (National DPP) program is available at almost 2,000 sites across the United States including many YMCAs, with a mix of online and in-person options. This program is covered for eligible individuals by Medicare and many private insurers and cost for non-covered patients is variable and often income-based or free. Initial evaluation of the real-world evidence for implementation of the National DPP have been promising with 35% achieving 5% weight loss and 42% meeting the activity goal of 150 minutes per week (82). Locations with the best participant retention and attendance share the following qualities: referrals from healthcare providers or health systems, provision of non-monetary incentives for participation, and use of cultural adaptations to address participant needs (83). The National DPP provides an affordable, easy and local referral source so that the provider can be assured their patients are receiving evidence-based lifestyle management in an ongoing program.

RESOURCES

Figure 1 below shows the age-adjusted prevalence of obesity in adults by race and ethnicity, and sex from the Centers for Disease Control 2017 National Center for Health Statistics Data Brief (5).

Figure 1.

Prevalence of Obesity by Race/Ethnicity and Sex

REFERENCES

1.

Sikorski C, Luppa M, Kaiser M, et al. The stigma of obesity in the general public and its implications for public health - A systematic review. BMC Public Health. 2011;11(1):661. [PMC free article: PMC3175190] [PubMed: 21859493]

2.

Tsai AG, Histon T, Kyle TK, Rubenstein N, Donahoo WT. Evidence of a gap in understanding obesity among physicians. Obes Sci Pract. 2018;4(1):46–51. [PMC free article: PMC5818759] [PubMed: 29479464]

3.

Allison (chair) DB, Downey (co-chair) M, Atkinson RL, et al. Obesity as a Disease: A White Paper on Evidence and Arguments Commissioned by the Council of The Obesity Society. Obesity. 2008;16(6):1161-1177. [PubMed: 18464753]

4.

Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in obesity and severe obesity prevalence in usyouth and adults by sex and age, 2007-2008 to 2015-2016. JAMA - J Am Med Assoc. 2018;319(16):1723–1725. [PMC free article: PMC5876828] [PubMed: 29570750]

5.

Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity among adults and youth : United States, 2015–2016. (U.S.) NC for HS, ed. https://stacks​.cdc.gov/view/cdc/49223. [PubMed: 29155689]

6.

Fernández JR, Shiver MD. Using genetic admixture to study the biology of obesity traits and to map genes in admixed populations. Nutr Rev. 2004;62(7 Pt 2):S69–S74. [PubMed: 15387470]

7.

Cardel M, Higgins PB, Willig AL, et al. African genetic admixture is associated with body composition and fat distribution in a cross-sectional study of children. Int J Obes. 2011;35(1):60–65. [PMC free article: PMC3804117] [PubMed: 20877287]

8.

Kronenfeld LW, Reba-Harrelson L, Von Holle A, Reyes ML, Bulik CM. Ethnic and racial differences in body size perception and satisfaction. Body Image. 2010;7(2):131–136. d. [PMC free article: PMC3593344] [PubMed: 20096656]

9.

Braveman PA, Cubbin C, Egerter S, Williams DR, Pamuk E. Socioeconomic Disparities in Health in the United States: What the Patterns Tell Us. Am J Public Health. 2010;100(S1):S186–S196. [PMC free article: PMC2837459] [PubMed: 20147693] [CrossRef]

10.

Fontenot K, Semega J, Kollar M. Income and and Poverty Poverty the United States. 2018 September;2017:2018.

11.

Ryan CL, Bauman K. Educational attainment in the United States: 2015 population characteristics. United States Census Bur. 2016;2010:20–578.

12.

Kivimäki M, Davey Smith G, Juonala M, et al. Socioeconomic position in childhood and adult cardiovascular risk factors, vascular structure, and function: Cardiovascular risk in young Finns study. Heart. 2006 [PMC free article: PMC1860895] [PubMed: 16159979]

13.

Ogden CL, Fakhouri TH, Carroll MD, et al. Prevalence of Obesity Among Adults, by Household Income and Education — United States, 2011–2014. MMWR Morb Mortal Wkly Rep. 2017;66(50):1369. [PMC free article: PMC5751581] [PubMed: 29267260] [CrossRef]

14.

O’Donoghue G, Kennedy A, Puggina A, et al. Socio-economic determinants of physical activity across the life course: A “DEterminants of DIet and Physical ACtivity” (DEDIPAC) umbrella literature review. Henchoz Y, ed. PLoS One. 2018;13(1):e0190737. [PMC free article: PMC5774703] [PubMed: 29351286]

15.

Darmon N, Drewnowski A. Does social class predict diet quality? Am J Clin Nutr. 2008;87(5):1107–1117. [PubMed: 18469226]

16.

Gurka MJ, Filipp SL, DeBoer MD. Geographical variation in the prevalence of obesity, metabolic syndrome, and diabetes among US adults. Nutr Diabetes. 2018;8(1):14. [PMC free article: PMC5856741] [PubMed: 29549249]

17.

Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature. 2019;569(7755):260–264. [PMC free article: PMC6784868] [PubMed: 31068725]

18.

Wen M, Fan JX, Kowaleski-Jones L, Wan N. Rural–Urban Disparities in Obesity Prevalence Among Working Age Adults in the United States: Exploring the Mechanisms. Am J Heal Promot. 2018;32(2):400–408. [PubMed: 29214811]

19.

Maddock J. The relationship between obesity and the prevalence of fast food restaurants: State-level analysis. Am J Heal Promot. 2004;19(2):137–143. [PubMed: 15559714]

20.

Li F, Harmer P, Cardinal BJ, Bosworth M, Johnson-Shelton D. Obesity and the built environment: does the density of neighborhood fast-food outlets matter? Am J Health Promot. 2009;23(3):203–209. [PMC free article: PMC2730045] [PubMed: 19149426]

21.

Mazidi M, Speakman JR. Higher densities of fast-food and full-service restaurants are not associated with obesity prevalence. Am J Clin Nutr. 2017;106(2):603–613. [PubMed: 28566310]

22.

Moore L V., Diez Roux A V. Associations of Neighborhood Characteristics With the Location and Type of Food Stores. Am J Public Health. 2006;96(2):325–331. [PMC free article: PMC1470485] [PubMed: 16380567]

23.

Zenk SN, Schulz AJ, Israel BA, James SA, Bao S, Wilson ML. Neighborhood Racial Composition, Neighborhood Poverty, and the Spatial Accessibility of Supermarkets in Metropolitan Detroit. Am J Public Health. 2005;95(4):660–667. [PMC free article: PMC1449238] [PubMed: 15798127]

24.

Chen D, Jaenicke EC, Volpe RJ. Food Environments and Obesity: Household Diet Expenditure Versus Food Deserts. Am J Public Health. 2016;106(5):881–888. [PMC free article: PMC4985118] [PubMed: 26985622]

25.

Giskes K, van Lenthe F, Avendano-Pabon M, Brug J. A systematic review of environmental factors and obesogenic dietary intakes among adults: are we getting closer to understanding obesogenic environments? Obes Rev. 2011;12(5):e95–e106. [PubMed: 20604870]

26.

Dubowitz T, Ghosh-Dastidar M, Cohen DA, et al. Diet And Perceptions Change With Supermarket Introduction In A Food Desert, But Not Because Of Supermarket Use. Health Aff. 2015;34(11):1858–1868. [PMC free article: PMC4977027] [PubMed: 26526243]

27.

Hu FB. Resolved: there is sufficient scientific evidence that decreasing sugar-sweetened beverage consumption will reduce the prevalence of obesity and obesity-related diseases. Obes Rev. 2013;14(8):606–619. [PMC free article: PMC5325726] [PubMed: 23763695]

28.

Popkin BM, Hawkes C. Sweetening of the global diet, particularly beverages: Patterns, trends, and policy responses. Lancet Diabetes Endocrinol. 2016;4(2):174–186. [PMC free article: PMC4733620] [PubMed: 26654575]

29.

Young LR, Nestle M. The contribution of expanding portion sizes to the US obesity epidemic. Am J Public Health. 2002;92(2):246–249. [PMC free article: PMC1447051] [PubMed: 11818300]

30.

Livingstone MBE, Pourshahidi LK. Portion Size and Obesity. Adv Nutr. 2014;5(6):829–834. [PMC free article: PMC4224223] [PubMed: 25398749]

31.

Hall KD, Ayuketah A, Brychta R, et al. Clinical and Translational Report Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake Cell Metabolism Clinical and Translational Report Ultra-Processed Diets Cause Excess Ca. Cell Metab. 2019;30(1):1–11. [PMC free article: PMC7959109] [PubMed: 31269427]

32.

Boyland EJ, Nolan S, Kelly B, et al. Advertising as a cue to consume: a systematic review and meta-analysis of the effects of acute exposure to unhealthy food and nonalcoholic beverage advertising on intake in children and adults. Am J Clin Nutr. 2016;103(2):519–533. [PubMed: 26791177]

33.

Story M, French S. Food Advertising and Marketing Directed at Children and Adolescents in the US. Int J Behav Nutr Phys Act. 2004;1(1):3. [PMC free article: PMC416565] [PubMed: 15171786]

34.

Carlson A, Frazão E. Food costs, diet quality and energy balance in the United States. Physiol Behav. 2014;134(C):20–31. [PubMed: 24631301]

35.

Lee A, Mhurchu CN, Sacks G, et al. Monitoring the price and affordability of foods and diets globally. Obes Rev. 2012 November;2014(14):82–95. [PubMed: 24074213]

36.

Creatore MI, Glazier RH, Moineddin R, et al. Association of Neighborhood Walkability With Change in Overweight, Obesity, and Diabetes. JAMA. 2016;315(20):2211. [PubMed: 27218630]

37.

Ng SW, Popkin BM. Time use and physical activity: a shift away from movement across the globe. Obes Rev. 2012;13(8):659–680. [PMC free article: PMC3401184] [PubMed: 22694051]

38.

Salvo G, Lashewicz BM, Doyle-Baker PK, McCormack GR. Neighbourhood Built Environment Influences on Physical Activity among Adults: A Systematized Review of Qualitative Evidence. Int J Environ Res Public Health. 2018;15(5) [PMC free article: PMC5981936] [PubMed: 29724048]

39.

Smith M, Hosking J, Woodward A, et al. Systematic literature review of built environment effects on physical activity and active transport - an update and new findings on health equity. Int J Behav Nutr Phys Act. 2017;14(1):158. [PMC free article: PMC5693449] [PubMed: 29145884]

40.

Giles-Corti B, Donovan RJ. Socioeconomic status differences in recreational physical activity levels and real and perceived access to a supportive physical environment. Prev Med (Baltim). 2002. [PubMed: 12460528]

41.

van Lenthe F, Mackenbach J. Neighbourhood deprivation and overweight: the GLOBE study. Int J Obes. 2002;26(2):234–240. [PubMed: 11850756]

42.

Mayne SL, Jose A, Mo A, et al. Neighborhood disorder and obesity-related outcomes among women in Chicago. Int J Environ Res Public Health. 2018;15(7) [PMC free article: PMC6069019] [PubMed: 29970797]

43.

Rees-Punia E, Hathaway ED, Gay JL. Crime, perceived safety, and physical activity: A meta-analysis. Prev Med (Baltim). 2017 October;2018(111):307–313. [PubMed: 29157975]

44.

Ludwig J, Sanbonmatsu L, Gennetian L, et al. Neighborhoods, Obesity, and Diabetes — A Randomized Social Experiment. N Engl J Med. 2011 [PMC free article: PMC3410541] [PubMed: 22010917]

45.

Church TS, Thomas DM, Tudor-Locke C, et al. Trends over 5 Decades in U.S. Occupation-Related Physical Activity and Their Associations with Obesity. Lucia A, ed. PLoS One. 2011;6(5):e19657. [PMC free article: PMC3102055] [PubMed: 21647427]

46.

Grier SA, Kumanyika SK. The Context for Choice: Health Implications of Targeted Food and Beverage Marketing to African Americans. Am J Public Health. 2008;98(9):1616–1629. [PMC free article: PMC2509618] [PubMed: 18633097]

47.

Adeigbe RT, Baldwin S, Gallion K, Grier S, Ramirez AG. Food and Beverage Marketing to Latinos. Heal Educ Behav. 2015;42(5):569–582. [PubMed: 25504570]

48.

Cardel MI, Chavez S, Bian J, et al. Accuracy of weight loss information in Spanish search engine results on the internet. Obesity. 2016;24(11):2422–2434. [PMC free article: PMC5117437] [PubMed: 27653438]

49.

Robinson TN, Banda JA, Hale L, et al. Screen Media Exposure and Obesity in Children and Adolescents. Pediatrics. 2017;140 Suppl 2:S97–S101. [PMC free article: PMC5769928] [PubMed: 29093041]

50.

Banks E, Jorm L, Rogers K, Clements M, Bauman A. Screen-time, obesity, ageing and disability: findings from 91 266 participants in the 45 and Up Study. Public Health Nutr. 2011;14(1):34–43. [PubMed: 20409356]

51.

Mitchell JA, Rodriguez D, Schmitz KH, Audrain-McGovern J. Greater screen time is associated with adolescent obesity: A longitudinal study of the BMI distribution from Ages 14 to 18. Obesity. 2013;21(3):572–575. [PMC free article: PMC3630469] [PubMed: 23592665]

52.

Lee AM, Chavez S, Bian J, et al. Efficacy and effectiveness of mobile health technologies for facilitating physical activity in adolescents: Scoping review. JMIR mHealth uHealth. 2019;7(2) [PMC free article: PMC6390191] [PubMed: 30747716]

53.

D.E. S. G. T-M, S.J. J, S. W. Mobile apps for pediatric obesity prevention and treatment, healthy eating, and physical activity promotion: Just fun and games? Transl Behav Med. 2013;3(3):320–325. http://link​.springer​.com/article/10.1007/s13142-013-0206-3 Accessed June 10, 2016. [PMC free article: PMC3771006] [PubMed: 24073184]

54.

Hutchesson MJ, Rollo ME, Krukowski R, et al. eHealth interventions for the prevention and treatment of overweight and obesity in adults: a systematic review with meta-analysis. Obes Rev. 2015;16(5):376–392. [PubMed: 25753009]

55.

Abbott DH, Keverne EB, Bercovitch FB, et al. Are subordinates always stressed? A comparative analysis of rank differences in cortisol levels among primates. Horm Behav. 2003;43(1):67–82. http://www​.ncbi.nlm.nih​.gov/pubmed/12614636 Accessed April 16, 2019. [PubMed: 12614636]

56.

Sapolsky RM. Social Status and Health in Humans and Other Animals. Annu Rev Anthropol. 2004;33(1):393–418.

57.

Tamashiro KLK, Hegeman MA, Sakai RR. Chronic social stress in a changing dietary environment. Physiol Behav. 2006;89(4):536–542. [PubMed: 16843504]

58.

Wilson ME, Fisher J, Fischer A, Lee V, Harris RB, Bartness TJ. Quantifying food intake in socially housed monkeys: Social status effects on caloric consumption. Physiol Behav. 2008;94(4):586–594. [PMC free article: PMC2490628] [PubMed: 18486158]

59.

Razzoli M, Nyuyki-Dufe K, Gurney A, et al. Social stress shortens lifespan in mice. Aging Cell. 2018 [PMC free article: PMC6052478] [PubMed: 29806171]

60.

Daly M, Boyce C, Wood A. A social rank explanation of how money influences health. Heal Psychol. 2015. [PMC free article: PMC4507513] [PubMed: 25133843]

61.

Althoff T, Sosič R, Hicks JL, King AC, Delp SL, Leskovec J. Large-scale physical activity data reveal worldwide activity inequality. Nature. 2017;547(7663):336–339. [PMC free article: PMC5774986] [PubMed: 28693034]

62.

Bratanova B, Loughnan S, Klein O, Claassen A, Wood R. Poverty, inequality, and increased consumption of high calorie food: Experimental evidence for a causal link. Appetite. 2016;100:162–171. [PubMed: 26809142]

63.

Coleman-Jensen A, Rabbitt MP, Gregory CA, Singh A. Household Food Security in the United States in 2016. 2017.

64.

Hernandez DC, Reesor LM, Murillo R. Food insecurity and adult overweight/obesity: Gender and race/ethnic disparities. Appetite. 2017;117:373–378. [PubMed: 28739148]

65.

Tait CA, L’Abbé MR, Smith PM, Rosella LC. The association between food insecurity and incident type 2 diabetes in Canada: A population-based cohort study. PLoS One. 2018 [PMC free article: PMC5965821] [PubMed: 29791453]

66.

Dhurandhar EJ. The food-insecurity obesity paradox: A resource scarcity hypothesis. Physiol Behav. 2016 [PMC free article: PMC5394740] [PubMed: 27126969]

67.

Cardel MI, Tong S, Pavela G, et al. Youth Subjective Social Status (SSS) is Associated with Parent SSS, Income, and Food Insecurity but not Weight Loss Among Low-Income Hispanic Youth. Obesity. 2018;26(12):1923–1930. [PMC free article: PMC6249075] [PubMed: 30421861]

68.

Cornil Y, Chandon P. From Fan to Fat? Vicarious Losing Increases Unhealthy Eating, but Self-Affirmation Is an Effective Remedy. Psychol Sci. 2013;24(10):1936–1946. [PubMed: 23925307]

69.

Cardel MI, Johnson SL, Beck J, et al. The effects of experimentally manipulated social status on acute eating behavior: A randomized, crossover pilot study. Physiol Behav. 2015;162:93–101. [PMC free article: PMC4899290] [PubMed: 27094920]

70.

Cheon BK, Hong Y-Y. Mere experience of low subjective socioeconomic status stimulates appetite and food intake. Proc Natl Acad Sci. 2017 [PMC free article: PMC5224403] [PubMed: 27994148]

71.

Frerichs L, Huang TTK, Chen DR. Associations of subjective social status with physical activity and body mass index across four asian countries. J Obes. 2014 [PMC free article: PMC4058138] [PubMed: 24971171]

72.

Rajala K, Kankaanpää A, Laine K, Itkonen H, Goodman E, Tammelin T. Associations of subjective social status with accelerometer-based physical activity and sedentary time among adolescents. J Sports Sci. 2018;(June):1–8. [PubMed: 29889652]

73.

Bernardo C de O, Bastos JL, González-Chica DA, Peres MA, Paradies YC. Interpersonal discrimination and markers of adiposity in longitudinal studies: a systematic review. Obes Rev. 2017;18(9):1040–1049. [PubMed: 28569010]

74.

Hunte HER, Williams DR. The association between perceived discrimination and obesity in a population-based multiracial and multiethnic adult sample. Am J Public Health. 2009;99(7):1285–1292. [PMC free article: PMC2696650] [PubMed: 18923119]

75.

Leroy JL, Gadsden P, Gonzalez de Cossio T, Gertler P. Cash and in-Kind Transfers Lead to Excess Weight Gain in a Population of Women with a High Prevalence of Overweight in Rural Mexico. J Nutr. 2013 [PubMed: 23343672]

76.

American Diabetes Association AD. 1. Improving Care and Promoting Health in Populations: Standards of Medical Care in Diabetes-2019. Diabetes Care. 2019;42 Suppl 1:S7–S12. [PubMed: 30559227]

77.

Bazemore AW, Cottrell EK, Gold R, et al. “Community vital signs” : incorporating geocoded social determinants into electronic records to promote patient and population health. J Am Med Informatics Assoc. 2016;23(2):407–412. [PubMed: 26174867]

78.

Gold R, Bunce A, Cowburn S, et al. Adoption of Social Determinants of Health EHR Tools by Community Health Centers. Ann Fam Med. 2018;16(5):399–407. [PMC free article: PMC6131002] [PubMed: 30201636]

79.

Gundersen C, Engelhard EE, Crumbaugh AS, Seligman HK. Brief assessment of food insecurity accurately identifies high-risk US adults. Public Health Nutr. 2017;20(8):1367–1371. [PMC free article: PMC10261547] [PubMed: 28215190]

80.

Stenmark SH, Steiner JF, Marpadga S, Debor M, Underhill K, Seligman H. Lessons Learned from Implementation of the Food Insecurity Screening and Referral Program at Kaiser Permanente Colorado. Perm J. 2018;22:18–093. [PMC free article: PMC6175601] [PubMed: 30296400]

81.

Diabetes Prevention Program (DPP) | NIDDK. National Institute of Diabetes and Digestive and Kidney Disease. https://www​.niddk.nih​.gov/about-niddk/research-areas​/diabetes​/diabetes-prevention-program-dpp. Published 2018. Accessed May 6, 2019.

82.

Ely EK, Gruss SM, Luman ET, et al. A National Effort to Prevent Type 2 Diabetes: Participant-Level Evaluation of CDC’s National Diabetes Prevention Program. Diabetes Care. 2017;40(10):1331–1341. [PMC free article: PMC5606310] [PubMed: 28500215]

83.

Nhim K, Gruss SM, Porterfield DS, et al. Using a RE-AIM framework to identify promising practices in National Diabetes Prevention Program implementation. Implement Sci. 2019;14(1):81. [PMC free article: PMC6694543] [PubMed: 31412894]