[80]. Screen time during childhood is also a predictor of overweight and obesity during adulthood. A study in New Zealand showed that 17% of the prevalence of overweight/obesity at age 26 could be attributed to more than 2 hours of screen time per day during childhood and adolescence [81]. Similarly, among adults, a longitudinal study in England found a significant association between watching TV for more than 6 hours a day and central obesity [82].
Crime
The association between crime levels, obesity, and weight‐related behaviors (e.g. physical activity and diet) has been widely explored [83]. Lack of safety in urban areas may be an important pathway by which physical activity decreases among both adults and children and particularly among women and girls [84]. High levels of perceived or objective crime may make residents reluctant to use neighborhood amenities even if they are available [85]. For example, one study in Nigeria of approximately 2000 randomly selected urban residents found that feeling unsafe from crime or traffic was associated with overweight even after adjusting for neighborhood sociodemographic variables [86]. Similar associations have been reported in high‐income countries. For example, low criminality is associated with lower BMI and obesity prevalence among women in Spain [87]. A study conducted on almost 15,000 patients recruited from primary care clinics in Chicago has shown that recurrent exposure to high rates of violent crime was associated with obesity [88]. Similarly, studies in the United States have shown lower rates of physical activity and higher rates of obesity among children living in unsafe neighborhoods [89,90]. This association is particularly relevant for ethnic minorities who may be more likely to live in neighborhoods with higher crime rates [91–93]. A study in the US state of Indiana found that access to walking trails is associated with lower childhood obesity in low‐crime neighborhoods but not in high‐crime neighborhoods [90]. These findings highlight the need for multisectoral action to prevent obesity. Without addressing high violent crime rates, physical activity promotion interventions are unlikely to be effective.
Culture
Dimensions of national culture have also been explored in relation to obesity. The most frequently used metric has been the Geert Hofstede dimensions: power distance, individualism, masculinity, uncertainty avoidance, long‐term orientation, and indulgence. Of these, individualism is the most consistently significant dimension, with countries ranking high on individualism having the highest prevalence of obesity [94]. This observation lends support to the idea that putting the onus on individuals to prevent obesity is unlikely to be successful and may simply result in stigmatization, anxiety, and low self‐esteem for individuals experiencing obesity.
Body size perceptions and preferences also play a role [95–98]. For example, several studies support the idea that Latina mothers prefer a heavier child body size – it is considered healthier, cuter, resilient to illness, and a sign of good parenting compared to a leaner child body size [99–102]. In sub‐Saharan Africa, large body sizes are preferred because they are associated with beauty, health, and social status [96,103–108]. This preference for large body sizes is even more vital in places where AIDS is prevalent because being thin is considered a sign of HIV [105,106,109].
Racial and ethnic disparities in obesity persist in the United States, where Blacks and Hispanics are disproportionately affected [4]. Many studies have explored the underlying drivers of these observations and have found that differences in known obesity risk factors explain a small proportion (less than one‐fifth) of these disparities [110]. For example, while poverty plays an underlying role in obesity disparities, income does not explain all of the observed differences [111]. Food environments may partly explain some of the differences. In particular, having limited access to grocery stores (e.g. food deserts) and high levels of unhealthy food outlets like fast‐food restaurants (e.g. food swamps). For example, one analysis of BRFSS found that 14% of the gap in mean age‐adjusted adult obesity prevalence between counties with large black populations and those with small black populations could be explained by disparities in the food environment [112]. Neighborhood walkability has also been identified as a significant driver [113]. However, in all of these studies, a sizable proportion of the disparities could not be explained, suggesting there is still much to be done in terms of research to understand – and intervene to reduce – racial and ethnic disparities in obesity in the United States.
Conclusions
In the United States, the prevalence of obesity continues to increase among adolescents and adults, although it is declining among 2‐ to 5‐year‐olds and has stabilized among 6‐ to 11‐year‐olds [4,31]. Around the world, no country is on track to meet global targets of halting the rise in obesity [10]. Given the strong association between obesity and COVID‐19 [13], there is more urgency now than ever to address the growing prevalence of obesity. To tackle obesity at the population level, social determinants of weight gain must first be addressed. In high‐income countries such as the United States, this will involve addressing poverty, unhealthy cities that are not conducive to active transport or outdoor exercise, and unhealthy food environments that promote the consumption of ultra‐processed foods. It will also involve addressing cultural drivers such as individualism, which blame individuals rather than the contexts in which individuals live. There are early signs that obesity is plateauing in certain population subgroups, particularly young children, where many prevention efforts have focused to date. This observation suggests that the obesity pandemic is not inevitable, and with concerted efforts, we can prevent obesity.
References
1 1. The NCD Risk Factor Collaboration (NCD‐RisC). Worldwide trends in body‐mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population‐based measurement studies in 128· 9 million children, adolescents, and adults. Lancet 2017; 390(10113):2627–42.
2 2. The NCD Risk Factor Collaboration (NCD‐RisC). Trends in adult body‐mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population‐based measurement studies with 19.2 million participants. Lancet 2016; 387(10026):1377–96.
3 3. Thomas DM, Weedermann M, Fuemmeler BF, et al. Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends. Obesity 2014; 22(2):590–7.
4 4. Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity and severe obesity among adults: United States, 2017–2018. NCHS Data Brief, no 360. Hyattsville, MD: National Center for Health Statistics, 2020.
5 5. Rtveladze K, Marsh T, Barquera S, et al. Obesity prevalence in Mexico: impact on health and economic burden. Public Health Nutr 2014; 17(1):233–9.
6 6. Rtveladze K, Marsh T, Webber L, et al. Obesity trends in Russia. The impact on health and healthcare costs. Health 2012; 4(12):1471–84.
7 7. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. WHO Technical Report Series 894. Geneva: World Health Organization, 2000.
8 8. Office of Disease Prevention and Health Promotion. Healthy People 2030. Objectives and data. Reduce the proportion of adults with obesity — NWS‐03. 2020, Available from: https://health.gov/healthypeople/objectives‐and‐data/browse‐objectives/overweight‐and‐obesity/reduce‐proportion‐adults‐obesity‐nws‐03 (accessed August 19 2020).
9 9. World Health Organization. Comprehensive Implementation Plan on Maternal, Infant and Young Child Nutrition. Geneva: World Health Organization, 2014.
10 10. Lobstein T, Brinsden H. Atlas of Childhood Obesity. London: World Obesity Federation, 2019.
11 11.
