Executive Summary

The latest epidemiological data suggest that the incidence of obesity among children may have stabilized, although at a dangerously high level. There are currently about 13 million obese children and adolescents in the US. Moreover, significant racial/ethnic and socioeconomic disparities exist and may be widening. This report consists of an expansive, annotated literature review in two broad sections—(1) intervention studies geared at preventing childhood obesity and (2) studies that examine some of the social determinants of health underlying the epidemic.

Intervention Studies

The evidence on intervention studies designed to prevent or reduce childhood obesity suggests that the most successful strategies incorporate a long-term, multi-pronged community-based approach, targeting younger children. It is crucial that schools play a key role in this partnership

in terms of providing opportunities for healthier nutrition and physical activity and that there is parental involvement and support from the community.

A related approach, which has garnered considerable attention among researchers, involves applying a systems science framework in which sophisticated modeling is utilized to characterize the complex interactions and feedback loops across social, environmental and organizational systems. Research on the efficacy of this approach, however, is only just emerging. The least developed area is at the distal end of the model used in the systems science framework— the political economy of the system itself. By this we mean the ways in which the macroeconomic and political forces that shape and develop the institutions, policies, and programs affect the incidence, prevalence, and prevention of obesity.

School-based interventions alone are inadequate, largely because they do not account for child behavior outside of school, require sustained staff resources and training, and have little impact on addressing obesogenic features in the community. It is worth highlighting the US Department of Agriculture’s Fresh Fruit and Vegetable program, which targets low-income schools and provides funding for distribution of free fresh fruits and vegetables. Preliminary studies have found promising evidence that this approach can reduce obesity rates in participating schools. Follow-up studies are needed to confirm these findings.

Studies focused on early childhood interventions have mainly been implemented in children five and younger and conducted in childcare facilities. There is certainly some logic to focusing on young children whose health behaviors are still developing, but the evidence from these studies is decidedly mixed and may be affected by the implementation of effective regulations that prescribe adequate physical activity and nutritional standards.

Technology-related interventions take advantage of platforms such as computer programs, video games, Internet sites, and apps for mobile devices to target health behavior outcomes such as diet quality, physical activity levels, and sedentary time. Unfortunately, the studies to date have been of short duration and poor quality, resulting in disappointing results. However, this is a rapidly emerging field and may hold some promise in the years ahead.

Interventions brought about by laws or policies have significant potential for positive change in health behaviors and weight outcomes, not only for children across the socioeconomic spectrum, but also for people of all ages in the population as a whole. These interventions include local or organizational policies and practices as well as large-scale government regulations and programs. Much of the research in this area focuses on food and beverage prices, subsidies, and taxes, as well as the built environment, housing, and economic policy. The majority of studies have applied sophisticated modeling techniques; true experimental designs are rare in this area. Studies have, for example, demonstrated efficacy, and occasionally cost effectiveness, in reducing child obesity by improving early childcare standards, eliminating tax subsidies for television advertising of unhealthy food directed at children, subsidies for fruit and vegetable consumption, imposing a sugar-sweetened beverage (SSB) tax, and increasing active physical education time in schools.

Modeling studies, however, have limitations, most obviously their practical application in the real world. They also suffer from some of the mundane problems facing the other intervention studies including

the failure to adequately capture the multitude of mediating or moderating factors that may influence the relationships between specific policies and child weight. Despite these caveats, there are at least four reasons why these types of approaches should be considered and pursued more seriously: (1) They are built on a reasonably strong evidence base (e.g. studies linking prices or advertising exposure to consumption); (2) the predicted effect size on obesity reduction is substantial (and larger than most of the intervention studies discussed earlier); (3) they have the potential to reach large segments of the population, particularly poor and minority communities; and (4) because of their modest cost, and in some cases, cost savings, they are more likely to be sustainable over the long term.

Social Determinants of Health

The social determinants of health broadly speaking include the social, cultural, and economic conditions that provide the framework for policies that shape the conditions under which people live. We felt it important to examine some of these conditions, such as poverty, income, housing, and the power of the industrial food system, because they shape people’s lives in many ways, including influencing their patterns of food consumption and physical activity. Moreover, mainstream researchers and policymakers have devoted inadequate attention to these underlying conditions, which may play an important role in curbing the obesity epidemic.

Systematic data reviews have examined the relationship between local food environments and childhood obesity and found little evidence linking the two. Reviews analyzing the relationship between the built environment, physical activity, and obesity— although somewhat mixed—do show an overall modestly beneficial effect. It is well understood that ultimately obesity is a function of energy balance between calories expended through physical activity and calories taken in through food and beverage consumption. It therefore makes sense that some studies in both of these fields of inquiry demonstrate beneficial findings. However, beyond the problem of the weak study designs that characterize many of these studies (most studies are cross-sectional and observational, which prevents strong inferences of causality), the inconsistency of study findings suggests that the overall focus of these inquiries overemphasizes the proximal causes of obesity at the expense of the more distal ones. In other words, there has been a lack of attention to the political, economic, and social conditions that shape the environment and the population-based behaviors that generate obesity.

Over the past decade, the obesity field has been dominated by the use of a behavioral framework in which the individual is the primary unit of analysis and intervention. Although social and cultural factors are often considered, they are generally not the central focus. This has begun to change modestly as studies have started addressing the role of the physical, social, and economic context of neighborhoods where people live. However, researchers have not yet given as much attention to the underlying context or to policies that generate these conditions and constrain people’s choices. While building bike paths and pedestrian walkways, improving access to parks and recreational facilities, or improving access to supermarkets, are amenities any community might favor, they are unlikely to curb the obesity epidemic without addressing the underlying social determinants of health.

Social epidemiology has energized research on health and place by demonstrating a strong social gradient that exists in health status in general and is starkly visible in the obesity epidemic. However, this has yet to be fully embraced by the mainstream in obesity research or by funders or policymakers. Poverty, income, neighborhood deprivation, inadequate housing, residential segregation, and the political economy of our industrial food system lie at the heart of the epidemic and are key to understanding the disproportionate burden of child obesity on poor and vulnerable populations.

Inclusion Criteria

Our review of intervention studies reports outcomes in terms of anthropometric measures of obesity or other health behavior outcomes such as diet quality, physical activity levels, or sedentary behavior levels. The main search included articles published between January 2010 and October 2015. The literature on the social determinants of obesity begins in 2000. Studies were gathered through systematic searches of electronic databases such as PubMed, as well as reference lists of relevant articles, independent searches, and websites from relevant organizations. Titles and abstracts were screened by two independent reviewers. Full copies of the relevant articles were retrieved and assessed independently for eligibility. Preference was given to (1) interventions that targeted children from minority groups or low-income families; (2) articles that reported results separately for these subgroups; and (3) articles that examined whether interventions increased or decreased disparities between these groups and their peers. To produce a comprehensive assessment, we also included strategies that would affect children across the socioeconomic gradient. Included articles were published in English, with no limits placed on setting or country. Both measured and self reported outcomes were included. No limits were placed on study design but preference was given to systematic reviews. Included articles examined children from birth to 18 years of age; articles that included adults in the study sample were also used if results for children were reported separately.

We relied more heavily on systematic data reviews (SDRs) when they were available because, in general, they produce a less biased picture of the literature in a given field than individual studies. However, it was not always possible to find an SDR on each specific topic. Additionally, there is often a lengthy time lag between publication or pre-publication of individual studies and the publication of a review, and with the quickly evolving literature on obesity, many of the most recent studies are not included. Moreover, there are other limitations to reviews that must be considered, including that the results of many reviews are often provisional and may be overturned by a single large or robust study. In this report, the SDRs dominate our review of intervention studies but are less prevalent in the studies focused on the social determinants of health.

Abbreviations

ATLAS — Active Teen Leaders Avoiding Screen-Time
BMI — Body mass index
BPC — CATCH BP and Community
CAFTA DR — Dominican Republic-Central America Free Trade Agreement
CATCH BP (or BP) — Coordinated Approach to Child Health BasicPlus
CDC — Center for Disease Control and Prevention
CF&B — Competitive food and beverage
CFBAIR — Children’s Food and Beverage Advertising Initiative
CI — Confidence interval
CV — Cardiovascular
DID — — Difference-in-differences
DPHO — — District Public Health Office
ECE — Early care and education
EMI — Ecological momentary intervention
FFVP — Fresh fruit and vegetable program
FV — Fruit and vegetable
GuS — Growing Up Strong Program
HIP — Healthy Incentives Program
HOPS — Healthier Options for Public Schoolchildren
HUD — Department of Housing and Urban Development
IOM — Institute of Medicine
MSA — Metropolitan Statistical Area
MTO — Moving to Opportunity
MVPA — Moderate to vigorous physical activity
N — Sample size
NAFTA — North American Free Trade Agreement
NR — Not reported
NYS — New York State
NYC — New York City
OWG — OrganWise Guys
PA — Physical activity
PE — Physical education
RCT — Randomized controlled trial
SD — Standard deviation
SE — Socioeconomic
SEP — Socioeconomic position
SES — Socioeconomic status
SNAP — Supplemental Nutrition Assistance Program
SDR — Systematic Data Review
SR — Systematic review
SSB — Sugar-sweetened beverage
TV — Television
TV AD — Television advertising
UK — United Kingdom
US — United States
USDA — United States Department of Agriculture
WIC — Special Supplemental Nutrition Program for Women, Infants, and Children
WHO — World Health Organization
WOC — Whole-of-community

Intervention Studies

The following is a summary of the types of interventions detailed in the enclosed table (see Appendix).

School-Based Interventions

  • The majority of these interventions incorporate both physical activity and nutrition components and show higher efficacy when targeting multiple behaviors and environments is those with longer duration.
  • Similar strategies seem to be successful in minority and low-income children, as well as appropriate cultural tailoring of intervention components.

The vast majority of childhood obesity intervention studies are school-based. They are implemented in school or after-school settings, sometimes include other components including family involvement, and usually focus on the school food environment, physical education, and nutrition education. We have included 12 SDRs conducted since 2011 in which the primary question revolved around the efficacy of a school-based intervention. Overall, these reviews show that some modest gains in reducing obesity can be made in the school context, a setting that provides the opportunity to reach a large, diverse population of children. The most effective studies appear to be those aiming to modify multiple health behaviors across a variety of school environments, with community and family involvement playing integral roles in these strategies. Intervention duration is important in achieving reductions in anthropometric measures and may help to improve sustainability. Many reviews found larger positive changes in measurements of health behaviors such as diet quality and physical activity time than in weight outcomes such as BMI or BMI z-scores.

Several SDRs examined the effects of school-based interventions on minority children and those from low income families. Robinson et al. found multicomponent strategies involving several school settings to have the most success with African American children.[1] Another review by Holub and colleagues that focused on Latino children found promising results for the same strategies, as well as for the cultural tailoring of interventions to targeted subgroups.[2]Two additional reviews focused on the efficacy of school-based interventions specifically in minorities reached similar conclusions regarding the importance of multiple school environments, community and family involvement, and cultural sensitivity.[3], [4]

There have been some recent studies that show promise but have not been included in the latest published SDRs. For example, the analysis by Qian and colleagues of the USDA’s Fresh Fruit and Vegetable Program, which targets low-income schools and provides funding for distribution of free fresh fruits and vegetables, found a 3% decrease in obesity rates for participating schools and a 1.8% reduction in overweight rate.[5] Sanchez-Vaznaugh et al. found evidence that school competitive food and beverage (CF&B) policies (which restrict the sale of foods such as soda, candy, and chips and are called competitive foods and beverages because they are available alongside and compete with school meal programs) have a positive effect on overweight/ obesity trends. However, the improvements were much smaller in children from low-income neighborhoods.[6] These results highlight the importance of separating and examining study results by subpopulation to determine equity.

A few limitations are common to most of the school- based intervention reviews and studies, including an inability in many cases to account for children’s behaviors during time spent out of school, a lack of long-term intervention evaluation or follow-up, and a scarcity of studies with stratified outcomes to compare effects in different subgroups of the population. A nearly universal problem is the heterogeneity of included studies. Heterogeneity is used here to describe differences in study design, population, outcomes measures, and other variables that make it difficult for many of the SDRs to make direct comparisons in intervention effectiveness or to perform meta- analyses.

Community-Based Interventions

  • Higher efficacy is found when interventions target younger children, are longer in duration, and include multiple settings, but overall results in this area are mixed.
  • Community-based interventions are unlikely to increase socioeconomic inequalities and have been shown to reduce disparities in some cases.

 Community-wide interventions are promising in their broad reach, often encompassing multiple settings and engaging in numerous strategies to improve population health. Whole-of-community (WOC) interventions are designed to improve population weight status by targeting a specific area such as a town, village, or city with programs, policies, or environments that are conducive to obesity prevention. We have included four SDRs conducted since 2013 that evaluate WOC strategies. These interventions achieved varied levels of success in reducing adiposity measures (which include different ways to assess obesity, e.g. body mass index (BMI) or changes in BMI (BMI-z scores)). The most successful interventions tend to be longer in duration, target younger children (middle school or primary school), and include multiple settings both within the community. A review by Bleich et al. found that four of the nine studies examined achieved desirable changes in BMI or BMI z-scores,[7] while another review found improvement for at least one adiposity measure in seven of eight studies (meta-analysis of six trials found a mean difference in intervention participant BMI z-scores of -0.09 (CI from -0.16 to -0.02)).[8] Baker and colleagues examined community strategies that targeted physical activity in both children and adults but found no effects.[9]

One review by Boelsen-Robinson et al. examined the effectiveness of WOC interventions by socioeconomic status (SES) in order to identify characteristics likely to have an equitable effect on obesity prevalence.[10] Nine of ten WOC interventions included were found to be equally or more effective in lower SES groups, with positive changes in weight outcomes for children. The review concluded that WOC interventions are not only unlikely to increase socioeconomic (SE) inequalities in population weight but when designed specifically with disadvantaged communities in mind, have been shown to be effective in reducing disparities in weight outcomes. It is noted by the authors, however, that these types of interventions alone will not be sufficient to address the socioeconomic gradient in weight.

These reviews found the following limitations: selection bias of the included intervention communities, suboptimal study designs in some cases, a lack of studies that stratified results by socioeconomic status, and heterogeneity in these measures. As Wolfenden et al. stated, “The lack of trials and the limited diversity of community intervention approaches hinder an examination of specific features of interventions, which may have contributed to positive intervention effects.”[11]

Technology-Based Interventions

  • These interventions show potential to engage children and target health behavior outcomes utilizing various platforms.
  • Technology interventions have not been studied extensively; there are few high-quality studies of longer duration; and any positive outcomes tend to be short-lived.

The benefits of employing new technologies in childhood obesity interventions are promising, but the field is just emerging. Electronic interventions take advantage of platforms such as computer programs, video games, Internet sites, and apps for mobile devices to target health behavior outcomes such as diet quality, physical activity levels, and sedentary time. We have included three SDRs on technology- related interventions published since 2011. Numerous studies were able to demonstrate positive effects on health behaviors and/or showed decreases in adiposity measures, but any positive effects tend to be short- lived. Overall, it is difficult to determine the efficacy of these interventions due to a paucity of high quality, long-term studies thus far. Whittemore and colleagues compared school-based Internet programs targeting obesity prevention for adolescents and found positive dietary or physical activity outcomes in 10 of 12 included studies. However, only one study detected a significant decrease in BMI.[12] Another review, Nguyen et al., compared electronic media interventions.[13] Of the 24 included studies, six were stand-alone electronic interventions that demonstrated significant obesity reduction. The rest of the included studies did not separate effects of the electronic intervention from other intervention components such as school or community-based strategies. Another review examined the use of “Exergaming,” a combination of interactive video games and physical exercise as a tool to decrease childhood obesity.[14] Although these games have the potential to increase physical activity and reduce sedentary behavior, no rigorous attempt was made to evaluate their efficacy.

We included several additional studies that focus on the effectiveness of technology-based interventions in minority or low-SES populations. One study tested a mobile application that was found to slightly increase fruit and vegetable (FV) intake (+0.88 servings/ day) and make a small decrease (-0.33 servings/day) in sugar-sweetened beverage (SSB) consumption in minority girls.[15] However, these differences were not statistically significant in comparison with the control group and no significant differences were observed in BMI. Another study included a smartphone application and website in an obesity prevention intervention for adolescent boys recruited from schools in low-income communities, but no significant effects were found on body composition.[[16]

The main limitation in this area is a lack of relevant studies or studies with longer durations or follow-up. Many interventions are very brief with limited sustainability, small sample sizes, and targeted samples that may result in a lack of generalizability. Heterogeneity in intervention designs, outcomes, and components make comparison difficult, especially since few report BMI changes.

Policy Interventions

  • Policies affecting food and beverage prices have been shown to influence both purchasing and consumption but to what extent remains uncertain.
  • Due to the nature of these interventions, many studies employ modeling techniques with promising results.
  • Subsidies for fresh fruits and vegetables as well as a tax on sugar-sweetened beverages may be particularly effective in low-income or minority populations.

Interventions brought about by a law or policy change hold significant potential for positive effects on health behaviors and weight outcomes, not only for children across the socioeconomic spectrum but for the population as a whole. These interventions include local or organizational policies and practices as well as large-scale government regulations and programs. Our search included five SDRs published since 2011. Much of this research focuses on food and beverage prices, subsidies, and taxes, as well as the built environment, housing policy, and other factors. Reviews evaluating prices, subsidies, and taxes establish that they affect levels of purchasing and consumption of target products, but to what extent remains unclear. A review by Faulkner and colleagues evaluated 38 studies and seven SDRs and found weight outcomes consistently responsive to food and beverage prices.[17] Thow et al. reported that subsidies for healthy foods led to an increase in their consumption (although the effect on total caloric intake is unclear) and that SSB taxes can reduce consumption, but only in proportion to the taxes applied.[18] Thow also notes that some studies find taxes to be a bigger burden for low-income families.

Several studies predicted the effects of policy interventions using modeling techniques. Kristensen et al. applied microsimulation modeling to estimate the effects of specific policies 20 years after implementation.[19] They found that after-school PA programs could potentially reduce obesity among children ages 6-12 by 1.8 percentage points, while a SSB excise tax of $0.01/ounce could reduce obesity among adolescents ages 13-18 by 2.4 percentage points. Gortmaker and colleagues used modeling to analyze cost-effectiveness in addition to potential obesity reduction, drawing on four studies that each estimate possible 10-year policy costs and effects in cohort models.[20] Their analyses indicate that a SSB tax could reduce BMI up to 0.16 units per child, while policies improving early childcare standards, eliminating tax subsidies for TV advertising of unhealthy food directed at children, and increasing physical activity time in schools would all reduce BMI, but to a lesser extent (BMI reductions range from .02 to .028 units per person). For three of the four policies, there would be a potential net cost savings over the 10-year period; additionally, either imposing a SSB tax or eliminating tax subsidies would generate net tax revenue.

Several studies and systematic reviews examined the efficacy of laws and policies specifically in low-income and minority populations. Powell et al. suggest that subsidies for fresh fruits and vegetables are especially effective in reducing weight among low-income youth [21] and Faulkner et al. found FV subsidies and the SSB tax to be particularly promising for children and low-income households.[17] Weight outcomes of participation in food assistance programs were mixed: one study found no improvement after WIC changes meant to improve dietary intake and feeding practices among toddlers and infants,[22] while another found that subsidized meals were beneficial for children’s weight. However, in cities with high food prices (where benefits have the least purchasing power) food assistance may actually contribute to childhood obesity.[23] Two studies that examined housing mobility and child health found that housing mobility (using housing vouchers to move to higher income neighborhoods) reduced obesity among adults but had little impact on child health or BMI and in some cases worsened health in the treatment group.[24],[25]

An obvious limitation in this area is the difficulty of studying the effects of laws or policies without implementation on a large scale or for an extended period of time. Many of the interventions studied may need longer durations to show effects. Modeling studies often have limited data with which to predict direct associations between policy and BMI. Other limitations include the scarcity of policy studies that directly assess influence on BMI or weight and the multitude of other factors that may influence the relationships between specific policies and child weight.

Early Childhood Interventions

  • The majority of these interventions are carried out in childcare facilities; outcomes are mixed but do identify areas for improvement.
  • There is a higher efficacy when both nutrition and physical activity components are included, as well as the addition of cultural tailoring for ethnic minority children.

Interventions focusing on obesity treatment or prevention in early childhood target children at an age when health behaviors are still developing. These interventions have the opportunity to establish patterns that will affect health risks over the course of their lives. Implemented in children five and under, the majority of studies focus on strategies carried out in childcare facilities. Our search included five reviews in this area published since 2011. Findings are mixed, identifying several important areas for improvement that are likely to positively influence child weight and health behaviors. Zhou et al. compared interventions taking place in childcare settings—only seven out of 15 studies showed relative improvements in adiposity, but all seven employed both physical activity and nutrition components, supporting the notion that interventions are more effective when multiple strategies are used in combination.[26] Bond and colleagues examined both weight outcomes and cost-effectiveness of interventions for children under five.[27] Of the four randomized control trials (RCTs) which met inclusion criteria, only one (the African American subgroup of Hip-Hop Jr.) showed a significant improvement in weight measures with a 24-month BMI increase in the intervention group of 0.48 kg/m2 versus a 1.14 kg/m2 increase in the control group. Larson et al. found a lack of strong state regulations in childcare settings, with opportunities for improvement in food nutritional quality and amount of quality physical activity time.[28]

Additional studies assessed the effects of early childhood interventions on low-income children or those belonging to racial/ethnic minorities. Many reviews also examined interventions directed toward this population, or at least compared effectiveness in these children versus their peers. Bender & Clark analyzed the effects of cultural adaptations on study outcomes in US ethnic minority preschool children and found a relative absence of appropriately adapted interventions.[29] However, when employed, these types of modifications were found to have potential to enhance intervention effectiveness. Laws et al. examined early childhood interventions targeting children from socioeconomically disadvantaged or indigenous families.[30] Mean differences in BMI between intervention and control groups ranged from -0.29 kg/m2 to -0.54 kg/m2. Interventions had greater impact when initiated in infancy than those started in preschool (ages 3-5). Sekhobo et al. examined obesity prevalence in low-income children in relation to the enactment of new regulations in New York City licensed childcare centers.[31] Average annual change in obesity prevalence was -2.6% for high-risk neighborhoods versus -1.6% in low-risk neighborhoods and results suggest a narrowing of the gap in obesity prevalence. Another study found that enrollment of low-income children in Head Start resulted in a significant decline in mean BMI z-score at a rate of -0.82 units by the end of the second year of enrollment.[32]

Common limitations in this area include a need for longer intervention duration and extended follow-up periods. There is a lack of studies examining long-term efficacy, as well as those that compare results based on socioeconomic status. Bond et al. sought to review the cost-effectiveness of early childhood interventions and were unable to find any studies that fit inclusion criteria.[27]

Comparing Multiple Interventions

  • Interventions achieve more positive outcomes when multiple settings and levels, and both nutrition and physical activity are targeted. Intervention duration and the inclusion of home or family components also improve efficacy.
  • Reviews focusing on reducing disparities in child obesity found that interventions did not increase inequalities and may reduce them in some cases.
  • Higher-level interventions, such as those targeting policy and environmental or economic factors, have the most potential to reduce inequalities.

Many reviews attempt to compare multiple types of interventions to determine which are most effective or what components are common to those with positive outcomes. Our search included 12 such reviews published since 2010. Most concluded that efficacy is increased in multi-setting and/or multi-level interventions, in those that influence both diet and PA, in interventions with longer durations, and in those that include a parental or home component. Several reviews performed meta-analyses. Peirson et al. could not identify any particular intervention strategy with consistent benefits, but overall found a small, significant reduction in BMI (-0.09 kg/m2, 95% CI) in an analysis of 90 studies in mixed-weight populations.[33] Wang et al. compared data from 147 articles, finding stronger evidence to support the efficacy of school-based interventions than interventions in childcare or home settings.[34] Meta-analysis found a small overall effect size, with an improvement in z-score of about 0.05 and in BMI of about 0.25. The meta-analysis performed by Waters et al. and published by the Cochrane Collaborative included 55 international studies, finding an overall mean difference in adiposity of -0.15 kg/m2, a small shift that could prove important if sustained over time.[35] Two reviews found higher success rates when interventions were initiated in younger children; one documented better results when interventions were introduced at middle school age or younger[36] and another reported stronger effects in children ages 6-12.[35]

The 12 reviews in this category include seven that specifically focus on targeting minority and low- income children or evaluate how interventions can reduce disparities in child obesity. Although most reviews were unable to identify specific strategies to reduce inequalities, two concluded that child obesity interventions did not increase inequalities and sometimes reduced or slowed the widening of disparities.[37],[38] McGill et al. examined the differential impact of healthy eating interventions by socioeconomic position.[39] This review, drawing upon evidence from all age groups, found upstream “price” interventions (such as taxes, subsidies, or economic incentives) to be the most likely to decrease inequalities in healthy eating outcomes. However, downstream “person” interventions (individual-based information and education) had the greatest potential to increase inequalities as these interventions have more benefits among higher income groups. Similarly, Beauchamp et al. (in a study that included adults as well as children) found community-based interventions or policies aimed at structural changes to the environment to be more effective in lower socioeconomic groups, while information provision was ineffective.[40] In children, Bambra et al. found school-delivered and environmental interventions to have the greatest potential influence on child obesity rates in disadvantaged areas.[37] The importance of cultural competence in intervention design and delivery is highlighted by Suarez-Balcazar et al., who found specific strategies that tend to work well for Latino and African American children.[41]

One limitation of these reviews is that the majority of studies take place in the school setting, making an unbiased comparison with other intervention settings more difficult. Another common difficulty is in specifying what particular intervention components produced positive results. Other limitations include heterogeneity of studies, lack of detailed subgroup analysis (gender, age, socioeconomic status, etc.), lack of studies that examine cost-effectiveness, publication bias, and weak study designs.

System Science Approach

  • This approach evaluates the complex interactions across the various systems affecting child obesity utilizing modeling techniques.
  • The Healthy Kids, Healthy Community Project, as well as the ongoing Childhood Obesity Research Demonstration project, apply this emerging technique to the study of child obesity.

Due to the complexity and multi-faceted causes of the obesity epidemic, a systems science approach has gained traction as a way to address the issue. This framework utilizes sophisticated modeling to characterize the complex interactions and feedback loops across social, environmental and organizational systems. The Institute of Medicine [42],[43] along with a number of individual investigators [44] has attempted to conceptualize its applicability to evaluating obesity prevention interventions. However, research on the efficacy of this approach is only just emerging. Most of the analyses conducted to date have not included hard outcome measures such as population measures of overweight or obesity. As James Sallis recently wrote, “Building systems models of childhood obesity with quantitative data has not been accomplished to my knowledge, so the application of systems thinking is in its early development in this field.”[52]

The most ambitious attempt to apply a system science approach to obesity has been the Robert Wood Johnson-funded Healthy Kids, Healthy Community (HKHC) project. However, its evaluation of projects in 49 communities across the country was “process oriented, monitoring community partnerships’ progress on their work plans, community engagement, revenue generation, and changes made to local and organizational policies and environments. Thus, the evaluation did not focus on changes in individual behaviors and health outcomes.”[53] A fuller description of these projects can be found in a special supplement of the Journal of Public Health Management published in May, 2015.[54]

Another ambitious project that is currently underway and may fall under the system science framework is known as the Childhood Obesity Research Demonstration Project (CORD).[55] It is funded by the CDC with moneys from the Affordable Care Act and will conduct multisite and multisector interventions in health care centers, schools, early care and education centers, communities, and the home in six rural and urban communities in Texas, Massachusetts, and California. According to the CDC, a summary report on project findings is expected by summer 2016.

There is little doubt that complex interactions take place between the individual, family, and community that loop back and affect one another and there is certainly promise in developing this approach further. However, the least developed area is at the distal end of the model—the political economy of the system itself. By this we mean the ways in which the macroeconomic and political forces that shape and develop the institutions, policies, and programs affect the incidence, prevalence, and prevention of obesity.

Social Determinants of Health:

Childhood Obesity and Race/Ethnic and Socioeconomic Disparities

The latest epidemiological data suggest that the incidence of obesity among children may have stabilized, albeit at a high level,[56] or declined in some regions around the country in recent years.[61],[62] The most authoritative national studies to date by Ogden and colleagues using data from NHANES report that, overall, approximately 17% (or 12.7 million) of children and adolescents aged 2–19 years are obese.[56],[57] These rates vary by age group—8.4% of 2-to 5-year-olds, 17.7% of 6-to 11-year-olds, and 20.5% of 12-to 19-year- olds. Significant disparities are also apparent by race/ ethnicity—rates are higher among Hispanics (22.4%) and non-Hispanic blacks (20.2%) than among non- Hispanic whites (14.1%).

Even if the epidemic is plateauing among children overall, it is doing so at an alarmingly high level that will have severe health and economic consequences as a significant proportion of children, particularly adolescents, become obese adults with the incipient health and economic consequences.[63],[64],[65] Moreover, there is evidence that racial and socioeconomic disparities may be widening,[66] although there is controversy over these trends and the metrics used to measure the disparities. [72]

As research on obesity, and particularly on obesity prevention, has intensified over the last decade, it is still dominated by a behavioral framework in which the individual is the primary unit of analysis and intervention. Even when socio-demographics such as income or education are taken into account, they are often only analyzed at the individual level. Although broader social and cultural factors are sometimes considered, they are generally not the central focus. This is not unique to obesity research, but pervades biomedical and health research in general. As Patricia O’Campo has stated:

The dominant explanatory model used in epidemiologic and social epidemiologic inquiry continues to be the biomedical or “disease-specific model,” which seeks to identify mostly individual-based risk markers and risk factors for specified health conditions. Thus, the study of macro-social policies and programs necessitates the expansion of the study designs used to understand and document contextual and macro-level influences on family and individual well-being.[73]

This focus on the individual is changing modestly as studies begin to address the role of the physical, social and economic context of neighborhoods where people live. However this trend seems less frequently applied to advancing our understanding of the economic and social forces that shape those neighborhoods or in the interventions or policies designed to reduce obesity.

Life-Course Perspective

A growing body of evidence supports a life-course perspective, which suggests that early and cumulative disadvantage play a central role in understanding health and health disparities over the lifespan.[74] Not only are overweight or obese children more likely to become overweight or obese adults, but these patterns are magnified for children of lower socioeconomic status and from racial and ethnic minorities.[63],[77],[78],[79],[80],[81] Moreover, these disparities have been shown to begin very early in life.[82],[83]

Neighborhood Disparities

Childhood obesity disparities by race/ethnicity and socioeconomic status are well established in the literature.[56],[[67],[84],[85] Generally, these disparities are also replicated at the neighborhood level—socially disadvantaged neighborhoods, especially in poor minority communities, experience higher levels of obesity.[86], [87],[88], [89] However, there is no consensus on precisely which factors influence BMI. There have been countless studies that have tried to link two key elements in these neighborhoods to obesity—poor access to healthy food (e.g. the presence of food deserts or the preponderance of fast food restaurants) and barriers to physical activity (e.g. pedestrian walkways, bike paths, park access), often modified by other social factors such as the prevalence or perception of crime.

Improving neighborhood access to healthy food and opportunities for physical activity has been one of the cornerstones of policy initiatives around the country for several years,[90],[91] but the scientific evidence supporting these notions and initiatives is quite mixed. For example, a recent systematic data review (SDR) by Laura Cobb and her colleagues of 71 studies representing 65 cohorts between 1990 and 2013 examined the relationship between local food environments and obesity and found little evidence linking the two.[92]The authors concluded that, “Despite the large number of studies, we found limited evidence for associations between the food environments and obesity.” [93]This does not prove that these associations are not true because, as the authors go on to say, “The predominately null associations should be interpreted cautiously due to the low quality of available studies.”[94] Yet this review, which analyzed associations for children and adults separately, found an even higher rate of null findings for children (85% overall).

Research findings on the relationship between the built environment, physical activity, and obesity are also somewhat mixed, although perhaps a bit less so than the studies on the food environment. A recent systematic review of 194 studies by Ferdinand et al. found an overall beneficial relationship between the built environment and PA or obesity.[95] This held true for the 68 studies focused specifically on children (<19 years of age). However, virtually all studies were observational, making causal inferences difficult to prove.

Additionally, there was a dearth of studies focused on minority populations, a result consistent with other reviews.[96] There have been at least two relatively recent critical reviews of other related systematic reviews highlighting multiple deficiencies in these studies including: incompleteness of reporting key methodological approaches; the lack of moderators, mediators, and objective and perceived measures of the built environment; and weak (mainly cross-sectional and observational) study designs that prevent strong inferences of causality.[97],[98] It is important to underscore the fact that despite the methodological weaknesses in many of these studies (those relating obesity to access to physical activity and food), many of these relationships may indeed be true. What we do not know with certainty is the precise role of these factors— do they have different impacts in more or less affluent communities? Are they causal? Are they associative and/or do they play more of a mediating or moderating role in the pathway towards obesity?

The concept of neighborhoods can be broadened by considering the social and economic policies that shape them. As Acevedo-Garcia & Osypuk state, “Research on place influences on health has largely focused on neighborhoods. However, a focus exclusively on neighborhoods limits our understanding of health disparities. Individual neighborhoods—and their qualities, risks, and resources—are part of metropolitan-area-wide neighborhood distributions. Neighborhoods are influenced by the larger economic and social context (e.g., housing and labor markets) of their metropolitan area.”[99]

Housing and Residential Segregation

Over the past two decades, social epidemiologists have increasingly turned their attention to the relationship between place and health, in part because of the limitations of individual-level factors in explaining health disparities.[100],[101],[102] A central part of this focus has been on the impact of residential segregation—the geographic separation of whites from ethnic minorities in residential areas—on health and health disparities.[103],[104] According to Acevedo-Garcia & Osypuk, “In conducting research on racial disparities in health, we cannot ignore residential segregation. Because of segregation, contextual differences by race are so pronounced that ignoring them may lead to a misestimation of the effect of individual-level factors.”[105]

There are a growing number of studies that directly examine the effects of residential segregation on obesity. Using an index of racial isolation (a common measure of segregation) as a neighborhood-level factor, Chang conducted multilevel analyses and found that greater racial isolation is associated with a higher BMI among non-Hispanic black adults (after adjusting for covariates including measures of [106] individual socioeconomic status). In a study of metropolitan-level segregation, Kershaw and colleagues found segregation was unrelated to obesity among men but had a beneficial effect on Mexican-American women and a strong adverse impact on African American women. These effects were not mediated by neighborhood poverty.[107] In a national study of 11,142 self-identified African American adults, Corral and colleagues found that at the metropolitan level, segregation was associated with obesity and overweight among African American adults (1-standard deviation increase in African American segregation was associated with a 0.423 increase in BMI and a 14 percent increase in the odds of being overweight; no gender breakdown).[108] More recently, Corral et al. conducted the first national study of segregation’s impact on Hispanics’ obesity rates.[109] Analyzing data on 8,785 Hispanic adults from 290 metropolitan statistical areas, they found that those living in highly segregated MSAs were 26.4% more likely to be obese compared to those living in low- segregated MSAs (after controlling for age, education, gender, and MSA poverty). However, this study did not examine heterogeneity in this relationship by gender or race. In a more nuanced study, Kershaw and colleagues demonstrated racial variation in the association between segregation and mean BMI among Hispanic women.[110]

Despite the burgeoning literature on residential segregation and health, we have only been able to identify two studies that examined this issue with respect to children and obesity— one by Rossen and Talih in 2014[111] and another by Ryabov in 2015.[112] The studies used different analytic approaches and datasets, but both found a statistically significant relationship between overweight and obesity and residential segregation. Moreover, both included multi-level modeling at the individual and neighborhood levels, an omission in many previous studies. The neighborhood effects, which characterize a community’s quality through infrastructure, maintenance, social capital, and other factors, are themselves affected by segregation and are essential to understanding the pathways between segregation, poverty, inequality, and adverse health outcomes such as obesity.

Ryabov used a nationally representative sample from the U.S. Panel Study on Income Dynamics merged with census data (for neighborhood variables) of 1,931 African American, Hispanic and non-Hispanic children and adolescents (data from interviews between 1997-2011). He reported that race-ethnic segregation accounted for between 5% and 20% of the difference in the odds of being overweight, obese, or having an obesity-related illness (a diagnosis of asthma, diabetes, or hypertension). Rossen and Talih’s study included multiple waves of NHANES (from 2001-2010) with a total of 18,199 non-Hispanic white, non-Hispanic black, and Mexican American children (ages 2-18). They used a novel approach to account for individual- level and contextual covariates, which allowed for more accurate comparisons between racial/ethnic groups than previously described in the literature. They stated, “racial/ethnic disparities in the prevalence and severity of excess weight were completely attenuated within matched groups, indicating that racial/ethnic differences were explained by social determinants such as neighborhood socioeconomic and demographic factors.”[113] The authors went on to conclude, “Our results highlight the importance of examining more upstream or distal factors such as neighborhood disadvantage or residential segregation in the context of weight disparities, rather than solely focusing on more proximal individual or family factors such as health behaviors.”[114]

Housing Mobility

A related area is housing mobility, which could provide tangible benefits to families living in segregated or otherwise economically and socially disadvantaged communities. The Movement to Opportunity Study (MTO) was conducted between 1994 and 1998 in four major American cities and was perhaps the largest randomized trial (4,608 families) ever conducted in the US to evaluate the impact of moving people from impoverished neighborhoods to more affluent ones.[115] Studies have shown that the MTO experiment had a significant impact on reducing obesity among adults, but it had little impact on improving childhood obesity.[116],[117]

Recent work done by Chetty, Hendren and Klein approached the MTO data from a new perspective.[118] Based on recent literature indicating that neighborhood disadvantage has a cumulative adverse impact,[119],[120] particularly on long-term economic wellbeing, they chose to examine the outcomes of children who moved to less impoverished areas before the age of 13 (average age = 8). In their recent analysis of the MTO data, they found robust evidence that children whose families move to low-poverty neighborhoods when they are young are far more likely to attend college, less likely to become single parents, and earn significantly more as adults than children who remain in those communities or move at later ages. In other words, the benefits of moving away from impoverished neighborhoods decline the longer children are exposed to the adverse conditions in those neighborhoods. This is important for both theoretical and policy implications. On one hand, it supports the powerful adverse impact of cumulative disadvantage and, on the other, it suggests that housing initiatives be tailored to families with young children. This stratified age approach has not yet been applied to health outcomes including obesity, but it is entirely possible that it would yield similar positive results.

Housing Instability

Housing instability has not yet been linked to obesity but there is considerable evidence that it is linked to poor access to care and adverse health outcomes among both children and adults.[121],[22],[123] Children who involuntarily move frequently do less well in school and suffer other emotional and social problems.[124] The foreclosure crisis stemming from the recent great recession, which by definition has resulted in massive housing instability, has affected more than eight million children.[125] In a study of foreclosure rates in four states (Arizona, California, Florida, and New Jersey), which comprised nearly half of the nation’s foreclosures in 2008, Currie and Telik found that housing foreclosures were associated with increased visits to hospitals and emergency rooms for preventable conditions with statistically significant effects for all age groups including children.[126]

Child Development Accounts

There is growing interest in promoting asset development, particularly among children, as a means of addressing the rising and dramatic wealth inequalities in our society. In 2011, due in part to the great recession, a fifth of American households had a median negative net worth, meaning that the value of a household’s liabilities exceeds the value of its assets.[127] However, racial and ethnic wealth inequalities have been longstanding—white households had a median net worth more than 15 times larger than black households and 13 times that of Latinos.[128]

Child development accounts also known as child savings accounts come in a number of different forms but generally are designed as a vehicle for long-term asset building and are begun for children as early as birth and allowed to grow over many years. They provide an opportunity to develop financial assets early in life that can be used to build financial security and support educational and career opportunities.[129],[130] In addition to the financial characteristics of these accounts, researchers are paying increasing attention to the potential impact of this type of asset-building on child health and wellbeing. Although these studies have not yet been linked with obesity, they generally show that household assets are positively associated with improved educational outcomes and fewer behavioral problems for children.[131],a href=”#_edn132″ name=”_ednref132″>[132],[133] One recent study by Huang and colleagues showed some preliminary evidence that child development accounts led to improved social and emotional development particularly among low- income children.[134] Grinstein-Weiss and colleagues reviewed the emerging literature to assess the impact of these accounts, but found that the evidence is too preliminary to definitively prove the long-term effects on children’s wellbeing. However, they still conclude that, “long-term asset-building programs—especially early, universal, and progressive programs—seem most likely to improve the wellbeing of low-income children.”[135]

Immigration and Acculturation

For immigrants, length of time spent living in the US is associated with the adoption of new lifestyle and health behaviors as well as a higher risk of becoming overweight or obese.[136],[137],[138] Initially, immigrants enjoy better health outcomes than their US peers, but this trend tends to diminish with length of residence or degree of acculturation. After controlling for a range of factors such as ethnicity, socioeconomic status, television viewing, and physical activity, “…first- generation immigrant children, overall, had 26% lower odds of obesity than native-born children.”[139] When compared with their US-born peers, studies have found that immigrant children and adolescents across ethnic groups tend to have significantly healthier dietary patterns, lower calorie and fat intake, and spend less time viewing television.[136] Physical activity is an exception, as foreign-born children tend to be less physically active.[140]

With increased exposure to the US environment, sedentary lifestyle and poor nutrition become more prevalent.[141],[142] Within 10–15 years of arriving in the US, the overweight and obesity rates seen in immigrants approach or even surpass that of the general population.[138] As traditional food is consumed less, intake of fat, sugar, and calories rises.[141] For example, Hispanics tend to consume more fast food and fat and less fruit and vegetables in association with acculturation, and both black and white immigrant children watch more television per day in each ensuing generation.[136] One study of Mexican children and adolescents found that both those that had been born in Mexico and raised in the US and those born and raised in the US were more likely to be obese than those born and raised in Mexico.[143] These patterns differ across immigrant groups. For example, Asian immigrants tend to maintain more positive health behaviors and outcomes with acculturation.[137] The association between acculturation and obesity is further supported in a 2015 study by Ishizawa & Jones, which found that immigrants living in areas with higher proportions of foreign-born residents and linguistically isolated households (in which no adult speaks only English and no adult speaks English “very well”) experience lower obesity levels. These results suggest that when immigrants live in a concentrated area, it may slow down acculturation and adoption of new [144] health behaviors, resulting in better weight outcomes.

A systematic review of obesity prevention interventions in US immigrant populations by Tovar et al. included five studies specific to children.[141] Those that targeted caregiver influence on child health showed improvements in BMI while those targeting pre-school or childcare settings saw no effects. Successful interventions had a cultural focus and targeted multiple behaviors, such as diet and physical activity and parent behaviors. Limitations of this review included relatively short study durations with few studies measuring degree of acculturation. A general lack of data emphasizes the need for further high quality research in this area. Finally, new and targeted approaches are needed among immigrant children to prevent their progression toward obesity in succeeding generations.

Breastfeeding

Breastfeeding is often viewed as a protective factor in relation to childhood obesity; initiation and duration of breast milk consumption have been linked in numerous studies to better weight outcomes over time through both nutritional and behavioral pathways.[145] The IOM, CDC, and American Academy of Pediatrics all recommend breastfeeding as a means to prevent obesity.[146] However, as the 2012 IOM report notes, in this area of research “…the nature of the study designs makes it difficult to infer causality.”[147] Studies reach mixed conclusions, and those that do find evidence of an association between breastfeeding and weight status later in life often do not, or cannot, control for important confounding factors. Other pre-and post- natal variables, especially those pertaining to a mother’s health status and behaviors, as well as early childhood factors such as nutrition and PA levels, also correlate with early child obesity incidence.[148] This makes it difficult to define exactly what effect breastfeeding has on later child weight outcomes, especially given that negative trends in these variables often tend to co- occur.

There is no lack of research supporting the hypothesis that breastfeeding results in more desirable child weight outcomes. One study saw a 38%-51% reduction in obesity risk at age nine depending on breastfeeding duration,[145] while a meta-analysis found a 22% decrease in risk of childhood obesity in breastfed children versus those who were never breastfed.[149] Unhealthy weight gain during infancy and early childhood increases the likelihood of obesity later in life.[150] The composition of breast milk, which is moderate in calories and nutrients, may slow patterns of growth in comparison with children who are formula-fed.[145],[149] Formula, in comparison, provides higher levels of fats and proteins which have been associated with higher weight in early childhood.[149] There are also possible behavioral explanations; some studies show that breastfeeding may help increase the mother’s awareness of her child’s hunger and satiety cues.[145]

Other researchers point out the difficulty of verifying a definite link between breastfeeding and healthy childhood weight. Lumeng et al. concluded that evidence to support breastfeeding as an obesity prevention strategy is lacking,[151] and a review by Monasta et al. notes that “it is difficult to establish a casual association with obesity.”[152] Another study found breastfeeding to be a factor in childhood overweight, but the effects were not statistically significant,[153] while a large 2013 RCT found that improving breastfeeding duration and exclusivity did not prevent overweight or obesity at age 11.[154]

Serious confounders of the relationship between breastfeeding and childhood obesity further impede reaching any definitive conclusions. Reviews have suggested that the associated positive effects of breastfeeding on reducing obesity risk are eliminated after controlling for maternal BMI, smoking, and socioeconomic factors.[155] Higher maternal weight and/or weight gain is considered to be a significant risk factor for high birth weight or later adiposity outcomes,[146],[148],[151],[156],[157] and overweight mothers are also less likely to breastfeed.[158] Dixon et al. state, “Maternal obesity is one of the strongest and most reliable predictors of later obesity in children. Infants born to overweight mothers are more likely to be born large for gestational age, are less likely to be breastfed, and are at higher risk for obesity and type 2 diabetes in later life.”[159] Maternal weight is also associated with lower socioeconomic status and education, two additional factors that may influence childhood obesity risk.[158]

The complex relationship between maternal weight and other variables associated with child weight outcomes confound any conclusions reached without controlling for all factors involved. Child obesity risk can be influenced by other maternal characteristics such as diabetes incidence, smoking status, experience of food insecurity, socioeconomic status, and experience of antenatal depression or stress. And as a child grows, risk is further influenced by sleep duration, physical activity, diet, and sedentary behavior.[148],[158] One study points out that it is possible that “…breastfeeding simply serves as a marker, albeit a powerful marker, of other nutritional or lifestyle-related exposures.”[160]

While the pathways by which breastfeeding may be associated with childhood obesity are uncertain, disparities in breastfeeding prevalence have been well documented. Minority women are less likely to have characteristics associated with breastfeeding initiation such as higher income and education levels.[161] Studies in ethnic minorities and poor communities find a negative association between breastfeeding and socioeconomic status,[149] as well as lower rates of breastfeeding initiation among black women than other ethnic groups. Initiation rates are higher in Hispanic and Asian populations, but are still lower than in white women.[162] Shafai et al. point out that most mothers enrolled in WIC do not breastfeed and that, in fact, the majority of the program’s expenditures are used to purchase formula.[163] One study that examined ethnicity and breastfeeding prevalence at age four did not find breastfeeding to have a mediating effect on obesity prevalence,[153] while another found “…that infant feeding practices were the primary mechanism mediating the role between socioeconomic status and early childhood obesity.”[164]

The Industrial Food System

Dramatic changes in the food system in the US over the past 50 years are intricately linked to the obesity epidemic. There is a growing consensus that the evolution of industrially produced and highly processed energy dense foods has lead to a significant increase in caloric intake since at least the 1970s and is one of the main drivers of the current epidemic.[165],[166],[167] According to the US Department of Agriculture, the daily calorie intake per person increased from 2,039 in 1970 to 2,544 in 2010, and refined grains and added fats and oils, products of the industrial food system, contributed 79% of the increase in calories over this time period.[168]

United States agricultural policy plays a pivotal role through its subsidies of crops like corn and soybeans which, when transformed into corn syrup and oils, are key ingredients in processed, refined, and calorie dense foods and beverages. Programs managing commodity crop production, first established to help keep prices stable and insure that farmers would have steady income, were shifted to maximize cheap production of these crops in the 1970s with the idea that US farmers could expand and profit in the world market.[169] By encouraging overproduction, these policies cause prices for commodity crops to fall (and lower prices for the food and beverage industry). Farmers are then reimbursed for their losses and the cycle continues.[170] In contrast, there are few incentives to grow fruits and vegetables due to the lack of subsidies.[169],[171] In the US, “…the real (inflation-adjusted) cost of fresh fruits and vegetables has risen nearly 40% in the past 20 years while the real cost of soda pop has declined more than 20% (converted to real dollars).”[172] The US is the world’s largest producer of both corn and soybeans and much of what is produced is also consumed within the country.

The surplus of cheap food produced as a result of these practices has a direct influence on other government policies. The USDA distributes surplus food through federal food assistance programs, many of which it administers, even though these foods are often already overconsumed when compared to USDA dietary guidelines.[170] The National School Lunch Program is affected as well—the USDA states that it “…must balance its responsibility to provide healthy school meals with its responsibility to support and promote US agricultural production.”[173] Many commodity crops are well suited for long-distance shipping and the export of these crops is often facilitated through trade agreements. Research on the potential impacts on diet of international trade policies such as NAFTA and CAFTA-DR (composed of the US, Dominican Republic, and several Central American countries) in low and middle-income countries finds that these agreements were likely to increase availability and lower prices of processed foods and their ingredients.[171]

By driving down prices for certain crops, US agricultural policy creates a food environment in which energy-dense foods become more affordable and increasingly represent a large portion of overall caloric intake.[174],[175] When prices for certain crops are low, the companies involved in food production are financially motivated to make as large a portion of their product consist of these inexpensive ingredients (specifically corn syrup and soybean oil) as possible. This results in foods with less nutritional value and higher caloric content being more readily available to the consumer at lower prices.[176] The added sugar intake from corn sweeteners alone rose 359% from 1970 to 2007[175] and, as of 2006, soy oil accounted for 20% of the average American’s daily calories.[170] The United States has “…the cheapest food in history when measured as a fraction of disposable income,”[177] — less money can purchase more calories. The rise in consumption of low-cost, high-fat and high-sugar “junk” foods and sweetened beverages produced from subsidized crops has paralleled the rise in the obesity epidemic.

In more descriptive literary terms, Specter recently wrote about America’s fast food consumption in The New Yorker:

Each month, more than two hundred million people eat at least one meal at one of the hundred and sixty thousand fast-food restaurants in the United States. McDonald’s alone serves twenty-six million people every day at its fourteen thousand American outlets—more than the population of Australia. Millions more visit Burger King, Wendy’s, Subway, Pizza Hut, Dunkin’ Donuts, In-N- Out Burger, as well as the other chains that occupy virtually every highway, strip mall, and town center in the nation.[178]

Although there is no definitive proof, the pattern of expenditures on fruits and vegetables by socioeconomic status may contribute to obesity disparities. For example, average expenditures on fruits and vegetables for low-income households fell between 1991 and 2000 at the same time these expenditures increased for higher income households.[183] As unhealthy food and beverages produced from subsidized crops become less expensive, they become more readily available to low-income families who are already at elevated risk for overweight and obesity.[174],[184] Due to their higher consumption of these high-calorie but low-nutrient products,[185],[186] changes in price are more likely to affect weight trends in both minorities and low-income populations.

Increased consumption of these foods and beverages is driven by their convenience as well as their affordability. Since the 1960s, partly as a result of dramatic increases in women’s participation in the paid labor force,[187] time spent on food preparation has decreased across all socioeconomic groups with low-income groups showing the greatest decline in the proportion of adults cooking.[188] This results in a higher proportion of foods consumed that require little preparation (and tend to be highly processed) as well as a greater proportion of foods consumed at home that were prepared elsewhere (such as fast-food restaurants, the main contributor to this increase).[189] These trends are alarming because the frequency of family meals and consumption of home-prepared dinners show positive effects on child dietary intake in low-income households.[190] Efforts to educate parents on purchasing and preparation of more nutritious foods on a budget, such as USDA SNAP- Education or the Cooking Matters program, may help families to increase fruit and vegetable intake, lower fast food consumption, and prepare healthier low-cost meals at home.[191],[192]

According to Cohen and colleagues, while under- consumption of healthy foods like fruits and vegetables increases risk for obesity, overconsumption of processed foods often produced from subsidized crops may have an even greater effect on overall calorie intake.[193] They found that overconsumption of discretionary calories from low-nutrient foods like candy, cookies, salty snacks, and SSBs was greater than under consumption of fruits and vegetables. Many question how great an impact increased physical activity or FV intake can make on obesity outcomes without finding a way to lower the consumption of energy-dense, subsidized food and beverages.[175],[182]

Marketing and Advertising

The marketing of foods and beverages low in nutritional value yet high in calories, sugar, fat, and sodium has been shown to have an impact both on the magnitude of the obesity epidemic and on disparities in obesity prevalence. The high proportion of inexpensive, calorie-dense foods consumed by poor and minority populations makes these groups a profitable advertising target. This is documented by a growing body of evidence demonstrating that advertisements featuring unhealthy food and beverage products are shown more frequently in media targeted at certain minority groups. Specifically, a disproportionate amount of unhealthy food marketing is directed at minority children.[194]

In addition to television advertising, the food and beverage industries have taken advantage of new ways to reach children and adolescents. Many companies have websites or online games targeting young people, foods are often tied into popular video games or movies, companies have a presence on social media, and they often sponsor activities or athletic events.[195] Unhealthy foods and drinks even have a marketing presence in schools, where logos are featured on signs, scoreboards, vending machines, and sports equipment, and related products are used in fundraisers. Companies also sponsor educational materials.[196]

After identifying marketing of unhealthy food and beverages as a driver of increased population caloric intake in recent years, the 2012 Institute of Medicine (IOM) report, Accelerating Progress in Obesity Prevention, concluded that, “In short, marketing works effectively to cause children to prefer, request, and consume sugary, fatty, and salty foods marketed to them.”[197]

Food advertising and marketing strategies exploit poor and vulnerable populations’ sensitivities to costs,[194] since lower quality diets are more affordable.[198] A recent review by Larson & Story found that “Substantial research shows low income and minority youth of all ages tend to consume less whole fruits, vegetables, whole grains, and low-fat milk; consume more fast food and sweetened beverages; and have poorer knowledge of nutritional recommendations for health.”[199] A research brief conducted by Kumanyika and colleagues at the African American Collaborative Obesity Research Network (AACORN) reported that sugar-sweetened beverages are consumed more often by minorities. Specifically, Black Americans consume more calories from weight gain-promoting SSBs than their White counterparts. This may be due in part to the fact that they are targeted for SSB marketing. Black adolescents in particular have shown significant increases in SSB consumption since the 1990s, while consumption among White adolescents remained relatively stable.[200]

Trends in higher advertising exposure for minorities and youth suggest that “ethnic minority youth are likely the most heavily targeted segment of the population.”[201] A recent study by the Rudd Center for Food Policy and Obesity analyzed advertising practices among 26 companies in the restaurant, food, and beverage industries.[202] They found that the 18% of brands advertising disproportionately frequently to children were significantly more likely to target their ads at minorities. The same report also found that Black children and adolescents are exposed to 70% more food-related ads than their White counterparts. In geographic areas with higher proportions of Black children or lower-income households, children are exposed to more television advertisements for sugar- sweetened beverages and fast-food restaurants.[203]

One study comparing Spanish and English-language advertising during children’s television programming found a significantly lower amount of advertisements for food and beverage products on Spanish-language channels. However, the nutritional quality of the foods featured in Spanish commercials was markedly worse. Industry self-regulation was also found to have considerably less positive results in Spanish-language marketing practices.[204] Minority children are also more often exposed to other advertising mediums, such as unhealthy food promotion on food packaging and in print ads.[205] Yancey et al. found a higher density of outdoor advertising in African American and Latino zip codes when compared with white areas.[206] The unhealthy diet promoted in advertisements is associated with child overweight and obesity as it influences child food preferences, the foods they request from caregivers, and short term food choices, making it a likely factor in child obesity prevalence and disparities.[197],[205],[207]

Industry guidelines related to reducing children’s exposure to advertising have been published and many companies have pledged to improve practices.[207] However, an analysis comparing the food advertisements that appeared during children’s TV programs in 2007 versus 2013 found no significant improvement in overall nutritional quality of the foods featured since industry self-regulation had been adopted.[208] A review published in 2013 found a noteworthy disparity in research evaluating the effectiveness of worldwide industry regulation and self-regulation.[209] Industry-sponsored studies indicate major reductions in unhealthy product promotion and children’s exposure to such advertising, while non- industry-sponsored reports find little to no reductions over recent years except in the case of state regulation. A review by Chambers et al., 2015 found that seven of nine real world studies (rather than modeling studies or controlled experiments) examining statutory regulation successfully reduced the exposure of children to advertisements for foods high in fat, sugar, and salt as well as the purchase of these foods.[210]

Attempts by the federal government to improve standards in the US have shown little result. A 2012 review found that the government had made no substantial progress towards the goals recommended by the IOM in 2005 to improve nutritional quality of children’s advertising.[211] The government has found it difficult to improve even voluntary standards. The Joint Task Force on Media and Childhood Obesity established in 2006 proved unable to reach agreement between the government and food and media industries on standards to determine what foods qualify as “healthy” or set limits on advertising of unhealthy foods during children’s programming. The Interagency Working Group of Food Marketed to Children, formed in 2009, proposed voluntary standards for industry self- regulation which were found by Congress to be “overly restrictive and unrealistic” in 2011 and never released.[212] The White House Task Force on Childhood Obesity was established in 2010 to make recommendations for improving child obesity. It recommends strategies to decrease child obesity through industry self-regulation but also points out that government intervention may be necessary.[212] The position of the World Health Organization is that “industry self-regulation will not be sufficient.”[213]
Regulation of the food and beverage industries will be more effective if these industries are monitored using publicly available data,[214] which could also shed light on the food industry’s influence over the policies that relate to its financial interests. According to the 2012 IOM report,[197] reducing child and adolescent exposure to the marketing of unhealthy foods will accelerate obesity prevention. Furthermore, “These actions also may help reduce disparities in obesity rates for those youth who have greater exposure to media, including Black, Hispanic, and Asian youth.”[215]

Other countries have already enacted policies with the goal of reducing child-directed advertising—various advertising bans exist in Quebec, Norway, Sweden, and the UK. Modeling studies indicate promising results if similar policies were established in the US. For example, the potential result of eliminating tax subsidies for TV advertising of unhealthy products to children would be a decrease in BMI of up to 0.028 units per child[216] and banning television advertising of fast foods directed at children could reduce child and adolescent obesity up to one percentage point by 2032.[217]

Conflicts of Interest

Funding provided by the food and beverage industries is used to support a large portion of obesity and nutrition-related research. This creates opportunities for research beyond what government resources provide, but it raises questions regarding the validity of such studies and the necessity for closer examination of how funding may bias outcomes. Research evaluating the effects of certain food and beverages on overweight and obesity trends has potential to generate negative associations with such products, thereby creating a motivation for companies to fund studies with outcomes in their best interests. Even if funding does not directly influence results, it may be more likely to be used to support research questions that are expected to produce favorable outcomes for industry.[218]

One way studies can be examined for bias due to funding source is to compare research reporting quality; lower quality may reflect poor study design or analysis that might in turn skew results. Two studies examining this aspect of obesity and nutrition research were not able to detect significant differences in study quality in relation to funding source.[219],[220] Another study, which examined financial sponsorship of articles pertaining to soft drinks, juice, milk and health, found that no studies with all industry support reached an unfavorable conclusion and articles funded entirely by industry were “…approximately four to eight times more likely to be favorable to the financial interests of the sponsors than articles without industry-related funding.”[221] In 2013, Bes-Rastrollo et al. conducted a review of systematic reviews regarding research and potential conflicts of interest in studies of sugar-sweetened beverage consumption and weight gain or obesity and concluded:

Among those reviews without any reported conflict of interest, 83.3% of the conclusions (10/12) were that SSB consumption could be a potential risk factor for weight gain. In contrast, the same percentage of conclusions, 83.3% (5/6), of those SRs disclosing some financial conflict of interest with the food industry were that the scientific evidence was insufficient to support a positive association between SSB consumption and weight gain or obesity. Those reviews with conflicts of interest were five times more likely to present a conclusion of no positive association than those without them.[222]

The support provided by the food and beverage industries to groups responsible for conducting research and creating health guidelines in the United States is a major cause for concern. Coca-Cola has given millions of dollars in recent years to both the Academy of Pediatrics and the Academy of Nutrition and Dietetics, just two of the many health-related groups with financial ties to the company. This may in turn influence health recommendations and help to minimize research findings that are not beneficial to the company.[223] However, funding from sources other than the federal government is currently a driver in the field of nutrition and obesity research. In order for industry-supported research to establish credibility, clear guidelines need to be established to avoid possible conflicts of interest. Without greater transparency and more rigorous standards, research integrity cannot be maintained.

Conclusion

There are hopeful signs that the epidemic of childhood obesity may have stabilized, albeit at a very high level, with approximately 13 million obese children and adolescents. But there is also troubling evidence that socioeconomic and racial and ethnic disparities may be widening.

It is unlikely that the current state of intervention strategies will stem the tide of the epidemic nor significantly close the gaps in racial/ethnic and socioeconomic disparities without addressing the underlying social determinants of health. A systems science approach, which attempts to address obesity at the community level in a holistic way, is emerging and has potential, but it must meaningfully incorporate the underlying social and economic conditions into its models.

A number of policy-related proposals, mainly based on modeling studies, are promising and should be explored further. These include improving early childcare standards, eliminating tax subsidies for television advertising of unhealthy food directed at children, subsidies for fruit and vegetable consumption, imposing a sugar-sweetened beverage tax, and increasing active physical education time and improved nutrition in schools.

We know that some of our national agricultural policies and private commercial interests from the food and beverage industry are at odds with the public’s health. This must be addressed. Finally, there should be a greater emphasis in future research and policy on addressing upstream factors, such as reducing poverty and inequality, neighborhood disadvantage, and residential segregation to reduce racial/ethnic and socioeconomic weight disparities, rather than only focusing on individual or family factors such as health behaviors or education. It will take leadership and a sustained political movement to make progress along these lines, but there are promising signs that it has already begun.

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