Variations in Obesity Rates between US Counties: Impacts of Activity Access, Food Environments, and Settlement Patterns

National-level and state-level prevalence of overweight and obesity among children, adolescents, and adults in the USA, 1990-2021, and forecasts up to 2050

BACKGROUND

Over the past several decades, the overweight and obesity epidemic in the USA has resulted in a significant health and economic burden. Understanding current trends and future trajectories at both national and state levels is crucial for assessing the success of existing interventions and informing future health policy changes. We estimated the prevalence of overweight and obesity from 1990 to 2021 with forecasts to 2050 for children and adolescents (aged 5-24 years) and adults (aged ≥25 years) at the national level. Additionally, we derived state-specific estimates and projections for older adolescents (aged 15-24 years) and adults for all 50 states and Washington, DC.

METHODS

In this analysis, self-reported and measured anthropometric data were extracted from 134 unique sources, which included all major national surveillance survey data. Adjustments were made to correct for self-reporting bias. For individuals older than 18 years, overweight was defined as having a BMI of 25 kg/m2 to less than 30 kg/m2 and obesity was defined as a BMI of 30 kg/m2 or higher, and for individuals younger than 18 years definitions were based on International Obesity Task Force criteria. Historical trends of overweight and obesity prevalence from 1990 to 2021 were estimated using spatiotemporal Gaussian process regression models. A generalised ensemble modelling approach was then used to derive projected estimates up to 2050, assuming continuation of past trends and patterns. All estimates were calculated by age and sex at the national level, with estimates for older adolescents (aged 15-24 years) and adults aged (≥25 years) also calculated for 50 states and Washington, DC. 95% uncertainty intervals (UIs) were derived from the 2·5th and 97·5th percentiles of the posterior distributions of the respective estimates.

FINDINGS

In 2021, an estimated 15·1 million (95% UI 13·5-16·8) children and young adolescents (aged 5-14 years), 21·4 million (20·2-22·6) older adolescents (aged 15-24 years), and 172 million (169-174) adults (aged ≥25 years) had overweight or obesity in the USA. Texas had the highest age-standardised prevalence of overweight or obesity for male adolescents (aged 15-24 years), at 52·4% (47·4-57·6), whereas Mississippi had the highest for female adolescents (aged 15-24 years), at 63·0% (57·0-68·5). Among adults, the prevalence of overweight or obesity was highest in North Dakota for males, estimated at 80·6% (78·5-82·6), and in Mississippi for females at 79·9% (77·8-81·8). The prevalence of obesity has outpaced the increase in overweight over time, especially among adolescents. Between 1990 and 2021, the percentage change in the age-standardised prevalence of obesity increased by 158·4% (123·9-197·4) among male adolescents and 185·9% (139·4-237·1) among female adolescents (15-24 years). For adults, the percentage change in prevalence of obesity was 123·6% (112·4-136·4) in males and 99·9% (88·8-111·1) in females. Forecast results suggest that if past trends and patterns continue, an additional 3·33 million children and young adolescents (aged 5-14 years), 3·41 million older adolescents (aged 15-24 years), and 41·4 million adults (aged ≥25 years) will have overweight or obesity by 2050. By 2050, the total number of children and adolescents with overweight and obesity will reach 43·1 million (37·2-47·4) and the total number of adults with overweight and obesity will reach 213 million (202-221). In 2050, in most states, a projected one in three adolescents (aged 15-24 years) and two in three adults (≥25 years) will have obesity. Although southern states, such as Oklahoma, Mississippi, Alabama, Arkansas, West Virginia, and Kentucky, are forecast to continue to have a high prevalence of obesity, the highest percentage changes from 2021 are projected in states such as Utah for adolescents and Colorado for adults.

INTERPRETATION

Existing policies have failed to address overweight and obesity. Without major reform, the forecasted trends will be devastating at the individual and population level, and the associated disease burden and economic costs will continue to escalate. Stronger governance is needed to support and implement a multifaceted whole-system approach to disrupt the structural drivers of overweight and obesity at both national and local levels. Although clinical innovations should be leveraged to treat and manage existing obesity equitably, population-level prevention remains central to any intervention strategies, particularly for children and adolescents.

FUNDING

Bill & Melinda Gates Foundation.

Emergency Free School Meal Distribution During the COVID-19 Pandemic in High-Poverty Urban Settings

BACKGROUND

The coronavirus disease 2019 pandemic triggered nationwide school closures in March 2020, putting millions of children in the United States who were reliant on subsidized school meals at risk of experiencing hunger. In response, the US Department of Agriculture mobilized the Summer Food Service Program and Seamless Summer Option program to provide emergency free school meals. There is a need to investigate the effectiveness of these programs in covering underresourced communities during the pandemic.

OBJECTIVE

This study assessed associations between meal distribution and census tract demographics (ie, poverty level, race/ethnicity, and deprivation level based on social deprivation index score).

DESIGN

An observational study using longitudinal meal distribution data collected over an 18-month period following school closures (March 2020 to August 2021).

PARTICIPANTS AND SETTING

Monthly meal distribution data were collected for community sites serving 142 census tracts within 4 urban New Jersey cities predominantly populated by people with low incomes and from racial and ethnic minority groups.

MAIN OUTCOME MEASURES

Main outcome measures were the number of meals served monthly by Summer Food Service Program and Seamless Summer Option meal sites.

STATISTICAL ANALYSES PERFORMED

A 2-part multivariable regression approach was used to analyze the data.

RESULTS

In the first step, logistic regression models showed that high-deprivation tracts were more likely to serve meals during the observed period (odds ratio 3.43, 95% CI 1.001 to 11.77; P = 0.0499). In the second step, among tracts that served any meals during the observed period, mixed effects negative binomial regression models showed that high-poverty and high-deprivation tracts served comparatively more meals (incidence rate ratio [IRR] 2.83, 95% CI 2.29 to 3.51; P < 0.001 and IRR 1.94, 95% CI 1.65 to 2.28; P < 0.001, respectively).

CONCLUSIONS

Findings show that meal distribution during the pandemic was higher within census tracts with higher poverty and deprivation levels, indicating that underresourced communities with higher need had more free meals available during this unprecedented public health emergency.

Investigating the effects of rurality on stress, subjective well-being, and weight-related outcomes

Purpose

Rates of obesity are significantly higher for those living in a rural versus urban setting. High levels of stress and low levels of subjective well-being (SWB) have been linked to poor weight-related behaviors and outcomes, but it is unclear if these relationships differ as a function of rurality. This study investigated the extent to which living in a rural versus urban county ("rurality") moderated associations between stress / subjective wellbeing (predictors) and diet quality, dietary intake of added sugars, physical activity, and BMI (outcomes).

Methods

Participants were recruited from urban (n = 355) and rural (n = 347) counties in Washington State and self-reported psychological, demographic, and food frequency questionnaires while physical activity behavior was measured objectively.

Findings

After controlling for relevant covariates, levels of stress were positively associated with added sugar intake for those living in the urban county while this relationship was non-significant for those residing in the rural county. Similarly, SWB was negatively associated with added sugar intake, but only for urban residents. County of residence was also found to moderate the relationship between SWB and BMI. Higher SWB was inversely associated with BMI for those living in the urban county while no relationship was observed for rural county residents.

Conclusions

These findings support the hypothesis that the relationships between stress / SWB and weight function differentially based on the rurality of the residing county. This work adds to the growing body of literature highlighting the role stress and SWB play in the rural obesity disparity.

Food and Physical Activity Environment in the US-Affiliated Pacific Region: The Children's Healthy Living Program

OBJECTIVE

To describe the quality of food and physical activity (PA) environments by World Bank Income level in jurisdictions from the Children's Healthy Living Program.

DESIGN

Baseline cross-sectional community data were analyzed from 11 jurisdictions categorized by World Bank Income levels to describe exposure to different food and PA outlets. The Children's Healthy Living Program was a multilevel, multijurisdictional prevalence study and community intervention trial that reduced child obesity in the US-Affiliated Pacific region.

SETTING

US-Affiliated Pacific region.

PARTICIPANTS

Food (n = 426) and PA (n = 552) Outlets.

MAIN OUTCOME MEASURES

Physical activity and food scores that reflect the quality of the outlets that support being physically active and healthy eating options, respectively.

ANALYSIS

Descriptive statistics are presented as means ± SD or percentages.

RESULTS

High-income-income level jurisdictions had higher food and PA scores than middle-income level jurisdictions.

CONCLUSIONS AND IMPLICATIONS

The US-Affiliated Pacific region has limited quality food and PA outlets in underserved communities at risk for obesity. The findings in this paper can be used to develop tools and design interventions to improve the food and PA environment to increase a healthier, active lifestyle.

Spatial clustering patterns and regional variations for food and physical activity environments across the United States

This study examined spatial patterns of obesogenic environments for US counties. We mapped the geographic dispersion of food and physical activity (PA) environments, assessed spatial clustering, and identified food and PA environment differences across U.S. regions and rurality categories. Substantial low food score clusters were located in the South and high score clusters in the Midwest and West. Low PA score clusters were located in the South and high score clusters in the Northeast and Midwest (p < .0001). For region, the South had significantly lower food and PA environment scores. For rurality, rural counties had significantly higher food environment scores and metropolitan counties had significantly higher PA environment scores (p < .0001). This study highlights geographic clustering and disparities in food and PA access nationwide. State and region-wide environmental inequalities may be targeted using structural interventions and policy initiatives to improve food and PA access.

Spatial Clustering of County-Level COVID-19 Rates in the U.S

Despite the widespread prevalence of cases associated with the coronavirus disease 2019 (COVID-19) pandemic, little is known about the spatial clustering of COVID-19 in the United States. Data on COVID-19 cases were used to identify U.S. counties that have both high and low COVID-19 incident proportions and clusters. Our results suggest that there are a variety of sociodemographic variables that are associated with the severity of COVID-19 county-level incident proportions. As the pandemic evolved, communities of color were disproportionately impacted. Subsequently, it shifted from communities of color and metropolitan areas to rural areas in the U.S. Our final period showed limited differences in county characteristics, suggesting that COVID-19 infections were more widespread. The findings might address the systemic barriers and health disparities that may result in high incident proportions of COVID-19 clusters.

Area-Level Determinants in Colorectal Cancer Spatial Clustering Studies: A Systematic Review

The increasing pattern of colorectal cancer (CRC) in specific geographic region, compounded by interaction of multifactorial determinants, showed the tendency to cluster. The review aimed to identify and synthesize available evidence on clustering patterns of CRC incidence, specifically related to the associated determinants. Articles were systematically searched from four databases, Scopus, Web of Science, PubMed, and EBSCOHost. The approach for identification of the final articles follows PRISMA guidelines. Selected full-text articles were published between 2016 and 2021 of English language and spatial studies focusing on CRC cluster identification. Articles of systematic reviews, conference proceedings, book chapters, and reports were excluded. Of the final 12 articles, data on the spatial statistics used and associated factors were extracted. Identified factors linked with CRC cluster were further classified into ecology (health care accessibility, urbanicity, dirty streets, tree coverage), biology (age, sex, ethnicity, overweight and obesity, daily consumption of milk and fruit), and social determinants (median income level, smoking status, health cost, employment status, housing violations, and domestic violence). Future spatial studies that incorporate physical environment related to CRC cluster and the potential interaction between the ecology, biology and social determinants are warranted to provide more insights to the complex mechanism of CRC cluster pattern.

Disparities in Obesity Prevalence in Iranian Adults: Cross-Sectional Study Using Data from the 2016 STEPS Survey

INTRODUCTION

This paper outlines the prevalence, disparities, and social determinants of preobesity and obesity in Iranian adults.

METHODS

Data on 28,321 adults who participated in the 2016 National Survey of the Risk Factors of Noncommunicable Diseases (STEPS) survey were analyzed. The body mass index (BMI) was calculated from physically measured height and weight. To assess the association between sociodemographic factors and the prevalence of preobesity and obesity, a χ2 test and a logistic regression model were used. Socioeconomic inequality was quantified by a concentration index. Disparities in provincial mean BMI and concentration indices were shown on the map of Iran using geographic information system analysis.

RESULTS

Overall, 60.3% of the participants were affected by preobesity or obesity. The preobesity prevalence was 39% in men and 35.2% in women. The obesity prevalence was 15.6% in men and 30.4% in women. The mean BMI for the country was 26.5. Higher ranges were observed across the northwestern and central territories. Female individuals in the age group 48-57 years who were married and lived in urban settings had an increased risk of being preobese or obese. The concentration index revealed a prorich inequality, with a greater magnitude among women.

CONCLUSION

The findings suggest that policies aimed at reducing preobesity and obesity should remain a public health priority in Iran. However, a greater emphasis should be placed on the northwestern and central territories and on higher socioeconomic groups.

Multimethod, multidataset analysis reveals paradoxical relationships between sociodemographic factors, Hispanic ethnicity and diabetes

INTRODUCTION

Population-level and individual-level analyses have strengths and limitations as do 'blackbox' machine learning (ML) and traditional, interpretable models. Diabetes mellitus (DM) is a leading cause of morbidity and mortality with complex sociodemographic dynamics that have not been analyzed in a way that leverages population-level and individual-level data as well as traditional epidemiological and ML models. We analyzed complementary individual-level and county-level datasets with both regression and ML methods to study the association between sociodemographic factors and DM.

RESEARCH DESIGN AND METHODS

County-level DM prevalence, demographics, and socioeconomic status (SES) factors were extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data. Analogous individual-level data were extracted from 2007 to 2016 National Health and Nutrition Examination Survey studies and corrected for oversampling with survey weights. We used multivariate linear (logistic) regression and ML regression (classification) models for county (individual) data. Regression and ML models were compared using measures of explained variation (area under the receiver operating characteristic curve (AUC) and R2).

RESULTS

Among the 3138 counties assessed, the mean DM prevalence was 11.4% (range: 3.0%-21.1%). Among the 12 824 individuals assessed, 1688 met DM criteria (13.2% unweighted; 10.2% weighted). Age, gender, race/ethnicity, income, and education were associated with DM at the county and individual levels. Higher county Hispanic ethnic density was negatively associated with county DM prevalence, while Hispanic ethnicity was positively associated with individual DM. ML outperformed regression in both datasets (mean R2 of 0.679 vs 0.610, respectively (p<0.001) for county-level data; mean AUC of 0.737 vs 0.727 (p<0.0427) for individual-level data).

CONCLUSIONS

Hispanic individuals are at higher risk of DM, while counties with larger Hispanic populations have lower DM prevalence. Analyses of population-level and individual-level data with multiple methods may afford more confidence in results and identify areas for further study.

Development of a national childhood obesogenic environment index in the United States: differences by region and rurality

Background

Diverse environmental factors are associated with physical activity (PA) and healthy eating (HE) among youth. However, no study has created a comprehensive obesogenic environment index for children that can be applied at a large geographic scale. The purpose of this study was to describe the development of a childhood obesogenic environment index (COEI) at the county level across the United States.

Methods

A comprehensive search of review articles (n = 20) and input from experts (n = 12) were used to identify community-level variables associated with youth PA, HE, or overweight/obesity for potential inclusion in the index. Based on strength of associations in the literature, expert ratings, expertise of team members, and data source availability, 10 key variables were identified – six related to HE (# per 1000 residents for grocery/superstores, farmers markets, fast food restaurants, full-service restaurants, and convenience stores; as well as percentage of births at baby (breastfeeding)-friendly facilities) and four related to PA (percentage of population living close to exercise opportunities, percentage of population < 1 mile from a school, a composite walkability index, and number of violent crimes per 1000 residents). Data for each variable for all counties in the U.S. (n = 3142) were collected from publicly available sources. For each variable, all counties were ranked and assigned percentiles ranging from 0 to 100. Positive environmental variables (e.g., grocery stores, exercise opportunities) were reverse scored such that higher values for all variables indicated a more obesogenic environment. Finally, for each county, a total obesogenic environment index score was generated by calculating the average percentile for all 10 variables.

Results

The average COEI percentile ranged from 24.5–81.0 (M = 50.02,s.d. = 9.01) across US counties and was depicted spatially on a choropleth map. Obesogenic counties were more prevalent (F = 130.43,p < .0001) in the South region of the U.S. (M = 53.0,s.d. = 8.3) compared to the Northeast (M = 43.2,s.d. = 6.9), Midwest (M = 48.1,s.d. = 8.5), and West (M = 48.4,s.d. = 9.8). When examined by rurality, there were also significant differences (F = 175.86,p < .0001) between metropolitan (M = 46.5,s.d. = 8.4), micropolitan (M = 50.3,s.d. = 8.1), and rural counties (M = 52.9,s.d. = 8.8) across the U.S.

Conclusion

The COEI can be applied to benchmark obesogenic environments and identify geographic disparities and intervention targets. Future research can examine associations with obesity and other health outcomes.

Can Age-Friendly Planning Promote Equity in Community Health Across the Rural-Urban Divide in the US?

In the US, rural communities face challenges to meet the community health needs of older adults and children. Meanwhile, rural areas lag in age-friendly built environment and services. AARP, a US based organization promoting livability for all ages, has developed a Livability Index based on the World Health Organization's (WHO) domains of age-friendly communities: health, housing, neighborhood, transportation, environment, engagement, and opportunity. This study links the 2018 AARP Livability Index categories with demographic structure and socio-economic factors from the American Community Survey at the county level in the US to examine if the physical, built and social environment differentiate communities with better community health across the rural-urban divide. Results show that the neighborhood built environment has the largest impact on community health for all county types. Although rural areas lag in community health, those which give more attention to engagement and opportunity rank higher. Rural communities with more African Americans, children, and poor Whites, rank lower on community health. While neighborhood characteristics have the strongest link to community health, a broader approach with attention to age, race, poverty and engagement and opportunity is needed for rural areas.

Impact of Changes in the Food, Built, and Socioeconomic Environment on BMI in US Counties, BRFSS 2003-2012

OBJECTIVE

Researchers have linked geographic disparities in obesity to community-level characteristics, yet many prior observational studies have ignored temporality and potential for bias.

METHODS

Repeated cross-sectional data were used from the Behavioral Risk Factor Surveillance System (BRFSS) (2003-2012) to examine the influence of county-level characteristics (active commuting, unemployment, percentage of limited-service restaurants and convenience stores) on BMI. Each exposure was calculated using mean values over the 5-year period prior to BMI measurement; values were standardized; and then variables were decomposed into (1) county means from 2003 to 2012 and (2) county-mean-centered values for each year. Cross-sectional (between-county) and longitudinal (within-county) associations were estimated using a random-effects within-between model, adjusting for individual characteristics, survey method, and year, with nested random intercepts for county-years within counties within states.

RESULTS

A negative between-county association for active commuting (β = -0.19; 95% CI: -0.23 to -0.16) and positive associations for unemployment (β = 0.17; 95% CI: 0.14 to 0.19) and limited-service restaurants (β = 0.13; 95% CI: 0.11 to 0.14) were observed. An SD increase in active commuting within counties was associated with a 0.51-kg/m² (95% CI: -0.72 to -0.31) decrease in BMI over time.

CONCLUSIONS

These results suggest that community-level characteristics play an important role in shaping geographic disparities in BMI between and within communities over time.

An Area-Level Indicator of Latent Soda Demand: Spatial Statistical Modeling of Grocery Store Transaction Data to Characterize the Nutritional Landscape in Montreal, Canada

Measurement of neighborhood dietary patterns at high spatial resolution allows public health agencies to identify and monitor communities with an elevated risk of nutrition-related chronic diseases. Currently, data on diet are obtained primarily through nutrition surveys, which produce measurements at low spatial resolutions. The availability of store-level grocery transaction data provides an opportunity to refine the measurement of neighborhood dietary patterns. We used these data to develop an indicator of area-level latent demand for soda in the Census Metropolitan Area of Montreal in 2012 by applying a hierarchical Bayesian spatial model to data on soda sales from 1,097 chain retail food outlets. The utility of the indicator of latent soda demand was evaluated by assessing its association with the neighborhood relative risk of prevalent type 2 diabetes mellitus. The indicator improved the fit of the disease-mapping model (deviance information criterion: 2,140 with the indicator and 2,148 without) and enables a novel approach to nutrition surveillance.

Using data from online geocoding services for the assessment of environmental obesogenic factors: a feasibility study

BACKGROUND

The increasing prevalence of obesity is a major public health problem in many countries. Built environment factors are known to be associated with obesity, which is an important risk factor for type 2 diabetes. Online geocoding services could be used to identify regions with a high concentration of obesogenic factors. The aim of our study was to examine the feasibility of integrating information from online geocoding services for the assessment of obesogenic environments.

METHODS

We identified environmental factors associated with obesity from the literature and translated these factors into variables from the online geocoding services Google Maps and OpenStreetMap (OSM). We tested whether spatial data points can be downloaded from these services and processed and visualized on maps. True- and false-positive values, false-negative values, sensitivities and positive predictive values of the processed data were determined using search engines and in-field inspections within four pilot areas in Bavaria, Germany.

RESULTS

Several environmental factors could be identified from the literature that were either positively or negatively correlated with weight outcomes in previous studies. The diversity of query variables was higher in OSM compared with Google Maps. In each pilot area, query results from Google showed a higher absolute number of true-positive hits and of false-positive hits, but a lower number of false-negative hits during the validation process. The positive predictive value of database hits was higher in OSM and ranged between 81 and 100% compared with a range of 63-89% for Google Maps. In contrast, sensitivities were higher in Google Maps (between 59 and 98%) than in OSM (between 20 and 64%).

CONCLUSIONS

It was possible to operationalize obesogenic factors identified from the literature with data and variables available from geocoding services. The validity of Google Maps and OSM was reasonable. The assessment of environmental obesogenic factors via geocoding services could potentially be applied in diabetes surveillance.

Data Collection Instruments for Obesogenic Environments in Adults: A Scoping Review

The rise in obesity prevalence has increased research interest in the obesogenic environment and its influence on excess weight. The aim of the present study was to review and map data collection instruments for obesogenic environments in adults in order to provide an overview of the existing evidence and enable comparisons. Through the scoping review method, different databases and webpages were searched between January 1997 and May 2018. Instruments were included if they targeted adults. The documents were categorised as food environment or built environment. In terms of results, 92 instruments were found: 46 instruments measuring the food environment, 42 measuring the built environment, and 4 that characterised both environments. Numerous diverse instruments have been developed to characterise the obesogenic environment, and some of them have been developed based on existing ones; however, most of them have not been validated and there is very little similarity between them, hindering comparison of the results obtained. In addition, most of them were developed and used in the United States and were written in English. In conclusion, there is a need for a robust instrument, improving or combining existing ones, for use within and across countries, and more sophisticated study designs where the environment is contemplated in an interdisciplinary approach.

Identification of Factors Associated With Variation in US County-Level Obesity Prevalence Rates Using Epidemiologic vs Machine Learning Models

IMPORTANCE

Obesity is a leading cause of high health care expenditures, disability, and premature mortality. Previous studies have documented geographic disparities in obesity prevalence.

OBJECTIVE

To identify county-level factors associated with obesity using traditional epidemiologic and machine learning methods.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional study using linear regression models and machine learning models to evaluate the associations between county-level obesity and county-level demographic, socioeconomic, health care, and environmental factors from summarized statistical data extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data from each of 3138 US counties. The explanatory power of the linear multivariate regression and the top performing machine learning model were compared using mean R2 measured in 30-fold cross validation.

EXPOSURES

County-level demographic factors (population; rural status; census region; and race/ethnicity, sex, and age composition), socioeconomic factors (median income, unemployment rate, and percentage of population with some college education), health care factors (rate of uninsured adults and primary care physicians), and environmental factors (access to healthy foods and access to exercise opportunities).

MAIN OUTCOMES AND MEASURES

County-level obesity prevalence in 2018, its association with each county-level factor, and the percentage of variation in county-level obesity prevalence explained by linear multivariate and gradient boosting machine regression measured with R2.

RESULTS

Among the 3138 counties studied, the mean (range) obesity prevalence was 31.5% (12.8%-47.8%). In multivariate regressions, demographic factors explained 44.9% of variation in obesity prevalence; socioeconomic factors, 33.0%; environmental factors, 15.5%; and health care factors, 9.1%. The county-level factors with the strongest association with obesity were census region, median household income, and percentage of population with some college education. R2 values of univariate regressions of obesity prevalence were 0.238 for census region, 0.218 for median household income, and 0.160 for percentage of population with some college education. Multivariate linear regression and gradient boosting machine regression (the best-performing machine learning model) of obesity prevalence using all county-level demographic, socioeconomic, health care, and environmental factors had R2 values of 0.58 and 0.66, respectively (P < .001).

CONCLUSIONS AND RELEVANCE

Obesity prevalence varies significantly between counties. County-level demographic, socioeconomic, health care, and environmental factors explain the majority of variation in county-level obesity prevalence. Using machine learning models may explain significantly more of the variation in obesity prevalence..

Childhood obesity and the associated roles of neighborhood and biologic stress

Exposure to violence and obesity continues to be growing epidemics, particularly among children. Our objective was to increase our understanding of the association between neighborhood violence exposure and children's weight and how biologic stress may mediate this relation. A matched, community-recruited cross-sectional study of 90 children, ages 5–16 years, from 52 neighborhoods took place in the greater New Orleans, LA area between 2012 and 2013. Children were matched on their propensity for living in a high violence neighborhood and previous exposure to Hurricane Katrina. Primary neighborhood exposure included violent crime, operationalized as crime rates within specific radii of children's home. Rates of exposure within 500, 1000 and 2000 meter radii from the child's home were calculated. Primary outcomes were body mass index (BMI) and waist circumference, and the primary mediator was telomere length (TL), a marker of cellular aging. Significant variation in obesity and TL was observed at the neighborhood level and violent crime was significantly associated with weight status, with an increase of 1.24 units in BMI for each additional violent crime in the child's neighborhood and a significant mediated or indirect effect of TL in the crime-BMI relation (0.32, 95% bootstrapped CI = 0.05, 0.81; 32% total mediated effect). Findings strengthen existing evidence linking neighborhood violence to childhood health and identify biologic stress, indexed by TL, as one mechanistic pathway by which neighborhood violence may influence childhood obesity. Neighborhood violence may be an important target for interventions focused on reducing obesity and other stress related health outcomes in children.

Area-Level Walkability and the Geographic Distribution of High Body Mass in Sydney, Australia: A Spatial Analysis Using the 45 and Up Study

Improving the walkability of built environments to promote healthy lifestyles and reduce high body mass is increasingly considered in regional development plans. Walkability indexes have the potential to inform, benchmark and monitor these plans if they are associated with variation in body mass outcomes at spatial scales used for health and urban planning. We assessed relationships between area-level walkability and prevalence and geographic variation in overweight and obesity using an Australian population-based cohort comprising 92,157 Sydney respondents to the 45 and Up Study baseline survey between January 2006 and April 2009. Individual-level data on overweight and obesity were aggregated to 2006 Australian postal areas and analysed as a function of area-level Sydney Walkability Index quartiles using conditional auto regression spatial models adjusted for demographic, social, economic, health and socioeconomic factors. Both overweight and obesity were highly clustered with higher-than-expected prevalence concentrated in the urban sprawl region of western Sydney, and lower-than-expected prevalence in central and eastern Sydney. In fully adjusted spatial models, prevalence of overweight and obesity was 6% and 11% lower in medium-high versus low, and 10% and 15% lower in high versus low walkability postcodes, respectively. Postal area walkability explained approximately 20% and 9% of the excess spatial variation in overweight and obesity that remained after accounting for other individual- and area-level factors. These findings provide support for the potential of area-level walkability indexes to inform, benchmark and monitor regional plans aimed at targeted approaches to reducing population-levels of high body mass through environmental interventions. Future research should consider potential confounding due to neighbourhood self-selection on area-level walkability relations.

Obesity and Urban Environments

Obesity is a major public health issue, affecting both developed and developing societies. Obesity increases the risk for heart disease, stroke, some cancers, and type II diabetes. While individual behaviours are important risk factors, impacts on obesity and overweight of the urban physical and social environment have figured large in the recent epidemiological literature, though evidence is incomplete and from a limited range of countries. Prominent among identified environmental influences are urban layout and sprawl, healthy food access, exercise access, and the neighbourhood social environment. This paper reviews the literature and highlights the special issue contributions within that literature.

Geographic Association Between Income Inequality and Obesity Among Adults in New York State

Introduction

In addition to economic factors and geographic area poverty, area income inequality — the extent to which income is distributed in an uneven manner across a population — has been found to influence health outcomes and obesity. We used a spatial-based approach to describe interactions between neighboring areas with the objective of generating new insights into the relationships between county-level income inequality, poverty, and obesity prevalence across New York State (NYS).

Methods

We used data from the 2015 American Community Survey and 2013 obesity estimates from the Centers for Disease Control and Prevention for NYS to examine correlations between county-level economic factors and obesity. Spatial mapping and analysis were conducted with ArcMap. Ordinary least squares modeling with adjusting variables was used to examine associations between county-level obesity percentages and county-level income inequality (Gini index). Univariate spatial analysis was conducted between obesity and Gini index, and globally weighted regression and Hot Spot Analysis were used to view spatial clustering.

Results

Although higher income inequality was associated with lower obesity rates, a higher percentage of poverty was associated with higher obesity rates. A higher percentage of Hispanic population was associated with lower obesity rates. When tested spatially, higher income inequality was associated with a greater decrease in obesity in southern and eastern NYS counties than in the northern and western counties, with some differences by sex present in this association.

Conclusion

Increased income inequality and lower poverty percentage were significantly linked to lower obesity rates across NYS counties for men. Income inequality influence differed by geographic location. These findings indicate that in areas with high income inequality, currently unknown aspects of the environment may benefit low-income residents. Future studies should also include environmental factors possibly linked to obesity.

A Community-Driven Approach to Generate Urban Policy Recommendations for Obesity Prevention

There is an increasing research interest in targeting interventions at the neighborhood level to prevent obesity. Healthy urban environments require including residents’ perspectives to help understanding how urban environments relate to residents’ food choices and physical activity levels. We describe an innovative community-driven process aimed to develop environmental recommendations for obesity prevention. We conducted this study in a low-income area in Madrid (Spain), using a collaborative citizen science approach. First, 36 participants of two previous Photovoice projects translated their findings into policy recommendations, using an adapted logical framework approach. Second, the research team grouped these recommendations into strategies for obesity prevention, using the deductive analytical strategy of successive approximation. Third, through a nominal group session including participants, researchers, public health practitioners and local policy-makers, we discussed and prioritized the obesity prevention recommendations. Participants identified 12 policy recommendations related to their food choices and 18 related to their physical activity. The research team grouped these into 11 concrete recommendations for obesity prevention. The ‘top-three’ ranked recommendations were: (1) to adequate and increase the number of public open spaces; (2) to improve the access and cost of existing sports facilities and (3) to reduce the cost of gluten-free and diabetic products.