National Economic Development and Disparities in Body Mass Index: A Cross-Sectional Study of Data from 38 Countries

Background: Increases in body mass index (BMI) and the prevalence of overweight in low- and middle income countries (LMICs) are often ascribed to changes in global trade patterns or increases in national income. These changes are likely to affect populations within LMICs differently based on their place of residence or socioeconomic status (SES). Objective: Using nationally representative survey data from 38 countries and national economic indicators from the World Bank and other international organizations, we estimated ecological and multilevel models to assess the association between national levels of gross domestic product (GDP), foreign direct investment (FDI), and mean tariffs and BMI. Design: We used linear regression to estimate the ecological association between average annual change in economic indicators and BMI, and multilevel linear or ordered multinomial models to estimate associations between national economic indicators and individual BMI or over- and underweight. We also included cross-level interaction terms to highlight differences in the association of BMI with national economic indicators by type of residence or socioeconomic status (SES). Results: There was a positive but non-significant association of GDP and mean BMI. This positive association of GDP and BMI was greater among rural residents and the poor. There were no significant ecological associations between measures of trade openness and mean BMI, but FDI was positively associated with BMI among the poorest respondents and in rural areas and tariff levels were negatively associated with BMI among poor and rural respondents. Conclusion: Measures of national income and trade openness have different associations with the BMI across populations within developing countries. These divergent findings underscore the complexity of the effects of development on health and the importance of considering how the health effects of “globalizing” economic and cultural trends are modified by individual-level wealth and residence

Racial differences in the built environment—body mass index relationship? A geospatial analysis of adolescents in urban neighborhoods

Background: Built environment features of neighborhoods may be related to obesity among adolescents and potentially related to obesity-related health disparities. The purpose of this study was to investigate spatial relationships between various built environment features and body mass index (BMI) z-score among adolescents, and to investigate if race/ethnicity modifies these relationships. A secondary objective was to evaluate the sensitivity of findings to the spatial scale of analysis (i.e. 400- and 800-meter street network buffers). Methods: Data come from the 2008 Boston Youth Survey, a school-based sample of public high school students in Boston, MA. Analyses include data collected from students who had georeferenced residential information and complete and valid data to compute BMI z-score (n = 1,034). We built a spatial database using GIS with various features related to access to walking destinations and to community design. Spatial autocorrelation in key study variables was calculated with the Global Moran’s I statistic. We fit conventional ordinary least squares (OLS) regression and spatial simultaneous autoregressive error models that control for the spatial autocorrelation in the data as appropriate. Models were conducted using the total sample of adolescents as well as including an interaction term for race/ethnicity, adjusting for several potential individual- and neighborhood-level confounders and clustering of students within schools. Results: We found significant positive spatial autocorrelation in the built environment features examined (Global Moran’s I most ≥ 0.60; all p = 0.001) but not in BMI z-score (Global Moran’s I = 0.07, p = 0.28). Because we found significant spatial autocorrelation in our OLS regression residuals, we fit spatial autoregressive models. Most built environment features were not associated with BMI z-score. Density of bus stops was associated with a higher BMI z-score among Whites (Coefficient: 0.029, p < 0.05). The interaction term for Asians in the association between retail destinations and BMI z-score was statistically significant and indicated an inverse association. Sidewalk completeness was significantly associated with a higher BMI z-score for the total sample (Coefficient: 0.010, p < 0.05). These significant associations were found for the 800-meter buffer. Conclusion: Some relationships between the built environment and adolescent BMI z-score were in the unexpected direction. Our findings overall suggest that the built environment does not explain a large proportion of the variation in adolescent BMI z-score or racial disparities in adolescent obesity. However, there are some differences by race/ethnicity that require further research among adolescents

School-Based Programs: Lessons Learned from CATCH, Planet Health, and Not-On-Tobacco

Establishing healthy habits in youth can help prevent many chronic health problems later in life that are attributable to unhealthy eating, sedentary lifestyle, and overweight. For this reason, many public health professionals are interested in working with school systems to reach children in school settings. However, a lack of familiarity with how schools operate can be a substantial impediment to developing effective partnerships with schools. We describe lessons learned from three successful school health promotion programs that were developed and disseminated through collaborations between public health professionals, academic institutions, and school personnel. The programs include two focused on physical activity and good nutrition for elementary and middle school children — Coordinated Approach to Child Health (CATCH) and Planet Health — and one focused on smoking cessation among adolescents — Not-On-Tobacco (N-O-T). Important features of these school health programs include 1) identification of staff and resources required for program implementation and dissemination; 2) involvement of stakeholders (e.g., teachers, students, other school personnel, parents, nonprofit organizations, professional organizations) during all phases of program development and dissemination; 3) planning for dissemination of programs early in the development and testing process; and 4) rigorous evaluation of interventions to determine their effectiveness. The authors provide advice based on lessons learned from these programs to those who wish to work with young people in schools

Play Across Boston: A Community Initiative to Reduce Disparities in Access to After-School Physical Activity Programs for Inner-City Youths

Background: In 1999, the Centers for Disease Control and Prevention (CDC) funded Play Across Boston to address disparities in access to physical activity facilities and programs for Boston, Mass, inner-city youths. Context: Local stakeholders worked with the Harvard School of Public Health Prevention Research Center and Northeastern University's Center for the Study of Sport in Society to improve opportunities for youth physical activity through censuses of facilities and programs and dissemination of results. Methods: Play Across Boston staff conducted a facility census among 230 public recreational complexes and a program census of 86% of 274 physical activity programs for Boston inner-city youths aged 5 to 18 years during nonschool hours for the 1999 to 2000 school year and summer of 2000. Comparison data were collected from three suburban communities: one low income, one medium income, and one high income. Consequences: Although Boston has a substantial sports and recreational infrastructure, the ratio of youths to facilities in inner-city Boston was twice the ratio found in the medium- and high-income suburban comparison communities. The low-income suburban comparison community had the highest number of youths per recreational facility with 137 youths per facility, followed by Boston with 117 youths per facility. The ratio of youths to facilities differed among Boston neighborhoods. Boston youths participated less in school-year physical activities than youths in medium- and high-income communities, and less advantaged Boston neighborhoods had lower levels of participation than more advantaged Boston neighborhoods. Girls participated less than boys. Interpretation: Play Across Boston successfully developed and implemented a rigorous needs assessment with local relevance and important implications for public health research on physical activity and the environment. Boston Mayor Thomas M. Menino called the Play Across Boston report a "playbook" for future sports and recreation planning by the city of Boston and its community partners

Identifying Sources of Children’s Consumption of Junk Food in Boston After-School Programs, April–May 2011

Introduction: Little is known about how the nutrition environment in after-school settings may affect children’s dietary intake. We measured the nutritional quality of after-school snacks provided by programs participating in the National School Lunch Program or the Child and Adult Care Food Program and compared them with snacks brought from home or purchased elsewhere (nonprogram snacks). We quantified the effect of nonprogram snacks on the dietary intake of children who also received program-provided snacks during after-school time. Our study objective was to determine how different sources of snacks affect children’s snack consumption in after-school settings. Methods: We recorded snacks served to and brought in by 298 children in 18 after-school programs in Boston, Massachusetts, on 5 program days in April and May 2011. We measured children’s snack consumption on 2 program days using a validated observation protocol. We then calculated within-child change-in-change models to estimate the effect of nonprogram snacks on children’s dietary intake after school. Results: Nonprogram snacks contained more sugary beverages and candy than program-provided snacks. Having a nonprogram snack was associated with significantly higher consumption of total calories (+114.7 kcal, P < .001), sugar-sweetened beverages (+0.5 oz, P = .01), desserts (+0.3 servings, P < .001), and foods with added sugars (+0.5 servings; P < .001) during the snack period. Conclusion: On days when children brought their own after-school snack, they consumed more salty and sugary foods and nearly twice as many calories than on days when they consumed only program-provided snacks. Policy strategies limiting nonprogram snacks or setting nutritional standards for them in after-school settings should be explored further as a way to promote child health

The cost of a primary care-based childhood obesity prevention intervention

Background: United States pediatric guidelines recommend that childhood obesity counseling be conducted in the primary care setting. Primary care-based interventions can be effective in improving health behaviors, but also costly. The purpose of this study was to evaluate the cost of a primary care-based obesity prevention intervention targeting children between the ages of two and six years who are at elevated risk for obesity, measured against usual care. Methods: High Five for Kids was a cluster-randomized controlled clinical trial that aimed to modify children’s nutrition and TV viewing habits through a motivational interviewing intervention. We assessed visit-related costs from a societal perspective, including provider-incurred direct medical costs, provider-incurred equipment costs, parent time costs and parent out-of-pocket costs, in 2011 dollars for the intervention (n = 253) and usual care (n = 192) groups. We conducted a net cost analysis using both societal and health plan costing perspectives and conducted one-way sensitivity and uncertainty analyses on results. Results: The total costs for the intervention group and usual care groups in the first year of the intervention were $65,643 (95$64,522, $66,842]) and$12,192 (95% CI [$11,393,$13,174]). The mean costs for the intervention and usual care groups were $259 (95$255, $264]) and$63 (95% CI [$59,$69]) per child, respectively, for a incremental difference of $196 (95$191, $202]) per child. Children in the intervention group attended a mean of 2.4 of a possible 4 in-person visits and received 0.45 of a possible 2 counseling phone calls. Provider-incurred costs were the primary driver of cost estimates in sensitivity analyses. Conclusions: High Five for Kids was a resource-intensive intervention. Further studies are needed to assess the cost-effectiveness of the intervention relative to other pediatric obesity interventions. Trial registration ClinicalTrials.gov Identifier: NCT00377767