Disparities in healthy food access are well documented in cross-sectional studies in communities across the United States. However, longitudinal studies examining changes in food environments within various neighborhood contexts are scarce. In a sample of 142 census tracts in four low-income, high-minority cities in New Jersey, United States, we examined the availability of different types of food stores by census tract characteristics over time (2009–2017). Outlets were classified as supermarkets, small grocery stores, convenience stores, and pharmacies using multiple sources of data and a rigorous protocol. Census tracts were categorized by median household income and race/ethnicity of the population each year. Significant declines were observed in convenience store prevalence in lower- and medium-income and majority black tracts (p for trend: 0.004, 0.031, and 0.006 respectively), while a slight increase was observed in the prevalence of supermarkets in medium-income tracts (p for trend: 0.059). The decline in prevalence of convenience stores in lower-income and minority neighborhoods is likely attributable to declining incomes in these already poor communities. Compared to non-Hispanic neighborhoods, Hispanic communities had a higher prevalence of small groceries and convenience stores. This higher prevalence of smaller stores, coupled with shopping practices of Hispanic consumers, suggests that efforts to upgrade smaller stores in Hispanic communities may be more sustainable.

School food and physical activity (PA) environments can influence children’s dietary and physical activity behaviors. However, evidence on whether school environment is associated with students’ weight status is less definitive. In this study, we examined the association between students’ body mass index (BMI) and measures of school food and PA environments. We calculated BMI from nurse-measured data collected on 19,188 6–19-year-old students from 90 public schools in four low-income cities in New Jersey in 2015–2016. Based on a questionnaire administered to school nurses, we constructed 6 food and 3 PA indices capturing the healthfulness of key dimensions in the school food and PA environment domains. Multilevel linear models, stratified by school level (elementary and secondary), examined the association between BMI z-scores and indices of the school environment. The food and PA domains were modeled separately and then combined. Joint significance of indices within each domain was tested. Analyses were conducted in 2019–2020. In the combined model for elementary schools, indices in both the food and PA domains were jointly significant (p = 0.005 and p < 0.001, respectively). With regard to specific indices in the model, students’ BMI z-score was 0.03 units lower for each additional outdoor PA facility (95% CI [−0.06, −0.00]; p = 0.036). Similarly, for secondary schools, both the food and PA domains were jointly significant (p = 0.004 and p = 0.020, respectively). Each additional unhealthy item in vending machines was associated with a 0.12 unit increase in BMI z-score (95% CI [+0.00, 0.23]; p = 0.042). Overall, healthier food and PA environments were associated with lower student BMI.

The tables and graphs in this chartbook were created using data collected by Camden Public Schools for the school year 2008-2009. Rutgers Center for State Health Policy obtained de-identified data from the schools and computed a BMI score and a BMI percentile (BMIPCT) for each child. Weight status is defined using the following BMIPCT categories.

BMIPCT

BMIPCT < 85

BMIPCT ~ 85

BMIPCT ~ 95

BMIPCT ~ 97

Weight Status

Not Overweight or Obese

Overweight and Obese

Obese

Very Obese

BMIPCT categories are presented at the city level and in sub-group analysis by age, gender, and race. Aggregate data are also presented at the school level, with notation, where representativeness of the data was a concern.

Tables and graphs on pages 5, 7, 9, and 11 show comparisons with national estimates (National Health and Nutrition Examination Survey, 2007-2008). The national data are representative of all 2-19 year old children in the US.

Each graph and table is accompanied by brief summary statements. Readers are encouraged to review the actual data presented in tables and graphs as there is much more detail.

The New Jersey Childhood Obesity Study, funded by the Robert Wood Johnson Foundation, aims to provide vital information for planning, implementing and evaluating interventions aimed at preventing childhood obesity in five New Jersey municipalities: Camden, Newark, New Brunswick, Trenton, and Vineland.

These five communities are being supported by RWJF's New Jersey Partnership for Healthy Kids program to plan and implement policy and environmental change strategies to prevent childhood obesity.

Effective interventions for addressing childhood obesity require community-specific information on who is most at risk and on contributing factors that can be addressed through tailored interventions that meet the needs of the community.

Using a comprehensive research study, the Center for State Health Policy at Rutgers University is working collaboratively with the State Program Office for New Jersey Partnership for Healthy I<ids and the five communities to address these information needs. The main components of the study include:

• A household survey of 1700 families with 3 -18 year old children

• De-identified heights and weights data from public school districts

• Assessment of the food and physical activity environments using objective data

Data books and maps based on the results of the study are being shared with the community coalitions in the five communities to help them plan their interventions.

The maps in this chartbook describe the food environment in Vineland in terms of access to supermarkets, smaller grocery stores, convenience stores, and limited service restaurants. Research shows that when residents have access to healthy food outlets, they tend to eat healthy.

• Food environment maps were created using geo-coded commercially available data of food outlets (Info USA, 2008 and Trade Dimensions, 2008) in Vineland and in a 1 mile buffer area around Vineland.

•Using the commercial data and additional investigation, food outlets were classified into different categories based on their likelihood of carrying healthy choices: supermarkets carry most healthy choices; smaller grocery stores carry fewer healthy choices; convenience stores and limited service restaurants are likely to carry mostly unhealthy choices.

• Access to different types of food outlets was computed at the census block group level based on concentration of stores / restaurants per unit area and is reported as food outlet densities.

• Food outlet density maps are compared with Census 2000 data to visualize accessibility of healthy foods in neighborhoods with different characteristics.

Data Sources: Info USA food outlet 2008 data

Trade Dimensions food outlet 2008 data

Census 2000 data

New Jersey Department of Education 2008-2009 data

The maps in this chartbook describe the food environment in Trenton in terms of access to supermarkets, smaller grocery stores, convenience stores, and limited service restaurants. Research shows that when residents have access to healthy food outlets, they tend to eat healthy.

•Food environment maps were created using geo-coded commercially available data of food outlets (InfoUSA, 2008 and Trade Dimensions, 2008) in Trenton and in a 1 mile buffer area around Trenton.

•Using the commercial data and additional investigation, food outlets were classified into different categories based on their likelihood of carrying healthy choices: supermarkets carry most healthy choices; smaller grocery stores carry fewer healthy choices; convenience stores and limited service restaurants are likely to carry mostly unhealthy choices.

• Access to different types of food outlets was computed at the census block group level based on concentration of stores / restaurants per unit area and is reported as food outlet densities.

•Food outlet density maps are compared with Census 2000 data to visualize accessibility of healthy foods in neighborhoods with different characteristics.

Data Sources: Info USA food outlet 2008 data

Trade Dimensions food outlet 2008 data

Census 2000 data

New Jersey Department of Education 2008-2009 data