Programs and Communities
The collection collates collections by schools, centers, programs, and research groups.
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- Creators: Yedidia, Michael J., 1946-
- Creators: Benard, Jacqueline
- Creators: Link, Denise
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.
In response to lack of access to healthy foods, many low-income communities are instituting local healthy corner store programs. Some stores also participate in the United States Department of Agriculture's Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and the Supplemental Nutrition Assistance Program (SNAP). This study used two assessment tools to compare the healthfulness of offerings at stores participating in local healthy store programs (upgraded stores), WIC, and/or SNAP to that of similar non-participating stores.
Based on store audits conducted in 315 New Jersey corner stores in 2014, we calculated healthy food availability scores using subsections of the Nutrition Environment Measures Survey for Corner Stores (NEMS-CS-Availability) and a short-form corner store audit tool (SCAT). We used multivariable regression to examine associations between program participation and scores on both instruments.
Adjusting for store and block group characteristics, stores participating in a local healthy store program had significantly higher SCAT scores than did non-participating stores (upgraded: M = 3.18, 95% CI 2.65–3.71; non-upgraded: M = 2.52, 95% CI 2.32–2.73); scores on the NEMS-CS-Availability did not differ (upgraded: M = 12.8, 95% CI 11.6–14.1; non-upgraded: M = 12.5, 95% CI 12.0–13.0). WIC-participating stores had significantly higher scores compared to non-participating stores on both tools. Stores participating in SNAP only (and not in WIC) scored significantly lower on both instruments compared to non-SNAP stores.
WIC-participating and non-SNAP corner stores had higher healthfulness scores on both assessment tools. Upgraded stores had higher healthfulness scores compared to non-upgraded stores on the SCAT.
Objective: The Social Ecological Model (SEM) has been used to describe the aetiology of childhood obesity and to develop a framework for prevention. The current paper applies the SEM to data collected at multiple levels, representing different layers of the SEM, and examines the unique and relative contribution of each layer to children's weight status.
Design: Cross-sectional survey of randomly selected households with children living in low-income diverse communities.
Setting: A telephone survey conducted in 2009-2010 collected information on parental perceptions of their neighbourhoods, and household, parent and child demographic characteristics. Parents provided measured height and weight data for their children. Geocoded data were used to calculate proximity of a child's residence to food and physical activity outlets.
Subjects: Analysis based on 560 children whose parents participated in the survey and provided measured heights and weights.
Results: Multiple logistic regression models were estimated to determine the joint contribution of elements within each layer of the SEM as well as the relative contribution of each layer. Layers of the SEM representing parental perceptions of their neighbourhoods, parent demographics and neighbourhood characteristics made the strongest contributions to predicting whether a child was overweight or obese. Layers of the SEM representing food and physical activity environments made smaller, but still significant, contributions to predicting children's weight status.
Conclusions: The approach used herein supports using the SEM for predicting child weight status and uncovers some of the most promising domains and strategies for childhood obesity prevention that can be used for designing interventions.
Methods: An academic detailing session was provided by the Florida Department of Health in Broward County to primary care providers (PCPs) at a private clinic in the Southeastern US. The Conceptual Model of Nursing and Population Health (CMNPH) and the Promoting Action on Research Implementation in Health Services Framework (PARIHS) were used as guides for project design and evidence-based practice implementation. There were five participants in the academic detailing session including: a physician, a physician assistant (PA), and three medical students. PCP PrEP knowledge, attitudes, and behaviors were measured using pre and post intervention surveys. PrEP prescription, HIV/Sexually Transmitted Infection (STI) screening, and HIV testing were measured using aggregate data queries for the time periods of four and eight weeks before and four and eight weeks after the educational intervention. No personal identifying information was obtained.
Results: Pre and post surveys were analyzed using Wilcoxon signed rank testing to examine differences between matched pairs of ordinal data. Significant PrEP screening and HIV/STI testing were analyzed pre and post intervention using paired t tests to compare pre and post intervention practices. Significant differences were found in the results (Z = 2.03, 1.84, and 1.83 respectively, p > 0.1) The academic detailing intervention significantly improved knowledge, attitudes, and behaviors of primary care providers and medical students regarding PrEP. The mean of active qualifying ICD 10 codes prior to the intervention versus post intervention were 5 (sd = 5.64) vs. 4.2 (sd = 4.87) respectively. No significant difference was found between active ICD 10 codes for patient visits before compared to after the intervention (t (9) = 1.12, p>.1).
Conclusions: This project found that academic detailing improved provider self-reported knowledge, attitudes, and behaviors regarding PrEP. However, self-reported survey scores were not correlated with changes in clinical practice based on ICD 10 codes. Additional clinical implications may include fostering clinical outreach and cooperation between the county health department and local primary care clinics. Further research is needed on the effects of PrEP academic detailing on clinical practice.
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 ew 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 Kids 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 Camden 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 Camden and in a 1 mile buffer area around Camden.
•Using the commercial data and additional investigation, food outlets were classified into different categories based on their likeliliood 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 food s 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 ew Brunswick 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 New Brunswick and in a 1 mile buffer area around New Brunswick.
•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 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