Modified Retail Food Environment Index Research Articles

Neighborhood physical environments and change in cardiometabolic risk factors over 14 years in the study of Women's health across the nation

BackgroundNeighborhood physical environments may influence cardiometabolic health, but prior studies have been inconsistent, and few included long follow-up periods. MethodsChanges in cardiometabolic risk factors were measured for up to 14 years in 2830 midlife women in the Study of Women's Health Across the Nation, a multi-ethnic/racial cohort of women from seven U.S. sites. Data on neighborhood food retail environments (modified Retail Food Environment Index) and walkability (National Walkability Index) were obtained for each woman's residence at each follow-up. Data on neighborhood access to green space, parks, and supermarkets were available for subsets (32–42%) of women. Models tested whether rates of change in cardiometabolic outcomes differed based on neighborhood characteristics, independent of sociodemographic and health-related covariates. ResultsLiving in more (vs. less) walkable neighborhoods was associated with favorable changes in blood pressure outcomes (SBP: −0.27 mmHg/year, p = 0.002; DBP: −0.22 mmHg/year, p < 0.0001; hypertension status: ratio of ORs = 0.79, p < 0.0001), and small declines in waist circumference (−0.09 cm/year, p = 0.03). Small-magnitude associations were also observed between low park access and greater increases in blood pressure outcomes (SBP: 0.37 mmHg/year, p = 0.003; DBP: 0.15 mmHg/year, p = 0.04; hypertension status: ratio of ORs = 1.16, p = .04), though associations involving DBP and hypertension were only present after adjustment for sociodemographic variables. Other associations were statistically unreliable or contrary to hypotheses. ConclusionNeighborhood walkability may have a meaningful influence on trajectories of blood pressure outcomes in women from midlife to early older adulthood, suggesting the need to better understand how individuals interact with their neighborhood environments in pursuit of cardiometabolic health.

Deserts, Swamps and Food Oases: Mapping around the Schools in Bahia, Brazil and Implications for Ensuring Food and Nutritional Security.

Deserts, swamps and food oases terms have been used to characterize the food environment according to the identified food availability. Food swamps are defined as neighborhoods with a predominance of food establishments selling ultra-processed foods compared to establishments selling healthy options. In contrast, food oases are areas with easy access to healthy and nutritious foods, such as fruits, vegetables, and other fresh foods. Food deserts describe densely populated urban areas where residents face difficulty obtaining healthy food. In this context, this work aimed to map deserts, swamps, and food oases around federal schools in Bahia, Brazil, emphasizing the importance of implementing the Brazilian National School Feeding Program (PNAE) in these schools, to guarantee food security and nutrition. An ecological study was carried out in all 35 federal schools in Bahia, Brazil, using an 800 m buffer analysis, with the school as the centroid. The geographic coordinates of schools and food establishments were initially obtained using Google Maps and later confirmed onsite. To evaluate food deserts and swamps, the methodology proposed by the CDC was used and the Modified Retail Food Environment Index (mRFEI) was calculated; when the result was equal to zero, the surrounding area was considered a food desert and values between 0.01 and 20 determined food swamps; for values above 20, the neighborhood was classified as mixed. Food oases were considered regions containing at least one supermarket within the analyzed buffer. Descriptive analyses were carried out with frequency measurements, measures of central tendency (mean and median) and dispersion (standard deviation). The food environment of schools was compared considering the number of students impacted, the area where the school was located (urban or rural) and the size of the municipalities. The average number of food establishments found was 22.39 (±13.03), with the highest averages for snack bars (7.33 ± 4.43), grocery stores (5.83 ± 4.09) and restaurants (2.94 ± 2.19). Food deserts and mixed environments were identified in 40% of the sample, while swamps represented 20% and oases 65%. An association was observed between food deserts and social vulnerability, making it necessary to emphasize the importance of adequate implementation of the PNAE in these schools to reduce food and nutritional insecurity, guaranteeing the human right to adequate and healthy food and providing better nutrition and health perspectives within the school environment and impact on students' lives through food and nutrition education actions, which are also part of the context of PNAE activities.

Predicting incident cardiovascular disease among African-American adults: A deep learning approach to evaluate social determinants of health in the Jackson heart study.

The present study sought to leverage machine learning approaches to determine whether social determinants of health improve prediction of incident cardiovascular disease (CVD). Participants in the Jackson Heart study with no history of CVD at baseline were followed over a 10-year period to determine first CVD events (i.e., coronary heart disease, stroke, heart failure). Three modeling algorithms (i.e., Deep Neural Network, Random Survival Forest, Penalized Cox Proportional Hazards) were used to evaluate three feature sets (i.e., demographics and standard/biobehavioral CVD risk factors [FS1], FS1 combined with psychosocial and socioeconomic CVD risk factors [FS2], and FS2 combined with environmental features [FS3]) as predictors of 10-year CVD risk. Contrary to hypothesis, overall predictive accuracy did not improve when adding social determinants of health. However, social determinants of health comprised eight of the top 15 predictors of first CVD events. The social determinates of health indicators included four socioeconomic factors (insurance status and types), one psychosocial factor (discrimination burden), and three environmental factors (density of outdoor physical activity resources, including instructional and water activities; modified retail food environment index excluding alcohol; and favorable food stores). Findings suggest that whereas understanding biological determinants may identify who is currently at risk for developing CVD and in need of secondary prevention, understanding upstream social determinants of CVD risk could guide primary prevention efforts by identifying where and how policy and community-level interventions could be targeted to facilitate changes in individual health behaviors.

Refining large-scale spatial metrics of food access

Large scale metrics of food access are frequently used to identify spatial inequities and for targeting local government intervention. The Food Access Research Atlas by the USDA and the Modified Retail Food Environment Index by the CDC are two such measures frequently used at the census-tract level. This paper uses spatial analyses to identify ways in which they can be refined for better actionability. Outcomes from this paper are threefold: (i) Upon examination of the average food environment, it finds that we might be undercounting rural census tracts with low food access relative to urban tracts; (ii) Disaggregating the often-conflated goals of physical access for food equity and nutritional access for public health might help targeting interventions; (iii) Data on food environments, and underlying assumptions on food consumption behavior should be investigated with greater caution.

SynthEco - A multi-layered digital ecosystem for analysing complex human behaviour in context

Introduction &amp; BackgroundHuman behaviour is multi-faceted and complex, with different dimensions interacting and impacting each other and individuals operating in an environmental context. In order to understand this behaviour better, the combination of data from different sources is useful to uncover some of those interactions and complexities. We present a multi-layered digital ecosystem based on a data platform providing statistically representative synthetic population derived from census data at different geo-spatial granularity, which we call SynthEco. This platform is enriched with individual data stemming from cohorts and cross-sectional surveys and geo-scanning of different layers of socio-environmental actors and conditions to create a complex digital ecosystem.&#x0D; Objectives &amp; ApproachThe objective of SynthEco is to allow for the analysis of behaviour, as well as health and wellbeing outcomes, through the integration of cohort and cross-sectional data into a geospatially anchored synthetic population embedded into environmental data which is forming the backdrop. We demonstrate the use of this platform on the example of Montreal, Canada. The synthetic population is first generated from census data using iterative proportional fitting, which allows for the creation of a population data set that is artificial yet statistically representative for a given geospatial granularity, such as a city. Each individual household is assigned a geospatial location, which allows for the consideration of their surrounding environment including enterprises or institutions such as schools, hospitals and the local food environment. Through fuzzy matching and statistical extrapolation, different cohort and cross-sectional survey data are then merged to individual records, in order to describe them in more detail. This includes health, as well as financial wellbeing or social environment descriptors.&#x0D; Relevance to Digital FootprintsThere are two important points made through the presented work in relation to Digital Footprints data: the first is the technical approach to merging multiple datasets describing different dimensions of interacting human characteristics and behaviour by anchoring them into a synthetic population through fuzzy record matching. The second is the consideration of a spatial dimension when describing human behaviour. This is especially important when describing behaviour within local environments, such as the interaction with local food outlets.&#x0D; ResultsRecent work in this context includes an analysis of the food environment in Montreal, Canada. It introduces a way of utilising the synthetic population to predict the healthfulness of their local environment in terms of healthy food outlets, as well as providing a platform for the analysis of food environment surveillance and intervention simulations. For this purpose, the healthfulness of different census tract regions in Montreal is calculated to identify food deserts, food swamps, as well as healthy areas as defined through the Modified Retail Food Environment Index. We test different machine learning approaches to then predict these healthfulness scores using census variables from the synthetic population in their respective census tract, achieving accuracy scores of around 0.53 to 0.60. This demonstrates that census data has some limited predictive power in explaining the healthiness of the local food environment, which could be especially relevant for situations in which no information on the retailers is available to local policy makers. Future work can extend this approach to also include further data describing the population, stemming from the integrated cohorts and survey data, which could improve the prediction accuracy or help in identifying areas of concern.&#x0D; Conclusions &amp; ImplicationsThe presented SynthEco platform views individuals as agents nested within modular systems of systems, trying to capture both internal systems and processes as well as environmental ones within which individuals are operating. The platform thus enables the application of computational systems modelling for the analysis of individual human behaviour in contexts. As demonstrated through the example of using SynthEco in the context of healthier food environments, the approach is especially relevant to practitioners and policy makers interested in local intervention strategies and identifying areas for targeted policy related to different dimensions of health and wellbeing.

Impact of Food Environments on Obesity Rates: A State-Level Analysis.

Limited access to healthy food in areas that are predominantly food deserts or food swamps may be associated with obesity. Other unhealthy behaviors may also be associated with obesity and poor food environments. We calculated Modified Retail Food Environment Index (mRFEI) to assess food retailers. Using data collected from the Behavioral Risk Factor Surveillance System (BRFSS) survey, the NJ Department of Health (NJDOH), and the US Census Bureau, we conducted a cross-sectional analysis of the interaction of obesity with the food environment and assessed smoking, leisure-time physical activity (LPA), and poor sleep. There were 17.9% food deserts and 9.3% food swamps in NJ. There was a statistically significant negative correlation between mRFEI and obesity rate (Pearson's r -0.13, p < 0.001), suggesting that lack of access to healthy food is associated with obesity. Regression analysis was significantly and independently associated with increased obesity prevalence (adjusted R square 0.74 and p=0.008). Obesity correlated positively with unhealthy behaviors. Each unhealthy behavior was negatively correlated with mRFEI. The mean prevalence for smoking, LPA, and sleep <7 hours was 15.4 (12.5-18.6), 26.5 (22.5-32.3), and 37.3 (34.9-40.4), respectively. Obesity tracks with food deserts and especially food swamps. It is also correlated with other unhealthy behaviors (smoking, LPA, and poor sleep).

A longitudinal assessment of racial and ethnic inequities in food environment exposure and retail market concentration.

This paper assesses trends in food environment and market concentration and racial and ethnic inequities in food environment exposure and food retail market concentration at the US census tract level from 2000 to 2019. Establishment-level data from the National Establishment Time Series were used to measure food environment exposure and food retail market concentration. We linked that dataset to race, ethnicity and social vulnerability information from the American Community Survey and the Agency for Toxic Substances and Disease Registry. A geospatial hot-spot analysis was conducted to identify relatively low and high healthy food access clusters based on the modified Retail Food Environment Index (mRFEI). The associations were assessed using two-way fixed effects regression models. Census tracts spanning all US states. 69 904 US census tracts. The geospatial analysis revealed clear patterns of areas with high and low mRFEI values. Our empirical findings point to disparities in food environment exposure and market concentration by race. The analysis shows that Asian Americans are likelier to live in neighbourhoods with a low food environment exposure and low retail market concentration. These adverse effects are more pronounced in metro areas. The robustness analysis for the social vulnerability index confirms these results. US food policies must address disparities in neighbourhood food environments and foster a healthy, profitable, equitable and sustainable food system. Our findings may inform equity-oriented neighbourhood, land use and food systems planning. Identifying priority areas for investment and policy interventions is essential for equity-oriented neighbourhood planning.

777 Food Swamps, Food Deserts, and Wound Healing in Burn Patients at an Urban Academic Center

Abstract Introduction Many victims of burn injury live in food deserts or food swamps. Food deserts are low-income areas with minimal or no access to nutritious foods while food swamps are areas with much greater access to unhealthy, high-calorie foods. We examined the relationship between residency in a food swamp or food desert by census tract and wound healing. Methods We performed a retrospective review of burn patients who underwent split-thickness skin grafting at an ABA verified urban academic burn center between September 2018 and August 2022. Inclusion criteria were burn area less than 20% total body surface area (TBSA), age ≥ 18, and single operation for split-thickness skin grafting. Patient residential locations were used in conjunction with Modified Retail Food Environment Index (mREFI) data to classify residency in food deserts and food swamps. The primary outcome was time to donor site healing. Time-to-event Cox proportional regression analysis was performed to evaluate risk factors for poor wound healing. Results A total of 238 patients were included in the study, of whom 110 (46.2%) were female and 128 (53.8%) were male. Mean age was 48, BMI was 28.5, TBSA 3.7, and average days to donor site healing was 26. In categorizing residency by mREFI data, 45 (18.9%) individuals had good access, 11 (4.6%) resided in food deserts, and 182 (76.5%) resided in food swamps. Although time to healing was longer on average in the food desert (25.8 days) and food swamp (26.4 days) groups compared to the group with good access to food within ½ mile (22.9 days), this difference was not statistically significant. Not having diabetes was found to be associated with shorter time to wound healing (HR=1.68, 95% CI [1.15, 2.47], p=0.007). Living in a good access area was associated with faster wound healing (compared to swamp: HR=1.23, 95% CI [0.86, 1.75], compare to desert: HR=1.06, 95% CI [0.51, 2.20]), however this association was not statistically significant. Conclusions Residency in a food swamp or food desert does not significantly influence time to healing in burn injured patients. However, not having diabetes was associated with a shorter time to wound healing. This is the first study to compare residency in food deserts or food swamps by census tract level data with time to wound healing in an urban burn population. Applicability of Research to Practice Wound healing is a complex and important process known to be affected by malnutrition. Our data contributes to the current body of literature on nutrition and wound healing.

778 Epidemiology of Food Access in an Urban Burn Population

Abstract Introduction Poverty is a known risk factor for burn injury and is associated with residency in food deserts and food swamps. Food deserts are areas with low or no access to nutritious, healthy foods while food swamps are areas with much higher access to high-calorie, unhealthy foods than to foods with good nutritious value. The purpose of our investigation was to determine the prevalence of residency in food deserts and food swamps in burn patients evaluated at an urban academic center and to determine the relationship between residency in these areas and the presence of patient comorbidities. Methods We performed a retrospective chart review of patients with burn injuries seen at an ABA verified urban academic center between January 2016 and January 2022. All patients over age 18 seen in the emergency department or admitted to the burn service were included in the study. Patient residential locations were used in conjunction with the Modified Retail Food Environment Index (mREFI) data to classify residency in food deserts and food swamps. Patient demographics and comorbidities were also recorded. Chi square and ANOVA analyses were completed to determine differences between the food desert, food swamp, and good access groups. Results A total of 3064 subjects were included in the study, of these 1370 (44.7%) were female and 1694 (55.3%) were male. In terms of access, 96 (3.1%) individuals lived in food deserts, 477 (15.6%) lived in good access areas, and 2490 (81.3%) resided in food swamps. This difference was found to be statistically significant (p &amp;lt; 0.0001). Age, sex, and BMI did not differ significantly between the three groups. A significantly higher percentage of food swamp residents had a diagnosis of diabetes (15.0%) as compared to subjects living in good food access areas (11.7%) and food deserts (8.3%), (p &amp;lt; 0.043). Hypertension was also found to be more prevalent in food swamps (24.8%) than food deserts (20.8%) and good access areas (19.3%), (p &amp;lt; 0.028). Finally, smoking was more common in food swamps (45.3%) than good access areas (40.4%) and food deserts (27.9%), (p &amp;lt; 0.0028). Conclusions A majority of burn patients reside in food swamps and residency in these areas is associated with a higher prevalence of hypertension, diabetes, and tobacco smoking. This is the first study to investigate the epidemiology of food access in an urban burn population. Applicability of Research to Practice Nutrition is known to be important to overall health and to wound healing in the setting of an acute injury. Our data contributes to the current literature on the role of nutrition in burns.

What counts? Adding nuance to retail food environment measurement tools in a Canadian context.

Limitations of traditional geospatial measures, like the modified Retail Food Environment Index (mRFEI), are well documented. In response, we aimed to: (1) extend existing food environment measures by inductively developing subcategories to increase the granularity of healthy v. less healthy food retailers; (2) establish replicable coding processes and procedures; and (3) demonstrate how a food retailer codebook and database can be used in healthy public policy advocacy. We expanded the mRFEI measure such that 'healthy' food retailers included grocery stores, supermarkets, hypermarkets, wholesalers, bulk food stores, produce outlets, butchers, delis, fish and seafood shops, juice/smoothie bars, and fresh and healthy quick-service retailers; and 'less healthy' food retailers included fast-food restaurants, convenience stores, coffee shops, dollar stores, pharmacies, bubble tea restaurants, candy stores, frozen dessert restaurants, bakeries, and food trucks. Based on 2021 government food premise licences, we used geographic information systems software to evaluate spatial accessibility of healthy and less healthy food retailers across census tracts and in proximity to schools, calculating differences between the traditional v. expanded mRFEI. Calgary and Edmonton, Canada. N/A. Of the 10 828 food retailers geocoded, 26 % were included using traditional mRFEI measures, while 53 % were included using our expanded categorisation. Changes in mean mRFEI across census tracts were minimal, but the healthfulness of food environments surrounding schools significantly decreased. Overall, we show how our mRFEI adaptation, and transparent reporting on its use, can promote more nuanced and comprehensive food environment assessments to better support local research, policy and practice innovations.

Historical redlining and food environments: A study of 102 urban areas in the United States.

Unhealthy food environments are disproportionally concentrated in neighborhoods with clustering of racial/ethnic minorities and poverty. This disparity has been blamed, in part, on market self-regulation. This explanation risks overlooking past and current practices of racial segregation that have created and reinforced the obstacles blocking investments from food retailers in marginalized neighborhoods. We fill this gap by investigating how the long-term ramifications of redlining, discriminatory housing practices enacted by federal Home Owner Lending Corporation (HOLC) in the 1930s, has evolved generations later to disproportionally exposing neighborhoods to unhealthy food environments. We overlaid historical redlining maps over 2010 food environment observations at the census tract level to identify areas with less healthy food environments and to assess the historical context of those areas. For 11,651 census tracts within 102 U.S. urban areas, we described the healthiness of food environments as measured by the modified retail food environment index (mRFEI). Using hurdle models with random effects, we further examined the association between redlining housing practice and food environments. The results indicate that historically redlined neighborhoods show a higher likelihood for unhealthy retail food environments even for census tracts with present-day economic and racial privilege. The current evidence shows how structural discrimination manifested by unjust housing practices and racial residential segregation fueled an uneven food environment where minority neighborhoods disproportionally bore the brunt of restrictive food access. It highlights an urgent need to ameliorate patterns of housing inequality as a fix to unequal food environments.

Food Environments and Hepatocellular Carcinoma Incidence.

Research into the potential impact of the food environment on liver cancer incidence has been limited, though there is evidence showing that specific foods and nutrients may be potential risk or preventive factors. Data on hepatocellular carcinoma (HCC) cases were obtained from the Surveillance, Epidemiology, and End Results (SEER) cancer registries. The county-level food environment was assessed using the Modified Retail Food Environment Index (mRFEI), a continuous score that measures the number of healthy and less healthy food retailers within counties. Poisson regression with robust variance estimation was used to calculate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the association between mRFEI scores and HCC risk, adjusting for individual- and county-level factors. The county-level food environment was not associated with HCC risk after adjustment for individual-level age at diagnosis, sex, race/ethnicity, year, and SEER registry and county-level measures for health conditions, lifestyle factors, and socioeconomic status (adjusted IRR: 0.99, 95% CI: 0.96, 1.01). The county-level food environment, measured using mRFEI scores, was not associated with HCC risk.

The effect of the food environment on fresh produce served in family child care homes.

Higher produce consumption in childhood decreases risks of short- and long-term malnutrition, obesity, and disease. Children in early care programs, including family child care homes (FCCHs), receive 50-67% of daily nutrition while in care. Procuring nutritious foods requires grocer access, which is absent in food deserts (FDs). To determine if FCCH food environment (FE) impacted distance to grocers and amount of fresh produce served. Using a cross-sectional design, Modified Retail Food Environment Index scores determined census tract FD status. FCCH and grocer addresses were geocoded and distance to the nearest grocers was calculated. Fresh produce was observed during two lunches. FE did not influence distance to grocers or fresh produce served. Non-desert FCCHs tended to serve fresh produce more frequently. The amount of fresh produce served was overall low. Further studies are warranted to inform policies aimed to reduce provider barriers regarding service of fresh produce.

Predicting access to healthful food retailers with machine learning

Many U.S. households lack access to healthful food and rely on inexpensive, processed food with low nutritional value. Surveying access to healthful food is costly and finding the factors that affect access remains convoluted owing to the multidimensional nature of socioeconomic variables. We utilize machine learning with census tract data to predict the modified Retail Food Environment Index (mRFEI), which refers to the percentage of healthful food retailers in a tract and agnostically extract the features of no access—corresponding to a “food desert” and low access—corresponding to a “food swamp.” Our model detects food deserts and food swamps with a prediction accuracy of 72% out of the sample. We find that food deserts and food swamps are intrinsically different and require separate policy attention. Food deserts are lightly populated rural tracts with low ethnic diversity, whereas swamps are predominantly small, densely populated, urban tracts, with more non-white residents who lack vehicle access. Overall access to healthful food retailers is mainly explained by population density, presence of black population, property value, and income. We also show that our model can be used to obtain sensible predictions of access to healthful food retailers for any U.S. census tract.

Examining the relationship between food environment and gestational diabetes

Studies have shown an association between the incidence of gestational diabetes and living in neighborhoods oversaturated with unhealthy foods. This study sought to determine if the food environment also affects the management of gestational diabetes. We hypothesized that living in areas with a higher quality of food decreased the risk of requiring medication to treat gestational diabetes. This was a retrospective cohort study of singleton births at the Christiana Care Health System between 2015 and 2018. Patients with gestational diabetes who live in Delaware (N=1327) were geocoded and classified according to their census tract food environment. The food environment was assessed using the modified Retail Food Environment Index, which measures the percentage of healthy food retailers among all food retailers within a half-mile radius of the census tract boundaries. The modified Retail Food Environment Index scores were divided into 3 categories: poor (modified Retail Food Environment Index score, 0-3), average (modified Retail Food Environment Index score, 4-10), and good or above average (modified Retail Food Environment Index score, ≥11) food environments. The primary outcome was the prevalence of A2 gestational diabetes mellitus. Several neonatal and obstetrical outcomes were also examined including type II diabetes mellitus (defined as a 2-hour glucose tolerance test with at least 1 value above the threshold), cesarean delivery, shoulder dystocia, admission to the neonatal intensive care unit, neonatal hypoglycemia, neonatal hyperbilirubinemia, neonatal respiratory distress syndrome, and macrosomia. A total of 689 (52%) women were diagnosed as having A2 gestational diabetes mellitus. Women in the average or good or above average food environment groups had a lower prevalence of A2 gestational diabetes mellitus than women in the poor food environment group (modified Retail Food Environment Index score, 4-10 [adjusted odds ratio, 0.58; 95% confidence interval, 0.37-0.92] and modified Retail Food Environment Index score, ≥11 [adjusted odds ratio, 0.56; 95% confidence interval, 0.40-0.82]). They also had a lower prevalence of type II diabetes mellitus (modified Retail Food Environment Index score, 4-10 [adjusted odds ratio, 0.25; 95% confidence interval, 0.09-0.72] and modified Retail Food Environment Index score, ≥11 [adjusted odds ratio, 0.48; 95% confidence interval, 0.27-0.86]). There were no differences in the other secondary outcomes of interest. The food environment affects the requirement for medication to obtain glycemic levels that are within the target range for those with gestational diabetes.

Does Adherence to Mediterranean Diet Mediate the Association Between Food Environment and Obesity Among Non-Hispanic Black and White Older US Adults? A Path Analysis.

This study aims to test the hypothesis that in addition to a direct effect of food environment on obesity, food environment is indirectly associated with obesity through consuming Mediterranean diet (MD). Cross-sectional secondary data analysis. Nationwide community-dwelling residency. A total of 20 897 non-Hispanic black and white adults aged ≥45 years who participated in the REasons for Geographic and Racial Differences in Stroke study and completed baseline assessment during January 2003 and October 2007. The Modified Retail Food Environment Index (mRFEI; 0-100) was used as food environment indicator. The MD score (0-9) was calculated to indicate the dietary pattern adherence. Body mass index (BMI; kg/m2) was used to estimate obesity. Path analysis was used to quantify the pathways between food environment, MD adherence, and obesity. Proper data transformation was made using Box-Cox power transformation to meet certain analysis assumptions. The participants were from 49 states of the United States, with the majority (64.42%) residing in the South. Most of the participants were retired, female, white, married, having less than college graduate education, having annual household income ≤75 000, and having health insurance. The means of mRFEI was 10.92 (standard deviation [SD] = 10.19), MD score was 4.36 (SD = 1.70), and the BMI was 28.96 kg/m2 (SD = 5.90). Access to healthy food outlets (β = .04, P < .0001) and MD adherence (β = .08, P < .0001) had significant and inverse relationships with BMI, respectively. Mediterranean diet adherence mediated the relationship between food environment and obesity among a subpopulation who had an annual household income of <$75 000 (β = -.02, P = .0391). Population-tailored interventions/policies to modify food environment and promote MD consumption are needed in order to combat the obesity crisis in the United States.

P34 County-Level Associations Between Food Retail Outlet Availability and Violent Crime Rate

Background Violent crime is a major public health issue. Currently, information on the relationship between food retail outlet availability and violent crime occurrence is limited. Objective Evaluate US county-level associations between the availability of healthy (grocery stores, supercenters, and farmers’ markets) and unhealthy (convenience stores and fast food restaurants) and violent crime rate after adjusting for socio-demographic factors. We hypothesized that counties with fewer healthy food retailers would have a higher violent crime rate. Study Design, Settings, and Participants Data collected in 2014 on 3,126 counties were obtained from two sources: The United States Department of Agriculture Food Environment Atlas and the United States Department of Justice Uniform Crime Reporting Program. Measures on resident socio-demographics (e.g., % Non-Hispanic black, % Hispanic, poverty rate, etc.), per capita food retail outlets, and per capita violent crimes were analyzed. Measurable Outcome/Analysis Violent crime rate reflects the total number of murders, robberies, forcible rapes, and aggravated assaults per 10,000 county residents. The CDC's Modified Retail Food Environment Index (mRFEI) was applied to assess the healthfulness of each county's retail food environment. Multivariable-adjusted linear regression models were used to examine associations between food retail outlet availability and violent crime rate. Results After adjusting for socio-demographic measures, increased availability of grocery stores (P = .01) and farmers’ markets (P Conclusion US counties with fewer healthy food retailers had a higher violent crime rate after adjusting for socio-demographic factors. These findings highlight the need for additional research on the role of the retail food environment in community violence.

The association between food deserts and short sleep duration among young adults in the United States: variation by race and ethnicity

BackgroundBoth food deserts and short sleep duration are serious public health problems that affect the lives of millions of Americans. Although recent research has begun to link community characteristics to sleep problems, the relationship between living in a food desert and sleep behavior has gone largely unexplored. MethodsUsing data from the National Longitudinal Study from Adolescent to Adult Health and data on food retailers from the Modified Retail Food Environment Index, this study applies multivariable logistic regression to assess the association between living in a food desert and short sleep duration. Models were stratified by race and ethnicity to examine potential moderating effects. Supplemental analyses are conducted where the comparison category is restricted to respondents who live in census tracts with low access to healthy food retailers but do not live in food deserts. ResultsIn adjusted logistic regression models, living in a food desert is not associated with short sleep duration. When the sample is stratified, living in a food desert has a positive and significant association with short sleep duration among Hispanic respondents in both the main analysis (odds ratio = 1.7; 95% confidence interval = 1.0-2.7) and supplemental models (odds ratio = 1.9; 95% confidence interval = 1.2-3.2). ConclusionsLiving in a food desert is not associated with short sleep duration among young adults. Study results indicate that living in a food desert is associated with increased odds for short sleep duration among Hispanics. Potential explanations for this finding are discussed in the context of extant research.

Association between Spatial Access to Food Outlets, Frequency of Grocery Shopping, and Objectively-Assessed and Self-Reported Fruit and Vegetable Consumption.

Because supermarkets are a critical part of the community food environment, the purpose of this paper is to examine the association between accessibility to the supermarket where participants were surveyed, frequency of shopping at the supermarket, and self-reported and objectively-assessed fruit and vegetable consumption. Accessibility was assessed using Geographic Information Systems (GIS) measured distance and multiple versions of the modified Retail Food Environment Index (mRFEI), including a localized road network buffer version. Frequency of shopping was assessed using self-report. The National Cancer Institute Fruit and Vegetable screener was used to calculate daily servings of fruits and vegetables. Skin carotenoids were assessed using the “Veggie Meter™” which utilizes reflection spectroscopy to non-invasively assess skin carotenoids as an objective measure of fruit and vegetable consumption. Bivariate and multivariable statistics were used to examine the associations in RStudio. There was a positive association between skin carotenoids and the Special Supplemental Nutrition Program for Women Infants and Children (WIC) and mRFEI scores, suggesting that WIC participation and a healthier food environment were associated with objectively-assessed fruit and vegetable consumption (skin carotenoids). Future research should examine these associations using longitudinal study designs and larger sample sizes.

Access to healthy food retailers among formerly incarcerated individuals.

To assess access to healthy food retailers among formerly incarcerated individuals. Using linked data from the National Longitudinal Study of Adolescent to Adult Health and the Modified Retail Food Environment Index, the present study applies multivariate logistic regression to assess the association between incarceration and (i) living in a food desert and (ii) having low access to healthy food retailers. To account for unobserved heterogeneity, additional analyses are performed comparing formerly incarcerated individuals with persons arrested or convicted for a crime but not previously incarcerated. Sample of respondents living in urban census tracts in the USA. Adults (n 10390) aged 24-34 years. In adjusted logistic regression models, prior incarceration was not significantly associated with living in a food desert (OR=1·097; 95% CI 0·896, 1·343). Prior incarceration significantly increased the likelihood of living in a census tract with low access to healthy food retailers (OR=1·265; 95% CI 1·069, 1·498). This significant association remained when comparing formerly incarcerated individuals with those who had been arrested or convicted of a crime, but not previously incarcerated (OR=1·246; 95% CI 1·032, 1·503). Formerly incarcerated individuals are more likely to live in areas with low access to healthy food retailers compared with their non-incarcerated counterparts. Because lower access healthy food retailers may be associated with worse health and dietary behaviour, disparities in local food retail environments may exacerbate health inequalities among formerly incarcerated individuals.