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.

The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study.

Prostate cancer is one of the leading causes of cancer-related mortality among men in the United States. We examined the role of neighborhood obesogenic attributes on prostate cancer risk and mortality in the Southern Community Cohort Study (SCCS). From the total of 34,166 SCCS male participants, 28,356 were included in the analysis. We assessed the relationship between neighborhood obesogenic factors [neighborhood socioeconomic status (nSES) and neighborhood obesogenic environment indices including the restaurant environment index, the retail food environment index, parks, recreational facilities, and businesses] and prostate cancer risk and mortality by controlling for individual-level factors using a multivariable Cox proportional hazards model. We further stratified prostate cancer risk analysis by race and body mass index (BMI). Median follow-up time was 133 months [interquartile range (IQR): 103, 152], and the mean age was 51.62 (SD: ± 8.42) years. There were 1,524 (5.37%) prostate cancer diagnoses and 98 (6.43%) prostate cancer deaths during follow-up. Compared to participants residing in the wealthiest quintile, those residing in the poorest quintile had a higher risk of prostate cancer (aHR = 1.32, 95% CI 1.12-1.57, p = 0.001), particularly among non-obese men with a BMI < 30 (aHR = 1.46, 95% CI 1.07-1.98, p = 0.016). The restaurant environment index was associated with a higher prostate cancer risk in overweight (BMI ≥ 25) White men (aHR = 3.37, 95% CI 1.04-10.94, p = 0.043, quintile 1 vs. None). Obese Black individuals without any neighborhood recreational facilities had a 42% higher risk (aHR = 1.42, 95% CI 1.04-1.94, p = 0.026) compared to those with any access. Compared to residents in the wealthiest quintile and most walkable area, those residing within the poorest quintile (aHR = 3.43, 95% CI 1.54-7.64, p = 0.003) or the least walkable area (aHR = 3.45, 95% CI 1.22-9.78, p = 0.020) had a higher risk of prostate cancer death. Living in a lower-nSES area was associated with a higher prostate cancer risk, particularly among Black men. Restaurant and retail food environment indices were also associated with a higher prostate cancer risk, with stronger associations within overweight White individuals. Finally, residing in a low-SES neighborhood or the least walkable areas were associated with a higher risk of prostate cancer mortality.

Weight Loss Maintainers Sustain High Diet Quality in Diverse Residential Retail Food Environments

BackgroundThe relationship between the retail food environment and diet quality has received minimal investigation among weight loss maintainers. ObjectiveThe aim of this study was to investigate the association between the residential retail food environment and diet quality in weight loss maintainers from WeightWatchers in the United States. DesignCross-sectional data were collected between January 2018 and February 2020. The Retail Food Environment Index (RFEI), based on geocoded home addresses, classified the environment as follows: RFEI <1.6 = healthiest; RFEI 1.6 to <2.5 = moderately healthy; RFEI 2.5 to <4.0 = moderately unhealthy; RFEI ≥4.0 = least healthy. Dietary data were obtained using a food frequency questionnaire. Participants/SettingAdult participants (n = 1,159) who had lost weight using WeightWatchers and maintained ≥9.1-kg weight loss for ≥1 year (mean 24.7-kg loss for 3.4 years). Main Outcome MeasuresHealthy Eating Index 2015 (HEI-2015) component and total scores (0-100; higher scores indicate better alignment with the 2015-2020 Dietary Guidelines for Americans). Statistical Analyses PerformedRegression models included RFEI category, the independent variable, and HEI-2015 and component scores (outcomes) controlling for age, sex, race and ethnicity, educational attainment, and household income. ResultsCompared with individuals living in the healthiest food environments (mean HEI-2015 score = 71.5) those in the unhealthiest environments had a mean HEI-2015 score of 70.1 (95% CI 68.8 to 71.3), those in moderately unhealthy environments had a score of 71.3 (95% CI 70.3 to 73.1) and those in moderately healthy environments had a score of 70.3 (95% CI 68.9 to 71.2), indicating a nonlinear relationship. Compared with those in the healthiest environments, those in the least healthy environments had an approximately 0.47 lower added sugar HEI-2015 component score (95% CI –0.86 to –0.08), indicating approximately 5% higher added sugar intake. ConclusionsWeight loss maintainers maintained high diet quality in diverse retail food environments. Compared with those in the healthiest food environments, those in the least healthy had a higher consumption of added sugars.

What North American retail food environment indices miss in Guatemala: Cultural considerations for the study of place and health

We evaluated the cross-context validity and equivalence of the US- and Canada-originated Retail Food Environment Index (RFEI) and modified RFEI (mRFEI) against a retail food environment dataset from the indigenous-majority city of Quetzaltenango (Xela), Guatemala. The RFEI/mRFEI failed to identify 77% of retailers and misclassified the healthiness of 42% of the remaining retailers in Xela, inaccurately labeling the city a food swamp. The RFEI/mRFEI are not currently suitable for mapping retail food environments in places like Quetzaltenango. Alternative functional and temporal classifications of retail food environments may provide measures with greater contextual fit, highlighting important cultural considerations for the study of place and dietary 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.

The Impact of Fast-Food Density on Obesity during the COVID-19 Lockdown in the UK: A Multi-Timepoint Study on British Cohort Data

Poor food environments are considered to trigger obesity and related health complications by restricting the local food options to predominantly low quality, energy-dense foods. This study investigated the impact of the food environment on obesity with a focus on any changes that might have occurred around the COVID lockdown period in the UK when majority of the population relied on food delivery and the local food environments. The proportion of fast-food retailers in the area and the Retail Food Environment Index (RFEI) were calculated for participants of the 1970 British Cohort Study (BCS70) at three timepoints: pre-COVID (2016), the first UK nation-wide lockdown (April–May 2020) and post lockdown (September–October 2020). The association of the food environment and the odds of obesity was estimated through multivariable logistic regression, with adjustments being made for selected socioeconomic variables. A model using the fast-food proportion as the sole predictor estimated that higher fast-food proportion increased the odds of obesity by 2.41 in 2016, 2.89 during the lockdown and 1.34 post lockdown, compared with 1.87, 2.23, and 0.73, respectively, for the same three periods with adjustments being made for select socioeconomic variables. On the other hand, RFEI increased the odds of obesity only slightly at 1.01, 1.02 and 1.03, respectively, with the model with adjustments yielding respective similar values. The fast-food proportion model indicates that proximity to a poor food environment is linked to obesity, especially during the COVID lockdown period, but the impact of a poor-food environment is limited if the RFEI is used as its indicator. The findings will add much needed insights on the UK data and will inform public health planning and policy.

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.

A healthier retail food environment around the home is associated with longer duration of weight-loss maintenance among successful weight-loss maintainers

Few studies have examined associations between the retail food environment and weight maintenance. This study examined the residential Retail Food Environment Index (RFEI) of weight loss maintainers and associations with weight maintenance duration, perceived effort and difficulty managing weight, and coping and monitoring strategies.Participants were 6947 members of the WW Success Registry (enrolled January 2018–February 2020), a nationwide (United States) convenience sample of individuals who lost weight using Weight Watchers (WW) and maintained a ≥ 9.1 kg weight loss for ≥1 year (Mean 24.7 kg loss for 3.4 years). Home addresses were geo-coded and the RFEI (ratio of unhealthy [fast-food and convenience stores] to healthy [supermarkets, grocery stores, and fruit/vegetable vendors] outlets) was used to classify the healthfulness of the food environments. Validated questionnaires measured psychological coping and self-monitoring.Compared to individuals living in the healthiest food environments (RFEI<1.6), those in the least healthy food environments (RFEI ≥4.0) maintained weight loss for 0.5 years less (3.2 vs 3.7 years; 95% CI between-group difference = 0.20, 0.80), reported statistically higher scores but not clinically relevant differences on perceived effort (4.6 vs. 4.5; 95% between-group difference = 0.01, 0.21) and difficulty managing their weight (3.1 vs. 3.0; 95% CI between-group difference = 0.01, 0.17) and practice of self-monitoring (2.7 vs. 2.6; 95% CI between-group difference = 0.01, 0.14). No differences in psychological coping were observed.Weight loss maintainers living in the least healthy retail food environments maintained weight loss for a shorter duration compared to those in the healthiest food environments.

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.

Abstract 85: Food Swamps Are Associated With Incident Stroke In The Health And Retirement Study

Introduction: The role of “food swamps”, an area characterized by a high-density of establishments selling fast-food and junk food relative to healthier options, on incident stroke is not well studied. Hypothesis We hypothesized that a higher retail food environment index (RFEI), indicative of food swamps, would be associated with greater odds of incident stroke. Methods: The sample comprised of community-dwelling stroke-free participants aged ≥50 years who enrolled in the 2010 epoch of the Health and Retirement Study (HRS), which is representative of the US population. If a participant moved to a new area during follow-up through 2016, we only considered incident strokes reported before relocation. The traditional RFEI is a county’s ratio of the number of fast-food restaurants and convenience stores to the number of grocers. The expanded RFEI additionally includes full-service restaurants as unhealthy food options, and farmers’ markets and specialized food stores as healthy food retailers. We averaged RFEI across all included follow-up years and dichotomized RFEI using a threshold of 5, previously shown to discriminate obesity rates. We used logistic regression models to assess the association between RFEI groups and incident stroke, adjusting for key covariates and weighting to account for survey design. Results: Among 84,023,542 participants (mean age 64±10 years, 54% female, 84% white) in weighted analysis, 3,224,378 (3.8%) reported an incident stroke during follow-up. The average traditional and expanded RFEI were 6.5±2.7 (72% in ≥5 group) and 6.9±2.3 (84% in ≥5 group), respectively. In fully adjusted weighted analyses, the higher traditional RFEI group had greater odds of incident stroke compared to the lower group (OR [95% CI]: 1.135 [1.132-1.138]). We found a similar association with expanded RFEI groups and incident stroke (OR [95% CI]: 1.095 [1.092-1.098]). Conclusions: Among community-dwelling adults in HRS, RFEI was associated with incident stroke, independent of demographics and health characteristics. Results highlight the potential importance of an area’s food options as a structural determinant in stroke, especially given most participants resided in areas with 6 times the amount of relative unhealthy to healthy food choices.

Enhancing the Retail Food Environment Index (RFEI) with Neighborhood Commuting Patterns: A Hybrid Human-Environment Measure.

The Retail Food Environment Index (RFEI) and its variants have been widely used in public health to measure people’s accessibility to healthy food. These indices are purely environmental as they only concern the geographic distribution of food retailers, but fail to include human factors, such as demographics, socio-economy, and mobility, which also shape the food environment. The exclusion of human factors limits the explanatory power of RFEIs in identifying neighborhoods of the greatest concern. In this study, we first proposed a hybrid approach to integrate human and environmental factors into the RFEI. We then demonstrated this approach by incorporating neighborhood commuting patterns into a traditional RFEI: we devised a multi-origin RFEI (MO_RFEI) that allows people to access food from both homes and workplaces, and further an enhanced RFEI (eRFEI) that allows people to access food with different transportation modes. We compared the traditional and proposed RFEIs in a case study of Florida, USA, and found that the eRFEI identified fewer and more clustered underserved populations, allowing policymakers to intervene more effectively. The eRFEI depicts more realistic human shopping behaviors and better represents the food environment. Our study enriches the literature by offering a new and generic approach for assimilating a neighborhood context into food environment measures.

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.

Higher Neighborhood Population Density Is Associated with Lower Potassium Intake in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

Current U.S. dietary guidelines recommend a daily potassium intake of 3400 mg/day for men and 2600 mg/day for women. Sub-optimal access to nutrient-rich foods may limit potassium intake and increase cardiometabolic risk. We examined the association of neighborhood characteristics related to food availability with potassium intake in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). 13,835 participants completed a 24-h dietary recall assessment and had complete covariates. Self-reported potassium intake was calibrated with an objective 24-h urinary potassium biomarker, using equations developed in the SOL Nutrition & Physical Activity Assessment Study (SOLNAS, N = 440). Neighborhood population density, median household income, Hispanic/Latino diversity, and a retail food environment index by census tract were obtained. Linear regression assessed associations with 24-h potassium intake, adjusting for individual-level and neighborhood confounders. Mean 24-h potassium was 2629 mg/day based on the SOLNAS biomarker and 2702 mg/day using multiple imputation and HCHS/SOL biomarker calibration. Compared with the lowest quartile of neighborhood population density, living in the highest quartile was associated with a 26% lower potassium intake in SOLNAS (adjusted fold-change 0.74, 95% CI 0.59–0.94) and a 39% lower intake in HCHS/SOL (adjusted fold-change 0.61 95% CI 0.45–0.84). Results were only partially explained by the retail food environment. The mechanisms by which population density affects potassium intake should be further studied.

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.