Socioeconomic Context and the Food Landscape in Texas: Results from Hotspot Analysis and Border/Non-Border Comparison of Unhealthy Food Environments

Abstract

Purpose: The purpose of this paper is to describe the food landscape of Texas using the CDC’s Modified Retail Food Environment (mRFEI) and to make comparisons by border/non-border. Methods: The Modified Retail Food Environment index (mRFEI (2008)) is an index developed by the CDC that measures what percent of the total food vendors in a census track sell healthy food. The range of values is 0 (unhealthy areas with limited access to fruits and vegetables) to (100—Healthy). These data were linked to 2010 US Census socioeconomic and ethnic concentration data. Spatial analysis and GIS techniques were applied to assess the differences between border and non-border regions. Variables of interest were mRFEI score, median income, total population, percent total population less than five years, median age, % receiving food stamps, % Hispanic, and % with a bachelor degree. Results: Findings from this study reveal that food environment in Texas tends to be characteristic of a “food desert”. Analysis also demonstrates differences by border/non-border location and percent of the population that is foreign born and by percent of families who receive food stamps. Conclusions: Identifying the relationship between socioeconomic disparity, ethnic concentration and mRFEI score could be a fundamental step in improving health in disadvantage communities, particularly those on the Texas-Mexico border.

The use and misuse of ratio and proportion exposure measures in food environment research

“…In many instances, the denominator of the proportion measure is the total number of food stores (n.b. commonly this is the sum of ‘healthy’ and ‘unhealthy’ food stores in a binary classification) [ 19 , 25 , 29 – 33 , 35 , 40 , 41 , 45 , 46 ]. If the types of foods stores included in these categorisations are restrictive (e.g., healthy stores only include supermarkets and greengrocers, while unhealthy stores only include fast food restaurant and convenience stores), then numerous other stores selling food (e.g., fish mongers, bakeries, and others outlined by Lucan et al [ 44 ]) will be excluded from the denominator.…”

A Border Versus Non-Border Comparison of Food Environment, Poverty, and Ethnic Composition in Texas Urban Settings

“…Findings revealed that the relationship between socioeconomic characteristics, ethnic context and food environment varies by urban setting, and depends on border/non-border location. Access to healthy food options is said to differentially place certain race/ethnic groups and socioeconomic strata at higher-than-average risk for obesity and related adverse health outcomes ( 1 , 3 , 10 , 15 , 18 , 19 , 24 – 26 ). This is exemplified in the Texas–Mexico border region, where there is a high concentration of poverty and persons of Mexican origin, coupled with a higher–than-average prevalence of obesity ( 30 ), which places Mexican American residents in the region at increased risk for diabetes and uncontrolled hypertension ( 20 – 27 ).…”

“…While few studies have compared border to non-border food environments, the findings from this study are consistent with what is known, and contribute to mounting evidence that contextual influences may differ based on geographic proximity to the border and therefore be responsible for risk variation in obesity and related chronic diseases. For example, Salinas et al ( 26 ) using the same data by census tract across the State of Texas found that census tracts in the U.S.-Mexico border region have, on average, better food environments than non-border census tracts. Nonetheless, the relationship between the mRFEI and % foreign born and % families on food stamps varied by border/non-border location ( 26 ).…”

“…For example, Salinas et al ( 26 ) using the same data by census tract across the State of Texas found that census tracts in the U.S.-Mexico border region have, on average, better food environments than non-border census tracts. Nonetheless, the relationship between the mRFEI and % foreign born and % families on food stamps varied by border/non-border location ( 26 ). In this study, the border and near-border urban settings had higher mRFEI scores and higher Hispanic ethnic and immigrant concentration was associated with better food environments.…”

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

“…Local health departments increasingly are adopting geographic information system (GIS) software for epidemiological analyses [4,5] and to build analytic capacity [6]. Historically, the geographic unit of analysis most often used has been the county [7][8][9] or zip code [10], often due to availability of geography-related data. Using the county as the aggregation unit, however, precludes identifying specific high and low risk locales within the county, which may covary with the socioeconomics of local population, environmental exposure, or geographic access to health care services.…”