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'Assessing access to fresh fruits and vegetables in two school zones of Escambia County'
Background: Understanding how neighborhood food environments influence dietary behaviors and chronic disease risk, particularly in underserved communities, is essential for developing effective interventions and policies to reduce health disparities and improve population health. Integrating resident perceptions, objective assessments of retail food environments, and spatial analyses provides a comprehensive approach to identifying barriers to healthy food access.
Objective: This study assessed neighborhood food environments within two elementary school zones in Escambia County, Florida, by examining residents’ food purchasing behaviors, evaluating healthy food availability in corner stores, and identifying geographic disparities in food access.
Methods: A cross-sectional observational study was conducted from August 2017 to May 2018. Forty-seven adult residents completed a modified Nutrition Environment Measures Survey in Communities (NEMS-P), and sixteen corner stores across eight ZIP codes were assessed using the Nutrition Environment Measures Survey for Corner Stores (NEMS-CS). Descriptive statistics were used to summarize participant characteristics and store audit findings, while internal consistency was evaluated using Cronbach’s alpha. Geographic Information Systems (GIS), standardized Food Environment Index (FEI) scores, and composite NEMS-CS scores were used to examine spatial patterns and disparities in food access.
Results: Participants were predominantly Black/African American (48.9%), with 44.7% reporting annual household incomes below $20,000 and 42.6% receiving SNAP/EBT benefits. Food quality, taste, nutrition, and price were the primary factors influencing food purchasing decisions. Convenience stores and gas stations represented 56.3% of surveyed food outlets. ZIP codes 32504 and 32506 demonstrated more favorable NEMS-CS composite scores compared with ZIP codes 32505 and 32514. Neighborhood Food Environment Perceptions (α = 0.885) and Ease of Access to Foods (α = 0.914) demonstrated strong internal consistency. GIS analyses identified geographic variation in healthy food access, reflected by differences in FEI scores and community health indicators across the study area.
Conclusions: Although residents demonstrated high awareness of nutrition and healthy food choices, structural and geographic barriers may restrict equitable access to healthier foods. Combining resident perceptions, retail food environment assessments, and GIS-based spatial analyses provided a multidimensional evaluation of neighborhood food environments. These findings highlight geographic inequities in healthy food availability and support targeted, community-based strategies to improve food access and address obesity-related health disparities
Graph showing NEMS-CS scores of healthy food items in different corner stores by zip codes of Escambia County.
The graph is created by primary data using an Excel spreadsheet.
3D Excel Map depicts corner store addresses
There are 16 corner stores surveyed- created from 3D Maps
There are 16 corner stores surveyed- created from 3D Maps
An interactive site was created to spot the corner store using ArcGIS tool
An interactive site was created to spot the corner store using ArcGIS tool
This image is a screenshot of the video shoot created using Excel Maps
Geo-Spatial maps created to locate Corner stores
GIS-based integration of S-FEI, obesity prevalence, and self-reported health status provided a spatial representation of neighborhood-level differences. GIS approaches have been shown to improve understanding of how food outlet distribution and neighborhood characteristics influence food access patterns \cite{Peng2020GISFoodOutlet, LopezClass2010CommunityFoodResources}
GIS-based integration of S-FEI, obesity prevalence, and self-reported health status provided a spatial representation of neighborhood-level differences. GIS approaches have been shown to improve understanding of how food outlet distribution and neighborhood characteristics influence food access patterns \cite{Peng2020GISFoodOutlet, LopezClass2010CommunityFoodResources}
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