Food insecurity in Detroit: exploring the relationship between patient-reported food insecurity and proximity to healthful grocery stores.

Sara Santarossa,Katherine Scher,Mackenzie Taylor,Rachael Dombrowski,Aaron Sohaski,Mohammed Baseer,Christine Lm Joseph,Alex B Hill,Alexandra R Sitarik,Susan Hawkins,Alexander Plum

doi:10.1017/s1368980021003128

Sara Santarossa, Katherine Scher + Show 9 more

Open Access

https://doi.org/10.1017/s1368980021003128

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Abstract

The objective of the current study was to determine if patients of a large health care system in Detroit who self-identify as food insecure live further away from healthy grocery stores compared with food secure patients. Second, we explored whether food insecurity and distance to healthy grocery stores are related to ecological measures of vehicle availability in the area of residence. A secondary data analysis that uses baseline data from a pilot intervention/feasibility study. Detroit, Michigan, USA. Patients of Henry Ford Health System were screened for food insecurity to determine eligibility for a pilot intervention/feasibility study (i.e. Henry's Groceries for Health), conducted through a collaboration with Gleaners Community Foodbank of Southeastern Michigan. Only patients residing in Detroit city limits (including Highland Park and Hamtramck) were included in the secondary analysis. Of the 1,100 patients included in the analysis, 336 (31 %) were food insecure. After accounting for socio-demographic factors associated with food insecurity, we did not find evidence that food insecure patients lived further away from healthier grocery stores, nor was this modified by ecological measures of vehicle access. However, some neighbourhoods were identified as having a significantly higher risk of food insecurity. Food insecure patients in Detroit are perhaps limited by social and political determinants and not their immediate neighbourhood geography or physical access to healthy grocery stores. Future research should explore the complexity in linkages between household socio-economic factors, socio-cultural dynamics and the neighbourhood food environment.

Highlights

MethodsStudy setting This is a secondary data analysis that uses baseline data from a pilot intervention/feasibility study (i.e. Henry’s Groceries for Health) conducted through a collaboration between Henry Ford Health System (HFHS) and Gleaners Community Foodbank of Southeastern Michigan (Gleaners)
Using community-level data, persons reporting food insecurity tended to live in areas with: lower median household incomes (P = 0·014), higher percentages of people with income less than the poverty rate (P = 0·019), higher unemployment rates (P = 0·014), higher rates of no vehicle access (P = 0·022) and higher rates of public assistance or food stamps/Supplemental Nutrition Assistance Program (SNAP) (P = 0·014)
After accounting for socio-demographic factors associated with food insecurity, we did not find evidence that food insecure patients lived further away from healthier grocery stores, nor was this modified by ecological measures of vehicle access

Summary

Methods

Study setting This is a secondary data analysis that uses baseline data from a pilot intervention/feasibility study (i.e. Henry’s Groceries for Health) conducted through a collaboration between Henry Ford Health System (HFHS) and Gleaners Community Foodbank of Southeastern Michigan (Gleaners). Additional socio-demographic covariates were retrieved at the census tract level based on geocoded address, for both patients and grocery store locations These data were pulled from the 2014–2018 American Community Survey conducted by the United States Census Bureau and included median household income, income to poverty ratio, unemployment rate, uninsured rate, percentage without a vehicle and percentage with public assistance or food stamps/SNAP. Logistic regression was used to associate presence/absence of any NEMS-S scored grocery stores within a 0·5 and 1 mile radius with food insecurity; 2-mile radius was not examined in logistic regression analyses due to small sample sizes (only seventeen patients did not have a store within 2-miles of their home). In order to identify potential ‘hotspots’ of food insecurity, or sub-regions of Detroit where the risk of food insecurity is significantly elevated, spatial relative risk based on the ratio of two kernel densities were estimated using the R package sparr, using the symmetric case adaptive method[29]

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