Structural and Environmental Precarity: Tipping Points between Food Accessibility and Community Health Outcomes

Background:

Precarity concerns the insecurity or vulnerability of individuals, families, and communities that removes one’s agency, decreases certainty and autonomy, and questions the adequacy of meeting one’s financial needs. Food insecurity expounds the uncertainty of precarity in one’s environment and the vulnerability of community level resources and structural inequities that can tip the scales of population-level health concerns.

The study examines whether structural precarity at the macro-level (such as child poverty rates, Supplemental Nutrition Assistance Program (SNAP) benefits, and history of geographical deep and persistent poverty) and environmental precarity at the micro- and meso-levels (such as median family income and household food insecurity rate) can explain the relationship between food accessibility and community health outcomes. This study also tests paraliminality theory that posits the volatility of food insecurity due to structural and environmental economic factors.

Methods:

This analysis uses data from the United States Department of Agriculture 2020 Food Access Research Atlas, that uses 2010-2015 state, county, and census tract data from the U.S. Census Bureau, and store data from the Trade Dimensions' TDLinx directory and the U.S. Department of Commerce.

The analysis will assess four potential latent variables:

Environmental precarity is operationalized as factors at the micro- (family system) or meso- (direct community) level that are precarious at the county/state level: family median income, household food insecurity rate, change in low access to store.

Structural precarity is operationalized as factors at the macro-level that are precarious at the county level: child poverty rate, persistent poverty rate, family poverty rate, SNAP benefits per capita, and the general food tax rate.

Food accessibility is operationalized as factors that contribute to the availability of food resources at the county or state level: distance to grocers, SNAP authorized stores, school breakfast/lunch/summer program rate.

Health outcomes are examined via the adult diabetes rate for 2008 and 2013 and adult obesity rate for 2012 and 2017.

Structural equation modeling (SEM) is used to measure latent concepts and estimate the magnitude and directions of latent variable effects. SEM requires theoretical reasoning to comprehend the results; therefore, this analyses will provide quantitative evidence to test an application of paraliminality theory. Stata 18 will be used to clean the raw data and run descriptive analyses. MPlus 8 will be used to run the SEM model. Currently, the raw data is being cleaned and prepared to run the SEM model. Preliminary results will be available in time to present at the conference.

Implications:

The use of census-level data brings attention to the complexities of food accessibility that considers micro- and meso-level structural and environmental factors. This study uses theoretical framing that positions the role of structural and environmental economic factors as joint considerations of food accessibility equity. This study can lead to more concrete usage of data that is concentrated at the census-tract level to reveal inequities that can be measured and targeted for family policy responses that either extend the reach of SNAP programs or how supplemental food programs are created/enacted in local municipalities.