Dissertations & Theses (Open Access)

Date of Award

7-2020

Degree Name

Doctor of Philosophy (PhD)

Advisor(s)

Linda Highfield

Second Advisor

Frances Lee Revere

Third Advisor

Shreela Sharma

Abstract

The overall objective of this research is to use spatial methods to better understand food insecurity and SNAP under-participation in Texas. Paper 1 assesses whether a sample of community dwelling Medicare and Medicaid beneficiaries, who screen positive for food insecurity at healthcare locations in Harris County, exhibit a spatial pattern in terms of where they live. In other words, it tests whether or not there are statistically significant neighborhood hot spots or cold spots of food insecurity against a null hypothesis of complete spatial randomness. This approach is novel because it uses address-level data on patients who report being food insecure to test for statistically significant neighborhood hot spots or cold spots, instead of relying on extant factors like neighborhood poverty rates, or the presence of grocery stores. Using address-level food insecurity screening data is often difficult because few organizations screen for food insecurity, and even fewer are willing to share their data due to privacy concerns. Paper 2 utilizes geographical information systems (GIS) to map census tract-level clusters and outliers of households that are eligible but not enrolled (EBNE) in the SNAP program. The implications of this analysis are vast. Knowing the locations of neighborhood-level clusters and outliers of SNAP EBNE households can inform interventions to address the “SNAP GAP” more effectively. Additionally, this method of identifying neighborhood-level clusters and outliers of SNAP EBNE households can be applied to other safety net programs including Medicaid, the Children’s Health Insurance Program (CHIP), Healthy Texas Women, and the Women, Infant, and Children (WIC) Program.

Download

Share

COinS