Growth and decline in SNAP generosity: Outcome and equity implications - Project Summary/Abstract This project will examine the effects of added Supplemental Nutrition Assistance Program (SNAP) benefits on healthcare outcomes and disparities by race/ethnicity and disability. SNAP has been found to alleviate food insecurity (albeit not fully), and is disproportionately utilized by racial/ethnic minorities and people with disabilities, who have greater sensitivities to food insecurity (given “thinner margins of health” stemming from exposures to other inequities). SNAP policy changes could therefore affect healthcare outcomes for racial/ethnic minorities and people with disabilities in different ways than for their counterparts. Further, these groups experience healthcare disparities (e.g., in disease prevention and management, unfavorable clinical events, and expenditures) that are likely affected by food insecurity: individuals exposed to food insecurity miss needed care and medications, use emergency and inpatient care, and require expenditures all at higher rates than unexposed individuals, and effects are elevated among people with chronic conditions. We will use both national Medicaid data and a novel set of linked claims, public health, and administrative SNAP data from Massachusetts to exploit a staggered set of natural experiments created by the Families First Coronavirus Response Act: (i) April 2020: ~60% of SNAP households were given variable amounts of added monthly benefits (up to over $1,000/month, with an average of $161/unit/month over the first year), (ii) May 2021: those with no/small boosts were given up to $95/month in added benefits, and (iii) since April 2021: 18 states have ended the new benefits, which will end for remaining states in March 2023. In MA, SNAP-linked data will allow us to use these changes to model dose-responses and treatment removal precisely. T-MSIS Medicaid data and state variability will broaden insights and improve the generalizability of our findings. As these SNAP changes occurred, multiple contemporaneous disruptions to healthcare, public health, social welfare policies, and economic conditions were occurring as well. To address this issue, we will leverage the significant heterogeneity in the geography and timing of the disruptions, in addition to the robust efforts that have taken place during the pandemic to provide detailed, publically available data on these disruptions. We will use confirmatory factor analysis, machine learning techniques, and an expert panel to guide our understanding of and adjustment for these factors (Aim 1). We will then use growth curve models to measure the effects of each SNAP change on healthcare outcomes (i.e., preventive use, unfavorable events, and expenditures) (Aim 2) and on healthcare disparities based on race/ethnicity and disability status (Aim 3) in MA and the US. Changes to SNAP’s funding and administration are routinely debated. This study will inform these debates by offering rigorous, actionable evidence on the potential public health impact of these policy choices.
