Growth and decline in SNAP generosity: Outcome and equity implications

SNAP 慷慨程度的增长和下降：结果和公平影响

• 批准号: 10975443
• 负责人: Rajan Anthony Sonik
• 金额: $ 69.19万
• 依托单位: BRANDEIS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10975443
• 关键词: Growth; decline; SNAP; generosity; Outcome

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.

项目总结/摘要 该项目将研究增加补充营养援助计划（SNAP）福利的影响 按种族/民族和残疾划分的医疗保健结果和差异。SNAP被发现可以减轻食物 不安全（尽管不是完全），少数种族/族裔和有 残疾人对粮食不安全更敏感（由于“健康边缘更薄”， 其他不公平现象）。因此，SNAP政策的变化可能会影响医疗保健结果， 少数种族/族裔和残疾人的情况与他们的同行不同。此外，这些 群体经历健康护理差异（例如，在疾病预防和管理方面， 活动和支出）：面临粮食不安全的个人 需要的护理和药物，使用急诊和住院护理，并要求以更高的速度支出 比未接触的人，和影响与慢性疾病的人之间升高。 我们将使用国家医疗补助数据和一套新的关联索赔，公共卫生和行政 来自马萨诸塞州的SNAP数据，利用家庭第一组织创建的一系列交错的自然实验 冠状病毒应对法案：（i）2020年4月：约60%的SNAP家庭获得了可变数量的补充 每月津贴（最高超过1 000美元/月，第一年平均为161美元/单位/月），㈡ 5月 2021年：那些没有/少量增加的人每月获得高达95美元的额外福利，以及（iii）自2021年4月以来：18 各州已经结束了新的福利，其余各州将于2023年3月结束。在MA中，SNAP相关 数据将使我们能够利用这些变化来精确地建模剂量反应和治疗去除。T-MSIS 医疗补助数据和州的可变性将拓宽我们的见解，提高我们的研究结果的普遍性。 随着这些SNAP变化的发生，医疗保健、公共卫生、 社会福利政策和经济条件也在发生变化。为了解决这个问题，我们将利用 在地理和中断的时间的显着异质性，除了强有力的努力， 在大流行期间发生的事件，以提供有关这些中断的详细的、可通过实验获得的数据。我们 将使用验证性因素分析，机器学习技术和专家小组来指导我们的 了解并调整这些因素（目标1）。然后，我们将使用增长曲线模型来衡量 每个SNAP变化对健康护理结果的影响（即，预防性使用、不良事件，以及 医疗保健支出（目标2）和基于种族/民族和残疾状况的医疗保健差异（目标3） 和美国。SNAP的资金和管理的变化经常被讨论。这项研究将告知这些 通过提供关于这些政策选择的潜在公共卫生影响的严格的、可操作的证据来进行辩论。