Growth and decline in SNAP generosity: Outcome and equity implications

项目摘要/摘要 该项目将审查增加补充营养援助计划(SNAP)福利的影响 关于医疗保健结果和按种族/族裔和残疾划分的差异。Snap被发现可以减轻食物的负担 不安全(尽管不是完全的)，并且被种族/族裔少数群体和有 残疾人，他们对粮食不安全有更大的敏感性(考虑到因以下原因而产生的“较薄的健康边缘” 暴露于其他不平等)。因此，Snap政策的变化可能会影响以下方面的医疗结果 种族/族裔少数群体和残疾人以不同于他们的同行的方式。此外，这些 群体面临医疗保健差异(例如，在疾病预防和管理、不利的临床 事件和支出)可能受到粮食不安全的影响：暴露在粮食不安全中的个人错过了 需要护理和药物，使用急诊和住院护理，并要求以更高的费率支出 与未接触病毒的人相比，这种影响在患有慢性病的人中更高。 我们将使用国家医疗补助数据和一组新的相互关联的索赔、公共卫生和行政 从马萨诸塞州抓拍数据，以利用这些家庭首先创造的一组交错的自然实验 冠状病毒应对法案：(I)2020年4月：约60%的SNAP家庭获得了不同数量的添加 每月福利(最高达1,000美元/月，第一年平均为161美元/单位/月)，(2)5月 2021年：那些没有/小幅增加福利的人每月最高可获得95美元的额外福利，以及(Iii)自2021年4月起：18 各州已经结束了新的福利，其余州的福利将于2023年3月结束。在MA中，SNAP链接 数据将使我们能够使用这些变化来精确地模拟剂量反应和治疗移除。T-MSIS 医疗补助数据和州的可变性将拓宽我们的洞察力，并提高我们研究结果的概括性。 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状况也在发生变化。为了解决这个问题，我们将利用 中断的地理和时间上的显著异质性，除了 在大流行期间进行了监测，以提供关于这些干扰的详细的、公开的数据。我们 将使用验证性因子分析、机器学习技术和专家小组来指导我们的 对这些因素的理解和调整(目标1)。然后我们将使用增长曲线模型来衡量 每次SNAP变化对医疗保健结果的影响(即预防性使用、不利事件和 支出)(目标2)和基于种族/族裔和残疾状况的医疗保健差距(目标3) 还有美国。SNAP的资金和管理方面的变化经常被辩论。这项研究将告知这些人 通过就这些政策选择对公共健康的潜在影响提供严格、可操作的证据，进行辩论。

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.