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.

项目摘要/摘要 该项目将研究增加补充营养援助计划（SNAP）福利的影响 关于种族/种族和残疾的医疗保健结果和差异。已经发现快照可以减轻食物 不安全感（尽管没有完全），并且被种族/族裔少数群体和有人数不成比例 残疾人对粮食不安全的敏感性更高（鉴于“健康的较薄”，源于 暴露于其他不平等）。因此，改变政策的变化可能会影响医疗保健结果 种族/族裔少数民族和残疾人的方式与同行不同。此外，这些 小组会体验医疗保健分布（例如，在疾病预防和管理方面，不利的临床 可能受粮食不安全影响的事件和支出）：暴露于粮食不安全的人 需要的护理和药物，使用紧急和住院护理，并需要以更高的价格支出 比出乎意料的个体和慢性病患者的影响升高。 我们将同时使用国家医疗补助数据和一套新颖的链接索赔，公共卫生和行政 从马萨诸塞州拍摄数据，以利用家庭首先创建的一组交错的自然实验 冠状病毒反应法案：（i）2020年4月：〜60％的snap家庭被添加了可变数量 每月福利（每月高达1,000美元以上，平均每月161美元/月），（ii）5月（ii） 2021年：没有/小靴子的人的额外收益最高为$ 95，（iii）自2021年4月：18 各州已经结束了新的福利，该福利将于2023年3月结束。 数据将使我们能够使用这些更改来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扩大见解并提高我们发现的普遍性。 随着这些快速变化的发生，对医疗保健，公共卫生的多次破坏， 社会福利政策和经济状况也发生了。为了解决这个问题，我们将利用 除了强大的努力之外，干扰地理和时机的重要异质性 大流行期间发生了有关这些中断的详细公开数据。我们 将使用确认因素分析，机器学习技术和专家小组来指导我们 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测量 每个快照变化对医疗保健结果的影响（即预防性，不利事件和 支出）（目标2）和基于种族/种族和残疾状况的医疗保健差异（目标3） 和美国。常规辩论SNAP的资金和管理的变化。这项研究将告知这些 通过提供有关这些政策选择潜在的公共卫生影响的严格，可行的证据来辩论。