Outcomes for Children with Asthma on Medicaid: Elucidating Key Determinants at the Policy, Plan, Neighborhood, and Person Levels to Address Disparities.

项目摘要/摘要 背景：哮喘是一种影响500多万美国儿童的慢性病，在 少数族裔儿童和来自低收入家庭的儿童。尽管哮喘治疗取得了进展， 儿童哮喘的临床护理和死亡率停滞不前，不同种族/民族之间的差异依然存在。 和社会经济团体。我提出了一个培训和研究计划，这将加深我对 循证临床哮喘护理与差异背后多因素的差异影响 在儿童哮喘方面，同时推出一个创新的、与政策相关的研究组合，该组合结合了多个 来源，关联数据，以进行关于医疗补助管理保健(MMC)计划的“自然政策实验”。 目的：提供证据，说明MMC计划的“附加”好处及其对 哮喘儿童的结局，考虑了个人、家庭和邻里层面的因素。这 证据将被用来模拟将患者分配到最符合他们需求的MMC计划的不同方式 并最终缩小健康差距。目的1.检验儿童哮喘的循证指标 不同MMC计划的护理质量和结果。目的2.评估个人、家庭和 社区因素--包括社会人口、经济和生物(共病)风险因素-- 与哮喘的结局有关。目的3.开发一种算法，将每个患者与MMC进行匹配 帮助他们获得尽可能好的哮喘结果的计划。研究设计：自然实验 使用2000-2021年行政纵向链接数据集的分析和模拟方法。方法：I 将收集有关MMC计划福利的详细数据，并依赖于既定的医疗补助准随机分配 MMC计划的受益人，指定一组旨在估计计划因果影响的回归模型 对哮喘相关结果的益处，无论是单独的，还是相对于健康的社会决定因素。这些 分析将使用南卡罗来纳州个人和地理层面的链接数据集，这些数据集包含健康、经济、 社会人口统计结果和合并症：医疗补助；生命统计；教育部和 少年司法部记录；美国社区调查数据。我将使用模拟方法来 评估不同医疗补助政策情景下的儿童健康结果，将每个儿童与最佳 MMC计划。培训计划：补充我在经济学和数据分析方面的现有技能，并支持我的 在走向独立的道路上，我将在以下方面获得必要的培训：1.临床哮喘护理的证据和情况 这将有助于构建精确的计划质量衡量标准；2.利益相关者的参与是(A) 与MMC计划和领导层确认计划覆盖的细节，(B)通报和传播研究 结果，以及(C)未来参与其他州的医疗补助计划；3.高级方法技能 策略模拟。影响：该项目将解决NHLBI的研究优先事项，以调查以下因素 通过改进评估影响暴露的方法来考虑人群之间的健康差异。

PROJECT SUMMARY/ABSTRACT Background: Asthma, a chronic condition that affects over five million US children, is more prevalent among racial/ethnic minority children and those from low-income families. Despite advances in asthma treatment, asthma clinical care and mortality rates in children have plateaued, and disparities persist across racial/ethnic and socioeconomic groups. I propose a training and research plan that will deepen my understanding of evidence-based clinical asthma care and the differential impacts of multifactorial causes underlying disparities in childhood asthma, while launching an innovative, policy-relevant research portfolio that combines multi- source, linked data to conduct “natural policy experiments” regarding Medicaid managed care (MMC) plans. Objective: To produce evidence regarding the “add-on” benefits of MMC plans and their relative effects on outcomes of children with asthma, accounting for factors at individual, family, and neighborhood levels. This evidence will be used to simulate different ways of assigning patients to MMC plans that best serve their needs and, ultimately, reduce health disparities. Aim 1. To examine evidence-based indicators of pediatric asthma care quality and outcomes across different MMC plans. Aim 2. To evaluate the role of individual, family, and neighborhood contributors—including sociodemographic, economic, and biological (comorbidity) risk factors— associated with asthma outcomes. Aim 3. To develop an algorithm that matches each patient with an MMC plan that helps them achieve the best possible asthma outcomes. Research Design: Natural experiment analyses and simulation methods using administrative longitudinal linked datasets from 2000-2021. Methods: I will collect detailed data on MMC plan benefits and rely on established quasi-random assignment of Medicaid beneficiaries to MMC plans, to specify a set of regression models aimed at estimating causal effects of plan benefits on asthma-related outcomes, individually and relative to the social determinants of health. These analyses will use individual and geographic-level linked South Carolina datasets that contain health, economic, sociodemographic outcomes, and comorbidities: Medicaid; Vital Statistics; Department of Education and Department of Juvenile Justice records; American Community Survey data. I will use simulation methods to evaluate child health outcomes under differing Medicaid policy scenarios, to match each child to an optimal MMC plan. Training Plan: To complement my existing skills in economics and data analysis and support my path to independence, I will gain essential training in: 1. Evidence and circumstances of clinical asthma care that will aid in constructing precise plan quality measures; 2. Stakeholder engagement that is key to (a) confirming details of plan coverage with MMC plans and leadership, (b) informing and disseminating research results, and (c) engaging with other states’ Medicaid programs in the future; 3. Advanced methodologic skills in policy simulations. Implications: This project will address NHLBI’s research priority to investigate factors that account for differences in health among populations via advancing methods for assessing impactful exposures.