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.
