Conditional cross-design synthesis estimators for generalizability in Medicaid.

While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population that is not well-represented by a randomized study but is reflected when additionally incorporating observational data. To generalize to a target population represented by a union of these data, we propose a novel class of conditional cross-design synthesis estimators that combine randomized and observational data, while addressing their estimates' respective biases-lack of overlap and unmeasured confounding. These methods enable estimating the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City, which requires obtaining estimates for the 7% of beneficiaries randomized to a plan and 93% who choose a plan, who do not resemble randomized beneficiaries. Our new estimators include outcome regression, propensity weighting, and double robust approaches. All use the covariate overlap between the randomized and observational data to remove potential unmeasured confounding bias. Applying these methods, we find substantial heterogeneity in spending effects across managed care plans. This has major implications for our understanding of Medicaid, where this heterogeneity has previously been hidden. Additionally, we demonstrate that unmeasured confounding rather than lack of overlap poses a larger concern in this setting.

Nudging Leads Consumers In Colorado To Shop But Not Switch ACA Marketplace Plans.

The Affordable Care Act (ACA) dramatically expanded the use of regulated marketplaces in health insurance, but consumers often fail to shop for plans during open enrollment periods. Typically these consumers are automatically reenrolled in their old plans, which potentially exposes them to unexpected increases in their insurance premiums and cost sharing. We conducted a randomized intervention to encourage enrollees in an ACA Marketplace to shop for plans. We tested the effect of letters and e-mails with personalized information about the savings on insurance premiums that they could realize from switching plans and the effect of generic communications that simply emphasized the possibility of saving. The personalized and generic messages both increased shopping on the Marketplace's website by 23 percent, but neither type of message had a significant effect on plan switching. These findings show that simple "nudges" with even generic information can promote shopping in health insurance marketplaces, but whether they can lead to switching remains an open question.

Risk-Adjustment Simulation: Plans May Have Incentives To Distort Mental Health And Substance Use Coverage.

Under the Affordable Care Act, the risk-adjustment program is designed to compensate health plans for enrolling people with poorer health status so that plans compete on cost and quality rather than the avoidance of high-cost individuals. This study examined health plan incentives to limit covered services for mental health and substance use disorders under the risk-adjustment system used in the health insurance Marketplaces. Through a simulation of the program on a population constructed to reflect Marketplace enrollees, we analyzed the cost consequences for plans enrolling people with mental health and substance use disorders. Our assessment points to systematic underpayment to plans for people with these diagnoses. We document how Marketplace risk adjustment does not remove incentives for plans to limit coverage for services associated with mental health and substance use disorders. Adding mental health and substance use diagnoses used in Medicare Part D risk adjustment is one potential policy step toward addressing this problem in the Marketplaces.