Mental Health Coverage and Payment in Private Health Plans

Harvard Medical School, Boston MA

Investigators

Linked publications & trials

Abstract

DESCRIPTION (provided by applicant): The Department of Health and Human Services (HHS) has released rules for risk adjustment in Exchanges created by the Affordable Care Act (ACA) and a recommended premium age structure, but is delaying the implementation of some other Exchange provisions. States have decided whether to run Exchanges autonomously or in partnership with the Federal government. States are also taking a wide range of policy postures on whether to expand Medicaid under the ACA, with 20 states currently declaring opposition to Medicaid expansion. State-driven policies will set a fluid ACA policy environment for years to come. In RO1-MH094290, Mental Health Coverage in Health Care Reform, our team assessed incentives for Exchange plans to supply care for mental health and other chronic conditions, finding that incentives for underservice were strongest for cancer and mental health care. In a major methodological development, MH094290 identified a simple modification of conventional risk adjustment methods, namely, imposing constraints on least-squares regressions, that can address efficiency problems associated with risk adjustment. Our work so far has used the Medical Expenditure Panel Survey (MEPS). In this competing renewal we propose to refine and extend our methods and to apply them to the very large data sets used for development of actual payment models. We plan to accomplish two aims: First, construct a simulation model of a Health Insurance Exchange using MEPS data to: a) confirm the presence of adverse incentives for mental health (and other chronic illnesses) in Exchanges based on current federal policy; and b) apply constrained regression techniques to address selection incentives within the framework of proposed risk adjustment policy. Second, improve on existing risk- adjustment methodology by: first, using a large MarketScan Exchange eligible adult population that is selected and weighted using the MEPS Exchange population so as to be representative of the spending and illness distribution of likely Exchange participants; second, accounting for payment system features (plan-set premiums and reinsurance) affecting plan risk and incentives; and third, applying the constrained-regression fix for incentives related to selection against those with mental and other chronic illnesses. In addition to academic papers, Aim 2 will produce a potentially implementable optimal risk adjustment model for adults that corrects selection-related incentives.