Background: The 2010 Patient Protection and Affordable Care Act reformed the individual and small group health insurance markets and established a risk adjustment program to create a level playing field for competition. A new set of predictive models for measuring enrollee risk across plans was developed for the Patient Protection and Affordable Care Act-reformed markets, referred to as the Department of Health and Human Services Hierarchical Condition Category (HHS-HCC) models. Beginning in 2018, selected prescription drug classes were added to the models as risk markers. Objective: We describe the motivations, concerns, methodology, and results of adding prescription drug utilization to the HHS-HCC models. Methods: Separate HHS-HCC models are estimated by enrollee age and plan actuarial value. We defined and added 10 prescription drug classes, called RXCs, to the HHS-HCC adult models. Results: Using selected RXCs alongside demographic and diagnostic indicators yielded modest overall improvement in HHS-HCC models’ predictive power. Also, adding RXCs captures the higher costs of enrollees taking certain expensive pharmaceuticals and allows imputation of diagnoses for enrollees utilizing a drug but lacking the associated diagnosis. Conclusions: Including selected drugs in risk adjustment improved the models’ predictive power. In addition, inclusion of selected drugs may discourage insurers from using formulary and drug benefit design to avoid enrollment of patients taking high-cost drugs, such as for HIV, multiple sclerosis, and rheumatoid arthritis, and improve access for enrollees taking these drugs. Adding RXCs also may improve plan risk measurement for plans with less complete diagnosis reporting.
Abstract:The Affordable Care Act provides for a program of risk adjustment in the individual and small group health insurance markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula.This article is the third of three in this issue of the Medicare & Medicaid Research Review that describe the ACA risk adjustment methodology and focuses on the risk transfer formula. In our first companion article, we discussed the key issues and choices in developing the methodology. In our second companion paper, we described the risk adjustment model that is used to calculate risk scores. In this article we present the risk transfer formula. We first describe how the plan risk score is combined with factors for the plan allowable premium rating, actuarial value, induced demand, geographic cost, and the statewide average premium in a formula that calculates transfers among plans. We then show how each plan factor is determined, as well as how the factors relate to each other in the risk transfer formula. The goal of risk transfers is to offset the effects of risk selection on plan costs while preserving premium differences due to factors such as actuarial value differences. Illustrative numerical simulations show the risk transfer formula operating as anticipated in hypothetical scenarios.
Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in 1 ABSTRACTThis paper proposes a difference-in-differences strategy to decompose the contributions of various types of discrimination to the black-white wage differential. The proposed estimation strategy is implemented using data from the Young Physicians Survey. The results suggest that potential discrimination plays a small role in the racial wage gap among physicians. At most, discrimination lowers the hourly wages of black physicians by 3.3 percent. Decomposition shows that consumer discrimination accounts for all of the potential discrimination in the physician market, and that the effect of firm discrimination may actually favor black physicians. Interpretations of the estimates, however, are complicated by the possibility that, relative to white physicians, black physicians negatively self-select into self-employment.
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