BackgroundThe ability to accurately detect differential resource use between persons of different socioeconomic status relies on the accuracy of health-needs adjustment measures. This study tests different approaches to morbidity adjustment in explanation of health care utilization inequity.MethodsA representative sample was selected of 10 percent (~270,000) adult enrolees of Clalit Health Services, Israel's largest health care organization. The Johns-Hopkins University Adjusted Clinical Groups® were used to assess each person's overall morbidity burden based on one year's (2009) diagnostic information. The odds of above average health care resource use (primary care visits, specialty visits, diagnostic tests, or hospitalizations) were tested using multivariate logistic regression models, separately adjusting for levels of health-need using data on age and gender, comorbidity (using the Charlson Comorbidity Index), or morbidity burden (using the Adjusted Clinical Groups). Model fit was assessed using tests of the Area Under the Receiver Operating Characteristics Curve and the Akaike Information Criteria.ResultsLow socioeconomic status was associated with higher morbidity burden (1.5-fold difference). Adjusting for health needs using age and gender or the Charlson index, persons of low socioeconomic status had greater odds of above average resource use for all types of services examined (primary care and specialist visits, diagnostic tests, or hospitalizations). In contrast, after adjustment for overall morbidity burden (using Adjusted Clinical Groups), low socioeconomic status was no longer associated with greater odds of specialty care or diagnostic tests (OR: 0.95, CI: 0.94-0.99; and OR: 0.91, CI: 0.86-0.96, for specialty visits and diagnostic respectively). Tests of model fit showed that adjustment using the comprehensive morbidity burden measure provided a better fit than age and gender or the Charlson Index.ConclusionsIdentification of socioeconomic differences in health care utilization is an important step in disparity reduction efforts. Adjustment for health-needs using a comprehensive morbidity burden diagnoses-based measure, this study showed relative underutilization in use of specialist and diagnostic services, and thus allowed for identification of inequity in health resources use, which could not be detected with less comprehensive forms of health-needs adjustments.
The Affordable Care Act calls for the establishment of state-level health insurance exchanges. The viability and success of these exchanges will require effective risk-adjustment strategies to compensate for differences in enrollees' health status across health plans. This article describes why the Affordable Care Act could lead to favorable or adverse risk selection across plans. It reviews provisions in the act and recent proposed regulations intended to mitigate the problem of risk selection. We performed a simulation that showed that under the premium rating restrictions in the law, large incentives for insurers to attract healthier enrollees will be likely to persist-resulting in substantial overpayment to plans with very healthy enrollees and underpayment to plans with very sick members. We conclude that risk adjustment based on patients' diagnoses, such as will be in place from 2014 on, will yield payments to insurers that will be more accurate than what will come solely from the age-adjusted and other rating allowed by the act. We also describe additional challenges of implementing risk adjustment.
This article describes the risk-adjusted payment methodology employed by the Maryland Medicaid program to pay managed care organizations. It also presents an empirical simulation analysis using claims data from 230,000 Maryland Medicaid recipients. This simulation suggests that the new payment model will help adjust for adverse or favorable selection. The article is intended for a wide audience, including state and national policy makers concerned with the design of managed care Medicaid programs and actuaries, analysts, and researchers involved in the design and implementation of risk-adjusted capitation payment systems.
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