Dataset Information

Risk adjustment in health insurance exchanges for individuals with mental illness.

ABSTRACT: In 2014, an estimated 15 million individuals who currently do not have health insurance, including many with chronic mental illness, are expected to obtain coverage through state insurance exchanges. The authors examined how two mechanisms in the Affordable Care Act (ACA), namely, risk adjustment and reinsurance, might perform to ensure the financial solvency of health plans that have a disproportionate share of enrollees with mental health conditions. Risk adjustment is an ACA provision requiring that a federal or state exchange move funds from insurance plans with healthier enrollees to plans with sicker enrollees. Reinsurance is a provision in which all plans in the state contribute to an overall pool of money that is used to reimburse costs to individual market plans for expenditures of any individual enrollee that exceed a high predetermined level.Using 2006--2007 claims data from a sample of private and public health plans, the authors compared expected health plan compensation under diagnosis-based risk adjustment with actual health care expenditures, under different assumptions for chronic mental health and medical conditions. Analyses were conducted with and without the addition of $100,000 reinsurance.Risk adjustment performed well for most plans. For some plans with a high share of enrollees with mental health conditions, underpayment was substantial enough to raise concern. Reinsurance appeared to be helpful in addressing the most serious underpayment problems remaining after risk adjustment. Risk adjustment performed similarly for health plan cohorts that had a disproportionate share of enrollees with chronic mental health and medical conditions.Cost models indicate that the regulatory provisions in the ACA requiring risk adjustment and reinsurance can help protect health plans covering treatment for mentally ill individuals against risk selection. This model analysis may be useful for advocates for individuals with mental illness in considering their own state's insurance exchange.

SUBMITTER: Barry CL

PROVIDER: S-EPMC4436690 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Risk adjustment in health insurance exchanges for individuals with mental illness.

Barry Colleen L CL Weiner Jonathan P JP Lemke Klaus K Busch Susan H SH

The American journal of psychiatry 20120701 7

<h4>Objective</h4>In 2014, an estimated 15 million individuals who currently do not have health insurance, including many with chronic mental illness, are expected to obtain coverage through state insurance exchanges. The authors examined how two mechanisms in the Affordable Care Act (ACA), namely, risk adjustment and reinsurance, might perform to ensure the financial solvency of health plans that have a disproportionate share of enrollees with mental health conditions. Risk adjustment is an ACA ...[more]

PMID: 22581274

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Project description:BackgroundAlthough national policies in China are comprehensive and instructive, a wide disparity exists between different cities. The current status of public mental health services by region in China remains unclear. This study aimed to investigate policies related to public mental health services, the contact coverage of public mental health services and outcomes of service users.MethodsA cross-sectional study was carried out in Southern China, between April 2021 and March 2022. Considering the geographical location, socioeconomic development levels, and prevalence of severe mental illness, four cities including Wuhan, Changsha, Guangzhou, and Shenzhen were selected. Relevant service providers were asked to report data on mental health policies and facility-related information, including mental health resources, registration rates of patients, management rates of patients, and medication rates of patients. Eligible patients were invited to report service user-related data, including contact coverage of public mental health services and their outcomes. SPSS 26.0 was used for data analysis.ResultsThe four cities in Southern China have made different efforts to develop public mental health services, primarily focusing on socio-economically disadvantaged individuals. Community health centers in Guangzhou and Shenzhen reported having more professional human resources on mental health and higher mental health budgets for patients. The contact coverage rates of most public mental services were higher than 80%. Patients in Changsha (B = 0.3; 95%CI: 0.1-0.5), Guangzhou (B = 0.2; 95%CI: 0.1-0.3), and Shenzhen (B = 0.3; 95%CI: 0.1-0.4) who received social medical assistance services reported higher levels of medication adherence. Patients in Wuhan (B = -6.5; 95%CI: -12.9--0.1), Guangzhou (B = -2.8; 95%CI: -5.5--0.1), and Shenzhen who received community-based rehabilitation services reported lower levels of disability (B = -2.6; 95%CI: -4.6--0.5).ConclusionsThere have been advances in public mental health services in the four Southern cities. The contact coverage rates of most public mental health services were higher than 80%. Patients' utilization of public mental services was associated with better health outcomes. To improve the quality of public mental health services, the government should try to engage service users, their family members, and supporters in the design, delivery, operationalization, and evaluation of these public mental health services in the future.

Project description:BackgroundPredictive modeling presents an opportunity to contain the expansion of medical expenditures by focusing on very few people. Evaluation of how risk adjustment models perform in predictive modeling in Taiwan or Asia has been rare. The aims of this study were to evaluate the performance of different risk adjustment models (the ACG risk adjustment system and prior expenditures) in predictive modeling, using Taiwan's National Health Insurance (NHI) claims data, and to compare characteristics of potentially high-expenditure subjects identified through different models.MethodsA random sample of NHI enrollees continuously enrolled in 2002 and 2003 (n = 164,562) was selected. Health status measures and total expenditures derived from 2002 NHI claims data were used to predict the possibility of becoming 2003 top users. Statistics-based indicators (C-statistics, sensitivity, & Predictive Positive Value) and characteristics of identified top groups by different models (expenditures and prevalence of manageable diseases) were presented.ResultsBoth diagnosis-based and prior expenditures models performed much better than the demographic model. Diagnosis-based models were better in identifying top users with manageable diseases; prior expenditures models were better in statistics-based indicators and identifying people with higher average expenditures. Prior expenditures status could correctly identify more actual top users than diagnosis-based or demographic models. The proportions of actual top users that could be identified by diagnosis-based models alone were much lower than that identified by prior expenditures status.ConclusionsPredicted top users identified by different models have different characteristics and there is little agreement between modes regarding which groups would be potentially top users; therefore, which model to use should depend on the purpose of predictive modeling. Prior expenditures are a more powerful tool than diagnosis-based risk adjusters in terms of correctly identifying more actual high expenditures users. There is still much room left for improvement of diagnosis-based models in predictive modeling.

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Risk adjustment in health insurance exchanges for individuals with mental illness.

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Risk adjustment in health insurance exchanges for individuals with mental illness.

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