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Income-related disparities in Medicare advantage behavioral health care quality.
Breslau, Joshua; Haviland, Amelia M; Klein, David J; Martino, Steven; Adams, John; Dembosky, Jacob W; Tamayo, Loida; Gaillot, Sarah; Overton, Yvette; Elliott, Marc N.
Afiliação
  • Breslau J; RAND Corporation, Pittsburgh, Pennsylvania, USA.
  • Haviland AM; RAND Corporation, Pittsburgh, Pennsylvania, USA.
  • Klein DJ; Public Policy & Management, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
  • Martino S; RAND Corporation, Pittsburgh, Pennsylvania, USA.
  • Adams J; RAND Corporation, Pittsburgh, Pennsylvania, USA.
  • Dembosky JW; Kaiser Permanente Center for Effectiveness & Safety Research and Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California, USA.
  • Tamayo L; RAND Corporation, Pittsburgh, Pennsylvania, USA.
  • Gaillot S; Centers for Medicare & Medicaid Services, Baltimore, Maryland, USA.
  • Overton Y; Centers for Medicare & Medicaid Services, Baltimore, Maryland, USA.
  • Elliott MN; Centers for Medicare & Medicaid Services, Baltimore, Maryland, USA.
Health Serv Res ; 58(3): 579-588, 2023 06.
Article em En | MEDLINE | ID: mdl-36579742
ABSTRACT

OBJECTIVE:

To inform efforts to improve equity in the quality of behavioral health care by examining income-related differences in performance on HEDIS behavioral health measures in Medicare Advantage (MA) plans. DATA SOURCES AND STUDY

SETTING:

Reporting Year 2019 MA HEDIS data were obtained and analyzed. STUDY

DESIGN:

Logistic regression models were used to estimate differences in performance related to enrollee income, adjusting for sex, age, and race-and-ethnicity. Low-income enrollees were identified by Dual Eligibility for Medicare and Medicaid or receipt of the Low-Income Subsidy (DE/LIS). Models without and with random effects for plans were used to estimate overall and within-plan differences in measure performance. Heterogeneity by race-and-ethnicity in the associations of low-income with behavioral health quality were examined using models with interaction terms. DATA COLLECTION/EXTRACTION

METHODS:

Data were included for all MA contracts in the 50 states and the District of Columbia that collect HEDIS data. PRINCIPAL

FINDINGS:

For six of the eight measures, enrollees with DE/LIS coverage were more likely to have behavioral health conditions that qualify for HEDIS measures than higher income enrollees. In mixed-effects logistic regression models, DE/LIS coverage was associated with statistically significantly worse overall performance on five measures, with four large (>5 percentage point) differences (-7.5 to -11.1 percentage points) related to follow-up after hospitalization and avoidance of drug-disease interactions. Where the differences were large, they were primarily within-plan rather than between-plan. Interactions between DE/LIS and race-and-ethnicity were statistically significant (p < 0.05) for all measures; income-based quality gaps were larger for White enrollees than for Black or Hispanic enrollees.

CONCLUSIONS:

Low income is associated with lower performance on behavioral health HEDIS measures in MA, but these associations differ across racial-and-ethnic groups. Improving care integration and addressing barriers to care for low-income enrollees may improve equity across income levels in behavioral health care.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Qualidade da Assistência à Saúde / Medicare Part C Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Qualidade da Assistência à Saúde / Medicare Part C Idioma: En Ano de publicação: 2023 Tipo de documento: Article