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Estimating health care delivery system value for each US state and testing key associations.
Dieleman, Joseph L; Kaldjian, Alexander S; Sahu, Maitreyi; Chen, Carina; Liu, Angela; Chapin, Abby; Scott, Kirstin Woody; Aravkin, Aleksandr; Zheng, Peng; Mokdad, Ali; Murray, Christopher Jl; Schulman, Kevin; Milstein, Arnold.
Afiliação
  • Dieleman JL; Institute for Health Metrics and Evaluation, Hans Rosling Center, University of Washington, Seattle, Washington, USA.
  • Kaldjian AS; Bluesquare SA, Brussels, Belgium.
  • Sahu M; Institute for Health Metrics and Evaluation, Hans Rosling Center, University of Washington, Seattle, Washington, USA.
  • Chen C; Institute for Health Metrics and Evaluation, Hans Rosling Center, University of Washington, Seattle, Washington, USA.
  • Liu A; Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
  • Chapin A; Institute for Health Metrics and Evaluation, Hans Rosling Center, University of Washington, Seattle, Washington, USA.
  • Scott KW; Department of Emergency Medicine, University of Michigan, Ann Arbor, Michigan, USA.
  • Aravkin A; Institute for Health Metrics and Evaluation and Department of Applied Mathematics, University of Washington, Seattle, Washington, USA.
  • Zheng P; Institute for Health Metrics and Evaluation, Hans Rosling Center, University of Washington, Seattle, Washington, USA.
  • Mokdad A; Institute for Health Metrics and Evaluation, Hans Rosling Center, University of Washington, Seattle, Washington, USA.
  • Murray CJ; Institute for Health Metrics and Evaluation, Hans Rosling Center, University of Washington, Seattle, Washington, USA.
  • Schulman K; Clinical Excellence Research Center, Stanford University, Stanford, California, USA.
  • Milstein A; Clinical Excellence Research Center, Stanford University, Stanford, California, USA.
Health Serv Res ; 57(3): 557-567, 2022 06.
Article em En | MEDLINE | ID: mdl-34028028
ABSTRACT

OBJECTIVE:

To estimate health care systems' value in treating major illnesses for each US state and identify system characteristics associated with value. DATA SOURCES Annual condition-specific death and incidence estimates for each US state from the Global Burden Disease 2019 Study and annual health care spending per person for each state from the National Health Expenditure Accounts. STUDY

DESIGN:

Using non-linear meta-stochastic frontier analysis, mortality incidence ratios for 136 major treatable illnesses were regressed separately on per capita health care spending and key covariates such as age, obesity, smoking, and educational attainment. State- and year-specific inefficiency estimates were extracted for each health condition and combined to create a single estimate of health care delivery system value for each US state for each year, 1991-2014. The association between changes in health care value and changes in 23 key health care system characteristics and state policies was measured. DATA COLLECTION/EXTRACTION

METHODS:

Not applicable. PRINCIPAL

FINDINGS:

US state with relatively high spending per person or relatively poor health-outcomes were shown to have low health care delivery system value. New Jersey, Maryland, Florida, Arizona, and New York attained the highest value scores in 2014 (81 [95% uncertainty interval 72-88], 80 [72-87], 80 [71-86], 77 [69-84], and 77 [66-85], respectively), after controlling for health care spending, age, obesity, smoking, physical activity, race, and educational attainment. Greater market concentration of hospitals and of insurers were associated with worse health care value (p-value ranging from <0.01 to 0.02). Higher hospital geographic density and use were also associated with worse health care value (p-value ranging from 0.03 to 0.05). Enrollment in Medicare Advantage HMOs was associated with better value, as was more generous Medicaid income eligibility (p-value 0.04 and 0.01).

CONCLUSIONS:

Substantial variation in the value of health care exists across states. Key health system characteristics such as market concentration and provider density were associated with value.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Medicare / Gastos em Saúde Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Aged / Humans País como assunto: America do norte Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Medicare / Gastos em Saúde Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Aged / Humans País como assunto: America do norte Idioma: En Ano de publicação: 2022 Tipo de documento: Article