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Estimating health care delivery system value for each US state and testing key associations
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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Blackwell Publishing Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9108083/ https://www.ncbi.nlm.nih.gov/pubmed/34028028 http://dx.doi.org/10.1111/1475-6773.13676 |
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author | 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 |
author_facet | 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 |
author_sort | Dieleman, Joseph L |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9108083 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Blackwell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-91080832022-05-20 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 Health Serv Res Hospitals and Health System Value and Quality 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. Blackwell Publishing Ltd 2021-05-24 2022-06 /pmc/articles/PMC9108083/ /pubmed/34028028 http://dx.doi.org/10.1111/1475-6773.13676 Text en © 2021 The Authors. Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Hospitals and Health System Value and Quality 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 Estimating health care delivery system value for each US state and testing key associations |
title | Estimating health care delivery system value for each US state and testing key associations |
title_full | Estimating health care delivery system value for each US state and testing key associations |
title_fullStr | Estimating health care delivery system value for each US state and testing key associations |
title_full_unstemmed | Estimating health care delivery system value for each US state and testing key associations |
title_short | Estimating health care delivery system value for each US state and testing key associations |
title_sort | estimating health care delivery system value for each us state and testing key associations |
topic | Hospitals and Health System Value and Quality |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9108083/ https://www.ncbi.nlm.nih.gov/pubmed/34028028 http://dx.doi.org/10.1111/1475-6773.13676 |
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