TY - JOUR
T1 - Estimating health care delivery system value for each US state and testing key associations
AU - Dieleman, Joseph L.
AU - Kaldjian, Alexander S.
AU - Sahu, Maitreyi
AU - Chen, Carina
AU - Liu, Angela
AU - Chapin, Abby
AU - Scott, Kirstin Woody
AU - Aravkin, Aleksandr
AU - Zheng, Peng
AU - Mokdad, Ali
AU - Murray, Christopher J.L.
AU - Schulman, Kevin
AU - Milstein, Arnold
N1 - Publisher Copyright:
© 2021 The Authors. Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.
PY - 2022/6
Y1 - 2022/6
N2 - 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.
AB - 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.
KW - comparative health systems
KW - health care costs
KW - state health policies
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U2 - 10.1111/1475-6773.13676
DO - 10.1111/1475-6773.13676
M3 - Article
C2 - 34028028
AN - SCOPUS:85106313206
SN - 0017-9124
VL - 57
SP - 557
EP - 567
JO - Health services research
JF - Health services research
IS - 3
ER -