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Community factors and hospital wide readmission rates: Does context matter?
BACKGROUND: The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain. OBJECTIVE: To estimate the influence of community factors on the Centers for Medicare & Medicaid Services risk-stan...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7584172/ https://www.ncbi.nlm.nih.gov/pubmed/33095775 http://dx.doi.org/10.1371/journal.pone.0240222 |
Sumario: | BACKGROUND: The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain. OBJECTIVE: To estimate the influence of community factors on the Centers for Medicare & Medicaid Services risk-standardized hospital-wide readmission measure (HWR)–a quality performance measure in the U.S. RESEARCH DESIGN: We assessed 71 community variables in 6 domains related to health outcomes: clinical care; health behaviors; social and economic factors; the physical environment; demographics; and social capital. SUBJECTS: Medicare fee-for-service patients eligible for the HWR measure between July 2014-June 2015 (n = 6,790,723). Patients were linked to community variables using their 5-digit zip code of residence. METHODS: We used a random forest algorithm to rank variables for their importance in predicting HWR scores. Variables were entered into 6 domain-specific multivariable regression models in order of decreasing importance. Variables with P-values <0.10 were retained for a final model, after eliminating any that were collinear. RESULTS: Among 71 community variables, 19 were retained in the 6 domain models and in the final model. Domains which explained the most to least variance in HWR were: physical environment (R(2) = 15%); clinical care (R(2) = 12%); demographics (R(2) = 11%); social and economic environment (R(2) = 7%); health behaviors (R(2) = 9%); and social capital (R(2) = 8%). In the final model, the 19 variables explained more than a quarter of the variance in readmission rates (R(2) = 27%). CONCLUSIONS: Readmissions for a wide range of clinical conditions are influenced by factors relating to the communities in which patients reside. These findings can be used to target efforts to keep patients out of the hospital. |
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