Community Factors and Hospital Readmission Rates
Principal Investigator(s): View help for Principal Investigator(s) Erica Spatz, Yale University
Version: View help for Version V1
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Project Citation:
. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-09-28. https://doi.org/10.3886/E122901V1
Project Description
Summary:
View help for Summary
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).
Research
Design We assessed 71 community
factors 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 factors using their 5-digit zip code of residence.
Methods We used a random forest algorithm to rank
factors for their importance in predicting hospital HWR scores. Factors were
entered into 6 domain-specific multivariable regression models in order of
decreasing importance. Factors with with P-values <0.10 were retained for a
final model, after eliminating any that were collinear.
Results Among 71 community factors, 19 were retained in the 6 domain models
and the final model. Domains which explained the most to least variance in HWR
were: physical environment (R2=15%); clinical care (12%); demographics
(11%); social and economic environment (7%); health behaviors (9%); and social
capital (8%). In the final model, the 19 factors explained more than a quarter
of the variance in readmission rate (R2=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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