Community factors and hospital wide readmission rates: Does context matter?

The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain. To estimate the influence of community factors on the Centers for Medicare & Medicaid Services risk-standardized hospital-wide...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2020-10, Vol.15 (10), p.e0240222-e0240222
Main Authors: Spatz, Erica S, Bernheim, Susannah M, Horwitz, Leora I, Herrin, Jeph
Format: Article
Language:English
Subjects:
Aged
Algorithms
Biology and Life Sciences
Community medicine
Demography
Earth Sciences
Economic factors
Employment
Environment
Environments
Ethnicity
Government programs
Health care access
Health risks
Hospital admission and discharge
Hospitalization
Humans
Management
Medicaid
Medicare
Medicine
Medicine and Health Sciences
Minority & ethnic groups
Patient outcomes
Patient Readmission
Patients
Public Health
Regression analysis
Regression models
Research design
Social behavior
Social capital
Social Environment
Social Sciences
Socioeconomic factors
Statistics
Variables
Variance
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The environment in which a patient lives influences their health outcomes. However, the degree to which community factors are associated with readmissions is uncertain. 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. 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. 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. 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
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0240222