Ontology highlight
ABSTRACT: Objective
To examine the role of patient, hospital, and community characteristics on racial and ethnic disparities in in-hospital postsurgical complications.Data sources
Healthcare Cost and Utilization Project, 2011 State Inpatient Databases; American Hospital Association Annual Survey of Hospitals; Area Health Resources Files; Centers for Medicare & Medicaid Services Hospital Compare database.Methods
Nonlinear hierarchical modeling was conducted to examine the odds of patients experiencing any in-hospital postsurgical complication, as defined by Agency for Healthcare Research and Quality Patient Safety Indicators.Principal findings
A total of 5,474,067 inpatient surgical discharges were assessed using multivariable logistic regression. Clinical risk, payer coverage, and community-level characteristics (especially income) completely attenuated the effect of race on the odds of postsurgical complications. Patients without private insurance were 30 to 50 percent more likely to have a complication; patients from low-income communities were nearly 12 percent more likely to experience a complication. Private, not-for-profit hospitals in small metropolitan or micropolitan areas and higher nurse-to-patient ratios led to fewer postsurgical complications.Conclusions
Race does not appear to be an important determinant of in-hospital postsurgical complications, but insurance and community characteristics have an effect. A population-based approach that includes improving the socioeconomic context may help reduce disparities in these outcomes.
SUBMITTER: Witt WP
PROVIDER: S-EPMC5264108 | biostudies-literature | 2017 Feb
REPOSITORIES: biostudies-literature
Witt Whitney P WP Coffey Rosanna M RM Lopez-Gonzalez Lorena L Barrett Marguerite L ML Moore Brian J BJ Andrews Roxanne M RM Washington Raynard E RE
Health services research 20160309 1
<h4>Objective</h4>To examine the role of patient, hospital, and community characteristics on racial and ethnic disparities in in-hospital postsurgical complications.<h4>Data sources</h4>Healthcare Cost and Utilization Project, 2011 State Inpatient Databases; American Hospital Association Annual Survey of Hospitals; Area Health Resources Files; Centers for Medicare & Medicaid Services Hospital Compare database.<h4>Methods</h4>Nonlinear hierarchical modeling was conducted to examine the odds of pa ...[more]