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Measuring Emergency Care Survival: The Implications of Risk Adjusting for Race and Poverty
Author(s) -
Ioannides Kimon L. H.,
Baehr Avi,
Karp David N.,
Wiebe Douglas J.,
Carr Brendan G.,
Holena Daniel N.,
Delgado M. Kit
Publication year - 2018
Publication title -
academic emergency medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.221
H-Index - 124
eISSN - 1553-2712
pISSN - 1069-6563
DOI - 10.1111/acem.13485
Subject(s) - medicine , confidence interval , medicaid , demography , ethnic group , odds ratio , quartile , logistic regression , poverty , decile , emergency medicine , mortality rate , race (biology) , myocardial infarction , emergency department , gerontology , health care , statistics , botany , mathematics , psychiatry , sociology , anthropology , economics , biology , economic growth
Objectives We determined the impact of including race, ethnicity, and poverty in risk adjustment models for emergency care–sensitive conditions mortality that could be used for hospital pay‐for‐performance initiatives. We hypothesized that adjusting for race, ethnicity, and poverty would bolster rankings for hospitals that cared for a disproportionate share of nonwhite, Hispanic, or poor patients. Methods We performed a cross‐sectional analysis of patients admitted from the emergency department to 157 hospitals in Pennsylvania with trauma, sepsis, stroke, cardiac arrest, and ST ‐elevation myocardial infarction. We used multivariable logistic regression models to predict in‐hospital mortality. We determined the predictive accuracy of adding patient race and ethnicity (dichotomized as non‐Hispanic white vs. all other Hispanic or nonwhite patients) and poverty (uninsured, on Medicaid, or lowest income quartile zip code vs. all others) to other patient‐level covariates. We then ranked each hospital on observed‐to‐expected mortality, with and without race, ethnicity, and poverty in the model, and examined characteristics of hospitals with large changes between models. Results The overall mortality rate among 170,750 inpatients was 6.9%. Mortality was significantly higher for nonwhite and Hispanic patients (adjusted odds ratio [ aOR ] = 1.27, 95% confidence interval [ CI ] = 1.19–1.36) and poor patients ( aOR = 1.21, 95% CI = 1.12–1.31). Adding race, ethnicity, and poverty to the risk adjustment model resulted in a small increase in C‐statistic (0.8260 to 0.8265, p = 0.002). No hospitals moved into or out of the highest‐performing decile when adjustment for race, ethnicity, and poverty was added, but the three hospitals that moved out of the lowest‐performing decile, relative to other hospitals, had significantly more nonwhite and Hispanic patients (68% vs. 11%, p < 0.001) and poor patients (56% vs. 10%, p < 0.001). Conclusions Sociodemographic risk adjustment of emergency care–sensitive mortality improves apparent performance of some hospitals treating a large number of nonwhite, Hispanic, or poor patients. This may help these hospitals avoid financial penalties in pay‐for‐performance programs.