Hospital Variation in 30-Day Mortality for Patients With Stroke; The Impact of Individual and Municipal Socio-Demographic Status

Abstract

Background Thirty-day mortality after hospitalization for stroke is commonly reported as a quality indicator. However, the impact of adjustment for individual and/or neighborhood sociodemographic status ( SDS ) has not been well documented. This study aims to evaluate the role of individual and contextual sociodemographic determinants in explaining the variation across hospitals in Norway and determine the impact when testing for hospitals with low or high mortality. Methods and Results Patient Administrative System data on all 45 448 patients admitted to hospitals in Norway with an incident stroke diagnosis from 2005 to 2009 were included. The data were merged with data from several databases to obtain information on vital status (dead/alive) and individual SDS variables. Logistic regression models were compared to estimate the predictive effect of individual and neighborhood SDS on 30-day mortality and to determine outlier hospitals. All individual SDS factors, except travel time, were statistically significant predictors of 30-day mortality. Of the municipal variables, only the municipal variable proportion of low income was statistically significant as a predictor of 30-day mortality. Including sociodemographic characteristics of the individual and other characteristics of the municipality improved the model fit. However, performance classification was only changed for 1 (out of 56) hospital, from "significantly high mortality" to "nonoutlier." Conclusions Our study showed that those stroke patients with a lower SDS have higher odds of dying after 30 days compared with those with a higher SDS , although this did not have a substantial impact when classifying providers as performing as expected, better than expected, or worse than expected.

Keywords: health disparities; hospital performance; quality indicators; socioeconomic position; statistical model.

Figure 1

Log odds ratio for death within 30 days vs proportion of low income in the municipality, risk‐adjusted, with 95% CI according to model 3. The red line is the log odds ratio of low income according to model 4 (not including individual sociodemographic status variables).

Figure 2

Comparison of the hospital‐level estimates on the linear predictive scale using different models. A, Model 1 without sociodemographic status (SDS) variables and model 2 with individual (ind) SDS variables. B, Model 2 with individual SDS and model 3 with individual and municipal (muni) SDS. C, Model 3 with individual (ind) and municipal SDS and model 4 with only municipal variables (none‐individual SDS variables). D, Model 1 without SDS variables and model 3 with individual and municipal SDS.