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.

Socioeconomic status and survival after stroke – using mediation and sensitivity analyses to assess the effect of stroke severity and unmeasured confounding

“…In a study on a similar cohort we found income to be more closely associated with post-stroke survival than education [5]. This has also been found in register based studies in Denmark and Norway [6,9]. Based on this we chose to use low income as a proxy for low SES.…”

“…SES is a multidimensional concept that can be measured in different ways. Several previous studies, including one on a similar cohort, found income to be more strongly associated with post-stroke survival than education [5,6,9]. As we were interested in a general effect of SES, not specifically a pure effect of low income we opted not to adjust for other SES variables, such as education level, in our analyses.…”

“…Patients with low socioeconomic status (SES) are at an increased risk of death after stroke, even in countries with universal access to healthcare [5][6][7][8][9][10]. The causes behind these differences have not been established but are likely multifaceted, with aspects related both to patient characteristics (risk factors, comorbidities, etc.)…”

Hospital physicians’ views on discharge and readmission processes: a qualitative study from Norway

“…In 2018, readmission rates varied from 11.8% to 18% in Norwegian hospitals 4. A reduction in readmission rates and the implementation of well-coordinated transitions between healthcare services have been common goals of Norwegian healthcare services.…”

“…Hospital readmissions are a well-known problem in healthcare services worldwide1–5 with the hospital discharge process as an influential factor 6–8. Hospital discharge procedures play a critical role in the quality of transitional care, specifically on the continuity of information about patients’ treatments, or recommendations for further care 6.…”