The use of national administrative data to describe the spatial distribution of in-hospital mortality following stroke in France, 2008–2011

Abstract

Background: In the context of implementing the National Stroke Plan in France, a spatial approach was used to measure inequalities in this disease. Using the national PMSI-MCO databases, we analyzed the in-hospital prevalence of stroke and established a map of in-hospital mortality rates with regard to the socio-demographic structure of the country. Methods:The principal characteristics of patients identified according to ICD10 codes relative to stroke (in accordance with earlier validation work) were studied. A map of standardized mortality rates at the level of PMSI geographic codes was established. An exploratory analysis (principal component analysis followed by ascending hierarchical classification) using INSEE socio-economic data and mortality rates was also carried out to identify different area profiles.Results: Between 2008 and 2011, the number of stroke patients increased by 3.85 %, notably for ischemic stroke in the 36-55 years age group (60 % of men). Over the same period, in-hospital mortality fell, and the map of standardized rates illustrated the diagonal of high mortality extending from the north-east to the south-west of the country. The most severely affected areas were also those with the least favorable socio-professional indicators. Conclusions:The PMSI-MCO database is a major source of data on the health status of the population. It can be used for the area-by-area observation of the performance of certain healthcare indicators, such as in-hospital mortality, or to follow the implementation of the National Stroke Plan. Our study showed the interplay between social and demographic factors and stroke-related in-hospital mortality. The map derived from the results of the exploratory analysis illustrated a variety of areas where social difficulties, aging and high mortality seemed to meet. The study raises questions about access to neuro-vascular care in isolated areas and in those in demographic decline. Telemedicine appears to be the solution favored by decision makers. The aging of the population managed for stroke must not mask the growing incidence in younger people, which raises questions about the development of classical (smoking, hypertension) or new (drug abuse) risk factors.

A novel approach for measuring residential socioeconomic factors associated with cardiovascular and metabolic health

“…Our cluster analysis identified six unique residential clusters for this study as shown in the dendrogram (Figure 1); the number of clusters formed is consistent with other studies using similar methodology to examine neighborhood-associated SES. 38,39 …”

“…25,38,39 Using PCA, researchers mathematically determine the most important factors representing the greatest variability in the geographic area. However, the cut-off for determining the importance of the factor components can be arbitrary.…”

Differentiating Incident from Recurrent Stroke Using Administrative Data: The Impact of Varying Lengths of Look-Back Periods on the Risk of Misclassification

“…Data linkage provides the opportunity to identify prior stroke, which is not possible with unlinked administrative data [37]. We recommend at least a 10-year look-back period to maximise the accuracy of estimates for incident and recurrent stroke.…”

A novel approach for measuring residential socioeconomic factors associated with cardiovascular and metabolic health

“…Our cluster analysis identified six unique residential clusters for this study as shown in the dendrogram ( Figure 1); the number of clusters formed is consistent with other studies using similar methodology to examine neighborhood-associated SES. 38,39 In the dendrogram, the outer ring is made up of 444 individual leaves, with each leaf representing a Census block group. Each leaf was also given a color, and the colors correspond to the cluster designation.…”

“…A handful of studies have also used combinations of PCA and hierarchical clustering to help define neighborhood SES. 25,38,39 Using PCA, researchers mathematically determine the most important factors representing the greatest variability in the geographic area. However, the cut-off for determining the importance of the factor components can be arbitrary.…”