A Shared-Frailty Spatial Scan Statistic Model for Time-to-Event Data.

Spatial scan statistics are well-known methods widely used to detect spatial clusters of events. Furthermore, several spatial scan statistics models have been applied to the spatial analysis of time-to-event data. However, these models do not take account of potential correlations between the observations of individuals within the same spatial unit or potential spatial dependence between spatial units. To overcome this problem, we have developed a scan statistic based on a Cox model with shared frailty and that takes account of the spatial dependence between spatial units. In simulation studies, we found that (i) conventional models of spatial scan statistics for time-to-event data fail to maintain the type I error in the presence of a correlation between the observations of individuals within the same spatial unit and (ii) our model performed well in the presence of such correlation and spatial dependence. We have applied our method to epidemiological data and the detection of spatial clusters of mortality in patients with end-stage renal disease in northern France.

How do territorial characteristics affect spatial inequalities in the risk of coronary heart disease?

BACKGROUND: Cardiovascular diseases remain the leading cause of death and disabilities worldwide, with coronary heart diseases being the most frequently diagnosed. Their multifactorial etiology involves individual, behavioral and territorial determinants, and thus requires the implementation of multidimensional approaches to assess links between territorial characteristics and the incidence of coronary heart diseases. CONTEXT AND OBJECTIVES: This study was carried out in a densely populated area located in the north of France with multiple sources of pollutants. The aim of this research was therefore to establish complex territorial profiles that have been characterized by the standardized incidence, thereby identifying the influences of determinants that can be related to a beneficial or a deleterious effect on cardiovascular health. METHODS: Forty-four variables related to economic, social, health, environment and services dimensions with an established or suspected impact on cardiovascular health were used to describe the multidimensional characteristics involved in cardiovascular health. RESULTS: Three complex territorial profiles have been highlighted and characterized by the standardized incidence rate (SIR) of coronary heart diseases after adjustment for age and gender. Profile 1 was characterized by an SIR of 0.895 (sd: 0.143) and a higher number of determinants that revealed favorable territorial conditions. Profiles 2 and 3 were characterized by SIRs of respectively 1.225 (sd: 0.242) and 1.119 (sd: 0.273). Territorial characteristics among these profiles of over-incidence were nevertheless dissimilar. Profile 2 revealed higher deprivation, lower vegetation and lower atmospheric pollution, while profile 3 displayed a rather privileged population with contrasted territorial conditions. CONCLUSION: This methodology permitted the characterization of the multidimensional determinants involved in cardiovascular health, whether they have a negative or a positive impact, and could provide stakeholders with a diagnostic tool to implement contextualized public health policies to prevent coronary heart diseases.