Developing a data-driven spatial approach to assessment of neighbourhood influences on the spatial distribution of myocardial infarction

Wahida Kihal-Talantikite,Claire Segala,Christiane Weber,Dominique Arveiler,Clive E Sabel,Denis Bard,Séverine Deguen,Gaelle Pedrono

doi:10.1186/s12942-017-0094-8

Abstract

BackgroundThere is a growing understanding of the role played by ‘neighbourhood’ in influencing health status. Various neighbourhood characteristics—such as socioeconomic environment, availability of amenities, and social cohesion, may be combined—and this could contribute to rising health inequalities. This study aims to combine a data-driven approach with clustering analysis techniques, to investigate neighbourhood characteristics that may explain the geographical distribution of the onset of myocardial infarction (MI) risk.MethodsAll MI events in patients aged 35–74 years occurring in the Strasbourg metropolitan area (SMA), from January 1, 2000 to December 31, 2007 were obtained from the Bas-Rhin coronary heart disease register. All cases were geocoded to the census block for the residential address. Each areal unit, characterized by contextual neighbourhood profile, included socioeconomic environment, availability of amenities (including leisure centres, libraries and parks, and transport) and psychosocial environment as well as specific annual rates standardized (per 100,000 inhabitants). A spatial scan statistic implemented in SaTScan was then used to identify statistically significant spatial clusters of high and low risk of MI.ResultMI incidence was non-randomly spatially distributed, with a cluster of high risk of MI in the northern part of the SMA [relative risk (RR) = 1.70, p = 0.001] and a cluster of low risk of MI located in the first and second periphery of SMA (RR 0.04, p value = 0.001). Our findings suggest that the location of low MI risk is characterized by a high socioeconomic level and a low level of access to various amenities; conversely, the location of high MI risk is characterized by a high level of socioeconomic deprivation—despite the fact that inhabitants have good access to the local recreational and leisure infrastructure.ConclusionOur data-driven approach highlights how the different contextual dimensions were inter-combined in the SMA. Our spatial approach allowed us to identify the neighbourhood characteristics of inhabitants living within a cluster of high versus low MI risk. Therefore, spatial data-driven analyses of routinely-collected data georeferenced by various sources may serve to guide policymakers in defining and promoting targeted actions at fine spatial level.

Highlights

There is a growing understanding of the role played by ‘neighbourhood’ in influencing health status
Our spatial approach allowed us to identify the neighbourhood characteristics of inhabitants living within a cluster of high versus low myocardial infarction (MI) risk
We proposed a data-driven approach developed at fine spatial scale level, aimed at the investigation of neighbourhood characteristics capable of explaining geographical distribution of the onset of MI risk

Summary

Introduction

There is a growing understanding of the role played by ‘neighbourhood’ in influencing health status. Since the early 2000s, there has been a growing number of studies demonstrating the role played by ‘place’ where people live ( referred to as ‘context’) in influencing health status [4,5,6,7]. The causal framework proposed by Pearce et al [16] uses three distinct domains to describe the various components of neighbourhood: physical characteristics (quality of outdoor environment and housing, traffic and physical disorder, etc.), (2) social characteristics (social network, social cohesion, etc.), and (3) community resources access (leisure facilities, healthcare, etc.). Characterization of neighbourhood in the domain of community resources access, food store accessibility [22], primary healthcare services, recreational facilities, and public open [23, 24] and green spaces [25, 26] has been investigated in the literature. The role of the social domain has so far been explored mainly through data on local violence [27, 28] and social cohesion (or social capital) [29]

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