Building spatial composite indicators to analyze environmental health inequalities on a regional scale

Environmental Health

Research

Olivier Ganry¹  Julien Caudeville²  Maxime Beauchamp²  Florence Carré²  Mahdi-Salim Saib²  Andre Cicolella²  Alain Trugeon³ 

[1] Epidemiology and public health department, University hospital of Amiens, Amiens, France;French National Institute for Industrial Environment and Risks, Parc Technologique Alata,BP 2, 60550, Verneuil-en-Halatte, France;Regional Observatory of Health and Social Issues in Picardie, Amiens, France;

关键词: Principal Component Analysis;    Spatial;    Heterogeneity;    Autocorrelation;    Deprivation;    Exposure;    Health;    Composite indicators;   

DOI  :  10.1186/s12940-015-0054-3

 received in 2014-12-03, accepted in 2015-08-10,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundReducing health inequalities involves the identification and characterization of social and exposure factors and the way they accumulate in a given area. The areas of accumulation then allow for prioritization of interventions. The present study aims to build spatial composite indicators based on the aggregation of environmental, social and health indicators and their inter-relationships.MethodPreliminary work was carried out firstly to homogenize spatial coverage, and secondly to study spatial variation of environmental (EI), socioeconomic (SI) and health (HI) indicators. The aggregation of the different indicators was performed using several methodologies for which results and decision-makers’ usability were compared.ResultsFour methodologies were tested: 1) A simple summation of normalized HI, EI and SI indicators (IC), 2) the sum of the normalized HI, EI and SI indicators weighted by the first principal component of a Principal Component Analysis (IC PCA), 3) the sum of normalized and weighted indicators of the first principal component of Local Principal Component Analysis (IC LPCA), and 4) the sum of normalized and weighted indicators of the first principal component of a Geographically Weighted Principal Component Analysis (IC GWPCA).ConclusionThe GWPCA is particularly adapted to taking into account the spatial heterogeneity and the spatial autocorrelation between SI, EI and HI. This approach invalidates the basic assumptions of many standard statistical analyses. Where socioeconomic indicators present high deprivation and where they are associated with potential modifiable health determinants, decision-makers can prioritize these areas for reducing inequalities by controlling the socioeconomic and health determinants.

【 授权许可】

CC BY   
© Saib et al. 2015

【 图 表 】

Fig. 6