ArticleEnvironmental health : a global access science source2015
Building spatial composite indicators to analyze environmental health inequalities on a regional scale.
Article in Environmental health : a global access science source, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comparing Global and Spatial Composite Measures of Neighborhood Socioeconomic Status Across US Counties.Journal of urban health : bulletin of the New York Academy of Medicine · 2022Article
- Inequalities in Health: Methodological Approaches to Spatial Differentiation.International journal of environmental research and public health · 2021Article
- Development of a spatial web tool to identify hotspots of environmental burdens in Wallonia (Belgium).Environmental science and pollution research international · 2020Article
- SisLeish: A multi-country standardized information system to monitor the status of Leishmaniasis in the Americas.PLoS neglected tropical diseases · 2017Article
- Local Geographic Variation of Public Services Inequality: Does the Neighborhood Scale Matter?International journal of environmental research and public health · 2016Article
- Devising a Composite Index to Analyze and Model Loneliness and Related Health Risks in the United Kingdom.Gerontology & geriatric medicineArticle
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Authors and funding
7 authors.
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Abstract
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.
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