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Open AccessArticle

Spatial Relationship Quantification between Environmental, Socioeconomic and Health Data at Different Geographic Levels

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French National Institute for Industrial Environment and Risks, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France
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University of Picardie Jules Verne, 33 rue St Leu, Amiens 80039, France
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University Hospital of Amiens, Place Victor Pauchet Amiens 80054, France
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Regional Observatory of Health and Social Issues in Picardie (OR2S), 3, rue des Louvels, Amiens 80036, France
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Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2014, 11(4), 3765-3786; https://doi.org/10.3390/ijerph110403765
Received: 31 October 2013 / Revised: 18 March 2014 / Accepted: 19 March 2014 / Published: 3 April 2014
(This article belongs to the Special Issue Inequalities in Health)
Spatial health inequalities have often been analyzed in terms of socioeconomic and environmental factors. The present study aimed to evaluate spatial relationships between spatial data collected at different spatial scales. The approach was illustrated using health outcomes (mortality attributable to cancer) initially aggregated to the county level, district socioeconomic covariates, and exposure data modeled on a regular grid. Geographically weighted regression (GWR) was used to quantify spatial relationships. The strongest associations were found when low deprivation was associated with lower lip, oral cavity and pharynx cancer mortality and when low environmental pollution was associated with low pleural cancer mortality. However, applying this approach to other areas or to other causes of death or with other indicators requires continuous exploratory analysis to assess the role of the modifiable areal unit problem (MAUP) and downscaling the health data on the study of the relationship, which will allow decision-makers to develop interventions where they are most needed. View Full-Text
Keywords: health inequalities; socioeconomic status; exposure indicator; geographic level; MAUP; Geographically Weighted Regression health inequalities; socioeconomic status; exposure indicator; geographic level; MAUP; Geographically Weighted Regression
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Saib, M.-S.; Caudeville, J.; Carre, F.; Ganry, O.; Trugeon, A.; Cicolella, A. Spatial Relationship Quantification between Environmental, Socioeconomic and Health Data at Different Geographic Levels. Int. J. Environ. Res. Public Health 2014, 11, 3765-3786.

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