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

Mahdi-Salim Saib, Julien Caudeville, Florence Carre, Olivier Ganry, Alain Trugeon and Andre Cicolella
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Mahdi-Salim Saib: French National Institute for Industrial Environment and Risks, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France
Julien Caudeville: French National Institute for Industrial Environment and Risks, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France
Florence Carre: French National Institute for Industrial Environment and Risks, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France
Olivier Ganry: University Hospital of Amiens, Place Victor Pauchet Amiens 80054, France
Alain Trugeon: Regional Observatory of Health and Social Issues in Picardie (OR2S), 3, rue des Louvels, Amiens 80036, France
Andre Cicolella: French National Institute for Industrial Environment and Risks, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France

IJERPH, 2014, vol. 11, issue 4, 1-22

Abstract: 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.

Keywords: health inequalities; socioeconomic status; exposure indicator; geographic level; MAUP; Geographically Weighted Regression (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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