Beyond the map: evidencing the spatial dimension of health inequalities.
- Deep Digital Phenotyping Research Unit
BACKGROUND: Spatial inequalities in health result from different exposures to health risk factors according to the features of geographical contexts, in terms of physical environment, social deprivation, and health care accessibility. Using a common geographical referential, which combines indices measuring these contextual features, could improve the comparability of studies and the understanding of the spatial dimension of health inequalities. METHODS: We developed the Geographical Classification for Health studies (GeoClasH) to distinguish French municipalities according to their ability to influence health outcomes. Ten contextual scores measuring physical and social environment as well as spatial accessibility of health care have been computed and combined to classify French municipalities through a K-means clustering. Age-standardized mortality rates according to the clusters of this classification have been calculated to assess its effectiveness. RESULTS: Significant lower mortality rates compared to the mainland France population were found in the Wealthy Metropolitan Areas (SMR = 0.868, 95% CI 0.863-0.873) and in the Residential Outskirts (SMR = 0.971, 95% CI 0.964-0.978), while significant excess mortality were found for Precarious Population Districts (SMR = 1.037, 95% CI 1.035-1.039), Agricultural and Industrial Plains (SMR = 1.066, 95% CI 1.063-1.070) and Rural Margins (SMR = 1.042, 95% CI 1.037-1.047). CONCLUSIONS: Our results evidence the comprehensive contribution of the geographical context in the constitution of health inequalities. To our knowledge, GeoClasH is the first nationwide classification that combines social, environmental and health care access scores at the municipality scale. It can therefore be used as a proxy to assess the geographical context of the individuals in public health studies.