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A statistical procedure to create a neighborhood socioeconomic index for health inequalities analysis

Benoît Lalloué123*, Jean-Marie Monnez3, Cindy Padilla12, Wahida Kihal1, Nolwenn Le Meur14, Denis Zmirou-Navier125 and Séverine Deguen12

Author Affiliations

1 EHESP Rennes, Sorbonne Paris Cité, Rennes, France

2 Inserm, UMR IRSET Institut de recherche sur la santé l’environnement et le travail - 1085, Rennes, France

3 Lorraine University, CNRS, INRIA UMR 7502, Institut Elie Cartan, Lorraine, France

4 UMR936 INSERM, Université de Rennes 1, Rennes, France

5 Lorraine University, Medical School, Lorraine, France

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International Journal for Equity in Health 2013, 12:21  doi:10.1186/1475-9276-12-21

Published: 28 March 2013



In order to study social health inequalities, contextual (or ecologic) data may constitute an appropriate alternative to individual socioeconomic characteristics. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. This work proposes a statistical procedure to create a neighborhood socioeconomic index.


The study setting is composed of three French urban areas. Socioeconomic data at the census block scale come from the 1999 census. Successive principal components analyses are used to select variables and create the index. Both metropolitan area-specific and global indices are tested and compared. Socioeconomic categories are drawn with hierarchical clustering as a reference to determine “optimal” thresholds able to create categories along a one-dimensional index.


Among the twenty variables finally selected in the index, 15 are common to the three metropolitan areas. The index explains at least 57% of the variance of these variables in each metropolitan area, with a contribution of more than 80% of the 15 common variables.


The proposed procedure is statistically justified and robust. It can be applied to multiple geographical areas or socioeconomic variables and provides meaningful information to public health bodies. We highlight the importance of the classification method. We propose an R package in order to use this procedure.

Socioeconomic status; Multidimensional index; Principal component analysis; Hierarchical classification; Small-area analysis