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

International Journal for Equity in Health

Séverine Deguen¹  Denis Zmirou-Navier⁴  Nolwenn Le Meur⁵  Wahida Kihal³  Cindy Padilla¹  Jean-Marie Monnez²  Benoît Lalloué² 

[1] Inserm, UMR IRSET Institut de recherche sur la santé l’environnement et le travail - 1085, Rennes, France;Lorraine University, CNRS, INRIA UMR 7502, Institut Elie Cartan, Lorraine, France;EHESP Rennes, Sorbonne Paris Cité, Rennes, France;Lorraine University, Medical School, Lorraine, France;UMR936 INSERM, Université de Rennes 1, Rennes, France

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

Others  :  825004
DOI  :  10.1186/1475-9276-12-21

 received in 2012-12-20, accepted in 2013-03-17,  发布年份 2013

【 摘 要 】

Introduction

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.

Methods

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.

Results

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.

Conclusions

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.

【 授权许可】

   
2013 Lalloué et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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