[co_inspiracion] Fwd: [EQ] A statistical procedure to create a neighborhood socioeconomic index for health inequalities analysis

From: Mario Parada <mario.parada@xxxxx>
To: co_inspiracion@xxxxxxxxxxxxx
Date: Wed, 15 May 2013 13:41:50 -0400
---------- Forwarded message ----------
From: Ruggiero, Mrs. Ana Lucia (WDC) <ruglucia@xxxxxxxx>
Date: 2013/5/15
Subject: [EQ] A statistical procedure to create a neighborhood
socioeconomic index for health inequalities analysis
To: EQUIDAD@xxxxxxxxxxxxxxxxx


** ** ** **

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


Benoît Lalloué1,2,3*, Jean-Marie Monnez3, Cindy Padilla1,2, Wahida Kihal1,
Nolwenn Le Meur1,4, Denis Zmirou-Navier1,2,5 and Séverine Deguen1,2****

** **

1 EHESP Rennes, Sorbonne Paris Cité, Rennes, France****

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

3 Lorraine University, Institut Elie Cartan, Lorraine, France****

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

5 **Lorraine** University, ****Medical School**, **Lorraine**, **France*****
***

** **

*International Journal for Equity in Health – March  2013

*

Available online at: http://bit.ly/10pDRfs

****

“…..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….”


****

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[co_inspiracion] Fwd: [EQ] A statistical procedure to create a neighborhood socioeconomic index for health inequalities analysis

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