 
[MLM15] Relative index of inequality and slope index of inequality: a structured regression framework for estimation.Revue Internationale avec comité de lecture : Journal Epidemiology, vol. 26(4), pp. 51827, 2015Mots clés: Relative index of inequality, slope index of inequality, inequalities in health, socioeconomic status, comparative research, least false parameter, Cox model, additive hazards model, Poisson regression
Résumé:
Background: The relative index of inequality and the slope index of inequality are the two major indices used in epidemiologic studies for the measurement of socioeconomic inequalities in health. Yet the current definitions of these indices are not adapted to their main purpose, which is to provide summary measures of the linear association between socioeconomic status and health in a way that enables valid betweenpopulation comparisons. The lack of appropriate definitions has dissuaded the application of suitable regression methods for estimating the slope index of inequality.
Methods: We suggest formally defining the relative and slope indices of inequality as socalled least false parameters, or more precisely, as the parameters that provide the best approximation of the relation between socioeconomic status and the health outcome by loglinear and linear models, respectively. From this standpoint, we establish a structured regression framework for inference on these indices. Guidelines for implementation of the methods, including R and SAS codes, are provided.
Results: The new definitions yield appropriate summary measures of the linear association across the entire socioeconomic scale, suitable for comparative studies in epidemiology. Our regressionbased approach for estimation of the slope index of inequality contributes to an advancement of the current methodology, which mainly consists of a heuristic formula relying on restrictive assumptions. A study of the educational inequalities in allcause and causespecific mortality in France is used for illustration.
Conclusion: The proposed definitions and methods should guide the use and estimation of these indices in future studies.
Commentaires:
http://www.ncbi.nlm.nih.gov/pubmed/26000548
Equipe:
msdma
Collaboration:
CESP
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UMR S 1136
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UvA
,
CepiDc
BibTeX

