The local Socio-Economic Health Deprivation Index: methods and results

Abstract

INTRODUCTION: A socio-economic (SE) deprivation index is a measure that aims to provide an indication of SE hardship and disadvantage in the population. Our aim was constructing 10 Socio-Economonic and Health Deprivation Indexes (SEHDI) by means of the same method. This particular method enables these indexes to be used to investigate the relationships between SE inequalities and aspects of health and prevention in the population. MATERIALS AND METHODS: Data on the demographic and SE situation of the populations were taken from the 2011 Census at the Census Tract (CT) level (2001 for Rome municipality). To construct the SEHDIs, variables displaying a statistically significant correlation with the SMRs of overall mortality were subjected to a tolerance test of linearity, in order to eliminate collinear variables. The variables selected underwent PCA factor analysis, in order to obtain the factors to be linearly combined into the SEHDI. The final values were scaled from minimum to maximum deprivation, and the quantitative scale was converted into five ordinal normalized population groups. The SEHDIs were validated at the SE level by comparing them with the trends of the main SE indexes used in the 2011 Census (2001 for Rome municipality), and at the health level by comparing them with the trends of some causes of death. Both comparisons were made by means of ANOVA. RESULTS: The 10 areas considered were: the municipalities of Cagliari, Ferrara, Florence, Foggia, Genoa, Rome, Palermo, Sassari, Siena, and the ULSS 7 Veneto area. For each one, a specific SEHDI was computed and the different variables comprising each index focused on particular aspects of SE and health deprivation at the area level. The SEHDIs showed good percentages of explained variance (from 72.2% to 49.1%) and a linear distribution of the main statistical SE indices and of overall mortality in each area; these findings were in line with the literature on the relationship between the SE condition and health status of the population. The distribution of cause-specific mortality across the SEHDIs deprivation clusters is analyzed in other articles, which deal with the findings of the study in each area. CONCLUSIONS: The SEHDIs showed good ability to identify the elements of SE inequalities that impact on the health conditions of populations; to depict the distribution of causes of death that are sensitive to SE differences concerning aspects of the social and family support structure. From a public health perspective, these results are relevant because they enable interventions of health promotion and prevention to be implemented on the basis of the characteristics that define deprivation groups.

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