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Regional indices of socio-economic and health inequalities: a tool for public health programming.


ABSTRACT: Objectives:The aim was to provide an affordable method of computing socio-economic (SE) deprivation indices at the regional level, in order to reveal the specific aspects of the relationship between SE inequalities and health outcomes. The Umbria Region Socio-Health Index (USHI) was computed and compared with the Italian National Deprivation Index at the Umbria regional level (NDI-U). Methods:The USHI was computed by applying factor analysis to census tract SE variables correlated with general mortality and validated through comparison with the NDI-U. Results:Overall mortality presented linear positive trends in USHI, while trends in NDI-U proved non-linear or non-significant. Similar results were obtained with regard to specific causes of death according to deprivation groups, gender and age. Conclusions:The USHI better describes a local population in terms of health-related SE status. Policy-makers could therefore adopt this method in order to obtain a better picture of SE-associated health conditions in regional populations and to target strategies for reducing health inequalities.

SUBMITTER: Lillini R 

PROVIDER: S-EPMC6953449 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Regional indices of socio-economic and health inequalities: a tool for public health programming.

Lillini R R   Masanotti G G   Bianconi F F   Gili A A   Stracci F F   LA Rosa F F   Vercelli M M  

Journal of preventive medicine and hygiene 20191220 4


<h4>Objectives</h4>The aim was to provide an affordable method of computing socio-economic (SE) deprivation indices at the regional level, in order to reveal the specific aspects of the relationship between SE inequalities and health outcomes. The Umbria Region Socio-Health Index (USHI) was computed and compared with the Italian National Deprivation Index at the Umbria regional level (NDI-U).<h4>Methods</h4>The USHI was computed by applying factor analysis to census tract SE variables correlated  ...[more]

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