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      Does the effect of gender modify the relationship between deprivation and mortality?

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          Abstract

          Background

          In this study we propose improvements to the method of elaborating deprivation indexes. First, in the selection of the variables, we incorporated a wider range of both objective and subjective measures. Second, in the statistical methodology, we used a distance indicator instead of the standard aggregating method principal component analysis. Third, we propose another methodological improvement, which consists in the use of a more robust statistical method to assess the relationship between deprivation and health responses in ecological regressions.

          Methods

          We conducted an ecological small-area analysis based on the residents of the Metropolitan region of Barcelona in the period 1994–2007. Standardized mortality rates, stratified by sex, were studied for four mortality causes: tumor of the bronquial, lung and trachea, diabetes mellitus type II, breast cancer, and prostate cancer. Socioeconomic conditions were summarized using a deprivation index. Sixteen socio-demographic variables available in the Spanish Census of Population and Housing were included. The deprivation index was constructed by aggregating the above-mentioned variables using the distance indicator, DP 2. For the estimation of the ecological regression we used hierarchical Bayesian models with some improvements.

          Results

          At greater deprivation, there is an increased risk of dying from diabetes for both sexes and of dying from lung cancer for men. On the other hand, at greater deprivation, there is a decreased risk of dying from breast cancer and lung cancer for women. We did not find a clear relationship in the case of prostate cancer (presenting an increased risk but only in the second quintile of deprivation).

          Conclusions

          We believe our results were obtained using a more robust methodology. First off, we have built a better index that allows us to directly collect the variability of contextual variables without having to use arbitrary weights. Secondly, we have solved two major problems that are present in spatial ecological regressions, i.e. those that use spatial data and, consequently, perform a spatial adjustment in order to obtain consistent estimators.

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          Most cited references 45

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          Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

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            Bayesian image restoration, with two applications in spatial statistics

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              Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review.

              Interest in the effects of neighbourhood or local area social characteristics on health has increased in recent years, but to date the existing evidence has not been systematically reviewed. Multilevel or contextual analyses of social factors and health represent a possible reconciliation between two divergent epidemiological paradigms-individual risk factor epidemiology and an ecological approach. Keyword searching of Index Medicus (Medline) and additional references from retrieved articles. All original studies of the effect of local area social characteristics on individual health outcomes, adjusted for individual socioeconomic status, published in English before 1 June 1998 and focused on populations in developed countries. The methodological challenges posed by the design and interpretation of multilevel studies of local area effects are discussed and results summarised with reference to type of health outcome. All but two of the 25 reviewed studies reported a statistically significant association between at least one measure of social environment and a health outcome (contextual effect), after adjusting for individual level socioeconomic status (compositional effect). Contextual effects were generally modest and much smaller than compositional effects. The evidence for modest neighbourhood effects on health is fairly consistent despite heterogeneity of study designs, substitution of local area measures for neighbourhood measures and probable measurement error. By drawing public health attention to the health risks associated with the social structure and ecology of neighbourhoods, innovative approaches to community level interventions may ensue.
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                Author and article information

                Affiliations
                [1 ]Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain
                [2 ]Research Group on Statistics, Econometrics and Health (GRECS), CIBER of Epidemiology and Public Health (CIBERESP), University of Girona, Campus de Montilivi, 17071, Girona, Spain
                Contributors
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central
                1471-2458
                2012
                30 July 2012
                : 12
                : 574
                22846597
                3778847
                1471-2458-12-574
                10.1186/1471-2458-12-574
                Copyright ©2012 Salcedo et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Research Article

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