Int J Health Geogr - A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka.

Tópicos

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Resumo

CKGROUND: The deprived physical environments present in slums are well-known to have adverse health effects on their residents. However, little is known about the health effects of the social environments in slums. Moreover, neighbourhood quantitative spatial analyses of the mental health status of slum residents are still rare. The aim of this paper is to study self-rated mental health data in several slums of Dhaka, Bangladesh, by accounting for neighbourhood social and physical associations using spatial statistics. We hypothesised that mental health would show a significant spatial pattern in different population groups, and that the spatial patterns would relate to spatially-correlated health-determining factors (HDF).METHODS: We applied a spatial epidemiological approach, including non-spatial ANOVA/ANCOVA, as well as global and local univariate and bivariate Moran's I statistics. The WHO-5 Well-being Index was used as a measure of self-rated mental health.RESULTS: We found that poor mental health (WHO-5 scores < 13) among the adult population (age =15) was prevalent in all slum settlements. We detected spatially autocorrelated WHO-5 scores (i.e., spatial clusters of poor and good mental health among different population groups). Further, we detected spatial associations between mental health and housing quality, sanitation, income generation, environmental health knowledge, education, age, gender, flood non-affectedness, and selected properties of the natural environment.CONCLUSIONS: Spatial patterns of mental health were detected and could be partly explained by spatially correlated HDF. We thereby showed that the socio-physical neighbourhood was significantly associated with health status, i.e., mental health at one location was spatially dependent on the mental health and HDF prevalent at neighbouring locations. Furthermore, the spatial patterns point to severe health disparities both within and between the slums. In addition to examining health outcomes, the methodology used here is also applicable to residuals of regression models, such as helping to avoid violating the assumption of data independence that underlies many statistical approaches. We assume that similar spatial structures can be found in other studies focussing on neighbourhood effects on health, and therefore argue for a more widespread incorporation of spatial statistics in epidemiological studies.

Resumo Limpo

ckground depriv physic environ present slum wellknown advers health effect resid howev littl known health effect social environ slum moreov neighbourhood quantit spatial analys mental health status slum resid still rare aim paper studi selfrat mental health data sever slum dhaka bangladesh account neighbourhood social physic associ use spatial statist hypothesis mental health show signific spatial pattern differ popul group spatial pattern relat spatiallycorrel healthdetermin factor hdfmethod appli spatial epidemiolog approach includ nonspati anovaancova well global local univari bivari moran statist wellb index use measur selfrat mental healthresult found poor mental health score among adult popul age preval slum settlement detect spatial autocorrel score ie spatial cluster poor good mental health among differ popul group detect spatial associ mental health hous qualiti sanit incom generat environment health knowledg educ age gender flood nonaffected select properti natur environmentconclus spatial pattern mental health detect part explain spatial correl hdf therebi show sociophys neighbourhood signific associ health status ie mental health one locat spatial depend mental health hdf preval neighbour locat furthermor spatial pattern point sever health dispar within slum addit examin health outcom methodolog use also applic residu regress model help avoid violat assumpt data independ under mani statist approach assum similar spatial structur can found studi focuss neighbourhood effect health therefor argu widespread incorpor spatial statist epidemiolog studi

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