Abstract
BackgroundThe 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).MethodsWe 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.ResultsWe 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.ConclusionsSpatial 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.
Highlights
The deprived physical environments present in slums are well-known to have adverse health effects on their residents
Variations in well-being among population groups and slums We found that poor well-being (WHO-5 scores < 13) among the adult population was predominant in all slums
Ignoring spatial structures and assuming normally distributed errors, an ANOVA/ ANCOVA analysis showed that the WHO-5 scores for males and females did not significantly differ (p = 0.21), but age had a significant negative effect
Summary
The deprived physical environments present in slums are well-known to have adverse health effects on their residents. To accomplish Target 11 of the United Nations’ Millennium Development Goals (namely, by 2020 to have achieved a significant improvement in the lives of at least 100 million slum dwellers) [11], public health risk assessments are urgently required. Such assessments need to consider the slum residents’ specific health problems that are related to the specific unhealthy sociophysical environments in and around slums. In addition to studying diseases and symptoms, research on the mental health of urban slum populations is of major importance, as mental and physical health complements each other [1,14,15]. The results of such assessments will support slum-upgrading strategies
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