Open Access Research

A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka

Oliver Gruebner1*, Md Mobarak H Khan2, Sven Lautenbach13, Daniel Müller14, Alexander Kraemer2, Tobia Lakes1 and Patrick Hostert1

Author affiliations

1 Geomatics Lab, Geography Department, Humboldt-Universität zu Berlin, Germany

2 Department of Public Health Medicine, University of Bielefeld, Germany

3 Department for Computational Landscape Ecology, UFZ-Helmholtz Centre for Environmental Research, Germany

4 Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO), Germany

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Citation and License

International Journal of Health Geographics 2011, 10:36  doi:10.1186/1476-072X-10-36

Published: 20 May 2011

Abstract

Background

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.