A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka.
A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka.
复制标题
达卡贫民窟的自我评估心理健康的空间流行病学分析。
DOI：
10.1186/1476-072x-10-36
复制
发表时间：
2011-05-20
影响因子：
4.9
通讯作者：
Hostert P
中科院分区：
文献类型：
--
作者：
Gruebner O;Khan MM;Lautenbach S;Müller D;Kraemer A;Lakes T;Hostert P
众所周知，贫民窟中物质环境匮乏，对居民的健康产生不利影响。然而，人们对贫民窟的社会环境对健康的影响知之甚少。此外，贫民窟居民心理健康状况的邻里定量空间分析仍然很少。本文的目的是研究自测心理健康数据在几个贫民窟的达卡，孟加拉国，占邻里社会和物理协会使用空间统计。我们假设心理健康在不同人群中表现出显着的空间模式，并且空间模式与空间相关的健康决定因素（HDF）有关。我们采用了空间流行病学方法，包括非空间ANOVA/ANCOVA，以及全球和当地的单变量和双变量Moran's I统计。WHO-5幸福指数被用来衡量自我评估的心理健康。我们发现，在所有贫民窟居住区，成年人（年龄≥15岁）的心理健康状况不佳（WHO-5分< 13）普遍存在。我们检测了空间自相关的WHO-5评分（即，不同人口群体中精神健康状况较差和良好的空间集群）。此外，我们发现心理健康和住房质量，卫生，创收，环境卫生知识，教育，年龄，性别，洪水不受影响，和自然环境的选定属性之间的空间关联。心理健康的空间模式被检测到，可以部分解释空间相关的HDF。因此，我们表明，社会物理邻居与健康状况显着相关，即，一个地点的心理健康在空间上依赖于邻近地点的心理健康和HDF流行。此外，空间格局表明，贫民窟内部和贫民窟之间存在严重的健康差距。除了检查健康结果外，这里使用的方法也适用于回归模型的残差，例如帮助避免违反作为许多统计方法基础的数据独立性假设。我们假设，类似的空间结构可以在其他研究中发现，重点对健康的邻里效应，因此，主张更广泛地纳入流行病学研究的空间统计。
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). 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. 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. 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.