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首页> 美国卫生研究院文献>BMJ Global Health >Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania
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Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania

机译：在多种空间分辨率下估算地理空间模型的不确定性：坦桑尼亚剖腹产的分娩方式

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Visualising maternal and newborn health (MNH) outcomes at fine spatial resolutions is crucial to ensuring the most vulnerable women and children are not left behind in improving health. Disaggregated data on life-saving MNH interventions remain difficult to obtain, however, necessitating the use of Bayesian geostatistical models to map outcomes at small geographical areas. While these methods have improved model parameter estimates and precision among spatially correlated health outcomes and allowed for the quantification of uncertainty, few studies have examined the trade-off between higher spatial resolution modelling and how associated uncertainty propagates. Here, we explored the trade-off between model outcomes and associated uncertainty at increasing spatial resolutions by quantifying the posterior distribution of delivery via caesarean section (c-section) in Tanzania. Overall, in modelling delivery via c-section at multiple spatial resolutions, we demonstrated poverty to be negatively correlated across spatial resolutions, suggesting important disparities in obtaining life-saving obstetric surgery persist across sociodemographic factors. Lastly, we found that while uncertainty increased with higher spatial resolution input, model precision was best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.
机译：以精细的空间分辨率可视化母婴健康（MNH）结果对于确保不让最脆弱的妇女和儿童改善健康至关重要。关于救生MNH干预措施的分类数据仍然难以获得，但是，有必要使用贝叶斯地统计学模型来绘制小地理区域的成果。虽然这些方法改善了模型参数估计值，并改善了与空间相关的健康结果之间的精度，并允许对不确定性进行量化，但很少有研究研究了较高空间分辨率模型与相关不确定性如何传播之间的权衡。在这里，我们通过量化坦桑尼亚剖腹产（c-section）分娩的后验分布，探索了在提高空间分辨率时模型结果与相关不确定性之间的权衡。总体而言，在通过剖腹产在多个空间分辨率下进行建模的研究中，我们证明了贫困与各个空间分辨率之间呈负相关，这表明在获得可挽救生命的产科手术方面的重要差异仍然存在于社会人口统计学因素中。最后，我们发现，尽管不确定性随较高的空间分辨率输入而增加，但模型精度最好以最高的空间分辨率近似表示，这表明在识别健康指标中隐藏的空间异质性之间进行重要的政策权衡。

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