Ruktanonchai Corrine Warren, Nieves Jeremiah J, Ruktanonchai Nick W, Nilsen Kristine, Steele Jessica E, Matthews Zoe, Tatem Andrew J
School of Geography & Environmental Science, University of Southampton, Southampton, UK.
Department of Social Statistics & Demography, University of Southampton, Southampton, UK.
BMJ Glob Health. 2020 Feb 10;4(Suppl 5):e002092. doi: 10.1136/bmjgh-2019-002092. eCollection 2019.
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.
在精细空间分辨率下可视化孕产妇和新生儿健康（MNH）结果对于确保最脆弱的妇女和儿童在改善健康过程中不被落下至关重要。然而，关于挽救生命的MNH干预措施的分类数据仍然难以获得，因此需要使用贝叶斯地理统计模型来绘制小地理区域内的结果。虽然这些方法改进了空间相关健康结果之间的模型参数估计和精度，并允许对不确定性进行量化，但很少有研究探讨更高空间分辨率建模与相关不确定性传播之间的权衡。在这里，我们通过量化坦桑尼亚剖宫产（c-section）分娩的后验分布，探索了在不断提高的空间分辨率下模型结果与相关不确定性之间的权衡。总体而言，在多个空间分辨率下对剖宫产分娩进行建模时，我们证明贫困在不同空间分辨率下呈负相关，这表明在获得挽救生命的产科手术方面，社会人口因素之间存在重要差异。最后，我们发现虽然不确定性随着更高空间分辨率的输入而增加，但模型精度在最高空间分辨率下最接近最佳值，这表明在识别健康指标中隐藏的空间异质性方面存在重要的政策权衡。
