Determinants of Low Birth Weight in Malawi: Bayesian Geo-Additive Modelling

Studies on factors of low birth weight in Malawi have neglected the flexible approach of using smooth functions for some covariates in models. Such flexible approach reveals detailed relationship of covariates with the response. The study aimed at investigating risk factors of low birth weight in Malawi by assuming a flexible approach for continuous covariates and geographical random effect. A Bayesian geo-additive model for birth weight in kilograms and size of the child at birth (less than average or average and higher) with district as a spatial effect using the 2010 Malawi demographic and health survey data was adopted. A Gaussian model for birth weight in kilograms and a binary logistic model for the binary outcome (size of child at birth) were fitted. Continuous covariates were modelled by the penalized (p) splines and spatial effects were smoothed by the two dimensional p-spline. The study found that child birth order, mother weight and height are significant predictors of birth weight. Secondary education for mother, birth order categories 2-3 and 4-5, wealth index of richer family and mother height were significant predictors of child size at birth. The area associated with low birth weight was Chitipa and areas with increased risk to less than average size at birth were Chitipa and Mchinji. The study found support for the flexible modelling of some covariates that clearly have nonlinear influences. Nevertheless there is no strong support for inclusion of geographical spatial analysis. The spatial patterns though point to the influence of omitted variables with some spatial structure or possibly epidemiological processes that account for this spatial structure and the maps generated could be used for targeting development efforts at a glance.

针对马拉维低出生体重影响因素的相关研究，往往忽略了在模型中为部分协变量引入平滑函数的灵活建模范式。此类灵活建模手段可细致揭示协变量与响应变量间的内在关联。本研究旨在以连续协变量的灵活建模思路结合地理随机效应，探究马拉维地区低出生体重的风险影响因素。研究采用2010年马拉维人口与健康调查（Malawi Demographic and Health Survey）数据，构建了以行政区为空间效应单元的贝叶斯地理加性（geo-additive）模型，分别对应千克级出生体重与新生儿出生体型（低于平均水平/平均及以上水平）两类响应变量。其中，针对千克级出生体重拟合高斯模型，针对新生儿出生体型这一二分类响应变量拟合二元logistic回归模型。连续协变量通过惩罚样条（penalized splines, p-splines）建模，空间效应则借助二维惩罚样条实现平滑处理。研究结果显示：新生儿出生胎次、母亲体重与身高均为出生体重的显著预测因子；母亲接受中等教育、出生胎次处于2-3胎与4-5胎区间、家庭富裕指数层级较高以及母亲身高，均为新生儿出生体型的显著预测因子。低出生体重高发区域为奇蒂帕（Chitipa），而新生儿出生体型低于平均水平的风险升高区域为奇蒂帕与姆钦吉（Mchinji）。本研究验证了针对存在非线性影响的部分协变量采用灵活建模方式的科学性。不过，本研究并未为纳入地理空间分析提供强有力的实证支撑。尽管如此，空间分布模式仍提示存在具备空间结构的遗漏变量，或存在可解释该空间结构的流行病学进程；而生成的空间分布图可用于快速定位区域发展干预的重点方向。