Faculty of Economic Sciences, University of Warsaw, 00-927 Warszawa, Poland.
Int J Environ Res Public Health. 2022 Sep 2;19(17):10967. doi: 10.3390/ijerph191710967.
This study presents the determinants of childhood stunting as the consequence of child malnutrition. We checked two groups of factors-the socio-economic situation and climate vulnerability-using disaggregated sub-regional data in the spatial context. Data related to the percentage of stunted children in Pakistan for 2017 were retrieved from MICS 2017-18 along with other features. We used three quantitative models: ordinary least squares regression (OLS) to examine the linear relationships among the selected features, spatial regression (SDEM) to identify and capture the spatial spillover effect, and the Extreme Gradient Boosting machine learning algorithm (XGBoost) to analyse the importance of spatial lag and generate predictions. The results showed a high degree of spatial clustering in childhood stunting at the sub-regional level. We found that a 1 percentage point (p.p.) increase in multi-dimensional poverty may translate into a 0.18 p.p. increase in childhood stunting. Furthermore, high climate vulnerability and common marriages before age 15 each exacerbated childhood stunting by another 1 p.p. On the contrary, high female literacy and their high exposure to mass media, together with low climate vulnerability, may reduce childhood stunting. Model diagnostics showed that the SDEM outperformed the OLS model, as AIC = 766 > AIC = 760. Furthermore, XGBoost generated the most accurate predictions in comparison to OLS and SDEM, having the lowest root-mean-square error (RMSE).
本研究探讨了导致儿童发育迟缓的决定因素，即儿童营养不良的后果。我们使用空间背景下的细分次区域数据检查了两组因素——社会经济状况和气候脆弱性。从 MICS 2017-18 中检索到了 2017 年巴基斯坦发育迟缓儿童的百分比数据以及其他特征。我们使用了三种定量模型：普通最小二乘法回归（OLS）来检验所选特征之间的线性关系，空间回归（SDEM）来识别和捕获空间溢出效应，以及极端梯度增强机器学习算法（XGBoost）来分析空间滞后的重要性并生成预测。结果表明，在次区域层面上，儿童发育迟缓存在高度的空间聚类。我们发现，多维贫困增加 1 个百分点可能导致儿童发育迟缓增加 0.18 个百分点。此外，高气候脆弱性和 15 岁前常见的婚姻使儿童发育迟缓恶化了另一个 1 个百分点。相反，女性高识字率和她们对大众媒体的高接触率，以及低气候脆弱性，可能会降低儿童发育迟缓的发生率。模型诊断表明，SDEM 优于 OLS 模型，因为 AIC=766＞AIC=760。此外，与 OLS 和 SDEM 相比，XGBoost 生成了最准确的预测，其均方根误差（RMSE）最低。
