Leveraging Data Science Applications to Improve Children's Environmental Health in Sub-Saharan Africa (DICE)

项目摘要 恶劣的环境条件，如空气污染和不安全的水和卫生设施，已被列为 儿童残疾调整年(DALY)的首要风险因素。人均死亡人数最高 在撒哈拉以南非洲(SSA)观察到的可归因于环境暴露的情况最多 注意到儿童中的疾病负担。拟议研究的总体目标是利用数据科学 应用程序建立环境PM2.5暴露对儿童健康影响的空间变异性 SSA，并进一步找出解释和调节因素。该项目的总体目标将是 通过以下具体目标实现：(1)建立环境PM2.5影响的空间变异性 暴露对SSA儿童健康的影响，并探讨邻里绿化对儿童健康的调节作用 营养，(2)通过整合土地利用回归在多时间尺度上估计环境PM2.5暴露 乌干达和加纳的(LUR)模型、高分辨率地面监测数据和移动监测数据，以及 (3)确定区域(区域、地区)和家庭层面的因素，以解释环境中的空间差异 PM2.5-儿童健康关系，并建立这些暴露风险概况的时间变化。这个 拟议的研究旨在创造新的知识，并为数据科学的潜力提供证据 在南非解决儿童环境健康问题，与DSI-Africa计划保持一致。 对于目标1，我们将利用数据科学工具来组合使用以下方法估计的地理空间PM2.5暴露 卫星遥感与儿童营养不良、急性呼吸道感染以及新生儿和婴儿的数据 从几波人口与健康调查(DHS)和多指标群收集的死亡人数 跨越几十年的调查(MICS)数据。我们将使用贝叶斯空间随机系数模型集 环境PM2.5和儿童健康结果之间空间变化关系的模拟框架 个人和地区层面混杂因素的利益控制。对于目标2，我们将应用机器学习 利用移动和固定技术开发坎帕拉和阿克拉土地利用回归(LUR)模型的技术 监测数据，并在以下数据条件下比较两个城市的模型；(1)使用 仅在两个城市提供一致的数据，以及(2)使用特定城市的数据来推导本地优化的模型。 此外，我们将评估模型从一个城市到另一个城市的可转移性，并确定大多数 这两个城市都有重要的时间和空间预测指标。对于目标3，我们将使用贝叶斯轮廓回归(BPR) 并利用目标1中的相同数据集来识别表征高PM2.5暴露的简档集群 并确定哪些暴露情况群组与儿童健康不良患病率的增加有关 结果。我们还将探讨研究国家中暴露情况的时间变化。 拟议的研究结果应有助于推动对空气污染控制的投资，以及 采取政策行动解决地区和家庭贫困问题，以帮助改善南沙地区的儿童健康和存活率。

Project Abstract Poor environmental conditions such as air pollution, and unsafe water and sanitation have been ranked among the top risk factors for disability-adjusted years (DALYs) in children. The highest number of deaths per capita attributable to environmental exposures have been observed in Sub-Saharan Africa (SSA) with the highest disease burden noted among children. The overall goal of the proposed research is to harness data science applications to establish the spatial variability in the impact of ambient PM2.5 exposure on children’s health in SSA and further identify the explanatory and moderating factors. The overall goal of the project would be achieved through the following specific aims: (1) Establish the spatial variability in the impact of ambient PM2.5 exposure on children’s health in SSA, and explore the effect modifying role of neighbourhood greenness and nutrition, (2) Estimate ambient PM2.5 exposures at multi-temporal scales by integrating land use regression (LUR) models, high-resolution ground monitoring data, and mobile monitoring data in Uganda and Ghana, and (3) Identify area - (regional, district) and household-level factors that explain the spatial variability in ambient PM2.5 – child health relationship and establish the temporal changes in these exposure risk profiles. The proposed research seeks to create new knowledge and provide evidence on the potential of data science for addressing children’s environmental health problems in SSA in alignment with the DSI-Africa program. For Aim 1, we will leverage data science tools to combine geospatial PM2.5 exposures estimated using satellite remote sensing with data on child undernutrition, acute respiratory infections, and neonatal and infant deaths assembled from several waves of Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) data spanning several decades. We will use a spatial random coefficient model set in a Bayesian framework to model the spatially varying relationship between ambient PM2.5 and the child health outcomes of interest controlling for individual- and area-level confounders. For Aim 2, we would apply machine learning techniques to develop a land use regression (LUR) model for Kampala and Accra leveraging mobile and fixed monitoring data and compare the models between the two cities under the following data conditions; (1) using only consistent data available in both cities and (2) using city-specific data to derive locally optimized models. We will in addition evaluate transferability of the models from one city to another, and also, identify the most important temporal and spatial predictors in both cities. For Aim 3, we will use Bayesian Profile Regression (BPR) and leveraging the same datasets in Aim 1 to identify profile clusters that characterize high PM2.5 exposures and determine which exposure profile clusters is associated with increase prevalence of adverse child health outcomes. We would also explore the temporal changes in exposure profiles in the study countries. The findings of the proposed resaerch should help trigger investment in air pollution control as well as policy action for addressing area and household poverty to help improve child health and survival in SSA.