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

利用数据科学应用改善撒哈拉以南非洲儿童的环境健康 (DICE)

• 批准号: 10714773
• 负责人: Adeladza Kofi Amegah
• 金额: $ 25万
• 依托单位: UNIVERSITY OF CAPE COAST
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-12 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714773
• 关键词: Acute respiratory infection; Address; Adolescence; Adult; Africa; Africa South of the Sahara; African; Air; Air Pollution; Area; Bayesian Modeling; Cessation of life; Child; Child Development; Child Health; Child Malnutrition; Childhood; Cities; Complex; Computers; Country; Data; Data Science; Data Set; Data Sources; Demographic and Health Surveys; Environment; Environmental Exposure; Environmental Health; Environmental Monitoring; Epidemiologist; Excess Mortality; Future; Ghana; Goals; Health; Household; Imagery; Individual; Internet of Things; Investments; Knowledge; Life; Machine Learning; Maps; Methods; Modeling; Monitor; Neighborhoods; Outcome; Outcome Assessment; Policies; Poverty; Prevalence; Province; Public Health; Research; Resolution; Respiratory Tract Infections; Risk; Risk Factors; Role; Sanitation; Scientist; Surveys; Techniques; Technology; Testing; Uganda; Water; air monitoring; air pollution control; ambient air pollution; burden of illness; disability; diverse data; early life exposure; fine particles; health data; improved; in utero; infant death; innovation; interest; land use; multidisciplinary; neonatal death; nutrition; programs; remote sensing; respiratory; response; sensor; tool; water quality; Acute respiratory infection; Address; Adolescence; Adult; Africa; Africa South of the Sahara; African; Air; Air Pollution; Area; Bayesian Modeling; Cessation of life; Child; Child Development; Child Health; Child Malnutrition; Childhood; Cities; Complex; Computers; Country; Data; Data Science; Data Set; Data Sources; Demographic and Health Surveys; Environment; Environmental Exposure; Environmental Health; Environmental Monitoring; Epidemiologist; Excess Mortality; Future; Ghana; Goals; Health; Household; Imagery; Individual; Internet of Things; Investments; Knowledge; Life; Machine Learning; Maps; Methods; Modeling; Monitor; Neighborhoods; Outcome; Outcome Assessment; Policies; Poverty; Prevalence; Province; Public Health; Research; Resolution; Respiratory Tract Infections; Risk; Risk Factors; Role; Sanitation; Scientist; Surveys; Techniques; Technology; Testing; Uganda; Water; air monitoring; air pollution control; ambient air pollution; burden of illness; disability; diverse data; early life exposure; fine particles; health data; improved; in utero; infant death; innovation; interest; land use; multidisciplinary; neonatal death; nutrition; programs; remote sensing; respiratory; response; sensor; tool; water quality

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.

项目摘要 诸如空气污染，不安全的水和卫生等环境状况较差已被排名 儿童残疾调整年（DALY）的最高风险因素。人均死亡人数最多 在撒哈拉以南非洲（SSA）中，归因于环境暴露 儿童中注意到疾病。拟议研究的总体目标是利用数据科学 在环境PM2.5暴露对儿童健康的影响中建立空间变异性的申请 SSA并进一步确定利用和调节因素。该项目的总体目标是 通过以下特定目的实现：（1）在环境PM2.5的影响中建立空间变异性 SSA儿童健康状况的暴露，并探索邻里绿色和 营养，（2）通过整合土地使用回归，估计在多时间尺度下暴露于周围环境PM2.5 （LUR）模型，高分辨率地面监控数据以及乌干达和加纳的移动监控数据，以及 （3）确定区域 - （区域，地区）和家庭水平的因素，这些因素解释了环境的空间变异性 PM2.5 - 儿童健康关系并建立这些暴露风险概况的临时变化。这 拟议的研究旨在创造新知识，并提供有关数据科学潜力的证据 在与DSI-AFRICA计划保持一致的SSA中儿童的环境健康问题。 对于AIM 1，我们将利用数据科学工具来结合使用地理空间PM2.5 卫星遥感与儿童不足的数据，急性呼吸道感染以及新生儿和婴儿 死亡人口和健康调查（DHS）和多个指示集群的死亡人数 调查（MIC）数据跨越了几十年。我们将使用贝叶斯中设置的空间随机系数模型 建模环境PM2.5与儿童健康成果之间的空间变化关系的框架 控制个人和地区级别的混杂因素的兴趣。对于AIM 2，我们将应用机器学习 开发坎帕拉和阿克拉的土地利用回归（LUR）模型的技术 监视数据并在以下数据条件下比较两个城市之间的模型； （1）使用 仅在城市和（2）中使用城市特定数据来推导本地优化模型的一致数据。 另外，我们还将评估模型从一个城市到另一个城市的可转让性，并确定最多的 两个城市中重要的临时和空间预测因子。对于AIM 3，我们将使用贝叶斯个人资料回归（BPR） 并利用AIM 1中的相同数据集识别表征高PM2.5暴露的配置簇 并确定哪些暴露概况集群与不良儿童健康的患病率增加有关 结果。我们还将探索研究国家的暴露概况的暂时变化。 拟议的Resaerch的发现应有助于触发对空气污染控制的投资以及 解决地区和家庭贫困的政策行动，以帮助改善SSA儿童健康和生存。