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

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 空气污染、不安全的水和卫生设施等恶劣的环境条件被列为 儿童残疾调整年（Disability-adjusted years，DADs）的主要风险因素。人均死亡人数最多的 在撒哈拉以南非洲（SSA）， 注意到儿童的疾病负担。拟议研究的总体目标是利用数据科学 应用程序，以确定环境PM2.5暴露对儿童健康影响的空间变异性， SSA，并进一步确定解释和调节因素。该项目的总体目标是 通过以下具体目标实现：（1）确定环境PM2.5影响的空间变异性 暴露对SSA儿童健康的影响，并探讨社区绿化和 （2）通过整合土地利用回归，估计多时间尺度下的环境PM2.5暴露 (LUR)乌干达和加纳的高分辨率地面监测数据和移动的监测数据， (3)确定区域（区域、地区）和家庭层面的因素，解释环境的空间变异性 PM2.5与儿童健康的关系，并建立这些暴露风险曲线的时间变化。的 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， 解决撒哈拉以南非洲地区儿童的环境健康问题，与非洲残疾人倡议方案保持一致。 对于目标1，我们将利用数据科学工具，将联合收割机地理空间PM2.5暴露量与使用 提供儿童营养不良、急性呼吸道感染和新生儿及婴儿数据的卫星遥感 从几波人口与健康调查和多指标类集调查收集的死亡人数 多指标类集调查（MICS）数据跨越几十年。我们将使用贝叶斯空间随机系数模型集， 框架，以模拟环境PM2.5与儿童健康结果之间的空间变化关系， 对个人和地区水平混杂因素的利益控制。对于目标2，我们将应用机器学习 为坎帕拉和阿克拉开发土地利用回归模型的技术，利用移动的和固定的 监测数据，并在以下数据条件下比较两个城市之间的模型：（1）使用 两个城市中只有一致的数据可用;（2）使用特定于城市的数据来推导本地优化的模型。 此外，我们还将评估模型从一个城市到另一个城市的可移植性，并确定最适合的模型。 重要的时间和空间预测在这两个城市。对于目标3，我们将使用贝叶斯轮廓回归（BPR） 并利用目标1中的相同数据集来确定表征高PM2.5暴露的特征的轮廓集群 并确定哪些暴露概况集群与不良儿童健康患病率的增加有关 结果。我们还将探讨研究国家接触情况的时间变化。 拟议研究的结果应有助于推动对空气污染控制的投资， 采取政策行动，解决地区和家庭贫困问题，以帮助改善撒南非洲的儿童健康和生存状况。