Novel use of mobile phone big data to evaluate the effect of climatic and non-climatic shocks on human mobility and women's health outcomes in sub-Saharan Africa

新颖地利用手机大数据来评估气候和非气候冲击对撒哈拉以南非洲地区人口流动和妇女健康结果的影响

• 批准号: 10722580
• 负责人: Maya Luetke
• 金额: $ 12.88万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10722580
• 关键词: Acceleration; Address; Adverse effects; Affect; Africa South of the Sahara; African; Award; Big Data; Categories; Cellular Phone; Climate; Communities; Conflict (Psychology); Country; Coupled; Cross-Sectional Studies; Data; Data Sources; Demographic and Health Surveys; Disasters; Disease Outbreaks; Domestic Violence; Emergency response; Empirical Research; Environment; Epidemic; Event; Exposure to; Face; Faculty; Food; Foundations; Frequencies; Future; Gender; Goals; Health; Healthcare; Human; Individual; Inequity; Intervention; Kenya; Knowledge; Learning; Letters; Life; Link; Location; Machine Learning; Maps; Measures; Mediating; Mediator; Mentors; Mentorship; Methodology; Methods; Minnesota; Mission; Monitor; Morbidity - disease rate; National Institute of Child Health and Human Development; Natural Disasters; Outcome; Pathway interactions; Patient Self-Report; Pattern; Performance; Personal Satisfaction; Persons; Phase; Policies; Population; Positioning Attribute; Poverty; Process; Rain; Recording of previous events; Research; Research Personnel; Resource-limited setting; Resources; Risk; Role; Services; Sex Discrimination; Shelter facility; Shock; Social Sciences; Social support; Solid; Source; Surveys; System; Temperature; Testing; Time; Training; Violence; Volatilization; Water; Woman; Women' s Health; Work; analytical method; artificial intelligence method; career; career development; career preparation; climate change; climate impact; climate variability; combat; experience; extreme weather; gender equity; global health; health data; health determinants; health disparity; health equity; health inequalities; human migration; innovation; intimate partner violence; machine learning algorithm; machine learning method; member; migration; mortality; multidisciplinary; novel; population health; prevent; programs; resilience; response; skills; social; social disparities; social norm; spatial epidemiology; spatial integration; statistics; temporal measurement; tenure track; theories; trend

PROJECT SUMMARY Weather extremes and natural disasters associated with climate change are wreaking havoc on human health worldwide, and these events will become more frequent and more intense in coming years. Other disruptive, non-climatic shocks – such as exposure to armed conflict and disease outbreaks – also have significant impacts on health globally. Shock-induced mobility patterns likely play a role in linking shocks to adverse health outcomes. Despite the importance of understanding these relationships, little research has been conducted to date due to a dearth of detailed temporal and spatial data on mobility patterns, particularly in settings with limited resources. Novel location data derived from mobile phone use promises to elucidate granular human mobility patterns. Leveraging established partnerships to obtain the data and machine learning methods to process it, the proposed research will provide critical information on the health of shock-affected populations in Kenya, an east African country with increasing climate variability and a history of armed conflict. Further, this project will demonstrate the utility of applying these data to address critical population health problems. The proposed Pathway to Independence Award will provide essential and synergistic training to position Dr. Luetke as a leading expert in using big data and artificial intelligence methods to elucidate the social and health implications of shocks and human mobility responses in the context of climate change and increasing global insecurity. The mentored phase of the proposed project will provide training in (1) geospatial methods and population-environment research, (2) machine learning for spatial big data, (3) demographic theories and methods related to human migration, and (4) career development activities to prepare to be an independent investigator and future tenure- track faculty member. The empirical research of the proposed project will address three primary aims: (Aim 1) Use artificial intelligence methods to process the mobile phone data to identify mobility patterns over time (2018- 2022) and space; (Aim 2) Test the role of climate variability, natural disasters, and armed conflict as mechanisms to explain changes in these mobility patterns; and (Aim 3) Quantify the effect of climate extremes, natural disasters, and armed conflict on women’s exposure to intimate partner violence and explore mobility patterns, particularly when crisis-induced, as a mediator of these associations. The training and research detailed in this proposal will form a solid foundation to launch a rigorous and sustainable research agenda and provide the pilot work for a future R01 proposal. The additional training and mentorship will be an important step toward establishing a rich, independent research career aimed at reducing social and health disparities. A strong interdisciplinary mentorship team and an outstanding supportive training environment at the Minnesota Population Center provide a foundation for Dr. Luetke to fill an important scientific niche on understanding the impacts of external shocks, migration, and health outcomes for women in Sub-Saharan Africa.

项目概要 与气候变化相关的极端天气和自然灾害正在对人类健康造成严重破坏 在世界范围内，这些事件在未来几年将变得更加频繁和激烈。 非气候冲击——例如武装冲突和疾病爆发——也会产生重大影响 冲击引起的流动模式可能在将冲击与不良健康联系起来方面发挥了作用。 尽管理解这些关系很重要，但很少有研究对此进行研究。 由于缺乏有关流动模式的详细时间和空间数据，特别是在资源有限的环境中，因此无法确定日期。 来自移动电话使用的新位置数据有望阐明人类的精细移动性。 利用已建立的合作伙伴关系来获取数据和机器学习方法来处理数据， 拟议的研究将提供有关肯尼亚受冲击影响人口健康的重要信息， 此外，该项目还将帮助气候变化加剧且历史悠久的东非国家。 展示应用这些数据来解决关键人口健康问题的效用。 独立之路奖将提供必要的协同培训，使 Luetke 博士成为领导者 使用大数据和人工智能方法阐明冲击对社会和健康影响的专家 以及气候变化和全球不安全局势加剧背景下的人员流动应对措施。 拟议项目的阶段将提供以下方面的培训：(1) 地理空间方法和人口环境 研究，（2）空间大数据的机器学习，（3）与人类相关的人口统计理论和方法 移民，以及（4）职业发展活动，为成为一名独立调查员和未来的终身职位做好准备- 拟议项目的实证研究将解决三个主要目标：（目标 1） 使用人工智能方法处理手机数据，以识别一段时间内的移动模式（2018- （目标 2）测试气候变化、自然灾害和武装冲突作为机制的作用 解释这些流动模式的变化；以及（目标 3）量化极端气候、自然现象的影响； 灾害和武装冲突对妇女遭受亲密伴侣暴力的影响并探索流动模式， 特别是在危机引发时，作为这些协会的调解者，本节详细介绍了培训和研究。 该提案将为启动严格且可持续的研究议程奠定坚实的基础，并提供试点 为未来的 R01 提案而努力 额外的培训和指导将是迈向未来的重要一步。 建立丰富、独立的研究事业，旨在减少社会和健康差距。 明尼苏达大学的跨学科导师团队和出色的支持性培训环境 人口中心为 Luetke 博士提供了一个基础，以填补重要的科学空白，了解人口 外部冲击、移民和健康状况对撒哈拉以南非洲地区妇女的影响。