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.

项目总结