哈尔滨工业大学（深圳）学术讲座

讲座人Speaker：Dr. Chi-Hua Li (澳门大学助理教授)

题目Title: Life-course Approach and Healthy Aging: Insights and Opportunities from Cross-National Research

时间Date： 2024 年 11月 15 日     Time：15:00-16:00

地点Venue： L栋 213 室

联系人Contact：叶辰飞 助理研究员

内容摘要Abstract： 

Healthy aging is a critical public health goal in the context of global population aging. Drawing on studies of prenatal famine exposure and its long-term health impacts across countries, as well as healthy aging research using data from the US Health and Retirement Study and its international counterparts across the world, Dr. Li discuss the critical insights gained from a life-course perspective and cross-national research. Life-course approach provides a powerful framework to examine how early-life exposures, social, biological, and environmental factors accumulate and interact over the lifespan to influence health outcomes. Cross-national studies provide unique opportunities to triangulate findings, identify country-level differences in disease risk factors and their management, and address diverse research questions based on varying resources and settings.

个人简介(About the speaker):

Dr. Chi-Hua Li is a population health scientist focusing on healthy aging. He earned his doctoral degree from Columbia University, where his dissertation focused on the long-term health impacts of prenatal famine exposure, analyzing data from China, Ukraine, and the Netherlands to explore the effects of prenatal undernutrition on later health outcomes. Dr. Li has completed two postdoctoral training programs. The first was a one-year fellowship at Columbia University. In 2022, he embarked on a two-year postdoctoral fellowship co-sponsored by the University of Michigan and Johns Hopkins University, where he expanded his research to study how risk factors contribute to the development and progression of cardiometabolic and neurodegenerative diseases through immune aging. Additionally, Dr. Li has a strong interest in epidemiologic research methodology and data science, encompassing causal inference and meta-research methods.