Establishing a unified evaluation and implementation framework to inform heat-health warning systems

Columbia University Health Sciences, New York NY

Investigators

Abstract

Project Summary Research: Extreme heat poses a considerable, ongoing health threat under climate change. Heat alerts inform local government officials and the public about upcoming extreme heat events, enabling them to take preventive measures in the hope of reducing health risks associated with extreme heat. Previous studies examining the health benefits of current heat warning systems showed inconsistent and inconclusive results. Therefore, Dr. Xiao Wu will develop (1) an evaluation framework to assess the effectiveness of heat warning systems that will address specific challenges embedded in associated space- and time-varying data and (2) an implementation framework to inform when and where to issue heat alerts to maximize health benefits. The proposed method developments, incorporating natural experimental and policy learning approaches, will provide methodologically robust frameworks for evaluating and implementing heat-health warning systems in practice. The frameworks developed will be used to study the causal effects of heat alerts on daily, cause- specific health outcomes for heat-related illnesses using (1) death and hospital admission records from a national Medicare cohort of adults aged 65 and older, who are particularly vulnerable to extreme heat, and (2) death records from New York State residents across all age groups. The research outcomes will provide actionable recommendations for the development of improved heat-health warning systems. The development of these methods, coupled with open-source software and community partnership building, will expedite public health responses to extreme heat and increase extreme heat resilience in communities. Career Development and Training: Throughout his prior training, Dr. Wu has developed highly quantitative skills in biostatistics and data science. This K01 award will provide him with critical training required for his transition to an independent researcher specializing in statistical and causal inference methods that address broad methodological needs in environmental and climate-relevant health research. With this award, he will pursue domain expertise in climate science and disaster policy, further expand his quantitative skills with statistical methods and software in policy and reinforcement learning, and learn risk communication and community-engaged research methods to enhance dissemination and implementation of research outcomes. Goal: Dr. Wu will build an independent research program to advance causal inference methods for quantifying the mitigation effects of emerging climate adaptation strategies on health and guiding evidence-based implementations to improve health. This K01 award will provide him with protected time and focused training to accomplish his proposed research, establish him as a leading environmental health biostatistician and data scientist who is well-versed in climate science and policy, and enable him to compete successfully for R01 funding opportunities.