PROJECT SUMMARY / ABSTRACT In recent decades, climate change has contributed to more frequent and extreme weather-related disasters (EWRD), such as heatwaves, floods, hurricanes, storms and power outage (PO). The impact of these EWRDs on human health has become a top public health priority. Research suggests that older adults, especially those with low socioeconomic status (SES) and minority populations, are disproportionately vulnerable to disaster hazards due to lack of access to the necessary resources for hazard mitigation or adaptation. What is now needed is a much more comprehensive way to effectively address these disparities, by considering social and contextual influences on both exposure and health responses to EWRDs. Currently, significant gaps remain in our understanding of how all meteorological factors jointly affect health, and how health effects may differ during transitional seasons. Major limitations on exposure assessment capacity, based on existing limited monitoring sites in each state (particularly in rural areas), are also apparent. In addition, few large studies have attempted to assess how the EWRDs-health may be modified by community and social contexts (e.g., greenness) in ways that produce health disparities. To fill these gaps, the proposed study will test a central hypothesis that vulnerable aging populations are particularly susceptible to the adverse health effects of extreme weather or EWRDs. Specifically, we propose to: 1) Improve exposure assessment by generating high-resolution gridded weather data; 2) Evaluate joint effects of multiple weather factors and disasters on cardio-respiratory diseases, Alzheimer/dementia, injuries, and renal diseases in vulnerable older adults, as well as the modifying effects of regional greenness and pandemic; and 3) Assess the impact of multiple community contextual factors in affecting health during EWRDs by developing predictive models and vulnerability/resilience indices. Results will serve as the basis for the development of effective communication strategies. HrGWD and weather simulations will be created using a state-of-the-art, two-stage downscaling models based on unique Mesonet data. In addition to utilizing NYS hospitalization and ED data, we will retrospectively follow-up readmission and other critical care indicators in a unique 18-year dynamic cohort in NYS, while also evaluating US COVID-19 infection/death rates after major EWRDs. We will use distributed lag non-linear models and interrupted time-series analysis to evaluate the impacts of emergent EWRDs on the most common and fatal diseases among the aging population. While causal influence analysis will be used to estimate the mediation effects from greenness and community factors, a predictive model selected from over 300 factors at the community level will be developed to identify vulnerability/resilience factors using machine-learning algorithms. Our multi-disciplinary and experienced research team, access to nu...