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Principal Investigator: Shao Lin
Organization: STATE UNIVERSITY OF NEW YORK AT ALBANY
Fiscal Year: 2024
Award: $401,135
Funding agency: National Institute on Aging
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 numerous geocoded datasets, innovative data mining/analysis methods, culturally
appropriate communication materials planned for vulnerable older adults, and successful prior partnerships
with government agencies maximize the feasibility of this project and our probability of success.
Terms: <AD dementia><Accidental Injury><Active Follow-up><Address><Adverse effects><Affect><Aging><Alzheimer Type Dementia><Alzheimer disease dementia><Alzheimer sclerosis><Alzheimer syndrome><Alzheimer's><Alzheimer's Disease><Alzheimers Dementia><Amentia><Application Context><Behavior><COPD><COVID crisis><COVID epidemic><COVID pandemic><COVID-19 crisis><COVID-19 epidemic><COVID-19 era><COVID-19 global health crisis><COVID-19 global pandemic><COVID-19 health crisis><COVID-19 infection><COVID-19 pandemic><COVID-19 period><COVID-19 public health crisis><COVID-19 virus infection><COVID-19 years><COVID19 infection><Cardiovascular Diseases><Cessation of life><Chronic Obstruction Pulmonary Disease><Chronic Obstructive Lung Disease><Chronic Obstructive Pulmonary Disease><Communication><Communities><Critical Care><Data><Data Science><Data Set><Death><Death Rate><Dementia><Development><Disasters><Disease><Disorder><Disparities><Disparity><Environment><Environmental Factor><Environmental Risk Factor><Environmental Wind><Ethics><Event><Exposure to><Face><Flooding><Floods><Government Agencies><Grant><Health><Heat Waves><History><Hospital Admission><Hospitalization><Human><Hurricane><Ice><Individual><Individuals from minority><Individuals of minority><Injury><Interruption><Intervention><Intervention Strategies><Investigators><Joints><Kidney Diseases><Maps><Mediation><Meteorologic Factors><Meteorological Factors><Methodology><Methods><Minority><Minority Groups><Minority People><Minority Population><Minority individual><Modeling><Modern Man><Monitor><Morbidity><Morbidity - disease rate><Natural Disasters><Negotiating><Negotiation><Nephropathy><Non-linear Models><Nonlinear Models><Outcome><Population><Population Heterogeneity><Position><Positioning Attribute><Precipitation><Predisposition><Preparedness><Primary Senile Degenerative Dementia><Probability><Radiation><Readiness><Recording of previous events><Renal Disease><Research><Research Personnel><Research Resources><Researchers><Resolution><Resources><Respiratory Disease><Respiratory System Disease><Respiratory System Disorder><Risk><Risk Factors><SARS-CoV-2 epidemic><SARS-CoV-2 global health crisis><SARS-CoV-2 global pandemic><SARS-CoV-2 infection><SARS-CoV-2 pandemic><SARS-CoV2 infection><SARS-coronavirus-2 epidemic><SARS-coronavirus-2 pandemic><Seasons><Severe Acute Respiratory Syndrome CoV 2 epidemic><Severe Acute Respiratory Syndrome CoV 2 pandemic><Severe acute respiratory syndrome coronavirus 2 epidemic><Severe acute respiratory syndrome coronavirus 2 infection><Severe acute respiratory syndrome coronavirus 2 pandemic><Site><Social Environment><Socio-economic status><Socioeconomic Status><Susceptibility><Techniques><Testing><Time Series Analysis><Unintentional Injury><Vulnerable Populations><Weather><Wildfire><Wind><Work><active followup><aged group><aged groups><aged individual><aged individuals><aged people><aged person><aged persons><aged population><aged populations><aging population><cardiovascular disorder><chronic obstructive pulmonary disorder><climate change><climatic changes><cohort><community factor><community setting><community-level factor><computer based prediction><contextual factors><coronavirus disease 2019 crisis><coronavirus disease 2019 epidemic><coronavirus disease 2019 global health crisis><coronavirus disease 2019 global pandemic><coronavirus disease 2019 health crisis><coronavirus disease 2019 infection><coronavirus disease 2019 pandemic><coronavirus disease 2019 public health crisis><coronavirus disease crisis><coronavirus disease epidemic><coronavirus disease pandemic><coronavirus disease-19 global pandemic><coronavirus disease-19 pandemic><data integration><data mining><datamining><deep learning><deep learning method><deep learning strategy><developmental><disparity in health><diverse populations><environmental risk><ethical><ethnic diversity><ethnically diverse><evidence base><experience><extreme heat><extreme weather><faces><facial><follow up><follow-up><followed up><followup><global climate change><hazard><health assessment><health data><health disparity><heatwave><heterogeneous population><histories><hospital re-admission><hospital readmission><improved><indexing><infected with COVID-19><infected with COVID19><infected with SARS-CoV-2><infected with SARS-CoV2><infected with coronavirus disease 2019><infected with severe acute respiratory syndrome coronavirus 2><injuries><innovate><innovation><innovative><interventional strategy><kidney disorder><low SES><low socio-economic position><low socio-economic status><low socioeconomic position><low socioeconomic status><machine learned algorithm><machine learning algorithm><machine learning based algorithm><member><meteorological data><mortality><mortality rate><mortality ratio><multidisciplinary><older adult><older adulthood><pandemic><pandemic disease><participatory sensing><population aging><population diversity><precipitations><predictive modeling><primary degenerative dementia><programs><public health priorities><racial diversity><racially diverse><re-admission><re-hospitalization><readmission><rehospitalization><remote sensing><renal disorder><resilience><resilience factor><resiliency factor><resilient><resolutions><response><risk prediction algorithm><risk prediction model><rural area><rural location><rural region><senile dementia of the Alzheimer type><severe acute respiratory syndrome coronavirus 2 global health crisis><severe acute respiratory syndrome coronavirus 2 global pandemic><simulation><social><social climate><social context><social factors><socio-economic position><socioeconomic position><socioenvironment><socioenvironmental><spelling><success><tropical cyclone><vulnerable community><vulnerable group><vulnerable individual><vulnerable people><weather data><weather disaster><weather factors><weather variables><weather-related disaster><weather-related factors><web site><website><wild fire><wildland fire>