Enhancing Machine Learning Explainability of Disaster Preparedness Models from the FEMA National Household Survey to Inform Tailored Population Health Interventions.

Abstract

Devastating mortality, morbidity, economic, and quality of life impacts have resulted from disasters in the United States. This study aimed to validate a preexisting machine learning (ML) model of household disaster preparedness. Data from 2021 to 23 Federal Emergency Management Agency's National Household Surveys (n = 21,294) were harmonized. Importance features from the preexisting random forest ML model were transferred and tested in multiple linear and logistic regression models with updated datasets. Multiple regression models explained 42%-53% of the variance in household disaster preparedness. Features that improved the odds of overall disaster preparedness included detailed evacuation plans (odds ratios [OR] = 3.5-5.5), detailed shelter plans (OR = 4.3-11.0), having flood insurance (OR = 1.5-2.0), and higher educational attainment (OR = 1.1). Having no specified source of disaster information lowered preparedness odds (OR = 0.11-0.53). When stratified further by older adults with Black racial identities (n = 350), television as a main source of disaster-related information demonstrated associations with increased preparedness odds (OR = 2.2). These results validate the importance of detailed evacuation and shelter planning and the need to consider flood insurance subsidies in population health management to prepare for disasters. Tailored preparedness education for older adults with low educational attainment and targeted television media for subpopulation disaster-related information are indicated. By demonstrating a feasible use case to import ML model findings for regression testing in new datasets, this process promises to enhance population management health equity for those in sites that do not yet utilize local ML.