Context: County and state health departments are increasingly conducting hazard vulnerability and jurisdictional risk (HVJR) assessments for public health emergency preparedness and mitigation planning and evaluation to improve the public health disaster response; however, integration and adoption of these assessments into practice are still relatively rare. While the quantitative methods associated with complex analytic and measurement methods, causal inference, and decision theory are common in public health research, they have not been widely used in public health preparedness and mitigation planning.
Objective: To address this gap, the Harvard School of Public Health PERLC's goal was to develop measurement, geospatial, and mechanistic models to aid public health practitioners in understanding the complexity of HVJR assessment and to determine the feasibility of using these methods for dynamic and predictive HVJR analyses.
Methods: We used systematic reviews, causal inference theory, structural equation modeling (SEM), and multivariate statistical methods to develop the conceptual and mechanistic HVJR models. Geospatial mapping was used to inform the hypothetical mechanistic model by visually examining the variability and patterns associated with county-level demographic, social, economic, hazards, and resource data. A simulation algorithm was developed for testing the feasibility of using SEM estimation.
Results: The conceptual model identified the predictive latent variables used in public health HVJR tools (hazard, vulnerability, and resilience), the outcomes (human, physical, and economic losses), and the corresponding measurement subcomponents. This model was translated into a hypothetical mechanistic model to explore and evaluate causal and measurement pathways. To test the feasibility of SEM estimation, the mechanistic model path diagram was translated into linear equations and solved simultaneously using simulated data representing 192 counties.
Conclusions: Measurement, geospatial, and mechanistic models can be used to confirm and validate existing and proposed HVJR models and potentially increase the predictive validity of these models for optimizing and improving public health preparedness planning.