First, to test a model of the drivers of frequent emergency department utilization conceptualized as falling within predisposing, enabling, and need dimensions. Second, to extend the model to include social networks and service quality as predictors of frequent utilization. Third, to illustrate the variation in thresholds that define frequent utilization in terms of the number of emergency department encounters by the predictors within the model. Primary data collection over an eight week period within a level-1 trauma urban hospital’s emergency department. Representative randomized sample of 1,443 adult patients triaged ESI levels 4–5. Physicians and research staff interviewed patients as they received services. Relationships with the outcome variable, utilization, were tested using logistic regression to establish odds-ratios. 70.6 percent of patients have two or more, 48.3 percent have three or more, 25.3 percent have four or more, and 14.9 percent have five or more emergency department visits within 12 months. Factors associated with frequent utilization include gender, race, poor mental health, mental health drugs, prescription drug abuse, social networks, employment, perceptions of service quality, seriousness of condition, persistence of condition, and previous hospital admittance. Interventions targeting associated factors will change global emergency department encounters, although the mutability varies. Policy interventions to address predisposing factors such as substance abuse or access to mental health treatment as well as interventions that speak to enabling factors such as promoting the resiliency of social networks may result in decreased frequency of emergency department utilization.

This paper examines nonprofits’ capacity for responding to simultaneous hurricane-pandemic threat, addressing: (1) strategies nonprofits use to deliver services during the COVID-19 pandemic, and (2) how natural hazards may affect nonprofit roles in emergency service delivery during a pandemic. Data come from a survey of New Orleans-based nonprofits demonstrating effects of pandemic on local nonprofit service delivery, and workshops with U.S. coastal community stakeholders exploring expectations for nonprofit roles in emergency operations nationwide. Nonprofits have applied resilient strategies including virtual operations, staff reductions, and funding diversification, but vulnerabilities remain. Findings guide a research agenda for building nonprofit and community resilience.

Severe catastrophic natural disasters adversely affect housing stock and regional capacity to build and repair houses, with unfavorable impacts on the business continuity of industrial and business organizations resting on that affected community. Restoring industrial capacity and business continuity is crucial for the organizations’ survival in the aftermath of a disaster but the process takes time while the affected region faces an unexpected surge in the demand for housing. Modeling approaches largely used in the supply chain management realm may support humanitarian logistics and policy-making by providing a testbed in which supply chain elements of the recovery process can be examined before implementing. This research presents a system dynamics model that considers the problem of housing recovery from the demand and supply perspective and provides significant insights for policymakers into how the production of permanent housing depends upon the uncertainties and feedback effects of material and labor. In order to highlight its utility, a hypothetical scenario in which a hurricane hits the U.S. Hampton Roads region, which is a major population center along the mid-Atlantic coast, has been configured by using actual data collected from both governmental and academic sources.

Introduction This article demonstrates the utility of the system dynamics approach to model and simulate US demand for ambulatory health care service both for the general population and for specific cohort subpopulations over the 5-year period, from 2003 to 2008. A system dynamics approach that is shown to meaningfully project demand for services has implications for health resource planning and for generating knowledge that is critical to assessing interventions. Methods The study uses a cohort-component method in combination with structural modeling to simulate ambulatory health care utilization. Data are drawn from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey. Results The simulation of the total population requiring ambulatory services between 2003 and 2008 is performed to test the functionality and validate the model. Results show a close agreement between the simulated and actual data; the percent error between the two is relatively low, 1.5% on average. In addition, simulations of purposively selected population subsets are executed (men, 18–24 years of age, white, African American, Hispanic, and insurance coverage), resulting in error between simulated and actual data, which is 7.05% on average. Conclusions The proposed model demonstrates that it is possible to represent and mimic, with reasonable accuracy, the demand for health care services by the total ambulatory population and the demand by selected population subsets. This model and its simulation demonstrate how these techniques can be used to identify disparities among population subsets and a vehicle to test the impact of health care interventions on ambulatory utilization. A system dynamics approach may be a useful tool for policy and strategic planners.

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

This study examines households' prospective evacuation behavior during a hurricane‐pandemic compound threat. Data from a 2020 survey of coastal Virginia households help answer two questions: (1) What factors associated with the threat and impacts of the COVID‐19 pandemic and hurricanes influence the prospective evacuation behavior of households during a compound hurricane‐pandemic event? (2) What are the equity implications for emergency management policies and practices to support evacuation and sheltering during a compound hurricane‐pandemic event? Households in the sample were split between those who stated they would evacuate away from the at‐risk region and those who would stay. Greater household vulnerability to hurricanes and COVID‐19 and having sufficient financial resources increase the likelihood of evacuation. Higher‐income households were more likely to have resources to evacuate and were less likely to suffer financial consequences from a hurricane or pandemic. Racial minorities are more vulnerable to the pandemic and face greater resource challenges when evacuating.