Joshua G. Behr

Disparate health implications stemming from the propensity of elderly and medically fragile populations to shelter in place during severe storm events.

Chronic conditions, disability limitations (mobility, cognitive, and sensory), and the need for assistance with activities of daily living are characteristics of elderly and medically fragile populations. Theory suggests that households with these vulnerability attributes are more likely to suffer storm-induced adverse and prolonged health consequences and, therefore, ought to evidence an increased propensity to evacuate prior to a severe storm event. Yet despite being more sensitive to storm disruption, the elderly and medically fragile populations are only slightly more likely to evacuate in the face of impending storms. This suggests, for these groups, there may be other factors such as income, transportation, and social and familial networks that may be attenuating the propensity to evacuate. The public health significance is found in that the propensity to shelter in place, rather than evacuate, may contribute to disparate health outcomes. Data illustrating the prevalence of these conditions and the propensity to shelter in place are derived from a sampling of Hampton Roads households following the 2011 Hurricane Irene.

A system dynamics model for simulating ambulatory health care demands.

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.

Black and Female Municipal Employment: A Substantive Benefit of Minority Political Incorporation?

Research has advanced the expectation that black political mobilization will result not only in gains in black descriptive representation but also change in public policy. Under the premise that city employment is a substantive benefit, this article documents the relationships between mayoral leadership, minority descriptive representation on the city council, and trends in black and female municipal employment across eight job categories in New Orleans, Louisiana rom 1978 through 1997. It is hypothesized that with growing minority political incorporation there will occur a distinct and identifiable temporal sequence in minority city employment gains across several job categories dependent upon the relative desirability of those jobs. Over the period studied the following is found: (1) patterns of racial job stratification based on job desirability, (2) distinct trends in black male and black female employment, (3) male-female differences in traditional gender-specific occupations, and (4) bifurcation of black female and non-black female employment trends in gender-specific occupations. Although black political incorporation was in its ascendancy through the 1980s and public policy has been substantially controlled by the black leadership for more than a decade, the realization of substantive benefits in the form of municipal jobs has been much slower than previously theorized.

A system dynamics approach to modeling the sensitivity of inappropriate emergency department utilization

Non-urgent Emergency Department utilization has been attributed with increasing congestion in the flow and treatment of patients and, by extension, conditions the quality of care and profitability of the Emergency Department. Interventions designed to divert populations to more appropriate care may be cautiously received by operations managers due to uncertainty about the impact an adopted intervention may have on the two values of congestion and profitability. System Dynamics (SD) modeling and simulation may be used to measure the sensitivity of these two, often-competing, values of congestion and profitability and, thus, provide an additional layer of information designed to inform strategic decision making.

Emergency Department Frequent Utilization for Non-Emergent Presentments: Results from a Regional Urban Trauma Center Study.

Objectives 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. Data Source Primary data collection over an eight week period within a level-1 trauma urban hospital’s emergency department. Study Design 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. Principal Findings 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. Conclusions 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.

Identifying Factors that Influence Terrorist Decisions and Target Selection

Currently, the U.S. Department of Homeland Security (DHS) elicits probabilistic judgments from the intelligence community on actions terrorists may take to attack the continental U.S. For example, how likely is the adversary to choose agent ‘x’ over agent ‘y’ or target ‘a’ over target ‘b’? Eliciting these types of judgments is difficult and time consuming. The National Academies and others have suggested that a better approach may be to elicit information on adversary’s preferences, perceptions, and capabilities and use this information to calculate probabilities of interest to DHS. Some terrorist groups are thinking about using weapons of mass destruction (WMD), each with its own values, perceptions of reality, and capabilities. This presentation details the findings on the factors & relationships among factors that lead to a terrorist decision to initiate an attack against the continental U.S as well as target selection. To accomplish this, we assembled international experts in WMD, adversary modeling, political science, terrorism, psychiatry, social sciences as well as experts from national laboratories, the Commonwealth of Virginia State Fusion Center, and Hampton Roads Emergency Management.This paper provides a summary of the findings from an Adaptive Adversary Workshop. In this paper, we provide an overview of the motivation for and design of the workshop as well as 19 emerging themes. The purpose of the workshop was to illicit expert opinions on terrorist decision-making and target selections in an effort to improve our understanding of adversaries (individuals, local/regional groups, transnational groups, states) who may initiate a bioterrorism attack in the form of releasing biological agents upon U.S. interests. Furthermore, these expert opinions are intended to be used to inform Bayesian Belief Network (BBN) models of terrorist networks. These models must be informed or populated with substantive information about the intelligent and adaptive adversary who may initiate an attack. To this end, a conceptual framework, informed broadly by the social sciences community, is intended to capture the terrorists’ motivations, methods, and decision calculi.

Quantifying the economic and demographic impact of transportation infrastructure investments

Investment in transportation infrastructure has been widely utilized as an instrument for inducing economic growth. Such investment usually leads to job creation and an increase in per capita income that attracts population through migration to the region. This increases the utilization of the transport infrastructure over time, resulting in high levels of congestion. The congestion negatively impacts the attractiveness of the region and the gross regional product (GRP). Regions cyclically invest in transport infrastructure that temporally spurs economic activity and migration, and reduces congestion. This research employs a system dynamics simulation approach to capture and mimic the behavior of these complex and cyclical relationships over time. Our approach suggests the modeling of key demographic, transportation infrastructure, travel behavior, and economic activity components to determine the impact of infrastructure investments on regional growth. Given a set of prospective investment scenarios, the model replicates and projects levels of productivity, travel demand, congestion, GRP, and net migration patterns over time. The model also provides insights into the duration of critical cyclical patterns given these prospective infrastructure investments. The simulation model presented in this paper seeks to be utilized as guidance to support decision-making processes that lead to the execution of more exhaustive transportation studies that organize the execution of such investments.

Population vulnerability to storm surge flooding in coastal Virginia, USA.

This study aims to assess the vulnerability of populations to storm surge flooding in 12 coastal localities of Virginia, USA. Population vulnerability is assessed by way of 3 physical factors (elevation, slope, and storm surge category), 3 built-up components (road availability, access to hospitals, and access to shelters), and 3 household conditions (storm preparedness, financial constraints to recovering from severe weather events, and health fragility). Fuzzy analysis is used to generate maps illustrating variation in several types of population vulnerability across the region. When considering physical factors and household conditions, the most vulnerable neighborhoods to sea level rise and storm surge flooding are largely found in urban areas. However, when considering access to critical infrastructure, we find rural residents to be more vulnerable than nonrural residents. These detailed assessments can inform both local and state governments in catastrophic planning. In addition, the methodology may be generalized to assess vulnerability in other coastal corridors and communities. The originality is highlighted by evaluating socioeconomic conditions at refined scale, incorporating a broader range of human perceptions and predispositions, and employing a geoinformatics approach combining physical, built-up, and socioeconomic conditions for population vulnerability assessment. Integr Environ Assess Manag 2016;12:500-509.

Modeling the Transition from Adverse to Healthy Sleep Behaviors among School Age Children: A Simulation Approach

This research models and simulates, by way of a System Dynamics, approach sleep behavior in the presence of intervention strategies. The authors draw upon the established compartmental Susceptible, Infection, and Recovery (SIR) model used in epidemiology to characterize the potential for children and adolescents to both develop adverse sleep behaviors and to recover healthy sleep behaviors as they progress through educational levels. The development of healthy sleep during childhood and adolescence is important to the sustainment of healthy behaviors into early adulthood. Interventions designed to alter unhealthy sleep-related behaviors adopted at an early age may have a salubrious impact upon later chronic disease development. Our initial analyses adequately reproduce the drift experienced by children and adolescents who develop adverse sleep behaviors as they mature and transition through school levels. The ability to evaluate the effectiveness of interventions is important to public health officials. Investments in intervention programs shown to have positive health outcomes are attractive to policy makers. Although such programs may not be cost effective in the near-term, the programs may be cost saving in the long-term. The System Dynamics approach simulates behavior over time and allows policymakers insight into both the short-and longer-term cost and benefits.

Housing recovery in the aftermath of a catastrophe

Understand housing recovery process after disaster from material resource viewpoint.System dynamics to study problem at reconstruction material supply level.Shows importance of timing in decision making for supplies in housing recovery.Results in anticipating demand requirements with uncertainties related to disaster. Background/purposeThe occurrence of catastrophic events proves disastrous as they cause significant physical damages, both at the human and material levels. Depending on the magnitude of the event, a natural phenomenon can potentially lead to loss of life, home destruction and alter the economic and social structures of the affected community. The purpose of this study is to gain a deeper insight into the housing recovery process following a catastrophic event from the material resources perspective. MethodA System Dynamics (SD) model is developed in this paper to study the problem at the reconstruction/repair material supply level in an affected area. The model describes the behavior of material resources in the housing reconstruction and recovery planning a catastrophic event. ResultsIt enables deeper understanding of the implications of the occurrence of a disaster on the housing material fluctuations. This model considers, due to the resources shortage created, the amount of material adjustments to make in the aftermath of a highly disruptive event. Theoretical results show satisfaction as the model displays expected results, reflecting the importance of timing in decision making for supplies, in the housing recovery progress. ContributionThe proposed model brings more insight into the types of the housing recovery and the material demands over time. It provides a means to anticipate the demand requirements and alleviate the populations suffering, considering the uncertainties associated with disaster.