Recommendations for modeling disaster responses in public health and medicine: a position paper of the society for medical decision making

Designing a resilience model for pharmaceutical supply chain during crises: a grounded theory approach

Background

During disasters or crises, the traditional models of supply chain encounter failure and skewedness under the inevitable and unknown pressures. The procurement and transformation of required equipment to the involved areas is considered as one of the main triggers of decreasing damages and losses during crisis. In this regard, a breakdown in pharmaceutical supply chain can lead to intensive, undesired consequences.

Methods

This was a qualitative study applying a grounded theory approach. The study was conducted with attending of 32 informant participants who were qualified in supply chain during natural disasters and crisis. In order to collect the data, deep semi-structured interviews were applied along with investigating the documents, observation, field notes and theoretical memos. For data analysis, a continuous comparison was used according to Corbin and Strauss method.

Results

Results of the study were categorized in 8 main categories as the main themes. “Wasting” appeared as the main factor of the resilience of pharmaceutical and consumable medical equipment supply chain. Wasting included two subthemes of loss of resources and wasting time.

Conclusion

In order to make resilience in pharmaceutical and consumable medical equipment during disasters, it is necessary to reinforce the various dimensions of the resilience model to increase the rate of supply chain responsiveness. This study particularly contributes to broadening and deepening our understanding of how to mitigate the risk of undesirable outcomes of pharmaceutical supply chain during the disasters or crises.

Towards integrated modeling of the long-term impacts of oil spills

Although great progress has been made to advance the scientific understanding of oil spills, tools for integrated assessment modeling of the long-term impacts on ecosystems, socioeconomics and human health are lacking. The objective of this study was to develop a conceptual framework that could be used to answer stakeholder questions about oil spill impacts and to identify knowledge gaps and future integration priorities. The framework was initially separated into four knowledge domains (ocean environment, biological ecosystems, socioeconomics, and human health) whose interactions were explored by gathering stakeholder questions through public engagement, assimilating expert input about existing models, and consolidating information through a system dynamics approach. This synthesis resulted in a causal loop diagram from which the interconnectivity of the system could be visualized. Results of this analysis indicate that the system naturally separates into two tiers, ocean environment and biological ecosystems versus socioeconomics and human health. As a result, ocean environment and ecosystem models could be used to provide input to explore human health and socioeconomic variables in hypothetical scenarios. At decadal-plus time scales, the analysis emphasized that human domains influence the natural domains through changes in oil-spill related laws and regulations. Although data gaps were identified in all four model domains, the socioeconomics and human health domains are the least established. Considerable future work is needed to address research gaps and to create fully coupled quantitative integrative assessment models that can be used in strategic decision-making that will optimize recoveries from future large oil spills.

Developing Public Health Emergency Response Leaders in Incident Management: A Scoping Review of Educational Interventions

During emergency responses, public health leaders frequently serve in incident management roles that differ from their routine job functions. Leaders' familiarity with incident management principles and functions can influence response outcomes. Therefore, training and exercises in incident management are often required for public health leaders. To describe existing methods of incident management training and exercises in the literature, we queried 6 English language databases and found 786 relevant articles. Five themes emerged: (1) experiential learning as an established approach to foster engaging and interactive learning environments and optimize training design; (2) technology-aided decision support tools are increasingly common for crisis decision-making; (3) integration of leadership training in the education continuum is needed for developing public health response leaders; (4) equal emphasis on competency and character is needed for developing capable and adaptable leaders; and (5) consistent evaluation methodologies and metrics are needed to assess the effectiveness of educational interventions.These findings offer important strategic and practical considerations for improving the design and delivery of educational interventions to develop public health emergency response leaders. This review and ongoing real-world events could facilitate further exploration of current practices, emerging trends, and challenges for continuous improvements in developing public health emergency response leaders.

Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action

INTRODUCTION

High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications.

METHODS

We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors.

RESULTS

A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies.

CONCLUSIONS

This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.

Identifying Factors That May Influence Decision-Making Related to the Distribution of Patients During a Mass Casualty Incident

OBJECTIVE

We aimed to identify and seek agreement on factors that may influence decision-making related to the distribution of patients during a mass casualty incident.

METHODS

A qualitative thematic analysis of a literature review identified 56 unique factors related to the distribution of patients in a mass casualty incident. A modified Delphi study was conducted and used purposive sampling to identify peer reviewers that had either (1) a peer-reviewed publication within the area of disaster management or (2) disaster management experience. In round one, peer reviewers ranked the 56 factors and identified an additional 8 factors that resulted in 64 factors being ranked during the two-round Delphi study. The criteria for agreement were defined as a median score greater than or equal to 7 (on a 9-point Likert scale) and a percentage distribution of 75% or greater of ratings being in the highest tertile.

RESULTS

Fifty-four disaster management peer reviewers, with hospital and prehospital practice settings most represented, assessed a total of 64 factors, of which 29 factors (45%) met the criteria for agreement.

CONCLUSIONS

Agreement from this formative study suggests that certain factors are influential to decision-making related to the distribution of patients during a mass casualty incident. (Disaster Med Public Health Preparedness. 2018;12:101-108).

Infectious Disease Information Collection System at the Scene of Disaster Relief Based on a Personal Digital Assistant

OBJECTIVE

The objective of this study was to build a database to collect infectious disease information at the scene of a disaster through the use of 128 epidemiological questionnaires and 47 types of options, with rapid acquisition of information regarding infectious disease and rapid questionnaire customization at the scene of disaster relief by use of a personal digital assistant (PDA).

METHODS

SQL Server 2005 (Microsoft Corp, Redmond, WA) was used to create the option database for the infectious disease investigation, to develop a client application for the PDA, and to deploy the application on the server side. The users accessed the server for data collection and questionnaire customization with the PDA.

RESULTS

A database with a set of comprehensive options was created and an application system was developed for the Android operating system (Google Inc, Mountain View, CA). On this basis, an infectious disease information collection system was built for use at the scene of disaster relief. The creation of an infectious disease information collection system and rapid questionnaire customization through the use of a PDA was achieved.

CONCLUSIONS

This system integrated computer technology and mobile communication technology to develop an infectious disease information collection system and to allow for rapid questionnaire customization at the scene of disaster relief. (Disaster Med Public Health Preparedness. 2017;11:668-673).

Risk-Based Decision Making for Reoccupation of Contaminated Areas Following a Wide-Area Anthrax Release

This article presents an analysis of postattack response strategies to mitigate the risks of reoccupying contaminated areas following a release of Bacillus anthracis spores (the bacterium responsible for causing anthrax) in an urban setting. The analysis is based on a hypothetical attack scenario in which individuals are exposed to B. anthracis spores during an initial aerosol release and then placed on prophylactic antibiotics that successfully protect them against the initial aerosol exposure. The risk from reoccupying buildings contaminated with spores due to their reaerosolization and inhalation is then evaluated. The response options considered include: decontamination of the buildings, vaccination of individuals reoccupying the buildings, extended evacuation of individuals from the contaminated buildings, and combinations of these options. The study uses a decision tree to estimate the costs and benefits of alternative response strategies across a range of exposure risks. Results for best estimates of model inputs suggest that the most cost-effective response for high-risk scenarios (individual chance of infection exceeding 11%) consists of evacuation and building decontamination. For infection risks between 4% and 11%, the preferred option is to evacuate for a short period, vaccinate, and then reoccupy once the vaccine has taken effect. For risks between 0.003% and 4%, the preferred option is to vaccinate only. For risks below 0.003%, none of the mitigation actions have positive expected monetary benefits. A sensitivity analysis indicates that for high-infection-likelihood scenarios, vaccination is recommended in the case where decontamination efficacy is less than 99.99%.

Systems engineering methods for enhancing the value stream in public health preparedness: the role of Markov models, simulation, and optimization

OBJECTIVES

Large-scale incidents such as the 2009 H1N1 outbreak, the 2011 European Escherichia coli outbreak, and Hurricane Sandy demonstrate the need for continuous improvement in emergency preparation, alert, and response systems globally. As questions relating to emergency preparedness and response continue to rise to the forefront, the field of industrial and systems engineering (ISE) emerges, as it provides sophisticated techniques that have the ability to model the system, simulate, and optimize complex systems, even under uncertainty.

METHODS

We applied three ISE techniques--Markov modeling, operations research (OR) or optimization, and computer simulation--to public health emergency preparedness.

RESULTS

We present three models developed through a four-year partnership with stakeholders from state and local public health for effectively, efficiently, and appropriately responding to potential public health threats: (1) an OR model for optimal alerting in response to a public health event, (2) simulation models developed to respond to communicable disease events from the perspective of public health, and (3) simulation models for implementing pandemic influenza vaccination clinics representative of clinics in operation for the 2009-2010 H1N1 vaccinations in North Carolina.

CONCLUSIONS

The methods employed by the ISE discipline offer powerful new insights to understand and improve public health emergency preparedness and response systems. The models can be used by public health practitioners not only to inform their planning decisions but also to provide a quantitative argument to support public health decision making and investment.

RE-PLAN: An Extensible Software Architecture to Facilitate Disaster Response Planning

Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or GIS expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer (RE-PLAN) framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface. Bio-E, a user-friendly tool built using this framework, was designed to develop and analyze the feasibility of ad hoc clinics for treating populations following a biological emergency event. In this article, the design and implementation of the RE-PLAN framework are described, and the functionality of the modules used in the Bio-E biological emergency mitigation tool are demonstrated.

State of the art in risk analysis of workforce criticality influencing disaster preparedness for interdependent systems

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.

Quantitative models of the dose-response and time course of inhalational anthrax in humans

Anthrax poses a community health risk due to accidental or intentional aerosol release. Reliable quantitative dose-response analyses are required to estimate the magnitude and timeline of potential consequences and the effect of public health intervention strategies under specific scenarios. Analyses of available data from exposures and infections of humans and non-human primates are often contradictory. We review existing quantitative inhalational anthrax dose-response models in light of criteria we propose for a model to be useful and defensible. To satisfy these criteria, we extend an existing mechanistic competing-risks model to create a novel Exposure–Infection–Symptomatic illness–Death (EISD) model and use experimental non-human primate data and human epidemiological data to optimize parameter values. The best fit to these data leads to estimates of a dose leading to infection in 50% of susceptible humans (ID₅₀) of 11,000 spores (95% confidence interval 7,200–17,000), ID₁₀ of 1,700 (1,100–2,600), and ID₁ of 160 (100–250). These estimates suggest that use of a threshold to human infection of 600 spores (as suggested in the literature) underestimates the infectivity of low doses, while an existing estimate of a 1% infection rate for a single spore overestimates low dose infectivity. We estimate the median time from exposure to onset of symptoms (incubation period) among untreated cases to be 9.9 days (7.7–13.1) for exposure to ID₅₀, 11.8 days (9.5–15.0) for ID₁₀, and 12.1 days (9.9–15.3) for ID₁. Our model is the first to provide incubation period estimates that are independently consistent with data from the largest known human outbreak. This model refines previous estimates of the distribution of early onset cases after a release and provides support for the recommended 60-day course of prophylactic antibiotic treatment for individuals exposed to low doses.

Anthrax poses a potential community health risk due to accidental or intentional aerosol release. We address the need for a transparent and defensible quantitative dose-response model for inhalational anthrax that is useful for risk assessors in estimating the magnitude and timeline of potential public health consequences should a release occur. Our synthesis of relevant data and previous modeling efforts identifies areas of improvement among many commonly cited dose-response models and estimates. To address those deficiencies, we provide a new model that is based on clear, transparent assumptions and published data from human and non-human primate exposures. Our resulting estimates provide important insight into the infectivity to humans of low inhaled doses of anthrax spores and the timeline of infections after an exposure event. These insights are critical to assessment of the impacts of delays in responding to a large scale aerosol release, as well as the recommended course of antibiotic administration to those potentially exposed.

Medical rehabilitation after natural disasters: why, when, and how?

Natural disasters can cause significant numbers of severe, disabling injuries, resulting in a public health emergency and requiring foreign assistance. However, since medical rehabilitation services are often poorly developed in disaster-affected regions and not highly prioritized by responding teams, physical and rehabilitation medicine (PRM) has historically been underemphasized in global disaster planning and response. Recent development of the specialties of "disaster medicine" and "disaster rehabilitation" has raised awareness of the critical importance of rehabilitation intervention during the immediate postdisaster emergency response. The World Health Organization Liaison Sub-Committee on Rehabilitation Disaster Relief of the International Society of Physical and Rehabilitation Medicine has authored this report to assess the role of emergency rehabilitation intervention after natural disasters based on current scientific evidence and subject matter expert accounts. Major disabling injury types are identified, and spinal cord injury, limb amputation, and traumatic brain injury are used as case studies to exemplify the challenges to effective management of disabling injuries after disasters. Evidence on the effectiveness of disaster rehabilitation interventions is presented. The authors then summarize the current state of disaster-related research, as well as lessons learned from PRM emergency rehabilitation response in recent disasters. Resulting recommendations for greater integration of PRM services into the immediate emergency disaster response are provided. This report aims to stimulate development of research and practice in the emerging discipline of disaster rehabilitation within organizations that provide medical rehabilitation services during the postdisaster emergency response.

A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels

BACKGROUND

In recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several concerns about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these concerns and identify means of enhancing the current models for higher operational use.

METHODS

We surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers.

RESULTS

While examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values.

CONCLUSIONS

To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.

Principles of Scarce Medical Resource Allocation in Natural Disaster Relief

Background. A variety of triage principles have been proposed. The authors sought to evaluate their effects on how many lives can be saved in a hypothetical disaster. Objective. To determine an optimal scarce resource–rationing principle in the emergency response domain, considering the trade-off between lifesaving efficiency and ethical issues. Method. A discrete event simulation model is developed to examine the efficiency of four resource-rationing principles: first come–first served, random, most serious first, and least serious first. Seven combinations of available resources are examined in the simulations to evaluate the performance of the principles under different levels of resource scarcity. Result. The simulation results indicate that the performance of the medical resource allocation principles is related to the level of the resource scarcity. When the level of the scarcity is high, the performances of the four principles differ significantly. The least serious first principle performs best, followed by the random principle; the most serious first principle acts worst. However, when the scarcity is relieved, there are no significant differences among the random, first come–first served, and least serious first principles, yet the most serious first principle still performs worst. Conclusion. Although the least serious first principle exhibits the highest efficiency, it is not ethically flawless. Considering the trade off between the lifesaving efficiency and the ethical issues, random selection is a relatively fair and efficient principle for allocating scarce medical resources in natural disaster responses.

Availability of a pediatric trauma center in a disaster surge decreases triage time of the pediatric surge population: a population kinetics model

Background

The concept of disaster surge has arisen in recent years to describe the phenomenon of severely increased demands on healthcare systems resulting from catastrophic mass casualty events (MCEs) such as natural disasters and terrorist attacks. The major challenge in dealing with a disaster surge is the efficient triage and utilization of the healthcare resources appropriate to the magnitude and character of the affected population in terms of its demographics and the types of injuries that have been sustained.

Results

In this paper a deterministic population kinetics model is used to predict the effect of the availability of a pediatric trauma center (PTC) upon the response to an arbitrary disaster surge as a function of the rates of pediatric patients' admission to adult and pediatric centers and the corresponding discharge rates of these centers. We find that adding a hypothetical pediatric trauma center to the response documented in an historical example (the Israeli Defense Forces field hospital that responded to the Haiti earthquake of 2010) would have allowed for a significant increase in the overall rate of admission of the pediatric surge cohort. This would have reduced the time to treatment in this example by approximately half. The time needed to completely treat all children affected by the disaster would have decreased by slightly more than a third, with the caveat that the PTC would have to have been approximately as fast as the adult center in discharging its patients. Lastly, if disaster death rates from other events reported in the literature are included in the model, availability of a PTC would result in a relative mortality risk reduction of 37%.

Conclusions

Our model provides a mathematical justification for aggressive inclusion of PTCs in planning for disasters by public health agencies.

Decision making for HIV prevention and treatment scale up: bridging the gap between theory and practice

BACKGROUND

Effectively controlling the HIV epidemic will require efficient use of limited resources. Despite ambitious global goals for HIV prevention and treatment scale up, few comprehensive practical tools exist to inform such decisions.

METHODS

We briefly summarize modeling approaches for resource allocation for epidemic control, and discuss the practical limitations of these models. We describe typical challenges of HIV resource allocation in practice and some of the tools used by decision makers. We identify the characteristics needed in a model that can effectively support planners in decision making about HIV prevention and treatment scale up.

RESULTS

An effective model to support HIV scale-up decisions will be flexible, with capability for parameter customization and incorporation of uncertainty. Such a model needs certain key technical features: it must capture epidemic effects; account for how intervention effectiveness depends on the target population and the level of scale up; capture benefit and cost differentials for packages of interventions versus single interventions, including both treatment and prevention interventions; incorporate key constraints on potential funding allocations; identify optimal or near-optimal solutions; and estimate the impact of HIV interventions on the health care system and the resulting resource needs. Additionally, an effective model needs a user-friendly design and structure, ease of calibration and validation, and accessibility to decision makers in all settings.

CONCLUSIONS

Resource allocation theory can make a significant contribution to decision making about HIV prevention and treatment scale up. What remains now is to develop models that can bridge the gap between theory and practice.