Modelling the impact of an influenza pandemic on critical care services in England

Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves

Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions.

Optimising acute stroke pathways through flexible use of bed capacity: a computer modelling study

Background

Optimising capacity along clinical pathways is essential to avoid severe hospital pressure and help ensure best patient outcomes and financial sustainability. Yet, typical approaches, using only average arrival rate and average lengths of stay, are known to underestimate the number of beds required. This study investigates the extent to which averages-based estimates can be complemented by a robust assessment of additional ‘flex capacity’ requirements, to be used at times of peak demand.

Methods

The setting was a major one million resident healthcare system in England, moving towards a centralised stroke pathway. A computer simulation was developed for modelling patient flow along the proposed stroke pathway, accounting for variability in patient arrivals, lengths of stay, and the time taken for transfer processes. The primary outcome measure was flex capacity utilisation over the simulation period.

Results

For the hyper-acute, acute, and rehabilitation units respectively, flex capacities of 45%, 45%, and 36% above the averages-based calculation would be required to ensure that only 1% of stroke presentations find the hyper-acute unit full and have to wait. For each unit some amount of flex capacity would be required approximately 30%, 20%, and 18% of the time respectively.

Conclusions

This study demonstrates the importance of appropriately capturing variability within capacity plans, and provides a practical and economical approach which can complement commonly-used averages-based methods. Results of this study have directly informed the healthcare system’s new configuration of stroke services.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08433-0.

Forecasting national and regional level intensive care unit bed demand during COVID-19: The case of Italy

Given the pressure on healthcare authorities to assess whether hospital capacity allows properly responding to outbreaks such as COVID-19, there is a need for simple, data-driven methods that may provide accurate forecasts of hospital bed demand. This study applies growth models to forecast the demand for Intensive Care Unit admissions in Italy during COVID-19. We show that, with only some mild assumptions on the functional form and using short time-series, the model fits past data well and can accurately forecast demand fourteen days ahead (the mean absolute percentage error (MAPE) of the cumulative fourteen days forecasts is 7.64). The model is then applied to derive regional-level forecasts by adopting hierarchical methods that ensure the consistency between national and regional level forecasts. Predictions are compared with current hospital capacity in the different Italian regions, with the aim to evaluate the adequacy of the expansion in the number of beds implemented during the COVID-19 crisis.

A Generalized Mechanistic Model for Assessing and Forecasting the Spread of the COVID-19 Pandemic

Since early 2020, the world has been afflicted with an unprecedented global pandemic. The SARS-CoV-19 (COVID-19) has levied massive economic and public health costs across many countries. Due to its virulence, the pathogen is rapidly propagating throughout the world in such a way that makes it incredibly challenging for officials to contain its spread. Therefore, there is a pressing need for national and local authorities to have tools that aid in their ability to assess and extrapolate the future trends of the spread of COVID-19, so they may make rational and informed decisions that minimize public harm. Mechanistic models are prominent mathematical tools that are used to characterize epidemics. In this paper, we propose a generalized mechanistic model with eight states characterizing the COVID-19 pandemic evolution from a susceptible state to discharged states while passing by quarantined and hospitalized states. The parameters of the model are determined by solving a fitting optimization problem with three observed inputs: the number of infected, deceased, and reported cases. The model's objective function is weighted over the training days so as to guide the fitting algorithm towards the latest pandemic period and lead to more accurate trend predictions for a stronger forecast. We solve the fitting problem with the Levenberg-Marquardt algorithm; we compare the performance of the model generated from this algorithm to the one of another state-of-the-art fitting algorithm as well as to the one of another compartmental model widely used in literature. We test the model on the COVID-19 data from four highly afflicted countries. The fitting algorithm has been validated graphically and through numerical metrics, and results show significantly accurate results for most of the countries. Once the model's parameters are estimated, forecasting results are derived and uncertainty regions of the expected scenarios are provided.

Development of a structured process for fair allocation of critical care resources in the setting of insufficient capacity: a discussion paper

Early in the COVID-19 pandemic there was widespread concern that healthcare systems would be overwhelmed, and specifically, that there would be insufficient critical care capacity in terms of beds, ventilators or staff to care for patients. In the UK, this was avoided by a threefold approach involving widespread, rapid expansion of critical care capacity, reduction of healthcare demand from non-COVID-19 sources by temporarily pausing much of normal healthcare delivery, and by governmental and societal responses that reduced demand through national lockdown. Despite high-level documents designed to help manage limited critical care capacity, none provided sufficient operational direction to enable use at the bedside in situations requiring triage. We present and describe the development of a structured process for fair allocation of critical care resources in the setting of insufficient capacity. The document combines a wide variety of factors known to impact on outcome from critical illness, integrated with broad-based clinical judgement to enable structured, explicit, transparent decision-making founded on robust ethical principles. It aims to improve communication and allocate resources fairly, while avoiding triage decisions based on a single disease, comorbidity, patient age or degree of frailty. It is designed to support and document decision-making. The document has not been needed to date, nor adopted as hospital policy. However, as the pandemic evolves, the resumption of necessary non-COVID-19 healthcare and economic activity mean capacity issues and the potential need for triage may yet return. The document is presented as a starting point for stakeholder feedback and discussion.

Model-Based Recursive Partitioning of Patients' Return Visits to Multispecialty Clinic During the 2009 H1N1 Pandemic Influenza (pH1N1)

Background During the 2009 H1N1 influenza pandemic (pH1N1), the proportion of outpatient visits to emergency departments, clinics and hospitals became elevated especially during the early months of the pandemic due to surges in sick, 'worried well' or returning patients seeking care. We determined the prevalence of return visits to a multispecialty clinic during the 2009 H1N1 influenza pandemic and identify subgroups at risk for return visits using model-based recursive partitioning technique. Methods This study was a retrospective analysis of ILI-related medical care visits to multispecialty clinic in Houston, Texas obtained as part of the Houston Health Department Influenza Sentinel Surveillance Project (ISSP) during the 2009 H1N1 pandemic influenza (April 2009 - March 2010). The data comprised of 2680 individuals who made a total of 2960 clinic visits. Return visit was defined as any visit following the index visit after the wash-out phase prior to the study period. We applied nominal logistic regression and recursive partitioning models to determine the independent predictors and the response probabilities of return visits. The sensitivity and specificity of the outcomes probabilities were determined using receiver operating characteristic (ROC) curve. Results Overall, 4.56% (Prob. 0.0%-17.5%) of the cohort had return visits with significant variations observed attributed to age group (76.0%), type of vaccine received by patients (18.4%) and Influenza A (pH1N1) test result (5.6%). Patients in age group 0-4 years were 9 times (aOR: 8.77, 95%CI: 3.39-29.95, p<0.0001) more likely than those who were 50⁺ years to have return visits. Similarly, patients who received either seasonal flu (aOR: 1.59, 95% CI 1.01-2.50, p=0.047) or pH1N1 (aOR: 1.74, 95%CI: 1.09-2.75, p=0.022) vaccines were about twice more likely to have return visits compared to those with no vaccination history. Model-based recursive partitioning yielded 19 splits with patients in subgroup I (patients of age group 0-4 years, who tested positive for pH1N1, and received both seasonal flu and pH1N1 vaccines) having the highest risk of return visits (Prob.=17.5%). The area under the curve (AUC) for both return and non-return visits was 72.9%, indicating a fairly accurate classification of the two groups. Conclusions Return visits in our cohort were more prevalent among children and young adults, and those that received either seasonal flu or pH1N1 or both vaccines. Understanding the dynamics in care-seeking behavior during pandemic would assist policymakers with appropriate resource allocation, and in the design of initiatives aimed at mitigating surges and recurrent utilization of the healthcare system.

Prediction of the increase in health services demand based on the analysis of reasons of calls received by a customer relationship management

Currently, customer relationship management (CRM) tools are very important in our society because they provide a comunication channel to the healthcare system for patients. Salud Responde is a CRM that provides many health services for the entire population of Andalusia, in southern Spain. The number and frequenzy of phone calls received change along the year. They depend on many factors, such as weekdays, seasons, vaccination campaigns, environmental factors, pandemic periods, etc. All these are the main reasons number of health calls changes along the year. This variability makes that the current management of resources for offering emergency services based on historical data is inefficient. The factors, which influence the phone calls along the year, are different from one period to another. Therefore, it is clear to demand an improved in the current management system. In this context, the main goal for this research is to develop an expert system able to identify and analyze, using different data mining algorithms, the most relevant factors to predict the variability of health service demand. Thus, here, it is proposed a methodology in which using reasons calls received in the CRM as input data, it is possible to predict in advance the healthcare resources demand.

Sensitivity Analysis of the Parameters of Korea's Pandemic Influenza Preparedness Plan

OBJECTIVES

Our aim was to evaluate Korea's Pandemic Influenza Preparedness Plan.

METHODS

We conducted a sensitivity analysis on the expected number of outpatients and hospital bed occupancy, with 1,000,000 parameter combinations, in a situation of pandemic influenza, using the mathematical simulation program InfluSim.

RESULTS

Given the available resources in Korea, antiviral treatment and social distancing must be combined to reduce the number of outpatients and hospitalizations sufficiently; any single intervention is not enough. The antiviral stockpile of 4-6% is sufficient for the expected eligible number of cases to be treated. However, the eligible number assumed (30% for severe cases and 26% for extremely severe cases) is very low compared to the corresponding number in European countries, where up to 90% of the population are assumed to be eligible for antiviral treatment.

CONCLUSIONS

A combination of antiviral treatment and social distancing can mitigate a pandemic, but will only bring it under control for the most optimistic parameter combinations.

Health systems' "surge capacity": state of the art and priorities for future research

CONTEXT

Over the past decade, a number of high-impact natural hazard events, together with the increased recognition of pandemic risks, have intensified interest in health systems' ability to prepare for, and cope with, "surges" (sudden large-scale escalations) in treatment needs. In this article, we identify key concepts and components associated with this emerging research theme. We consider the requirements for a standardized conceptual framework for future research capable of informing policy to reduce the morbidity and mortality impacts of such incidents. Here our objective is to appraise the consistency and utility of existing conceptualizations of health systems' surge capacity and their components, with a view to standardizing concepts and measurements to enable future research to generate a cumulative knowledge base for policy and practice.

METHODS

A systematic review of the literature on concepts of health systems' surge capacity, with a narrative summary of key concepts relevant to public health.

FINDINGS

The academic literature on surge capacity demonstrates considerable variation in its conceptualization, terms, definitions, and applications. This, together with an absence of detailed and comparable data, has hampered efforts to develop standardized conceptual models, measurements, and metrics. Some degree of consensus is evident for the components of surge capacity, but more work is needed to integrate them. The overwhelming concentration in the United States complicates the generalizability of existing approaches and findings.

CONCLUSIONS

The concept of surge capacity is a useful addition to the study of health systems' disaster and/or pandemic planning, mitigation, and response, and it has far-reaching policy implications. Even though research in this area has grown quickly, it has yet to fulfill its potential to generate knowledge to inform policy. Work is needed to generate robust conceptual and analytical frameworks, along with innovations in data collection and methodological approaches that enhance health systems' readiness for, and response to, unpredictable high-consequence surges in demand.

Health system resource gaps and associated mortality from pandemic influenza across six Asian territories

Background

Southeast Asia has been the focus of considerable investment in pandemic influenza preparedness. Given the wide variation in socio-economic conditions, health system capacity across the region is likely to impact to varying degrees on pandemic mitigation operations. We aimed to estimate and compare the resource gaps, and potential mortalities associated with those gaps, for responding to pandemic influenza within and between six territories in Asia.

Methods and Findings

We collected health system resource data from Cambodia, Indonesia (Jakarta and Bali), Lao PDR, Taiwan, Thailand and Vietnam. We applied a mathematical transmission model to simulate a “mild-to-moderate” pandemic influenza scenario to estimate resource needs, gaps, and attributable mortalities at province level within each territory. The results show that wide variations exist in resource capacities between and within the six territories, with substantial mortalities predicted as a result of resource gaps (referred to here as “avoidable” mortalities), particularly in poorer areas. Severe nationwide shortages of mechanical ventilators were estimated to be a major cause of avoidable mortalities in all territories except Taiwan. Other resources (oseltamivir, hospital beds and human resources) are inequitably distributed within countries. Estimates of resource gaps and avoidable mortalities were highly sensitive to model parameters defining the transmissibility and clinical severity of the pandemic scenario. However, geographic patterns observed within and across territories remained similar for the range of parameter values explored.

Conclusions

The findings have important implications for where (both geographically and in terms of which resource types) investment is most needed, and the potential impact of resource mobilization for mitigating the disease burden of an influenza pandemic. Effective mobilization of resources across administrative boundaries could go some way towards minimizing avoidable deaths.

Epidemiologic modeling with FluSurge for pandemic (H1N1) 2009 outbreak, Queensland, Australia

At the beginning of the pandemic (H1N1) 2009 outbreak, we estimated the potential surge in demand for hospital-based services in 4 Health Service Districts of Queensland, Australia, using the FluSurge model. Modifications to the model were made on the basis of emergent evidence and results provided to local hospitals to inform resource planning for the forthcoming pandemic. To evaluate the fit of the model, a comparison between the model's predictions and actual hospitalizations was made. In early 2010, a Web-based survey was undertaken to evaluate the model's usefulness. Predictions based on modified assumptions arising from the new pandemic gained better fit than results from the default model. The survey identified that the modeling support was helpful and useful to service planning for local hospitals. Our research illustrates an integrated framework involving post hoc comparison and evaluation for implementing epidemiologic modeling in response to a public health emergency.

Intensive Care Admission Triage during a Pandemic: A Survey of the Acceptability of Triage Tools

We conducted a survey of the UK Intensive Care Society regarding physician opinion of national guidance on ICU triage during a viral pandemic. Respondents graded agreement for seventeen triage criteria, ten from the Department of Health. We determined whether respondents accepted the whole tool on the basis of proportion of criteria agreed with. A modified tool was devised and acceptability compared. Five hundred and fifty questionnaires were returned (33.1% from senior physicians). Approximately half of senior physicians (49.5%) and 44.4% of other respondents found the tool acceptable. This improved to 68.7% and 59.2% for the modified tool. Chi-square analysis revealed no statistically significant difference between the opinions of senior physicians and other respondents (p=0.850 for the original tool, p=0.593 for the modified tool). A small change to the government guidelines produced a tool with improved acceptability among ICU physicians.

Estimates of the impact of a future influenza pandemic in China

Background  The next influenza pandemic will create a surge in demand for health resources in China, with its current population of >1·3 billion persons and under‐developed medical care and public health system. However, few pandemic impact data are available for China.

Objectives  We estimated the effects of a future influenza pandemic in China by examining pandemic scenarios of varying severity and described the time distribution of cases during a first wave.

Methods  We used a Monte‐Carlo simulation model and death rates, hospitalizations and outpatient visits for 1918‐ and 1968‐like pandemic scenarios and data from the literature or experts’ opinion to estimate four health outcomes: deaths, hospitalizations, outpatient medical visits and clinical illness for which medical care was not sought. For each of the two scenarios we estimated outcomes by week using a normal distribution.

Results  We estimated that a 1968 scenario in China would result in 460 000–700 000 deaths, 1·94–2·27 million hospitalizations, 111–117 million outpatient visits and 192–197 million illnesses for which medical care was not sought. Fifty‐two percent of hospitalizations occurred during the two‐peak weeks of the first wave. We estimated that patients at high‐risk of influenza complications (10–17% of the population) would account for 61–75% of all deaths. For a 1918 scenario, we estimated that 4·95–6·95 million deaths, 20·8–22·7 million hospitalizations and 101–108 million outpatient visits could occur.

Conclusion  Even a 1968 pandemic scenario will pose substantial challenges for the medical and public health system in China, and planning to manage these challenges is essential.

Health service resource needs for pandemic influenza in developing countries: a linked transmission dynamics, interventions and resource demand model

We used a mathematical model to describe a regional outbreak and extrapolate the underlying health-service resource needs. This model was designed to (i) estimate resource gaps and quantities of resources needed, (ii) show the effect of resource gaps, and (iii) highlight which particular resources should be improved. We ran the model, parameterized with data from the 2009 H1N1v pandemic, for two provinces in Thailand. The predicted number of preventable deaths due to resource shortcomings and the actual resource needs are presented for two provinces and for Thailand as a whole. The model highlights the potentially huge impact of health-system resource availability and of resource gaps on health outcomes during a pandemic and provides a means to indicate where efforts should be concentrated to effectively improve pandemic response programmes.

Pandemic influenza and pediatric intensive care

OBJECTIVE

To assess the adequacy of preparedness planning for an influenza pandemic by modeling the pediatric surge capacity of healthcare facility and pediatric intensive care unit (PICU) requirements over time. Governments and Public Health authorities have planned preparedness activities and training for a flu pandemic. PICU facilities will be the limiting factor in healthcare provision for children but detailed analyses for needs and demands in PICU care have not been published.

DESIGN

Based on the Center for Disease Control and Prevention and World Health Organization estimates and published models of the expected evolution of pandemic flu, we modeled the pediatric surge capacity of healthcare facility and PICU requirements over time. Various scenarios with different assumptions were explored. We compared these demands with estimates of maximal PICU capacity factoring in healthcare worker absenteeism as well as reported and more realistic estimates derived from semistructured telephone interviews with key stakeholders in ICUs in the study area.

SETTING

All hospitals and intensive care facilities in the Northern Region in The Netherlands with near 1.7 million inhabitants, of whom approximately 25% is <18 yrs.

MEASUREMENTS AND MAIN RESULTS

Using well-established modeling techniques, evidence-based medicine, and incorporating estimates from the Centers for Disease Control and Prevention and World Health Organization, we show that PICU capacity may suffice during an influenza pandemic. Even during the peak of the pandemic, most children requiring PICU admission may be served, even those who have nonflu-related conditions, provided that robust indications and decision rules are maintained, both for admission, as well as continuation (or discontinuation) of life support.

CONCLUSIONS

We recommend that a model, with assumptions that can be adapted with new information obtained during early stages of the pandemic that is evolving, be an integral part of a preparedness plan for a pandemic influenza with new human transmissible agent like influenza A virus.

Pandemic (H1N1) 2009 influenza

The clinical picture in severe cases of pandemic (H1N1) 2009 influenza is markedly different from the disease pattern seen during epidemics of seasonal influenza, in that many of those affected were previously healthy young people. Current predictions estimate that, during a pandemic wave, 12-30% of the population will develop clinical influenza (compared with 5-15% for seasonal influenza) with 4% of those patients requiring hospital admissions and one in five requiring critical care. This review covers the background, clinical presentation, diagnosis, and treatment. The role of immunization and antiviral drugs is discussed. Experience from the first wave of pandemic (H1N1) 2009 influenza suggests that a number of infected patients become critically ill and require intensive care admission. These patients rapidly develop severe progressive respiratory failure which is often associated with failure of other organs, or marked worsening of underlying airways disease. The critical care management of these patients and the implications for resources is reviewed. Guidance from a range of bodies has been produced in a relatively short period of time in response to pandemic (H1N1) 2009 influenza. Disease severity has the potential to change, especially if there is virus mutation. Clinicians must be prepared for the unexpected and continue to share their experiences to maximize patient outcomes.

An observational cohort study of triage for critical care provision during pandemic influenza: 'clipboard physicians' or 'evidenced based medicine'?

We assessed the impact of a United Kingdom government-recommended triage process, designed to guide the decision to admit patients to intensive care during an influenza pandemic, on patients in a teaching hospital intensive care unit. We found that applying the triage criteria to a current case-mix would result in 116 of the 255 patients (46%) admitted during the study period being denied intensive care treatment they would have otherwise received, of which 45 (39%) survived to hospital discharge. In turn, 69% of those categorised as too ill to warrant admission according to the criteria survived. The sensitivity and specificity of the triage category at ICU admission predicting mortality was 0.29 and 0.84, respectively. If the need for intensive care beds is estimated to be 275 patients per week, the triage criteria would not exclude enough patients to prevent the need for further rationing. We conclude that the proposed triage tool failed adequately to prioritise patients who would benefit from intensive care.

An assessment of the validity of SOFA score based triage in H1N1 critically ill patients during an influenza pandemic

Sequential Organ Failure Assessment (SOFA) score based triage of influenza A H1N1 critically ill patients has been proposed for surge capacity management as a guide for clinical decision making. We conducted a retrospective records review and SOFA scoring of critically ill patients with influenza A H1N1 in a mixed medical-surgical intensive care unit in an urban hospital. Eight critically ill patients with influenza A H1N1 were admitted to the intensive care unit. Their mean (range) age was 39 (26-52) years with a length of stay of 11 (3-17) days. All patients met SOFA score based triage admission criteria with a modal SOFA score of five. Five patients required invasive ventilation for a mean (range) of 5 (4-11) days. Five patients would have been considered for withdrawal of treatment using SOFA scoring guidelines at 48 h. All patients survived. We conclude that SOFA score based triage could lead to withdrawal of life support in critically ill patients who could survive with an acceptably low length of stay in the intensive care unit.

Outcome of critically ill patients with influenza virus infection

Background

Influenza is a major cause of morbidity and mortality, with its greatest burden on the elderly and patients with chronic co-morbidities in the intensive care unit (ICU). An accurate prognosis is essential for decision-making during pandemic as well as interpandemic periods.

Methods

A retrospective cohort study was conducted to determine prognostic factors influencing short term outcome of critically ill patients with confirmed influenza virus infection. Baseline characteristics, laboratory and diagnostic findings, ICU interventions and complications were abstracted from medical records using standard definitions and compared between hospital survivors and non-survivors with univariate and multivariate logistic regression analyses.

Results

111 patients met the inclusion criteria. Acute respiratory distress syndrome (ARDS) complicated ICU course in 25 (23%) of the patients, with mortality rate of 52%. Multivariate logistic regression analysis identified the following predictors of hospital mortality: Acute Physiology and Chronic Health Evaluation (APACHE) III predicted mortality (Odds ratio [OR] 1.49, 95% confidence interval [CI] 1.1–2.1 for 10% increase), ARDS (OR 7.7, 95% CI 2.3–29) and history of immunosuppression (OR 7.19, 95% CI 1.9–28).

Conclusions

APACHE III predicted mortality, the development of ARDS and the history of immunosuppression are independent risk factors for hospital mortality in critically ill patients with confirmed influenza virus infection.

Modelling the impact of an influenza A/H1N1 pandemic on critical care demand from early pathogenicity data: the case for sentinel reporting

Projected critical care demand for pandemic influenza H1N1 in England was estimated in this study. The effect of varying hospital admission rates under statistical uncertainty was examined. Early in a pandemic, uncertainty in epidemiological parameters leads to a wide range of credible scenarios, with projected demand ranging from insignificant to overwhelming. However, even small changes to input assumptions make the major incident scenario increasingly likely. Before any cases are admitted to hospital, 95% confidence limit on admission rates led to a range in predicted peak critical care bed occupancy of between 0% and 37% of total critical care bed capacity, half of these cases requiring ventilatory support. For hospital admission rates above 0.25%, critical care bed availability would be exceeded. Further, only 10% of critical care beds in England are in specialist paediatric units, but best estimates suggest that 30% of patients requiring critical care will be children. Paediatric intensive care facilities are likely to be quickly exhausted and suggest that older children should be managed in adult critical care units to allow resource optimisation. Crucially this study highlights the need for sentinel reporting and real-time modelling to guide rational decision making.

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

PURPOSE

Mathematical and simulation models are increasingly used to plan for and evaluate health sector responses to disasters, yet no clear consensus exists regarding best practices for the design, conduct, and reporting of such models. The authors examined a large selection of published health sector disaster response models to generate a set of best practice guidelines for such models.

METHODS

. The authors reviewed a spectrum of published disaster response models addressing public health or health care delivery, focusing in particular on the type of disaster and response decisions considered, decision makers targeted, choice of outcomes evaluated, modeling methodology, and reporting format. They developed initial recommendations for best practices for creating and reporting such models and refined these guidelines after soliciting feedback from response modeling experts and from members of the Society for Medical Decision Making.

RESULTS

. The authors propose 6 recommendations for model construction and reporting, inspired by the most exemplary models: health sector disaster response models should address real-world problems, be designed for maximum usability by response planners, strike the appropriate balance between simplicity and complexity, include appropriate outcomes that extend beyond those considered in traditional cost-effectiveness analyses, and be designed to evaluate the many uncertainties inherent in disaster response. Finally, good model reporting is particularly critical for disaster response models.

CONCLUSIONS

. Quantitative models are critical tools for planning effective health sector responses to disasters. The proposed recommendations can increase the applicability and interpretability of future models, thereby improving strategic, tactical, and operational aspects of preparedness planning and response.

The open-air treatment of pandemic influenza

The H1N1 "Spanish flu" outbreak of 1918-1919 was the most devastating pandemic on record, killing between 50 million and 100 million people. Should the next influenza pandemic prove equally virulent, there could be more than 300 million deaths globally. The conventional view is that little could have been done to prevent the H1N1 virus from spreading or to treat those infected; however, there is evidence to the contrary. Records from an "open-air" hospital in Boston, Massachusetts, suggest that some patients and staff were spared the worst of the outbreak. A combination of fresh air, sunlight, scrupulous standards of hygiene, and reusable face masks appears to have substantially reduced deaths among some patients and infections among medical staff. We argue that temporary hospitals should be a priority in emergency planning. Equally, other measures adopted during the 1918 pandemic merit more attention than they currently receive.

Pandemic influenza and excess intensive-care workload

In The Netherlands a major part of preparedness planning for an epidemic or pandemic consists of maintaining essential public services, e.g., by the police, fire departments, army personnel, and healthcare workers. We provide estimates for peak demand for healthcare workers, factoring in healthcare worker absenteeism and using estimates from published epidemiologic models on the expected evolution of pandemic influenza in relation to the impact on peak surge capacity of healthcare facilities and intensive care units (ICUs). Using various published scenarios, we estimate their effect in increasing the availability of healthcare workers for duty during a pandemic. We show that even during the peak of the pandemic, all patients requiring hospital and ICU admission can be served, including those who have non-influenza-related conditions. For this rigorous task differentiation, clear hierarchical management, unambiguous communication, and discipline are essential and we recommend informing and training non-ICU healthcare workers for duties in the ICU.

[Organization of intensive care in situation of avian flu pandemic]

The influenza pandemic will create a major increase in demand for hospital admissions, particularly for critical care services. The recommendations detailed herein have been elaborated by experts from medical societies potentially involved in this situation and focus on general hospital organization. Intensive care units will initially face high demand for admission; the Healthcare Authorities must therefore study how ICU capacity can be expanded. Pediatric intensive care units will be particularly affected by this situation of relative bed shortage, since young children, particularly infants, are expected to be affected by severe clinical forms of avian flu. Therefore, the weight threshold for admission to the adult ICU was lowered to 20 kg. Neonatal intensive care units (NICU) should remain, if possible, low viral density areas. Mixed (neonatal and pediatric) intensive care units could be dedicated to infants and children only. NICU admission of extreme premature babies should be limited in this difficult situation. Pediatric intensive care units (PICU) admission capacity could be doubled by using intermediate care and postoperative care units. The staff could be increased by doctors and nurses involved in canceled programmed activities. Healthcare workers transferred to PICU should be given special training.

Partially wrong? Partial equilibrium and the economic analysis of public health emergencies of international concern

We argue that traditional health economic analysis is ill-equipped to estimate the cost effectiveness and cost benefit of interventions that aim at controlling and/or preventing public health emergencies of international concern (such as pandemic influenza or severe acute respiratory syndrome). The implicit assumption of partial equilibrium within both the health sector itself and--if a wider perspective is adopted--the economy as a whole would be violated by such emergencies. We propose an alternative, with the specific aim of accounting for the behavioural changes and capacity problems that are expected to occur when such an outbreak strikes.

Modeling the effects of drug resistant influenza virus in a pandemic

Neuraminidase inhibitors (NI) play a major role in plans to mitigate future influenza pandemics. Modeling studies suggested that a pandemic may be contained at the source by early treatment and prophylaxis with antiviral drugs. Here, we examine the influence of NI resistant influenza strains on an influenza pandemic. We extend the freely available deterministic simulation program InfluSim to incorporate importations of resistant infections and the emergence of de novo resistance. The epidemic with the fully drug sensitive strain leads to a cumulative number of 19,500 outpatients and 258 hospitalizations, respectively, per 100,000 inhabitants. Development of de novo resistance alone increases the total number of outpatients by about 6% and hospitalizations by about 21%. If a resistant infection is introduced into the population after three weeks, the outcome dramatically deteriorates. Wide-spread use of NI treatment makes it highly likely that the resistant strain will spread if its fitness is high. This situation is further aggravated if a resistant virus is imported into a country in the early phase of an outbreak. As NI-resistant influenza infections with high fitness and pathogenicity have just been observed, the emergence of drug resistance in treated populations and the transmission of drug resistant strains is an important public health concern for seasonal and pandemic influenza.

Definitive care for the critically ill during a disaster: a framework for optimizing critical care surge capacity: from a Task Force for Mass Critical Care summit meeting, January 26-27, 2007, Chicago, IL

BACKGROUND

Plausible disasters may yield hundreds or thousands of critically ill victims. However, most countries, including those with widely available critical care services, lack sufficient specialized staff, medical equipment, and ICU space to provide timely, usual critical care for a large influx of additional patients. Shifting critical care disaster preparedness efforts to augment limited, essential critical care (emergency mass critical care [EMCC]), rather than to marginally increase unrestricted, individual-focused critical care may provide many additional people with access to life-sustaining interventions. In 2007, in response to the increasing concern over a severe influenza pandemic, the Task Force on Mass Critical Care (hereafter called the Task Force) convened to suggest the essential critical care therapeutics and interventions for EMCC.

TASK FORCE SUGGESTIONS

EMCC should include the following: (1) mechanical ventilation, (2) IV fluid resuscitation, (3) vasopressor administration, (4) medication administration for specific disease states (eg, antimicrobials and antidotes), (5) sedation and analgesia, and (6) select practices to reduce adverse consequences of critical illness and critical care delivery. Also, all hospitals with ICUs should prepare to deliver EMCC for a daily critical care census at three times their usual ICU capacity for up to 10 days.

DISCUSSION

By using the Task Force suggestions for EMCC, communities may better prepare to deliver augmented critical care in response to disasters. In light of current mass critical care data limitations, the Task Force suggestions were developed to guide preparedness but are not intended as strict policy mandates. Additional research is required to evaluate EMCC and revise the strategy as warranted.

[The organisation of intensive care in a situation of pandemic avian influenza]

The development of an epidemic of avian influenza will have a major impact on the organisation and structure of the facilities for treatment. This paper, the product of collaboration between the six learned societies concerned, analyses the impact of a possible pandemic on the various aspects of management of patients requiring intensive care. It describes the organisation of hospital pathways for flu and non-flu patients with, in particular, the necessary actions in terms of separation of care facilities, the triage of patients and the cancellation of non-urgent activities. It analyses the preconditions necessary for the efficient functioning of intensive care and the predictable limiting factors. It underlines the importance of training of medical and paramedical personnel. Finally, it tackles the specific problems of paediatric intensive care: organisation, capacity for admissions and training.

Pandemic flu: are we properly prepared?

This paper presents the proceedings of the 2006 conference 'Pandemic flu: Are we properly prepared?', held in London. This conference sought to provide an overview of the preparatory steps necessary if a potential UK pandemic influenza (flu) outbreak is to be mitigated. Topics included the history of pandemic flu, research and development, antiviral drugs, clinical assessment of flu, and critical care contingency planning. The ethical dilemmas relating to a flu pandemic were also discussed.

Definitive care for the critically ill during a disaster: medical resources for surge capacity: from a Task Force for Mass Critical Care summit meeting, January 26-27, 2007, Chicago, IL

BACKGROUND

Mass numbers of critically ill disaster victims will stress the abilities of health-care systems to maintain usual critical care services for all in need. To enhance the number of patients who can receive life-sustaining interventions, the Task Force on Mass Critical Care (hereafter termed the Task Force) has suggested a framework for providing limited, essential critical care, termed emergency mass critical care (EMCC). This article suggests medical equipment, concepts to expand treatment spaces, and staffing models for EMCC.

METHODS

Consensus suggestions for EMCC were derived from published clinical practice guidelines and medical resource utilization data for the everyday critical care conditions that are anticipated to predominate during mass critical care events. When necessary, expert opinion was used. TASK FORCE MAJOR SUGGESTIONS: The Task Force makes the following suggestions: (1) one mechanical ventilator that meets specific characteristics, as well as a set of consumable and durable medical equipment, should be provided for each EMCC patient; (2) EMCC should be provided in hospitals or similarly equipped structures; after ICUs, postanesthesia care units, and emergency departments all reach capacity, hospital locations should be repurposed for EMCC in the following order: (A) step-down units and large procedure suites, (B) telemetry units, and (C) hospital wards; and (3) hospitals can extend the provision of critical care using non-critical care personnel via a deliberate model of delegation to match staff competencies with patient needs.

DISCUSSION

By using the Task Force suggestions for adequate supplies of medical equipment, appropriate treatment space, and trained staff, communities may better prepare to deliver augmented essential critical care in response to disasters.

Pandemic influenza and hospital resources

Using estimates from the Centers for Disease Control and Prevention, the World Health Organization, and published models of the expected evolution of pandemic influenza, we modeled the surge capacity of healthcare facility and intensive care unit (ICU) requirements over time in northern Netherlands (approximately 1.7 million population). We compared the demands of various scenarios with estimates of maximum ICU capacity, factoring in healthcare worker absenteeism as well as reported and realistic estimates derived from semistructured telephone interviews with key management in ICUs in the study area. We show that even during the peak of the pandemic, most patients requiring ICU admission may be served, even those who have non-influenza-related conditions, provided that strong indications and decision-making rules are maintained for admission as well as for continuation (or discontinuation) of life support. Such a model should be integral to a preparedness plan for a pandemic with a new human-transmissible agent.

Clinical review: mass casualty triage--pandemic influenza and critical care

Worst case scenarios for pandemic influenza planning in the US involve over 700,000 patients requiring mechanical ventilation. UK planning predicts a 231% occupancy of current level 3 (intensive care unit) bed capacity. Critical care planners need to recognise that mortality is likely to be high and the risk to healthcare workers significant. Contingency planning should, therefore, be multi-faceted, involving a robust health command structure, the facility to expand critical care provision in terms of space, equipment and staff and cohorting of affected patients in the early stages. It should also be recognised that despite this expansion of critical care, demand will exceed supply and a process for triage needs to be developed that is valid, reproducible, transparent and consistent with distributive justice. We advocate the development and validation of physiological scores for use as a triage tool, coupled with candid public discussion of the process.

Pandemic preparedness

PURPOSE OF REVIEW

Pandemic influenza remains a threat to world health and will probably result in an overwhelming number of critically ill patients. Preparations should be made now to meet this threat.

RECENT FINDINGS

Limited data are available on which to base preparations. Adequate staffing is crucial to the functioning of an ICU and therefore occupational safety is of central concern. In the absence of knowledge of the method of spread of a pandemic disease, it would seem appropriate to take airborne and contact precautions, and the literature related to this area is reviewed. Methods of recruiting and training additional staff and the issues of bed capacity, stockpiling, triage and ethics are discussed.

SUMMARY

Extensive preparation is needed in advance of an epidemic. This should include occupational safety measures, stockpiling of equipment and drugs, staff training, development of triage policies, and discussion of the limits of duty of care to patients. These preparations take considerable time and therefore these issues should be tackled urgently.