Cost-effective proactive testing strategies during COVID-19 mass vaccination: A modelling study.

BACKGROUND: As SARS-CoV-2 vaccines are administered worldwide, the COVID-19 pandemic continues to exact significant human and economic costs. Mass testing of unvaccinated individuals followed by isolation of positive cases can substantially mitigate risks and be tailored to local epidemiological conditions to ensure cost effectiveness. METHODS: Using a multi-scale model that incorporates population-level SARS-CoV-2 transmission and individual-level viral load kinetics, we identify the optimal frequency of proactive SARS-CoV-2 testing, depending on the local transmission rate and proportion immunized. FINDINGS: Assuming a willingness-to-pay of US$100,000 per averted year of life lost (YLL) and a price of $10 per test, the optimal strategy under a rapid transmission scenario (Re ∼ 2.5) is daily testing until one third of the population is immunized and then weekly testing until half the population is immunized, combined with a 10-day isolation period of positive cases and their households. Under a low transmission scenario (Re ∼ 1.2), the optimal sequence is weekly testing until the population reaches 10% partial immunity, followed by monthly testing until 20% partial immunity, and no testing thereafter. INTERPRETATION: Mass proactive testing and case isolation is a cost effective strategy for mitigating the COVID-19 pandemic in the initial stages of the global SARS-CoV-2 vaccination campaign and in response to resurgences of vaccine-evasive variants. FUNDING: US National Institutes of Health, US Centers for Disease Control and Prevention, HK Innovation and Technology Commission, China National Natural Science Foundation, European Research Council, and EPSRC Impact Acceleration Grant.

Modelling the COVID-19 pandemic in context: an international participatory approach.

The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. More significantly, the pandemic has created tremendous challenges for decision-makers, who have had to implement highly disruptive containment measures with very little empirical scientific evidence to support their decision-making process. Given this lack of data, predictive mathematical models have played an increasingly prominent role. In high-income countries, there is a long-standing history of established research groups advising policymakers, whereas a general lack of translational capacity has meant that mathematical models frequently remain inaccessible to policymakers in low-income and middle-income countries. Here, we describe a participatory approach to modelling that aims to circumvent this gap. Our approach involved the creation of an international group of infectious disease modellers and other public health experts, which culminated in the establishment of the COVID-19 Modelling (CoMo) Consortium. Here, we describe how the consortium was formed, the way it functions, the mathematical model used and, crucially, the high degree of engagement fostered between CoMo Consortium members and their respective local policymakers and ministries of health.

Triage of Scarce Critical Care Resources in COVID-19 An Implementation Guide for Regional Allocation: An Expert Panel Report of the Task Force for Mass Critical Care and the American College of Chest Physicians.

Public health emergencies have the potential to place enormous strain on health systems. The current pandemic of the novel 2019 coronavirus disease has required hospitals in numerous countries to expand their surge capacity to meet the needs of patients with critical illness. When even surge capacity is exceeded, however, principles of critical care triage may be needed as a means to allocate scarce resources, such as mechanical ventilators or key medications. The goal of a triage system is to direct limited resources towards patients most likely to benefit from them. Implementing a triage system requires careful coordination between clinicians, health systems, local and regional governments, and the public, with a goal of transparency to maintain trust. We discuss the principles of tertiary triage and methods for implementing such a system, emphasizing that these systems should serve only as a last resort. Even under triage, we must uphold our obligation to care for all patients as best possible under difficult circumstances.

Impact of Superstorm Sandy on Medicare Patients' Utilization of Hospitals and Emergency Departments.

INTRODUCTION: National health security requires that healthcare facilities be prepared to provide rapid, effective emergency and trauma care to all patients affected by a catastrophic event. We sought to quantify changes in healthcare utilization patterns for an at-risk Medicare population before, during, and after Superstorm Sandy's 2012 landfall in New Jersey (NJ). METHODS: This study is a retrospective cohort study of Medicare beneficiaries impacted by Superstorm Sandy. We compared hospital emergency department (ED) and healthcare facility inpatient utilization in the weeks before and after Superstorm Sandy landfall using a 20% random sample of Medicare fee-for-service beneficiaries continuously enrolled in 2011 and 2012 (N=224,116). Outcome measures were pre-storm discharges (or transfers), average length of stay, service intensity weight, and post-storm ED visits resulting in either discharge or hospital admission. RESULTS: In the pre-storm week, hospital transfers from skilled nursing facilities (SNF) increased by 39% and inpatient discharges had a 0.3 day decreased mean length of stay compared to the prior year. In the post-storm week, ED visits increased by 14% statewide; of these additional "surge" patients, 20% were admitted to the hospital. The increase in ED demand was more than double the statewide average in the most highly impacted coastal regions (35% versus 14%). CONCLUSION: Superstorm Sandy impacted both pre- and post-storm patient movement in New Jersey; post-landfall ED surge was associated with overall storm impact, which was greatest in coastal counties. A significant increase in the number and severity of pre-storm transfer patients, in particular from SNF, as well as in post-storm ED visits and inpatient admissions, draws attention to the importance of collaborative regional approaches to healthcare in large-scale events.

Optimizing Health Care Coalitions: Conceptual Frameworks and a Research Agenda.

The US health care system has maintained an objective of preparedness for natural or manmade catastrophic events as part of its larger charge to deliver health services for the American population. In 2002, support for hospital-based preparedness activities was bolstered by the creation of the National Bioterrorism Hospital Preparedness Program, now called the Hospital Preparedness Program, in the US Department of Health and Human Services. Since 2012, this program has promoted linking health care facilities into health care coalitions that build key preparedness and emergency response capabilities. Recognizing that well-functioning health care coalitions can have a positive impact on the health outcomes of the populations they serve, this article informs efforts to optimize health care coalition activity. We first review the landscape of health care coalitions in the United States. Then, using principles from supply chain management and high-reliability organization theory, we present 2 frameworks extending beyond the Office of the Assistant Secretary for Preparedness and Response's current guidance in a way that may help health care coalition leaders gain conceptual insight into how different enterprises achieve similar ends relevant to emergency response. We conclude with a proposed research agenda to advance understanding of how coalitions can contribute to the day-to-day functioning of health care systems and disaster preparedness.

A tool for the economic analysis of mass prophylaxis operations with an application to H1N1 influenza vaccination clinics.

This article uses the 2009 H1N1 influenza vaccination program experience to introduce a cost analysis approach that may be relevant for planning mass prophylaxis operations, such as vaccination clinics at public health centers, work sites, schools, or pharmacy-based clinics. These costs are important for planning mass influenza vaccination activities and are relevant for all public health emergency preparedness scenarios requiring countermeasure dispensing. We demonstrate how costs vary depending on accounting perspective, staffing composition, and other factors. We also describe a mass vaccination clinic budgeting tool that clinic managers may use to estimate clinic costs and to examine how costs vary depending on the availability of volunteers or donated supplies and on the number of patients vaccinated per hour. Results from pilot tests with school-based H1N1 influenza vaccination clinic managers are described. The tool can also contribute to planning efforts for universal seasonal influenza vaccination.

Mechanical ventilators in US acute care hospitals.

OBJECTIVE: The supply and distribution of mechanical ventilation capacity is of profound importance for planning for severe public health emergencies. However, the capability of US health systems to provide mechanical ventilation for children and adults remains poorly quantified. The objective of this study was to determine the quantity of adult and pediatric mechanical ventilators at US acute care hospitals. METHODS: A total of 5,752 US acute care hospitals included in the 2007 American Hospital Association database were surveyed. We measured the quantities of mechanical ventilators and their features. RESULTS: Responding to the survey were 4305 (74.8%) hospitals, which accounted for 83.8% of US intensive care unit beds. Of the 52,118 full-feature mechanical ventilators owned by respondent hospitals, 24,204 (46.4%) are pediatric/neonatal capable. Accounting for nonrespondents, we estimate that there are 62,188 full-feature mechanical ventilators owned by US acute care hospitals. The median number of full-feature mechanical ventilators per 100,000 population for individual states is 19.7 (interquartile ratio 17.2-23.1), ranging from 11.9 to 77.6. The median number of pediatric-capable device full-feature mechanical ventilators per 100,000 population younger than 14 years old is 52.3 (interquartile ratio 43.1-63.9) and the range across states is 22.1 to 206.2. In addition, respondent hospitals reported owning 82,755 ventilators other than full-feature mechanical ventilators; we estimate that there are 98,738 devices other than full-feature ventilators at all of the US acute care hospitals. CONCLUSIONS: The number of mechanical ventilators per US population exceeds those reported by other developed countries, but there is wide variation across states in the population-adjusted supply. There are considerably more pediatric-capable ventilators than there are for adults only on a population-adjusted basis.

Effectiveness and cost-effectiveness of vaccination against pandemic influenza (H1N1) 2009.

BACKGROUND: Decisions on the timing and extent of vaccination against pandemic (H1N1) 2009 virus are complex. OBJECTIVE: To estimate the effectiveness and cost-effectiveness of pandemic influenza (H1N1) vaccination under different scenarios in October or November 2009. DESIGN: Compartmental epidemic model in conjunction with a Markov model of disease progression. DATA SOURCES: Literature and expert opinion. TARGET POPULATION: Residents of a major U.S. metropolitan city with a population of 8.3 million. TIME HORIZON: Lifetime. PERSPECTIVE: Societal. INTERVENTIONS: Vaccination in mid-October or mid-November 2009. OUTCOME MEASURES: Infections and deaths averted, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness. RESULTS OF BASE-CASE ANALYSIS: Assuming each primary infection causes 1.5 secondary infections, vaccinating 40% of the population in October or November would be cost-saving. Vaccination in October would avert 2051 deaths, gain 69 679 QALYs, and save $469 million compared with no vaccination; vaccination in November would avert 1468 deaths, gain 49 422 QALYs, and save $302 million. RESULTS OF SENSITIVITY ANALYSIS: Vaccination is even more cost-saving if longer incubation periods, lower rates of infectiousness, or increased implementation of nonpharmaceutical interventions delay time to the peak of the pandemic. Vaccination saves fewer lives and is less cost-effective if the epidemic peaks earlier than mid-October. LIMITATIONS: The model assumed homogenous mixing of case-patients and contacts; heterogeneous mixing would result in faster initial spread, followed by slower spread. Additional costs and savings not included in the model would make vaccination more cost-saving. CONCLUSION: Earlier vaccination against pandemic (H1N1) 2009 prevents more deaths and is more cost-saving. Complete population coverage is not necessary to reduce the viral reproductive rate sufficiently to help shorten the pandemic. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality and National Institute on Drug Abuse.

Uncertainty and operational considerations in mass prophylaxis workforce planning.

BACKGROUND: The public health response to an influenza pandemic or other large-scale health emergency may include mass prophylaxis using multiple points of dispensing (PODs) to deliver countermeasures rapidly to affected populations. Computer models created to date to determine "optimal" staffing levels at PODs typically assume stable patient demand for service. The authors investigated POD function under dynamic and uncertain operational environments. METHODS: The authors constructed a Monte Carlo simulation model of mass prophylaxis (the Dynamic POD Simulator, or D-PODS) to assess the consequences of nonstationary patient arrival patterns on POD function under a variety of POD layouts and staffing plans. Compared are the performance of a standard POD layout under steady-state and variable patient arrival rates that may mimic real-life variation in patient demand. RESULTS: To achieve similar performance, PODs functioning under nonstationary patient arrival rates require higher staffing levels than would be predicted using the assumption of stationary arrival rates. Furthermore, PODs may develop severe bottlenecks unless staffing levels vary over time to meet changing patient arrival patterns. Efficient POD networks therefore require command and control systems capable of dynamically adjusting intra- and inter-POD staff levels to meet demand. In addition, under real-world operating conditions of heightened uncertainty, fewer large PODs will require a smaller total staff than many small PODs to achieve comparable performance. CONCLUSIONS: Modeling environments that capture the effects of fundamental uncertainties in public health disasters are essential for the realistic evaluation of response mechanisms and policies. D-PODS quantifies POD operational efficiency under more realistic conditions than have been modeled previously. The authors' experiments demonstrate that effective POD staffing plans must be responsive to variation and uncertainty in POD arrival patterns. These experiments highlight the need for command and control systems to be created to manage emergency response successfully.

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.

Can modeling of HIV treatment processes improve outcomes? Capitalizing on an operations research approach to the global pandemic.

BACKGROUND: Mathematical modeling has been applied to a range of policy-level decisions on resource allocation for HIV care and treatment. We describe the application of classic operations research (OR) techniques to address logistical and resource management challenges in HIV treatment scale-up activities in resource-limited countries. METHODS: We review and categorize several of the major logistical and operational problems encountered over the last decade in the global scale-up of HIV care and antiretroviral treatment for people with AIDS. While there are unique features of HIV care and treatment that pose significant challenges to effective modeling and service improvement, we identify several analogous OR-based solutions that have been developed in the service, industrial, and health sectors. RESULTS: HIV treatment scale-up includes many processes that are amenable to mathematical and simulation modeling, including forecasting future demand for services; locating and sizing facilities for maximal efficiency; and determining optimal staffing levels at clinical centers. Optimization of clinical and logistical processes through modeling may improve outcomes, but successful OR-based interventions will require contextualization of response strategies, including appreciation of both existing health care systems and limitations in local health workforces. CONCLUSION: The modeling techniques developed in the engineering field of operations research have wide potential application to the variety of logistical problems encountered in HIV treatment scale-up in resource-limited settings. Increasing the number of cross-disciplinary collaborations between engineering and public health will help speed the appropriate development and application of these tools.

Anticipating demand for emergency health services due to medication-related adverse events after rapid mass prophylaxis campaigns.

OBJECTIVES: Mass prophylaxis against infectious disease outbreaks carries the risk of medication-related adverse events (MRAEs). The authors sought to define the relationship between the rapidity of mass prophylaxis dispensing and the subsequent demand for emergency health services due to predictable MRAEs. METHODS: The authors created a spreadsheet-based computer model that calculates scenario-specific predicted daily MRAE rates from user inputs by applying a probability distribution to the reported timing of MRAEs. A hypothetical two- to ten-day prophylaxis campaign for one million people using recent data from both smallpox vaccination and anthrax chemoprophylaxis campaigns was modeled. RESULTS: The length of a mass prophylaxis campaign plays an important role in determining the subsequent intensity in emergency services utilization due to real or suspected adverse events. A two-day smallpox vaccination scenario would produce an estimated 32,000 medical encounters and 1,960 hospitalizations, peaking at 5,246 health care encounters six days after the start of the campaign; in contrast, a ten-day campaign would lead to 41% lower peak surge, with a maximum of 3,106 encounters on the busiest day, ten days after initiation of the campaign. MRAEs with longer lead times, such as those associated with anthrax chemoprophylaxis, exhibit less variability based on campaign length (e.g., 124 out of an estimated 1,400 hospitalizations on day 20 after a two-day campaign versus 103 on day 24 after a ten-day campaign). CONCLUSIONS: The duration of a mass prophylaxis campaign may have a substantial impact on the timing and peak number of clinically significant MRAEs, with very short campaigns overwhelming existing emergency department (ED) capacity to treat real or suspected medication-related injuries. While better reporting of both incidence and timing of MRAEs in future prophylaxis campaigns should improve the application of this model to community-based emergency preparedness planning, these results highlight the need for coordination between public health and emergency medicine planning for infectious disease outbreaks to avoid preventable surges in ED utilization.

Upper extremity musculoskeletal symptoms and functional impairment associated with computer use among college students.

PURPOSE: Occupational computer use has been associated with upper extremity musculoskeletal disorders among working-age adults, but little is known about computer-related musculoskeletal problems among college students. We carried out a descriptive epidemiological study of computer use-associated symptoms, functional limitations, and medication and health care utilization in this population. SUBJECTS AND METHODS: Cross-sectional survey of 240 undergraduates in the second through fourth years at a residential dormitory at a four-year college with random housing assignments. RESULTS: 194 students returned useable surveys (81% response rate). 42% reported upper extremity pain or discomfort when using a computer in the preceding two weeks. 41% said this pain or discomfort caused functional limitation and 9% said that these symptoms hindered academic or extracurricular performance. 23% reported taking medications for upper extremity pain related to computing (4% regularly) and 16% had seen a health care provider for computer-related symptoms. Female students, students of racial/ethnic minority groups, and students who experienced symptoms with < or = 1 hour of computer use were more likely to report symptom-related functional limitation than others. CONCLUSION: College students report high rates of computer use-associated upper extremity musculoskeletal symptoms and symptom-related functional limitation. Future studies should more closely examine exposure, demographic, and ergonomic correlates of these symptoms and outcomes.

Assessment of upper extremity role functioning in students.

BACKGROUND: Upper extremity symptoms associated with use of computers and other upper extremity activities are common in students. Research on these disorders requires psychometrically sound measures of health-related student role function; no such measure is available currently. METHODS: Based upon input from students and clinicians, we developed a 10-item scale to measure student health-related role function. The measure was administered as part of a survey of 193 undergraduates at one university. A follow-up survey was administered 1 month later. The student health-related role function questionnaire was assessed for internal consistency, ceiling effects, convergent and discriminant validity, and responsiveness to self-reported change in functional status. RESULTS: Eighty-two percent of students who were given the survey completed it and 65% completed a follow-up survey 1 month later. The new measure was reliable (Cronbach's alpha 0.87). Forty-six percent of respondents reported "no difficulty" on all items of the health-related student role function measure while 64% reported "no difficulty" on all items of a generic upper extremity functional status measure. This finding indicates that the new measure was better able to detect functional limitations; it had a less prominent ceiling effect. The new measure had moderately high correlations with measures of symptom severity and pain, documenting convergent validity. It distinguished students who utilized clinician services, medications, or academic accommodation from students who did not utilize these resources, documenting discriminant validity. The measure was responsive to self-perceived change, as demonstrated by a highly significant association (P < 0.0001) between changes in score over a 1-month follow-up and students' perceptions of whether they had improved in functional abilities after the month, deteriorated or remained stable. CONCLUSIONS: The student health-related role functioning measure is reliable, valid and responsive to change. It is an appropriate measure for research on upper extremity symptoms in students.