How Many Intensive Care Beds are Justifiable for Hospital Pandemic Preparedness? A Cost-effectiveness Analysis for COVID-19 in Germany

COVID-19-associated costs and mortality in Germany: an incidence-based analysis from a payer's perspective

BACKGROUND

This study aims to estimate average COVID-19-associated healthcare costs per capita in Germany from a payer perspective. In addition, insights into COVID-19-associated mortality should be gained.

METHODS

For this purpose, a retrospective longitudinal analysis using health insurance claims data was performed. Patients affected by COVID-19 in Q1/2021 (investigation group (IG)) were compared to a matched non-COVID-19 control group (CG) (1:1 propensity score matching (PSM)). Mean values of healthcare costs in 2020 and 2021 were computed for both groups and then separated by age and by development of Post-COVID-19 Syndrome (PCS). Group differences were examined using Mann-Whitney U test (α = 0.05). Difference-in-Differences approach (DiD) was used to estimate average cost effects of COVID-19 in 2021. Concerning mortality, the number of deaths in 2021 was compared between IG and CG using χ2 test of independence.

RESULTS

A total of 8,014 insurants were included (n = 4,007 per group; n = 536 per group examining PCS patients only). Total healthcare costs varied a lot in the sample, were comparable between IG and CG in 2020, but were significantly higher in the IG in 2021 (DiD estimate = € 1,063 (in total); € 3,242 (PCS group)). This was more pronounced in the older age groups. High hospital costs of a minority of patients were the most influential driver of COVID-19-associated healthcare costs. Mortality was more than doubled in the IG (tripled in patients aged ≥ 60).

CONCLUSIONS

COVID-19 is associated with significantly increased healthcare costs and mortality, especially in older age groups. The additional development of PCS further increases the costs of COVID-19.

Mechanical ventilation as a major driver of COVID-19 hospitalization costs: a costing study in a German setting

BACKGROUND

While COVID-19 hospitalization costs are essential for policymakers to make informed health care resource decisions, little is known about these costs in western Europe. The aim of the current study is to analyze these costs for a German setting, track the development of these costs over time and analyze the daily costs.

METHODS

Administrative costing data was analyzed for 598 non-Intensive Care Unit (ICU) patients and 510 ICU patients diagnosed with COVID-19 at the Frankfurt University hospital. Descriptive statistics of total per patient hospitalization costs were obtained and assessed over time. Propensity scores were estimated for length of stay (LOS) at the general ward and mechanical ventilation (MV) duration, using covariate balancing propensity score for continuous treatment. Costs for each additional day in the general ward and each additional day in the ICU with and without MV were estimated by regressing the total hospitalization costs on the LOS and the presence or absence of several treatments using generalized linear models, while controlling for patient characteristics, comorbidities, and complications.

RESULTS

Median total per patient hospitalization costs were €3,010 (Q1 - Q3: €2,224-€5,273), €5,887 (Q1 - Q3: €3,054-€10,879) and €21,536 (Q1 - Q3: €7,504-€43,480), respectively, for non-ICU patients, non-MV and MV ICU patients. Total per patient hospitalization costs for non-ICU patients showed a slight increase over time, while total per patient hospitalization costs for ICU patients decreased over time. Each additional day in the general ward for non-ICU COVID-19 patients costed €463.66 (SE: 15.89). Costs for each additional day in the general ward and ICU without and with mechanical ventilation for ICU patients were estimated at €414.20 (SE: 22.17), €927.45 (SE: 45.52) and €2,224.84 (SE: 70.24).

CONCLUSIONS

This is, to our knowledge, the first study examining the costs of COVID-19 hospitalizations in Germany. Estimated costs were overall in agreement with costs found in literature for non-COVID-19 patients, except for higher estimated costs for mechanical ventilation. These estimated costs can potentially improve the precision of COVID-19 cost effectiveness studies in Germany and will thereby allow health care policymakers to provide better informed health care resource decisions in the future.

Cost-effectiveness analysis of isolation strategies for asymptomatic and mild symptom COVID-19 patients

BACKGROUND

Management of COVID-19 patients with mild and moderate symptoms could be isolated at home isolation (HI), community isolation (CI) or hospitel. However, it was still unclear which strategy was more cost-effective. Therefore, this study was conducted to evaluate this.

METHODS

This study used data from patients who initially stayed at HI, CI, and hospitel under supervision of Ramathibodi Hospital between April and October 2021. Outcomes of interest were hospitalisation and mortality. An incremental cost-effectiveness ratios (ICER) was calculated based on hospital perspective using home isolation as the reference.

RESULTS

From 7,077 patients, 4,349 2,356, and 372 were admitted at hospitel, HI, and CI, respectively. Most patients were females (57.04%) and the mean age was 40.42 (SD = 16.15). Average durations of stay were 4.47, 3.35, and 3.91 days for HI, CI, and hospitel, respectively. The average cost per day for staying in these corresponding places were 24.22, 63.69, and 65.23 US$. For hospitalisation, the ICER for hospitel was at 41.93 US$ to avoid one hospitalisation in 1,000 patients when compared to HI, while CI had more cost, but less cases avoided. The ICER for hospitel and CI were at 46.21 and 866.17 US$ to avoid one death in 1,000 patients.

CONCLUSIONS

HI may be cost-effective isolated strategy for preventing hospitalisation and death in developing countries with limited resources.

Early tracheostomy versus late tracheostomy in severe traumatic brain injury or stroke: A systematic review and meta-analysis

OBJECTIVES

We aim to ascertain whether the benefit of early tracheostomy can be found in patients with severe traumatic brain injury (TBI) and stroke and if the benefit will remain considering distinct pathologies.

DATA SOURCES

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol, a search through Lilacs, PubMed, and Cochrane databases was conducted.

REVIEW METHODS

Included studies were those written in English, French, Spanish, or Portuguese, with a formulated question, which compared outcomes between early and late trach (minimum of two outcomes), such as intensive care unit (ICU) length of stay (LOS), duration of mechanical ventilation (MV), hospital LOS, mortality rates, or ventilator-associated pneumonia (VAP). Likewise, patients presented exclusively with head injury or stroke had minimum hospital stay follow-up, and as for severe TBI patients, they presented Glasgow Coma Scale ≤8 at admission. Evaluated outcomes were the risk ratio (RR) of VAP, risk difference (RD) of mortality, and mean difference (MD) of the duration of MV, ICU LOS, and hospital LOS.

RESULTS

The early and late tracheostomy cohorts were composed of 6211 and 8140 patients, respectively. The meta-analysis demonstrated that the early tracheostomy cohort had a lower risk for VAP (RR: 0.73 [95% confidence interval {CI}, 0.66, 0.81] p < 0.00001), shorter duration of MV (MD: -4.40 days [95% CI, -8.28, -0.53] p = 0.03), and shorter ICU (MD: -6.93 days [95% CI, -8.75, -5.11] p < 0.00001) and hospital LOS (MD: -7.05 days [95% CI, -8.27, -5.84] p < 0.00001). The mortality rate did not demonstrate a statistical difference.

CONCLUSION

Early tracheostomy could optimise patient outcomes by patients' risk for VAP and decreasing MV durationand ICU and hospital LOS.

Cost of the COVID-19 pandemic versus the cost-effectiveness of mitigation strategies in EU/UK/OECD: a systematic review

OBJECTIVES

The economic burden of COVID-19 pandemic is substantial, with both direct and indirect costs playing a significant role.

DESIGN

A systematic literature review was conducted to estimate the cost of the COVID-19 pandemic and the cost-effectiveness of pharmaceutical or non-pharmaceutical interventions. All cost data were adjusted to the 2021 Euro, and interventions compared with null.

DATA SOURCES

Ovid MEDLINE and EMBASE were searched from January 2020 through 22 April 2021.

ELIGIBILITY CRITERIA

Studies regarding COVID-19 outbreak or public health preparedness measures or interventions with outcome measures related to the direct and indirect costs for disease and preparedness and/or response in countries of the European Union (EU), the European Economic Area (EEA), the UK and the Organisation for Economic Co-operation and Development (OECD) of all relevant epidemiological designs which estimate cost within the selected time frame were considered eligible.

DATA EXTRACTION AND SYNTHESIS

Studies were searched, screened and coded independently by two reviewers with high measure of inter-rater agreement. Data were extracted to a predefined data extraction sheet. The risk of bias was assessed using the Consensus on Health Economic Criteria checklist.

RESULTS

We included data from 41 economic studies. Ten studies evaluated the cost of the COVID-19 pandemic, while 31 assessed the cost-benefit of public health surveillance, preparedness and response measures. Overall, the economic burden of the COVID-19 pandemic was found to be substantial. Community screening, bed provision policies, investing in personal-protective-equipment and vaccination strategies were cost-effective. Physical distancing measures were associated with health benefits; however, their cost-effectiveness was dependent on the duration, compliance and the phase of the epidemic in which it was implemented.

CONCLUSIONS

COVID-19 pandemic is associated with substantial short-term and long-term economic costs to healthcare systems, payers and societies, while interventions including testing and screening policies, vaccination and physical distancing policies were identified as those presenting cost-effective options to deal with the pandemic, dependent on population vaccination and the Re at the stage of the pandemic.

The economics of the COVID-19 pandemic: economic evaluation of government mitigation and suppression policies, health system innovations, and models of care

Background

The COVID-19 pandemic has impacted the scope of health economics literature, which will increasingly examine value beyond health care interventions such as government policy and broad health system innovations.

Aim

The study analyzes economic evaluations and methodologies evaluating government policies suppressing or mitigating transmission and reducing COVID-19, broad health system innovations, and models of care. This can facilitate future economic evaluations and assist government and public health policy decisions during pandemics.

Methods

The Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was used. Methodological quality was quantified using the scoring criteria in European Journal of Health Economics, Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 Checklist and the National Institute for Health and Care Excellence's (NICE) Cost Benefit Analysis Checklist. PUBMED, Medline, and Google Scholar were searched from 2020-2021.

Results

Cost utility analysis (CUA) and cost benefit analysis (CBA) analyzing mortality, morbidity, quality adjusted life year (QALY) gained, national income loss, and value of production effectively evaluate government policies suppressing or mitigating COVID-19 transmission, disease, and impacting national income loss. The WHO's pandemic economic framework facilitates economic evaluations of social and movement restrictions. Social return on investment (SROI) links benefits to health and broader social improvements. Multi-criteria decision analysis (MCDA) can facilitate vaccine prioritization, equitable health access, and technology evaluation. Social welfare function (SWF) can account for social inequalities and population-wide policy impact. It is a generalization of CBA, and operationally, it is equal to an equity-weighted CBA. It can provide governments with a guideline for achieving the optimal distribution of income, which is vital during pandemics. Economic evaluations of broad health system innovations and care models addressing COVID-19 effectively use cost effectiveness analysis (CEA) that utilize decision trees and Monte Carlo models, and CUAs that effectively utilize decision trees and Markov models, respectively.

Conclusion

These methodologies are very instructive for governments, in addition to their current use of CBA and the value of a statistical life analytical tool. CUA and CBA effectively evaluate government policies suppressing or mitigating COVID-19 transmission, disease, and impacts on national income loss. CEA and CUA effectively evaluate broad health system innovations and care models addressing COVID-19. The WHO's framework, SROI, MCDA, and SWF can also facilitate government decision-making during pandemics.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-023-01919-z.

Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19

BACKGROUND

During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-CoV-2 with only limited information about transmission rates, disease severity and the likely effectiveness of public health interventions. In the face of such uncertainties, formal approaches to quantifying the value of information can help decision makers to prioritise research efforts.

METHODS

In this study we use Value of Information (VoI) analysis to quantify the likely benefit associated with reducing three key uncertainties present in the early stages of the COVID-19 pandemic: the basic reproduction number ([Formula: see text]), case severity (CS), and the relative infectiousness of children compared to adults (CI). The specific decision problem we consider is the optimal level of investment in intensive care unit (ICU) beds. Our analysis incorporates mathematical models of disease transmission and clinical pathways in order to estimate ICU demand and disease outcomes across a range of scenarios.

RESULTS

We found that VoI analysis enabled us to estimate the relative benefit of resolving different uncertainties about epidemiological and clinical aspects of SARS-CoV-2. Given the initial beliefs of an expert, obtaining more information about case severity had the highest parameter value of information, followed by the basic reproduction number [Formula: see text]. Resolving uncertainty about the relative infectiousness of children did not affect the decision about the number of ICU beds to be purchased for any COVID-19 outbreak scenarios defined by these three parameters.

CONCLUSION

For the scenarios where the value of information was high enough to justify monitoring, if CS and [Formula: see text] are known, management actions will not change when we learn about child infectiousness. VoI is an important tool for understanding the importance of each disease factor during outbreak preparedness and can help to prioritise the allocation of resources for relevant information.

Economic evaluation of strategies against coronavirus: a systematic review

BACKGROUND

The COVID-19 outbreak was defined as a pandemic on 11 March 2020 by the World Health Organization. After that, COVID-19 has enormously influenced health systems around the world, and it has claimed more than 4.2 million deaths until July 2021. The pandemic has led to global health, social and economic costs. This situation has prompted a crucial search for beneficial interventions and treatments, but little is known about their monetary value. This study is aimed at systematically reviewing the articles conducted on the economic evaluation of preventive, control and treatment strategies against COVID-19.

MATERIAL AND METHOD

We searched PubMed, Web of Science, Scopus, and Google Scholar from December 2019 to October 2021 to find applicable literature to the economic evaluation of strategies against COVID-19. Two researchers screened potentially eligible titles and abstracts. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist was used to quality assessment of studies.

RESULTS

Thirty-six studies were included in this review, and the average CHEERS score was 72. Cost-effectiveness analysis was the most common type of economic evaluation, used in 21 studies. And the quality-adjusted life year (QALY) was the main outcome applied to measure the effectiveness of interventions, which was used in 19 studies. In addition, articles were reported a wide range of incremental cost-effectiveness ratio (ICER), and the lowest cost per QALY ($321.14) was related to the use of vaccines.

CONCLUSION

Based on the results of this systematic review, it seems that all strategies are likely to be more cost-effective against COVID-19 than no intervention and vaccination was the most cost-effective strategy. This research provides insight for decision makers in choosing optimal interventions against the next waves of the current pandemic and possible future pandemics.

Cost-effectiveness of future lockdown policies against the COVID-19 pandemic

AIM

While the European Union (EU) has approved several COVID-19 vaccines, new variants of concern may be able to escape immunity. The purpose of this study is to project the cost-effectiveness of future lockdown policies in conjunction with a variant-adapted vaccine booster. The exemplary scenario foresees a 25% decline in the vaccine protection against severe disease.

METHODS

A decision model was constructed using, for example, information on age-specific fatality rates, intensive care unit (ICU) costs and outcomes, and herd protection threshold. The costs and benefits of a future lockdown strategy were determined from a societal viewpoint under three future scenarios-a booster shot's efficacy of 0%, 50%, and 95%.

RESULTS

The cost-effectiveness ratio of a lockdown policy in conjunction with a booster dose with 95% efficacy is €44,214 per life year gained. A lockdown is cost-effective when the probability of approving a booster dose with 95% efficacy is at least 48% (76% when considering uncertainty in input factors).

CONCLUSION

In this exemplary scenario, a future lockdown policy appears to be cost-effective if the probability of approving a variant-adapted vaccine booster with an efficacy of 95% is at least 48%.

COVID-19 and the forgone health benefits of elective operations

BACKGROUND AND AIM

The first SARS-CoV-2 pandemic wave in Germany involved a tradeoff between saving the lives of COVID-19 patients by providing sufficient intensive care unit (ICU) capacity and foregoing the health benefits of elective procedures. This study aims to quantify this tradeoff.

METHODS

The analysis is conducted at both the individual and population levels. The analysis calculates quality-adjusted life years (QALYs) to facilitate a comparison between the health gains from saving the lives of COVID-19 patients in the ICU and the health losses associated with postponing operative procedures. The QALYs gained from saving the lives of COVID-19 patients are calculated based on both the real-world ICU admissions and deaths averted from flattening the first wave. Scenario analysis was used to account for variation in input factors.

RESULTS

At the individual level, the resource-adjusted QALY gain of saving one COVID-19 life is predicted to be 3 to 15 times larger than the QALY loss of deferring one operation (the average multiplier is 9). The real-world QALY gain at the population level is estimated to fall within the range of the QALY loss due to delayed procedures. The modeled QALY gain by flattening the first wave is 3 to 31 times larger than the QALY loss due to delayed procedures (the average multiplier is 17).

CONCLUSION

During the first wave of the pandemic, the resource-adjusted health gain from treating one COVID-19 patient in the ICU was found to be much larger than the health loss from deferring one operation. At the population level, flattening the first wave led to a much larger health gain than the health loss from delaying operative procedures.

The implications of globalization on COVID-19 vaccination in Europe

Although globalization has left its mark on economic dynamism, causing conditionalities among various aspects (market openness, production networks, technological and information developments, migratory flows, international cooperation, humanitarian support, etc.), the less pleasant side of it should not be omitted, i.e. the emergence of the framework for the faster diffusion of epidemiological diseases. Thus, with the onset of the SARS-CoV-2 virus, its widespread circulation is a serious challenge for the provision of efficient solutions to combat it, especially in countries with fragile health systems, poor institutional quality and lack of resources. In this paper we aim to investigate the implications of globalization on the COVID-19 vaccination of the population. The period under analysis is January 1, 2021-January 1, 2022, using montly data, and the object of our study are 48 European states. To capture the relationship between globalization and the vaccination rate, we applied regression models, including a number of factors that may influence the progress of vaccination. In order to test the robustness of the results, the two-stage least squares (2SLS) regressions was used. The regression models developed underlined that globalization impacts the degree of vaccination. More globalized economies are more competitive in COVID-19 management, and the significance of this effect comes from better interconnection in global markets and easier access to medical discoveries. At the same time, countries with a higher vaccination rate are associated with higher levels of development. Based on the results obtained, we proposed some policy recommendations to increase the propensity to vaccinate, ensure equity in the distribution of vaccines and provide financial support to developing countries.

Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis

Aim

To develop an accurate lab score based on in-hospital patients’ potent clinical and biological parameters for predicting COVID-19 patient severity during hospital admission.

Methods

To conduct this retrospective analysis, a derivation cohort was constructed by including all the available biological and clinical parameters of 355 COVID positive patients (recovered = 285, deceased = 70), collected in November 2020-September 2021. For identifying potent biomarkers and clinical parameters to determine hospital admitted patient severity or mortality, the receiver operating characteristics (ROC) curve and Fischer’s test analysis was performed. Relative risk regression was estimated to develop laboratory scores for each clinical and routine biological parameter. Lab score was further validated by ROC curve analysis of the validation cohort which was built with 50 COVID positive hospital patients, admitted during October 2021-January 2022.

Results

Sensitivity vs. 1-specificity ROC curve (>0.7 Area Under the Curve, 95% CI) and univariate analysis (p<0.0001) of the derivation cohort identified five routine biomarkers (neutrophil, lymphocytes, neutrophil: lymphocytes, WBC count, ferritin) and three clinical parameters (patient age, pre-existing comorbidities, admitted with pneumonia) for the novel lab score development. Depending on the relative risk (p values and 95% CI) these clinical parameters were scored and attributed to both the derivation cohort (n = 355) and the validation cohort (n = 50). ROC curve analysis estimated the Area Under the Curve (AUC) of the derivation and validation cohort which was 0.914 (0.883–0.945, 95% CI) and 0.873 (0.778–0.969, 95% CI) respectively.

Conclusion

The development of proper lab scores, based on patients’ clinical parameters and routine biomarkers, would help physicians to predict patient risk at the time of their hospital admission and may improve hospital-admitted COVID-19 patients’ survivability.

Economic evaluations of interventions against viral pandemics: a scoping review

Objectives

The COVID-19 pandemic has led to suggestions that cost-effectiveness analyses should adopt a broader perspective when estimating costs. This review aims to provide an overview of economic evaluations of interventions against viral pandemics in terms of the perspective taken, types of costs included, comparators, type of economic model, data sources and methods for estimating productivity costs.

Study design

Scoping literature review.

Methods

Publications were eligible if they conducted a cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis or cost-minimisation analysis and evaluated interventions aimed at viral pandemics or for patients infected with viral pandemic disease. We searched PubMed, Embase and Scopus for relevant references and charted data from the selected full-text publications into a predefined spreadsheet based on research sub-questions, summary tables and figures.

Results

From 5410 references, 36 full-text publications fulfilled the inclusion criteria. The economic evaluations were mainly model based and included direct medical costs of hospital treatment. Around half of the studies included productivity costs and the proportion of total costs attributed to productivity costs ranged from 10% to 90%, depending on estimation methods, assumptions about valuation of time, type of intervention, severity of illness and degree of transmission.

Conclusions

Economic evaluations of interventions against viral pandemics differed in terms of estimation methods and reporting of productivity costs, even for similar interventions. Hence, the literature on economic evaluations for pandemic response would benefit from having standards for conducting and reporting economic evaluations, especially for productivity costs.

Cost-effectiveness of interventions for the prevention and control of COVID-19: Systematic review of 85 modelling studies

Background

We aimed to quantitatively summarise the health economic evaluation evidence of prevention and control programs addressing COVID-19 globally.

Methods

We did a systematic review and meta-analysis to assess the economic and health benefit of interventions for COVID-19. We searched PubMed, Embase, Web of Science, and Cochrane Library of economic evaluation from December 31, 2019, to March 22, 2022, to identify relevant literature. Meta-analyses were done using random-effects models to estimate pooled incremental net benefit (INB). Heterogeneity was assessed using I2 statistics and publication bias was assessed by Egger's test. This study is registered with PROSPERO, CRD42021267475.

Results

Of 16 860 studies identified, 85 articles were included in the systematic review, and 25 articles (10 studies about non-pharmacological interventions (NPIs), five studies about vaccinations and 10 studies about treatments) were included in the meta-analysis. The pooled INB of NPIs, vaccinations, and treatments were $1378.10 (95% CI = $1079.62, $1676.59), $254.80 (95% CI = $169.84, $339.77) and $4115.11 (95% CI = $1631.09, $6599.14), respectively. Sensitivity analyses showed similar findings.

Conclusions

NPIs, vaccinations, and treatments are all cost-effective in combating the COVID-19 pandemic. However, evidence was mostly from high-income and middle-income countries. Further studies from lower-income countries are needed.

Investigation of Statistical Machine Learning Models for COVID-19 Epidemic Process Simulation: Random Forest, K-Nearest Neighbors, Gradient Boosting

COVID-19 has become the largest pandemic in recent history to sweep the world. This study is devoted to developing and investigating three models of the COVID-19 epidemic process based on statistical machine learning and the evaluation of the results of their forecasting. The models developed are based on Random Forest, K-Nearest Neighbors, and Gradient Boosting methods. The models were studied for the adequacy and accuracy of predictive incidence for 3, 7, 10, 14, 21, and 30 days. The study used data on new cases of COVID-19 in Germany, Japan, South Korea, and Ukraine. These countries are selected because they have different dynamics of the COVID-19 epidemic process, and their governments have applied various control measures to contain the pandemic. The simulation results showed sufficient accuracy for practical use in the K-Nearest Neighbors and Gradient Boosting models. Public health agencies can use the models and their predictions to address various pandemic containment challenges. Such challenges are investigated depending on the duration of the constructed forecast.

Economic Burden of COVID-19: A Systematic Review

Objective

To review and qualitatively synthesize the evidence related to the economic burden of COVID-19, including healthcare resource utilization and costs.

Methods

A systematic review of studies that assessed the economic burden [eg, direct costs, productivity, macroeconomic impact due to non-pharmaceutical interventions (NPIs) and equity] of COVID-19 was conducted by searches in EMBASE, MEDLINE, MEDLINE-IN-PROCESS, and The Cochrane Library, as well as manual searches of unpublished research for the period between January 2020 to February 2021. Single reviewer data extraction was confirmed independently by a second reviewer.

Results

The screening process resulted in a total of 27 studies: 25 individual publications, and 2 systematic literature reviews, of narrower scopes, that fulfilled the inclusion criteria. The patients diagnosed with more severe COVID-19 were associated with higher costs. The main drivers for higher costs were consistent across countries and included ICU admission, in-hospital resource use such as mechanical ventilation, which lead to increase costs of $2082.65 ± 345.04 to $2990.76 ± 545.98. The most frequently reported indirect costs were due to productivity losses. On average, older COVID-19 patients incurred higher costs when compared to younger age groups. An estimation of a 20% COVID-19 infection rate based on a Monte Carlo simulation in the United States led to a total direct medical cost of $163.4 billion over the course of the pandemic.

Conclusion

The COVID-19 pandemic has generated a considerable economic burden on patients and the general population. Preventative measures such as NPIs only have partial success in lowering the economic costs of the pandemic. Implementing additional preventative measures such as large-scale vaccination is vital in reducing direct and indirect medical costs, decreased productivity, and GDP losses.

Mortality of Mechanically Ventilated COVID-19 Patients in Traditional versus Expanded ICUs in NY

RATIONALE

During the first wave of the coronavirus disease 2019 (COVID-19) pandemic in New York City, the number of mechanically ventilated COVID-19 patients rapidly surpassed the capacity of traditional Intensive Care Units (ICUs), resulting in health systems utilizing other areas as expanded ICUs to provide critical care.

OBJECTIVES

To evaluate the mortality of patients admitted to expanded ICUs compared with those admitted to traditional ICUs.

METHODS

Multicenter, retrospective, cohort study of mechanically ventilated patients with COVID-19 admitted to the ICUs at 11 Northwell Health hospitals in the greater New York City area between March 1, 2020 and April 30, 2020.

MEASUREMENTS

In-hospital mortality up to 28 days after intubation of COVID-19 patients.

RESULTS

Among 1,966 mechanically ventilated patients with COVID-19, 1,198 (61%) died within 28 days after intubation, 46 (2%) were transferred to other hospitals outside of the Northwell Health system, 722 (37%) survived in the hospital until 28 days or were discharged after recovery. The risk of mortality of mechanically ventilated patients admitted to expanded ICUs was not different from those admitted to traditional ICUs (HR, 1.07; 95% CI, 0.95-1.20; p = 0.28), while hospital occupancy for critically ill patients itself was associated with increased risk of mortality (HR, 1.28; 95% CI, 1.12-1.45; p < 0.001).

CONCLUSIONS

Although increased hospital occupancy for critically ill patients itself was associated with increased mortality, the risk of 28-day in-hospital mortality of mechanically ventilated patients with COVID-19 who were admitted to expanded ICUs was not different from those admitted to traditional ICUs.

Political Decision Making in the COVID-19 Pandemic: The Case of Germany from the Perspective of Risk Management

The COVID-19 pandemic is permanently changing modern social and economic coexistence. Most governments have declared infection control to be their top priority while citizens face great restrictions on their civil rights. A pandemic is an exemplary scenario in which political actors must decide about future, and thus uncertain, events. This paper tries to present a tool well established in the field of entrepreneurial and management decision making which could also be a first benchmark for political decisions. Our approach builds on the standard epidemiological SEIR model in combination with simulation techniques used in risk management. By our case study we want to demonstrate the opportunities that risk management techniques, especially risk analyses using Monte Carlo simulation, can provide to policy makers in general, and in a public health crisis in particular. Hence, our case study can be used as a framework for political decision making under incomplete information and uncertainty. Overall, we want to point out that a health policy that aims to provide comprehensive protection against infection should also be based on economic criteria. This is without prejudice to the integration of ethical considerations in the final political decision.

Cost-Effectiveness of COVID-19 Policy Measures: A Systematic Review

OBJECTIVES

The COVID-19 pandemic has had a major impact on our society, with drastic policy restrictions being implemented to contain the spread of the severe acute respiratory syndrome coronavirus 2. This study aimed to provide an overview of the available evidence on the cost-effectiveness of various coronavirus disease 2019 policy measures.

METHODS

A systematic literature search was conducted in PubMed, Embase, and Web of Science. Health economic evaluations considering both costs and outcomes were included. Their quality was comprehensively assessed using the Consensus Health Economic Criteria checklist. Next, the quality of the epidemiological models was evaluated.

RESULTS

A total of 3688 articles were identified (March 2021), of which 23 were included. The studies were heterogeneous with regard to methodological quality, contextual factors, strategies' content, adopted perspective, applied models, and outcomes used. Overall, testing/screening, social distancing, personal protective equipment, quarantine/isolation, and hygienic measures were found to be cost-effective. Furthermore, the most optimal choice and combination of strategies depended on the reproduction number and context. With a rising reproduction number, extending the testing strategy and early implementation of combined multiple restriction measures are most efficient.

CONCLUSIONS

The quality assessment highlighted numerous flaws and limitations in the study approaches; hence, their results should be interpreted with caution because the specific context (country, target group, etc) is a key driver for cost-effectiveness. Finally, including a societal perspective in future evaluations is key because this pandemic has an indirect impact on the onset and treatment of other conditions and on our global economy.

Balancing financial incentives during COVID-19: A comparison of provider payment adjustments across 20 countries

Provider payment mechanisms were adjusted in many countries in response to the COVID-19 pandemic in 2020. Our objective was to review adjustments for hospitals and healthcare professionals across 20 countries.

We developed an analytical framework distinguishing between payment adjustments compensating income loss and those covering extra costs related to COVID-19. Information was extracted from the Covid-19 Health System Response Monitor (HSRM) and classified according to the framework. We found that income loss was not a problem in countries where professionals were paid by salary or capitation and hospitals received global budgets. In countries where payment was based on activity, income loss was compensated through budgets and higher fees. New FFS payments were introduced to incentivize remote services. Payments for COVID-19 related costs included new fees for out- and inpatient services but also new PD and DRG tariffs for hospitals. Budgets covered the costs of adjusting wards, creating new (ICU) beds, and hiring staff.

We conclude that public payers assumed most of the COVID-19-related financial risk. In view of future pandemics policymakers should work to increase resilience of payment systems by: (1) having systems in place to rapidly adjust payment systems; (2) being aware of the economic incentives created by these adjustments such as cost-containment or increasing the number of patients or services, that can result in unintended consequences such as risk selection or overprovision of care; and (3) periodically evaluating the effects of payment adjustments on access and quality of care.

What Can Politics Learn from Management Decisions? A Case Study of Germany’s Exit from Nuclear Energy after Fukushima

The devastating nuclear disaster in Fukushima, Japan, in 2011, which was triggered by a tsunami in the wake of an earthquake, resulted in the decision to quickly phase out nuclear power and with it implicitly accelerated the German Energiewende (energy transition). To the outside observer, the decision appeared to be spontaneous and possibly due to a distorted perception of the associated risks of nuclear power. From the decision results not only the limiting uses of private property by conventional energy providers, but the exit from nuclear energy has also implications for the energy market. As with every human, political actors decide under uncertainty and incomplete information. Based on these parameters, we emphasize that the decision of a political actor is comparable to management decision-making. The paper takes this as an opportunity to examine the political decision to phase-out nuclear energy by discussing relevant parameters from the perspective of decision theory. We plead for a mandatory consideration of economic findings, especially from decision theory and risk management in political decision-making processes, especially in matters that affect future generations.