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Vardavas C, Zisis K, Nikitara K, Lagou I, Marou V, Aslanoglou K, Athanasakis K, Phalkey R, Leonardi-Bee J, Fernandez E, Condell O, Lamb F, Sandmann F, Pharris A, Deogan C, Suk JE. Cost of the COVID-19 pandemic versus the cost-effectiveness of mitigation strategies in EU/UK/OECD: a systematic review. BMJ Open 2023; 13:e077602. [PMID: 37907290 PMCID: PMC10619092 DOI: 10.1136/bmjopen-2023-077602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/18/2023] [Indexed: 11/02/2023] Open
Abstract
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.
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Affiliation(s)
- Constantine Vardavas
- School of Medicine, University of Crete, Heraklion, Greece
- Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine, Harvard University, Boston, Massachusetts, USA
| | - Konstantinos Zisis
- School of Medicine, University of Crete, Heraklion, Greece
- Department of Public Health Policy, University of West Attica, Egaleo, Greece
| | | | - Ioanna Lagou
- School of Medicine, University of Crete, Heraklion, Greece
| | - Valia Marou
- School of Medicine, University of Crete, Heraklion, Greece
| | | | | | - Revati Phalkey
- Public Health England, London, UK
- University of Nottingham, Nottingham, UK
| | - Jo Leonardi-Bee
- Centre for Evidence-Based Healthcare, School of Medicine, University of Nottingham, Nottingham, UK
| | - Esteve Fernandez
- Tobacco Control Unit, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Spain
| | - Orla Condell
- European Centre for Disease Prevention and Control, Solna, Sweden
| | - Favelle Lamb
- European Centre for Disease Prevention and Control, Solna, Sweden
| | - Frank Sandmann
- European Centre for Disease Prevention and Control, Solna, Sweden
| | | | - Charlotte Deogan
- European Centre for Disease Prevention and Control, Solna, Sweden
| | - Jonathan E Suk
- European Centre for Disease Prevention and Control, Solna, Sweden
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Bhattacharya P, Chen J, Hoops S, Machi D, Lewis B, Venkatramanan S, Wilson ML, Klahn B, Adiga A, Hurt B, Outten J, Adiga A, Warren A, Baek YY, Porebski P, Marathe A, Xie D, Swarup S, Vullikanti A, Mortveit H, Eubank S, Barrett CL, Marathe M. Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support. THE INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2023; 37:4-27. [PMID: 38603425 PMCID: PMC9596688 DOI: 10.1177/10943420221127034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.
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Affiliation(s)
- Parantapa Bhattacharya
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Jiangzhuo Chen
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Stefan Hoops
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Dustin Machi
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Bryan Lewis
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | | | - Mandy L Wilson
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Brian Klahn
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Aniruddha Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Benjamin Hurt
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Joseph Outten
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Abhijin Adiga
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Andrew Warren
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Young Yun Baek
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Przemyslaw Porebski
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Achla Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Dawen Xie
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Samarth Swarup
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
| | - Anil Vullikanti
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Henning Mortveit
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Eng. Systems and Environment, University of Virginia, Charlottesville, VA, USA
| | - Stephen Eubank
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Christopher L Barrett
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
| | - Madhav Marathe
- Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA
- Dept. of Computer Science, University of Virginia, Charlottesville, VA, USA
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Fernandez N, Low D, Cain M, Martin L, Merguerian P. Recovering from COVID - Improving operating room capacity. J Pediatr Urol 2022; 18:411.e1-411.e7. [PMID: 35999120 PMCID: PMC9299983 DOI: 10.1016/j.jpurol.2022.07.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 07/08/2022] [Accepted: 07/10/2022] [Indexed: 11/16/2022]
Abstract
INTRODUCTION COVID-19 pandemic required that health systems made great efforts to mitigate the impact of high demands of patients requiring treatment. Triaging surgical cases reduced operating room capacity. Immunizations, massive testing, and personal protective equipment enabled re-activation of operating rooms. Delayed and newly added cases has placed stress on the system. We hypothesize that standardization in practice for tasks performed between anesthesia ready and surgery start time, also known as "prepping time", can reduce operative time, improve efficiency and increase capacity. The aim of our project was to create and implement a best practice standardized prepping protocol, to explore its impact on operating room capacity. METHODS Once local policies allowed re-opening of the operating rooms, our multidisciplinary group developed a working plan following Adaptive Clinical Management (ACM) principles to optimize surgical prepping time. Using electronic medical record (EMR) data, surgeons with the lowest surgical prepping times were identified (positive deviants). Their surgical prepping time workflows were reviewed. A clinical standard work (CSW) protocol was created by the team leader. New CSW protocol was defined and implemented by the leader and then by the rest of the surgeons. Baseline data was automatically extracted from EMR and analyzed by statistical process control (SPC) charts using AdaptX. Balancing measures included "last case end time" and rates of surgical site infections. RESULTS A total of 2506 patients were included for analysis with 1333 prior to intervention and 1173 after. Team leader implementated the new CSW prepping protocol showing a special cause variation with an average time improvement from 14.6 min to 11.6 min and for all surgeons from 13.8 to 12.0 min. Total cases per month increased from 70 to 90 cases per month. Baseline 'Last Case End Time' was 15.7 min later than the scheduled. New CSW improve end time with an average of 20.8 min before the schedule. Baseline surgical site infection was 0.1% for the study population. No difference was seen after implementation. DISCUSSION Variations in performance can be quantified using funnel plots showing individual practices allowing best practice to be identified, tested and scaled. Implementation of our surgical prepping time protocol showed a sustainable increase in efficiency without affecting quality, safety or workload. This additional increase is estimated to represent approximately $2-2.5M additional revenue per year. CONCLUSION Adaptive clinical management is a practical solution to increase OR capacity by improving efficiency to reduce extra burden presented during COVID19 pandemic.
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Affiliation(s)
- Nicolas Fernandez
- Division of Pediatric Urology. Seattle Children's Hospital. University of Washington, USA
| | - Daniel Low
- Anesthesia Department. Seattle Children's Hospital. University of Washington, USA
| | - Mark Cain
- Division of Pediatric Urology. Seattle Children's Hospital. University of Washington, USA
| | - Lynn Martin
- Anesthesia Department. Seattle Children's Hospital. University of Washington, USA
| | - Paul Merguerian
- Division of Pediatric Urology. Seattle Children's Hospital. University of Washington, USA.
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