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Cheng CH, Yuen Z, Chen S, Wong KL, Chin JW, Chan TT, So RHY. Contactless Blood Oxygen Saturation Estimation from Facial Videos Using Deep Learning. Bioengineering (Basel) 2024; 11:251. [PMID: 38534525 DOI: 10.3390/bioengineering11030251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 02/26/2024] [Accepted: 03/02/2024] [Indexed: 03/28/2024] Open
Abstract
Blood oxygen saturation (SpO2) is an essential physiological parameter for evaluating a person's health. While conventional SpO2 measurement devices like pulse oximeters require skin contact, advanced computer vision technology can enable remote SpO2 monitoring through a regular camera without skin contact. In this paper, we propose novel deep learning models to measure SpO2 remotely from facial videos and evaluate them using a public benchmark database, VIPL-HR. We utilize a spatial-temporal representation to encode SpO2 information recorded by conventional RGB cameras and directly pass it into selected convolutional neural networks to predict SpO2. The best deep learning model achieves 1.274% in mean absolute error and 1.71% in root mean squared error, which exceed the international standard of 4% for an approved pulse oximeter. Our results significantly outperform the conventional analytical Ratio of Ratios model for contactless SpO2 measurement. Results of sensitivity analyses of the influence of spatial-temporal representation color spaces, subject scenarios, acquisition devices, and SpO2 ranges on the model performance are reported with explainability analyses to provide more insights for this emerging research field.
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Affiliation(s)
- Chun-Hong Cheng
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK
| | - Zhikun Yuen
- Department of Computer Science, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Shutao Chen
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
| | - Kwan-Long Wong
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
| | - Jing-Wei Chin
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
| | - Tsz-Tai Chan
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
| | - Richard H Y So
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
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Woods KL, Camins BC. Picking up the pieces: Lessons learned about optimal public health and acute-care hospital collaboration during pandemics. Antimicrob Steward Healthc Epidemiol 2023; 3:e125. [PMID: 37502241 PMCID: PMC10369428 DOI: 10.1017/ash.2023.184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/27/2023] [Accepted: 04/27/2023] [Indexed: 07/29/2023]
Affiliation(s)
- Krystina L. Woods
- Department of Infection Prevention, Mount Sinai West, New York, New York
- Division of Infectious Diseases, Department of Medicine, at Icahn School of Medicine, Mount Sinai, New York, New York
| | - Bernard C. Camins
- Division of Infectious Diseases, Department of Medicine, at Icahn School of Medicine, Mount Sinai, New York, New York
- Department of Infection Prevention, Mount Sinai Health System, New York, New York
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Winkler ML, Hooper DC, Shenoy ES. Infection Prevention and Control of Severe Acute Respiratory Syndrome Coronavirus 2 in Health Care Settings. Infect Dis Clin North Am 2022; 36:309-326. [PMID: 35636902 PMCID: PMC8806155 DOI: 10.1016/j.idc.2022.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The authors describe infection prevention and control approaches to severe acute respiratory syndrome coronavirus 2 in the health care setting, including a review of the chain of transmission and the hierarchy of controls, which are cornerstones of infection control and prevention. The authors also discuss lessons learned from nosocomial transmission events.
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Affiliation(s)
- Marisa L. Winkler
- Infection Control Unit, Massachusetts General Hospital, 55 Fruit Street, Bulfinch 334, Boston, MA 02114, USA,Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA,Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA,Corresponding author. Massachusetts General Hospital, 55 Fruit Street, Bulfinch 334, Boston, MA, 02114
| | - David C. Hooper
- Infection Control Unit, Massachusetts General Hospital, 55 Fruit Street, Bulfinch 334, Boston, MA 02114, USA,Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA,Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA
| | - Erica S. Shenoy
- Infection Control Unit, Massachusetts General Hospital, 55 Fruit Street, Bulfinch 334, Boston, MA 02114, USA,Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA,Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA
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Struyf T, Deeks JJ, Dinnes J, Takwoingi Y, Davenport C, Leeflang MM, Spijker R, Hooft L, Emperador D, Domen J, Tans A, Janssens S, Wickramasinghe D, Lannoy V, Horn SRA, Van den Bruel A. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19. Cochrane Database Syst Rev 2022; 5:CD013665. [PMID: 35593186 PMCID: PMC9121352 DOI: 10.1002/14651858.cd013665.pub3] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND COVID-19 illness is highly variable, ranging from infection with no symptoms through to pneumonia and life-threatening consequences. Symptoms such as fever, cough, or loss of sense of smell (anosmia) or taste (ageusia), can help flag early on if the disease is present. Such information could be used either to rule out COVID-19 disease, or to identify people who need to go for COVID-19 diagnostic tests. This is the second update of this review, which was first published in 2020. OBJECTIVES To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in primary care or to hospital outpatient settings, such as the emergency department or dedicated COVID-19 clinics, has COVID-19. SEARCH METHODS We undertook electronic searches up to 10 June 2021 in the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We used artificial intelligence text analysis to conduct an initial classification of documents. We did not apply any language restrictions. SELECTION CRITERIA Studies were eligible if they included people with clinically suspected COVID-19, or recruited known cases with COVID-19 and also controls without COVID-19 from a single-gate cohort. Studies were eligible when they recruited people presenting to primary care or hospital outpatient settings. Studies that included people who contracted SARS-CoV-2 infection while admitted to hospital were not eligible. The minimum eligible sample size of studies was 10 participants. All signs and symptoms were eligible for this review, including individual signs and symptoms or combinations. We accepted a range of reference standards. DATA COLLECTION AND ANALYSIS Pairs of review authors independently selected all studies, at both title and abstract, and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and assessed risk of bias using the QUADAS-2 checklist, and resolved disagreements by discussion with a third review author. Analyses were restricted to prospective studies only. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic (ROC) space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary prospective studies were available, and whenever heterogeneity across studies was deemed acceptable. MAIN RESULTS We identified 90 studies; for this update we focused on the results of 42 prospective studies with 52,608 participants. Prevalence of COVID-19 disease varied from 3.7% to 60.6% with a median of 27.4%. Thirty-five studies were set in emergency departments or outpatient test centres (46,878 participants), three in primary care settings (1230 participants), two in a mixed population of in- and outpatients in a paediatric hospital setting (493 participants), and two overlapping studies in nursing homes (4007 participants). The studies did not clearly distinguish mild COVID-19 disease from COVID-19 pneumonia, so we present the results for both conditions together. Twelve studies had a high risk of bias for selection of participants because they used a high level of preselection to decide whether reverse transcription polymerase chain reaction (RT-PCR) testing was needed, or because they enrolled a non-consecutive sample, or because they excluded individuals while they were part of the study base. We rated 36 of the 42 studies as high risk of bias for the index tests because there was little or no detail on how, by whom and when, the symptoms were measured. For most studies, eligibility for testing was dependent on the local case definition and testing criteria that were in effect at the time of the study, meaning most people who were included in studies had already been referred to health services based on the symptoms that we are evaluating in this review. The applicability of the results of this review iteration improved in comparison with the previous reviews. This version has more studies of people presenting to ambulatory settings, which is where the majority of assessments for COVID-19 take place. Only three studies presented any data on children separately, and only one focused specifically on older adults. We found data on 96 symptoms or combinations of signs and symptoms. Evidence on individual signs as diagnostic tests was rarely reported, so this review reports mainly on the diagnostic value of symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. RT-PCR was the most often used reference standard (40/42 studies). Only cough (11 studies) had a summary sensitivity above 50% (62.4%, 95% CI 50.6% to 72.9%)); its specificity was low (45.4%, 95% CI 33.5% to 57.9%)). Presence of fever had a sensitivity of 37.6% (95% CI 23.4% to 54.3%) and a specificity of 75.2% (95% CI 56.3% to 87.8%). The summary positive likelihood ratio of cough was 1.14 (95% CI 1.04 to 1.25) and that of fever 1.52 (95% CI 1.10 to 2.10). Sore throat had a summary positive likelihood ratio of 0.814 (95% CI 0.714 to 0.929), which means that its presence increases the probability of having an infectious disease other than COVID-19. Dyspnoea (12 studies) and fatigue (8 studies) had a sensitivity of 23.3% (95% CI 16.4% to 31.9%) and 40.2% (95% CI 19.4% to 65.1%) respectively. Their specificity was 75.7% (95% CI 65.2% to 83.9%) and 73.6% (95% CI 48.4% to 89.3%). The summary positive likelihood ratio of dyspnoea was 0.96 (95% CI 0.83 to 1.11) and that of fatigue 1.52 (95% CI 1.21 to 1.91), which means that the presence of fatigue slightly increases the probability of having COVID-19. Anosmia alone (7 studies), ageusia alone (5 studies), and anosmia or ageusia (6 studies) had summary sensitivities below 50% but summary specificities over 90%. Anosmia had a summary sensitivity of 26.4% (95% CI 13.8% to 44.6%) and a specificity of 94.2% (95% CI 90.6% to 96.5%). Ageusia had a summary sensitivity of 23.2% (95% CI 10.6% to 43.3%) and a specificity of 92.6% (95% CI 83.1% to 97.0%). Anosmia or ageusia had a summary sensitivity of 39.2% (95% CI 26.5% to 53.6%) and a specificity of 92.1% (95% CI 84.5% to 96.2%). The summary positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.55 (95% CI 3.46 to 5.97) and 4.99 (95% CI 3.22 to 7.75) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The summary positive likelihood ratio of ageusia alone was 3.14 (95% CI 1.79 to 5.51). Twenty-four studies assessed combinations of different signs and symptoms, mostly combining olfactory symptoms. By combining symptoms with other information such as contact or travel history, age, gender, and a local recent case detection rate, some multivariable prediction scores reached a sensitivity as high as 90%. AUTHORS' CONCLUSIONS Most individual symptoms included in this review have poor diagnostic accuracy. Neither absence nor presence of symptoms are accurate enough to rule in or rule out the disease. The presence of anosmia or ageusia may be useful as a red flag for the presence of COVID-19. The presence of cough also supports further testing. There is currently no evidence to support further testing with PCR in any individuals presenting only with upper respiratory symptoms such as sore throat, coryza or rhinorrhoea. Combinations of symptoms with other readily available information such as contact or travel history, or the local recent case detection rate may prove more useful and should be further investigated in an unselected population presenting to primary care or hospital outpatient settings. The diagnostic accuracy of symptoms for COVID-19 is moderate to low and any testing strategy using symptoms as selection mechanism will result in both large numbers of missed cases and large numbers of people requiring testing. Which one of these is minimised, is determined by the goal of COVID-19 testing strategies, that is, controlling the epidemic by isolating every possible case versus identifying those with clinically important disease so that they can be monitored or treated to optimise their prognosis. The former will require a testing strategy that uses very few symptoms as entry criterion for testing, the latter could focus on more specific symptoms such as fever and anosmia.
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Affiliation(s)
- Thomas Struyf
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Jonathan J Deeks
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Jacqueline Dinnes
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Yemisi Takwoingi
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Clare Davenport
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Mariska Mg Leeflang
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - René Spijker
- Medical Library, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Amsterdam, Netherlands
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Lotty Hooft
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Julie Domen
- Department of Primary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Anouk Tans
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | | | | | | | - Sebastiaan R A Horn
- Department of Primary Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Ann Van den Bruel
- Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
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van Zundert A, Intaprasert T, Wiepking F, Eley V. Are Non-Contact Thermometers an Option in Anaesthesia? A Narrative Review on Thermometry for Perioperative Medicine. Healthcare (Basel) 2022; 10:219. [PMID: 35206834 PMCID: PMC8872024 DOI: 10.3390/healthcare10020219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/21/2022] [Accepted: 01/21/2022] [Indexed: 11/22/2022] Open
Abstract
Measurement of core body temperature—clinical thermometry—provides critical information to anaesthetists during perioperative care. The value of this information is determined by the accuracy of the measurement device used. This accuracy must be maintained despite external influences such as the operating room temperature and the patient’s thermoregulatory defence. Presently, perioperative thermometers utilise invasive measurement sites. The public health challenge of the COVID-19 pandemic, however, has highlighted the use of non-invasive, non-contact infrared thermometers. The aim of this article is to review common existing thermometers used in perioperative care, their mechanisms of action, accuracy, and practicality in comparison to infrared non-contact thermometry used for population screening during a pandemic. Evidence currently shows that contact thermometry varies in accuracy and practicality depending on the site of measurements and the method of sterilisation or disposal between uses. Despite the benefits of being a non-invasive and non-contact device, infrared thermometry used for population temperature screening lacks the accuracy required in perioperative medicine. Inaccuracy may be a consequence of uncontrolled external temperatures, the patient’s actions prior to measurement, distance between the patient and the thermometer, and the different sites of measurement. A re-evaluation of non-contact thermometry is recommended, requiring new studies in more controlled environments.
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Zhang N, Jack Chan PT, Jia W, Dung CH, Zhao P, Lei H, Su B, Xue P, Zhang W, Xie J, Li Y. Analysis of efficacy of intervention strategies for COVID-19 transmission: A case study of Hong Kong. Environ Int 2021; 156:106723. [PMID: 34161908 PMCID: PMC8214805 DOI: 10.1016/j.envint.2021.106723] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/12/2021] [Accepted: 06/14/2021] [Indexed: 05/25/2023]
Abstract
By the end of February 2021, COVID-19 had spread to over 230 countries, with more than 100 million confirmed cases and 2.5 million deaths. To control infection spread with the least disruption to economic and societal activities, it is crucial to implement the various interventions effectively. In this study, we developed an agent-based SEIR model, using real demographic and geographic data from Hong Kong, to analyse the efficiency of various intervention strategies in preventing infection by the SARS-CoV-2 virus. Close contact route including short-range airborne is considered as the main transmission routes for COVID-19 spread. Contact tracing is not that useful if all other interventions have been fully deployed. The number of infected individuals could be halved if people reduced their close contact rate by 25%. For reducing transmission, students should be prioritized for vaccination rather than retired older people and preschool aged children. Home isolation, and taking the nucleic acid test (NAT) as soon as possible after symptom onset, are much more effective interventions than wearing masks in public places. Temperature screening in public places only disrupted the infection spread by a small amount when other interventions have been fully implemented. Our results may be useful for other highly populated cities, when choosing their intervention strategies to prevent outbreaks of COVID-19 and similar diseases.
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Affiliation(s)
- Nan Zhang
- Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China; Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Pak-To Jack Chan
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Wei Jia
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China; Zhejiang Institute of Research and Innovation, The University of Hong Kong, Lin An, Zhejiang, China
| | - Chung-Hin Dung
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Pengcheng Zhao
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Hao Lei
- School of Public Health, Zhejiang University, Hangzhou, China
| | - Boni Su
- China Electric Power Planning & Engineering Institute, Beijing, China
| | - Peng Xue
- Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China
| | - Weirong Zhang
- Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China
| | - Jingchao Xie
- Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China
| | - Yuguo Li
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China.
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Cheng CH, Wong KL, Chin JW, Chan TT, So RHY. Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda. Sensors (Basel) 2021; 21:6296. [PMID: 34577503 PMCID: PMC8473186 DOI: 10.3390/s21186296] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/13/2021] [Accepted: 09/16/2021] [Indexed: 01/05/2023]
Abstract
Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.
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Affiliation(s)
- Chun-Hong Cheng
- Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China; (J.-W.C.); (T.-T.C.); (R.H.Y.S.)
| | - Kwan-Long Wong
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China; (J.-W.C.); (T.-T.C.); (R.H.Y.S.)
- Department of Bioengineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Jing-Wei Chin
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China; (J.-W.C.); (T.-T.C.); (R.H.Y.S.)
- Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Tsz-Tai Chan
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China; (J.-W.C.); (T.-T.C.); (R.H.Y.S.)
- Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Richard H. Y. So
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China; (J.-W.C.); (T.-T.C.); (R.H.Y.S.)
- Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
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Nuertey BD, Ekremet K, Haidallah AR, Mumuni K, Addai J, Attibu RIE, Damah MC, Duorinaa E, Seidu AS, Adongo VC, Adatsi RK, Suri HC, Komei AAK, Abubakari BB, Weyori E, Allegye-Cudjoe E, Sylverken A, Owusu M, Phillips RO. Performance of COVID-19 associated symptoms and temperature checking as a screening tool for SARS-CoV-2 infection. PLoS One 2021; 16:e0257450. [PMID: 34534249 PMCID: PMC8448301 DOI: 10.1371/journal.pone.0257450] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 09/01/2021] [Indexed: 01/08/2023] Open
Abstract
INTRODUCTION Coronavirus disease-19 (COVID-19), which started in late December, 2019, has spread to affect 216 countries and territories around the world. Globally, the number of cases of SARS-CoV-2 infection has been growing exponentially. There is pressure on countries to flatten the curves and break transmission. Most countries are practicing partial or total lockdown, vaccination, massive education on hygiene, social distancing, isolation of cases, quarantine of exposed and various screening approaches such as temperature and symptom-based screening to break the transmission. Some studies outside Africa have found the screening for fever using non-contact thermometers to lack good sensitivity for detecting SARS-CoV-2 infection. The aim of this study was to determine the usefulness of clinical symptoms in accurately predicting a final diagnosis of COVID-19 disease in the Ghanaian setting. METHOD The study analysed screening and test data of COVID-19 suspected, probable and contacts for the months of March to August 2020. A total of 1,986 participants presenting to Tamale Teaching hospital were included in the study. Logistic regression and receiver operator characteristics (ROC) analysis were carried out. RESULTS Overall SARS-CoV-2 positivity rate was 16.8%. Those with symptoms had significantly higher positivity rate (21.6%) compared with asymptomatic (17.0%) [chi-squared 15.5, p-value, <0.001]. Patients that were positive for SARS-CoV-2 were 5.9 [3.9-8.8] times more likely to have loss of sense of smell and 5.9 [3.8-9.3] times more likely to having loss of sense of taste. Using history of fever as a screening tool correctly picked up only 14.8% of all true positives of SARS-CoV-2 infection and failed to pick up 86.2% of positive cases. Using cough alone would detect 22.4% and miss 87.6%. Non-contact thermometer used alone, as a screening tool for COVID-19 at a cut-off of 37.8 would only pick 4.8% of positive SARS-CoV-2 infected patients. CONCLUSION The use of fever alone or other symptoms individually [or in combination] as a screening tool for SARS-CoV-2 infection is not worthwhile based on ROC analysis. Use of temperature check as a COVID-19 screening tool to allow people into public space irrespective of the temperature cut-off is of little benefit in diagnosing infected persons. We recommend the use of facemask, hand hygiene, social distancing as effective means of preventing infection.
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Affiliation(s)
- Benjamin Demah Nuertey
- Tamale Teaching Hospital, COVID-19 Management Team, Accra, Ghana
- Community Health Department, University of Ghana Medical School, Accra, Ghana
- Public Health Department, Tamale Teaching Hospital, Tamale, Ghana
| | - Kwame Ekremet
- Tamale Teaching Hospital, COVID-19 Management Team, Accra, Ghana
| | | | - Kareem Mumuni
- Tamale Teaching Hospital, COVID-19 Management Team, Accra, Ghana
- Department of Obstetric and Gynaecology, University of Ghana Medical School, Accra, Ghana
| | - Joyce Addai
- Department of Medicine, Korle-Bu teaching Hospital, Accra, Ghana
| | - Rosemary Ivy E. Attibu
- Tamale Teaching Hospital, COVID-19 Management Team, Accra, Ghana
- Public Health Department, Tamale Teaching Hospital, Tamale, Ghana
| | - Michael C. Damah
- Tamale Teaching Hospital, COVID-19 Management Team, Accra, Ghana
- Pharmacy Department, Tamale Teaching Hospital, Tamale, Ghana
| | - Elvis Duorinaa
- Tamale Teaching Hospital, COVID-19 Management Team, Accra, Ghana
- Department of Surgery, Tamale Teaching Hospital, Tamale, Ghana
| | - Anwar Sadat Seidu
- Tamale Teaching Hospital, COVID-19 Management Team, Accra, Ghana
- Public Health Department, Tamale Teaching Hospital, Tamale, Ghana
| | - Victor C. Adongo
- Tamale Teaching Hospital, COVID-19 Management Team, Accra, Ghana
- Laboratory Department, Tamale Teaching Hospital, Tamale, Ghana
| | - Richard Kujo Adatsi
- Tamale Teaching Hospital, COVID-19 Management Team, Accra, Ghana
- Laboratory Department, Tamale Teaching Hospital, Tamale, Ghana
| | - Hisyovi Caedenas Suri
- Tamale Teaching Hospital, COVID-19 Management Team, Accra, Ghana
- Intensive Care Unit, Tamale Teaching Hospital, Tamale, Ghana
| | | | - Braimah Baba Abubakari
- Regional Health Directorate, Northern Region, Tamale, Ghana
- School of Medical Sciences, University for development studies, Tamale, Ghana
| | - Enoch Weyori
- Zonal Public Health Reference Laboratory, Tamale, Ghana
| | | | - Augustina Sylverken
- Department of Theoretical and Applied Biology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- Kumasi Centre for Collaborative Research, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Michael Owusu
- Kumasi Centre for Collaborative Research, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
- Department of Medical Diagnostics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Richard O. Phillips
- Kumasi Centre for Collaborative Research, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
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Hirner S, Pigoga JL, Naidoo AV, Calvello Hynes EJ, Omer YO, Wallis LA, Bills CB. Potential solutions for screening, triage, and severity scoring of suspected COVID-19 positive patients in low-resource settings: a scoping review. BMJ Open 2021; 11:e046130. [PMID: 34526332 PMCID: PMC8449848 DOI: 10.1136/bmjopen-2020-046130] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES Purposefully designed and validated screening, triage, and severity scoring tools are needed to reduce mortality of COVID-19 in low-resource settings (LRS). This review aimed to identify currently proposed and/or implemented methods of screening, triaging, and severity scoring of patients with suspected COVID-19 on initial presentation to the healthcare system and to evaluate the utility of these tools in LRS. DESIGN A scoping review was conducted to identify studies describing acute screening, triage, and severity scoring of patients with suspected COVID-19 published between 12 December 2019 and 1 April 2021. Extracted information included clinical features, use of laboratory and imaging studies, and relevant tool validation data. PARTICIPANT The initial search strategy yielded 15 232 articles; 124 met inclusion criteria. RESULTS Most studies were from China (n=41, 33.1%) or the United States (n=23, 18.5%). In total, 57 screening, 23 triage, and 54 severity scoring tools were described. A total of 51 tools-31 screening, 5 triage, and 15 severity scoring-were identified as feasible for use in LRS. A total of 37 studies provided validation data: 4 prospective and 33 retrospective, with none from low-income and lower middle-income countries. CONCLUSIONS This study identified a number of screening, triage, and severity scoring tools implemented and proposed for patients with suspected COVID-19. No tools were specifically designed and validated in LRS. Tools specific to resource limited contexts is crucial to reducing mortality in the current pandemic.
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Affiliation(s)
- Sarah Hirner
- University of Colorado Denver School of Medicine, Aurora, Colorado, USA
| | - Jennifer Lee Pigoga
- Division of Emergency Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
| | | | - Emilie J Calvello Hynes
- Department of Emergency Medicine, University of Colorado Denver School of Medicine, Aurora, Colorado, USA
| | - Yasein O Omer
- Division of Emergency Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
- Sudan Medical Specialization Board, Khartoum, Sudan
| | - Lee A Wallis
- Division of Emergency Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Corey B Bills
- Department of Emergency Medicine, University of Colorado Denver School of Medicine, Aurora, Colorado, USA
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10
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Attia ZI, Kapa S, Dugan J, Pereira N, Noseworthy PA, Jimenez FL, Cruz J, Carter RE, DeSimone DC, Signorino J, Halamka J, Chennaiah Gari NR, Madathala RS, Platonov PG, Gul F, Janssens SP, Narayan S, Upadhyay GA, Alenghat FJ, Lahiri MK, Dujardin K, Hermel M, Dominic P, Turk-Adawi K, Asaad N, Svensson A, Fernandez-Aviles F, Esakof DD, Bartunek J, Noheria A, Sridhar AR, Lanza GA, Cohoon K, Padmanabhan D, Pardo Gutierrez JA, Sinagra G, Merlo M, Zagari D, Rodriguez Escenaro BD, Pahlajani DB, Loncar G, Vukomanovic V, Jensen HK, Farkouh ME, Luescher TF, Su Ping CL, Peters NS, Friedman PA. Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram. Mayo Clin Proc 2021; 96:2081-2094. [PMID: 34353468 PMCID: PMC8327278 DOI: 10.1016/j.mayocp.2021.05.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 01/24/2023]
Abstract
OBJECTIVE To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). METHODS A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. RESULTS The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. CONCLUSION Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.
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Key Words
- ace2, angiotensin-converting enzyme 2
- ai, artificial intelligence
- ai-ecg, artificial intelligence–enhanced electrocardiogram
- auc, area under the curve
- covid-19, coronavirus infectious disease 19
- npv, negative predictive value
- pcr, polymerase chain reaction
- ppv, positive predictive value
- redcap, research electronic data capture
- sars-cov-2, severe acute respiratory syndrome coronavirus 2
- who, world health organization
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Affiliation(s)
- Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | - Jennifer Dugan
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | - Naveen Pereira
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | | | - Jessica Cruz
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN
| | - Rickey E Carter
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Jacksonville, FL
| | - Daniel C DeSimone
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN; Division of Infectious Diseases, Mayo Clinic College of Medicine, Rochester, MN
| | - John Signorino
- Department of Compliance, Mayo Clinic College of Medicine, Rochester, MN
| | - John Halamka
- Mayo Clinic Platform, Mayo Clinic College of Medicine, Rochester, MN
| | | | | | - Pyotr G Platonov
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Fahad Gul
- Division of Cardiology, Heart and Vascular Institute, Einstein Healthcare Network, Philadelphia, PA
| | - Stefan P Janssens
- Department of Cardiovascular Diseases, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Sanjiv Narayan
- Cardiovascular Institute and Department of Cardiovascular Medicine, Stanford University Medical Center, Stanford, CA
| | - Gaurav A Upadhyay
- Section of Cardiology, Department of Medicine, University of Chicago, Chicago, IL
| | - Francis J Alenghat
- Section of Cardiology, Department of Medicine, University of Chicago, Chicago, IL
| | - Marc K Lahiri
- Henry Ford Hospital, Heart and Vascular Institute, Detroit, MI
| | - Karl Dujardin
- Department of Cardiology, AZ Delta Hospital, AZ Delta Campus Rumbeke, Deltalaan, Belgium
| | - Melody Hermel
- Scripps Health and the Scripps Clinic Division of Cardiology, La Jolla, CA
| | - Paari Dominic
- Louisiana State University Health Sciences Center, Shreveport, LA
| | | | | | - Anneli Svensson
- Department of Cardiology and Department of Medical and Health Sciences, Linköping University Hospital, Linköping, Sweden
| | - Francisco Fernandez-Aviles
- Hospital General Universitario Gregorio Maranon, Instituto de Investigacion Sanitaria Gregorio Maranon, Universidad Complutense, Madrid, Spain
| | - Darryl D Esakof
- Department of Cardiology, Lahey Hospital & Medical Center, Burlington, MA
| | | | - Amit Noheria
- Department of Cardiovascular Medicine, The University of Kansas Health System, Kansas City, KS
| | - Arun R Sridhar
- Section of Cardiac Electrophysiology, University of Washington Medical Center, Seattle, WA
| | - Gaetano A Lanza
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Universita Cattolica del Sacro Cuore, Cardiology Institute, Rome, Italy
| | - Kevin Cohoon
- Division of Cardiovascular Medicine Froedtert & the Medical College of Wisconsin, Milwaukee, WI
| | - Deepak Padmanabhan
- Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bangalore, India
| | | | - Gianfranco Sinagra
- Cardiovascular Department "Ospedali Riuniti" and University of Trieste, Trieste, Italy
| | - Marco Merlo
- Cardiovascular Department "Ospedali Riuniti" and University of Trieste, Trieste, Italy
| | - Domenico Zagari
- Electrophysiology and Cardiac Pacing Unit, Humanitas Mater Domini Clinical Institute, Castellanza, Italy
| | | | | | - Goran Loncar
- Department of Cardiology, Institute for Cardiovascular Diseases Dedinje (ICVDD), Belgrade, Serbia
| | - Vladan Vukomanovic
- University Hospital Center "Dr Dragisa Misovic-Dedinje", Belgrade, Serbia
| | - Henrik K Jensen
- Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark
| | | | | | | | - Nicholas S Peters
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN.
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11
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Abstract
A central problem in the COVID-19 pandemic is that there is not enough testing to prevent infectious spread of SARS-CoV-2, causing surges and lockdowns with human and economic toll. Molecular tests that detect viral RNAs or antigens will be unable to rise to this challenge unless testing capacity increases by at least an order of magnitude while decreasing turnaround times. Here, we evaluate an alternative strategy based on the monitoring of olfactory dysfunction, a symptom identified in 76-83% of SARS-CoV-2 infections-including those with no other symptoms-when a standardized olfaction test is used. We model how screening for olfactory dysfunction, with reflexive molecular tests, could be beneficial in reducing community spread of SARS-CoV-2 by varying testing frequency and the prevalence, duration, and onset time of olfactory dysfunction. We find that monitoring olfactory dysfunction could reduce spread via regular screening, and could reduce risk when used at point-of-entry for single-day events. In light of these estimated impacts, and because olfactory tests can be mass produced at low cost and self-administered, we suggest that screening for olfactory dysfunction could be a high impact and cost-effective method for broad COVID-19 screening and surveillance.
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Affiliation(s)
- Daniel B Larremore
- Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA.
| | - Derek Toomre
- Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA.
| | - Roy Parker
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA.
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
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12
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Johansson MA, Wolford H, Paul P, Diaz PS, Chen TH, Brown CM, Cetron MS, Alvarado-Ramy F. Reducing travel-related SARS-CoV-2 transmission with layered mitigation measures: symptom monitoring, quarantine, and testing. BMC Med 2021; 19:94. [PMID: 33849546 PMCID: PMC8043777 DOI: 10.1186/s12916-021-01975-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 03/25/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Balancing the control of SARS-CoV-2 transmission with the resumption of travel is a global priority. Current recommendations include mitigation measures before, during, and after travel. Pre- and post-travel strategies including symptom monitoring, antigen or nucleic acid amplification testing, and quarantine can be combined in multiple ways considering different trade-offs in feasibility, adherence, effectiveness, cost, and adverse consequences. METHODS We used a mathematical model to analyze the expected effectiveness of symptom monitoring, testing, and quarantine under different estimates of the infectious period, test-positivity relative to time of infection, and test sensitivity to reduce the risk of transmission from infected travelers during and after travel. RESULTS If infection occurs 0-7 days prior to travel, immediate isolation following symptom onset prior to or during travel reduces risk of transmission while traveling by 30-35%. Pre-departure testing can further reduce risk, with testing closer to the time of travel being optimal even if test sensitivity is lower than an earlier test. For example, testing on the day of departure can reduce risk while traveling by 44-72%. For transmission risk after travel with infection time up to 7 days prior to arrival at the destination, isolation based on symptom monitoring reduced introduction risk at the destination by 42-56%. A 14-day quarantine after arrival, without symptom monitoring or testing, can reduce post-travel risk by 96-100% on its own. However, a shorter quarantine of 7 days combined with symptom monitoring and a test on day 5-6 after arrival is also effective (97--100%) at reducing introduction risk and is less burdensome, which may improve adherence. CONCLUSIONS Quarantine is an effective measure to reduce SARS-CoV-2 transmission risk from travelers and can be enhanced by the addition of symptom monitoring and testing. Optimal test timing depends on the effectiveness of quarantine: with low adherence or no quarantine, optimal test timing is close to the time of arrival; with effective quarantine, testing a few days later optimizes sensitivity to detect those infected immediately before or while traveling. These measures can complement recommendations such as social distancing, using masks, and hand hygiene, to further reduce risk during and after travel.
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Affiliation(s)
- Michael A Johansson
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, USA.
| | - Hannah Wolford
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, USA
| | - Prabasaj Paul
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, USA
| | - Pamela S Diaz
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, USA
| | - Tai-Ho Chen
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, USA
| | - Clive M Brown
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, USA
| | - Martin S Cetron
- COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, USA
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13
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Abstract
Coronavirus disease-2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has abruptly transformed the outlook of employer health benefits plans for 2020 and 2021. Containing the spread of the virus and facilitating care of those infected have quickly emerged as immediate priorities. Employers have adjusted health benefits coverage to make COVID-19 testing and treatment accessible and remove barriers to care in order to facilitate the containment of the disease. Employers also are introducing strategies focused on testing, surveillance, workplace modifications, and hygiene to keep workforces healthy and workplaces safe. This paper is intended to provide evidence-based perspectives for self-insured employers for managing population health during the COVID-19 pandemic. Such considerations include (1) return to work practices focused on mitigating the spread of COVID-19 through safety practices, testing and surveillance; and (2) anticipating the impact of COVID-19 on health benefits and costs (including adaptations in delivery of care, social and behavioral health needs, and managing interrupted care for chronic conditions).
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