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Murphy C, Lim WW, Mills C, Wong JY, Chen D, Xie Y, Li M, Gould S, Xin H, Cheung JK, Bhatt S, Cowling BJ, Donnelly CA. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20230132. [PMID: 37611629 PMCID: PMC10446910 DOI: 10.1098/rsta.2023.0132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/23/2023] [Indexed: 08/25/2023]
Abstract
Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.
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Affiliation(s)
- Caitriona Murphy
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Wey Wen Lim
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Cathal Mills
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jessica Y. Wong
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Dongxuan Chen
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Yanmy Xie
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Mingwei Li
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Susan Gould
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Hualei Xin
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Justin K. Cheung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Samir Bhatt
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Kobenhavn, Denmark
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Benjamin J. Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
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Henriques HR, Sousa D, Faria J, Pinto J, Costa A, Henriques MA, Durão MC. Learning from the covid-19 outbreaks in long-term care facilities: a systematic review. BMC Geriatr 2023; 23:618. [PMID: 37784017 PMCID: PMC10546730 DOI: 10.1186/s12877-023-04319-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 09/14/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has devastatingly affected Long-Term Care Facilities (LTCF), exposing aging people, staff members, and visitors. The world has learned through the pandemic and lessons can be taken to adopt effective measures to deal with COVID-19 outbreaks in LTCF. We aimed to systematically review the available evidence on the effect of measures to minimize the risk of transmission of COVID-19 in LTCs during outbreaks since 2021. METHODS The search method was guided by the preferred reporting items for systematic reviews (PRISMA) and the reporting guideline synthesis without meta-analysis (SWiM) in systematic reviews. The search was performed in April 2023. Observational and interventional studies from the databases of PubMed, Web of Science, Scopus, Cochrane Systematic Reviews, CINAHL, and Academic Search were systematically reviewed. We included studies conducted in the LTCF with outbreaks that quantitatively assess the effect of non-pharmacological measures on cases of COVID-19. Two review authors independently reviewed titles for inclusion, extracted data, and undertook the risk of bias according to pre-specified criteria. The quality of studies was analyzed using the Joanna Briggs Institute Critical Appraisal. RESULTS Thirteen studies were included, with 8442 LTCF experiencing COVID-19 outbreaks and 598 thousand participants (residents and staff members). Prevention and control of COVID-19 infection interventions were grouped into three themes: strategic, tactical, and operational measures. The strategic measures reveal the importance of COVID-19 prevention and control as LTCF structural characteristics, namely the LTCF size, new admissions, infection control surveillance, and architectural structure. At the tactical level, the lack of personal and long staff shifts is related to COVID-19's spread. Operational measures with a favorable effect on preventing COVID-19 transmission are sufficient. Personal protective equipment stock, correct mask use, signaling, social distancing, and resident cohorting. CONCLUSIONS Operational, tactical, and strategic approaches may have a favorable effect on preventing the spread of COVID-19 in LTCFs experiencing outbreaks. Given the heterogeneous nature of the measures, performing a meta-analysis was not possible. Future research should use more robust study designs to explore similar infection control measures in LTCFs during endemic situations with comparable outbreaks. TRIAL REGISTRATION The protocol of this systematic review was registered in PROSPERO (CRD42020214566).
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Affiliation(s)
- Helga Rafael Henriques
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal.
| | - Diana Sousa
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
| | - José Faria
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
| | - Joana Pinto
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
| | - Andreia Costa
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
- Instituto de Saúde Ambiental - ISAMB, Lisbon Medical School - Avenida Professor Egas Moniz MB, 1649-028, Lisbon, Portugal
| | - Maria Adriana Henriques
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
- Instituto de Saúde Ambiental - ISAMB, Lisbon Medical School - Avenida Professor Egas Moniz MB, 1649-028, Lisbon, Portugal
| | - Maria Cândida Durão
- Escola Superior de Enfermagem de Lisboa, CIDNUR - Nursing Research, Innovation and Development Centre of Lisbon, Avenida Prof Egas Moniz, 1600-190, Lisbon, Portugal
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Tulloch JS, Lawrenson K, Gordon AL, Ghebrehewet S, Ashton M, Peddie S, Parvulescu P. COVID-19 vaccine hesitancy in care home staff: A survey of Liverpool care homes. Vaccine 2023; 41:1290-1294. [PMID: 36669970 PMCID: PMC9826986 DOI: 10.1016/j.vaccine.2023.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 12/16/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023]
Abstract
Uptake of COVID-19 vaccine first doses in UK care homes has been higher among residents compared to staff. We aimed to identify causes of lower COVID-19 vaccine uptake amongst care home staff within Liverpool. An anonymised online survey was distributed to all care home managers, between the 21st and the 29th January 2021. 53 % of 87 care homes responded. The overall COVID-19 vaccination rate was 52.6 % (n = 1119). Reasons, identified by care home managers for staff being unvaccinated included: concerns about lack of vaccine research (37.0 %), staff being off-site during vaccination sessions (36.5 %), pregnancy and fertility concerns (5.6 %), and allergic reactions concerns (3.2 %). Care home managers wanted to tackle vaccine hesitancy through conversations with health professionals, and provision of evidence dispelling vaccine misinformation. Vaccine hesitancy and logistical issues were the main causes for reduced vaccine uptake among care home staff. The former could be addressed by targeted training, and public health communication campaigns to build confidence and acceptance of COVID-19 vaccines.
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Affiliation(s)
- John S.P. Tulloch
- Institute of Infection, Veterinary, and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Karen Lawrenson
- Public Health Department, Liverpool City Council, Liverpool, UK
| | - Adam L Gordon
- School of Medicine, University of Nottingham, Nottingham, UK
| | - Sam Ghebrehewet
- Public Health England North West, Cheshire & Merseyside Health Protection Team, Liverpool, UK
| | - Matthew Ashton
- Public Health Department, Liverpool City Council, Liverpool, UK
| | - Steve Peddie
- Adult Social Care Department, Liverpool City Council, Liverpool, UK
| | - Paula Parvulescu
- Public Health Department, Liverpool City Council, Liverpool, UK.
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Zhang J, Yu Y, Petrovic M, Pei X, Tian QB, Zhang L, Zhang WH. Impact of the COVID-19 pandemic and corresponding control measures on long-term care facilities: a systematic review and meta-analysis. Age Ageing 2023; 52:6987654. [PMID: 36668818 DOI: 10.1093/ageing/afac308] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 10/04/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Long-term care facilities (LTCFs) were high-risk settings for COVID-19 outbreaks. OBJECTIVE To assess the impacts of the COVID-19 pandemic on LTCFs, including rates of infection, hospitalisation, case fatality, and mortality, and to determine the association between control measures and SARS-CoV-2 infection rates in residents and staff. METHOD We conducted a systematic search of six databases for articles published between December 2019 and 5 November 2021, and performed meta-analyses and subgroup analyses to identify the impact of COVID-19 on LTCFs and the association between control measures and infection rate. RESULTS We included 108 studies from 19 countries. These studies included 1,902,044 residents and 255,498 staff from 81,572 LTCFs, among whom 296,024 residents and 36,807 staff were confirmed SARS-CoV-2 positive. The pooled infection rate was 32.63% (95%CI: 30.29 ~ 34.96%) for residents, whereas it was 10.33% (95%CI: 9.46 ~ 11.21%) for staff. In LTCFs that cancelled visits, new patient admissions, communal dining and group activities, and vaccinations, infection rates in residents and staff were lower than the global rate. We reported the residents' hospitalisation rate to be 29.09% (95%CI: 25.73 ~ 32.46%), with a case-fatality rate of 22.71% (95%CI: 21.31 ~ 24.11%) and mortality rate of 15.81% (95%CI: 14.32 ~ 17.30%). Significant publication biases were observed in the residents' case-fatality rate and the staff infection rate, but not in the infection, hospitalisation, or mortality rate of residents. CONCLUSION SARS-CoV-2 infection rates would be very high among LTCF residents and staff without appropriate control measures. Cancelling visits, communal dining and group activities, restricting new admissions, and increasing vaccination would significantly reduce the infection rates.
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Affiliation(s)
- Jun Zhang
- International Centre for Reproductive Health (ICRH), Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium.,The Research Center for Medical Sociology, Tsinghua University, 100084 Beijing, China
| | - Yushan Yu
- International Centre for Reproductive Health (ICRH), Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium
| | - Mirko Petrovic
- Section of Geriatrics, Department of Internal Medicine and Paediatrics, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium
| | - Xiaomei Pei
- Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, 050017 Shijiazhuang, Hebei, China
| | - Qing-Bao Tian
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, 710061 Xi'an, Shaanxi, China
| | - Lei Zhang
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne 3053, Australia.,Central Clinical School, Faculty of Medicine, Monash University, Melbourne 3800, Australia.,Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - Wei-Hong Zhang
- International Centre for Reproductive Health (ICRH), Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium.,School of Public Health, Université libre de Bruxelles (ULB), Bruxelles 1070, Belgium
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Mirabnejad M, Mohammadi H, Mirzabaghi M, Aghsami A, Jolai F, Yazdani M. Home Health Care Problem with Synchronization Visits and Considering Samples Transferring Time: A Case Study in Tehran, Iran. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15036. [PMID: 36429755 PMCID: PMC9690415 DOI: 10.3390/ijerph192215036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 10/31/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Health care facilities have not increased in response to the growing population. Therefore, government and health agencies are constantly seeking cost-effective alternatives so they can provide effective health care to their constituents. Around the world, health care organizations provide home health care (HHC) services to patients, especially the elderly, as an efficient alternative to hospital care. In addition, recent pandemics have demonstrated the importance of home health care as a means of preventing infection. This study is the first to simultaneously take into account nurses' working preferences and skill levels. Since transferring samples from the patient's home to the laboratory may affect the test results, this study takes into account the time it takes to transfer samples. In order to solve large instances, two metaheuristic algorithms are proposed: Genetic Algorithms and Particle Swarm Optimization. Nurses are assigned tasks according to their time windows and the tasks' time windows in a three-stage scheduling procedure. Using a case study set in Tehran, Iran, the proposed model is demonstrated. Even in emergencies, models can generate effective strategies. There are significant implications for health service management and health policymakers in countries where home health care services are receiving more attention. Furthermore, they contribute to the growing body of knowledge regarding health system strategies by providing new theoretical and practical insights.
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Affiliation(s)
- Mahyar Mirabnejad
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1439955961, Iran
| | - Hadi Mohammadi
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1439955961, Iran
| | - Mehrdad Mirzabaghi
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1439955961, Iran
| | - Amir Aghsami
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1439955961, Iran
- School of Industrial Engineering, K.N. Toosi University of Technology (KNTU), Tehran 1999143344, Iran
| | - Fariborz Jolai
- School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 1439955961, Iran
| | - Maziar Yazdani
- Research Centre for Integrated Transport Innovation, School of Civil and Environmental Engineering, The University of New South Wales, Sydney 2052, Australia
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Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: Update of a living systematic review and meta-analysis. PLoS Med 2022; 19:e1003987. [PMID: 35617363 PMCID: PMC9135333 DOI: 10.1371/journal.pmed.1003987] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/13/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Debate about the level of asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection continues. The amount of evidence is increasing and study designs have changed over time. We updated a living systematic review to address 3 questions: (1) Among people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? (2) What is the infectiousness of asymptomatic and presymptomatic, compared with symptomatic, SARS-CoV-2 infection? (3) What proportion of SARS-CoV-2 transmission in a population is accounted for by people who are asymptomatic or presymptomatic? METHODS AND FINDINGS The protocol was first published on 1 April 2020 and last updated on 18 June 2021. We searched PubMed, Embase, bioRxiv, and medRxiv, aggregated in a database of SARS-CoV-2 literature, most recently on 6 July 2021. Studies of people with PCR-diagnosed SARS-CoV-2, which documented symptom status at the beginning and end of follow-up, or mathematical modelling studies were included. Studies restricted to people already diagnosed, of single individuals or families, or without sufficient follow-up were excluded. One reviewer extracted data and a second verified the extraction, with disagreement resolved by discussion or a third reviewer. Risk of bias in empirical studies was assessed with a bespoke checklist and modelling studies with a published checklist. All data syntheses were done using random effects models. Review question (1): We included 130 studies. Heterogeneity was high so we did not estimate a mean proportion of asymptomatic infections overall (interquartile range (IQR) 14% to 50%, prediction interval 2% to 90%), or in 84 studies based on screening of defined populations (IQR 20% to 65%, prediction interval 4% to 94%). In 46 studies based on contact or outbreak investigations, the summary proportion asymptomatic was 19% (95% confidence interval (CI) 15% to 25%, prediction interval 2% to 70%). (2) The secondary attack rate in contacts of people with asymptomatic infection compared with symptomatic infection was 0.32 (95% CI 0.16 to 0.64, prediction interval 0.11 to 0.95, 8 studies). (3) In 13 modelling studies fit to data, the proportion of all SARS-CoV-2 transmission from presymptomatic individuals was higher than from asymptomatic individuals. Limitations of the evidence include high heterogeneity and high risks of selection and information bias in studies that were not designed to measure persistently asymptomatic infection, and limited information about variants of concern or in people who have been vaccinated. CONCLUSIONS Based on studies published up to July 2021, most SARS-CoV-2 infections were not persistently asymptomatic, and asymptomatic infections were less infectious than symptomatic infections. Summary estimates from meta-analysis may be misleading when variability between studies is extreme and prediction intervals should be presented. Future studies should determine the asymptomatic proportion of SARS-CoV-2 infections caused by variants of concern and in people with immunity following vaccination or previous infection. Without prospective longitudinal studies with methods that minimise selection and measurement biases, further updates with the study types included in this living systematic review are unlikely to be able to provide a reliable summary estimate of the proportion of asymptomatic infections caused by SARS-CoV-2. REVIEW PROTOCOL Open Science Framework (https://osf.io/9ewys/).
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Paranthaman K, Allen H, Chudasama D, Verlander NQ, Sedgwick J. Case-control study to estimate odds of death within 28 days of positive test for SARS-CoV-2 prior to vaccination for residents of long-term care facilities in England, 2020-2021. J Epidemiol Community Health 2021; 76:jech-2021-218135. [PMID: 34764218 PMCID: PMC8593275 DOI: 10.1136/jech-2021-218135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 10/30/2021] [Indexed: 11/04/2022]
Abstract
BACKGROUND Persons living in long-term care facilities (LTCFs) are presumed to be at higher risk of adverse outcomes from SARS-CoV-2 infection due to increasing age and frailty, but the magnitude of increased risk is not well quantified. METHODS After linking demographic and mortality data for cases with confirmed SARS-CoV-2 infection between March 2020 and January 2021 in England, a random sample of 6000 persons who died and 36 000 who did not die within 28 days of a positive test was obtained from the dataset of 3 020 800 patients. Based on an address-matching process, the residence type of each case was categorised into one of private home and residential or nursing LTCF. Univariable and multivariable logistic regression analysis was conducted. RESULTS Multivariable analysis showed that an interaction effect between age and residence type determined the outcome. Compared with a 60-year-old person not living in LTCF, the adjusted OR (aOR) for same-aged persons living in residential and nursing LTCFs was 1.77 (95% CI 1.21 to 2.6, p=0.0017) and 3.95 (95% CI 2.77 to 5.64, p<0.0001), respectively. At 90 years of age, aORs were 0.87 (95% CI 0.72 to 1.06, p=0.21) and 0.74 (95% CI 0.61 to 0.9, p=0.001), respectively. The model had an overall accuracy of 94.2% (94.2%) when applied to the full dataset of 2 978 800 patients. CONCLUSION This study found that residents of LTCFs in England had higher odds of death up to 80 years of age. Beyond 80 years, there was no difference in the odds of death for LTCF residents compared with those in the wider community.
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Affiliation(s)
| | - Hester Allen
- COVID-19 Epidemiology Cell, UK Health Security Agency, London, UK
| | - Dimple Chudasama
- COVID-19 Epidemiology Cell, UK Health Security Agency, London, UK
| | - Neville Q Verlander
- Statistics, Modelling and Economics Department, UK Health Security Agency, London, UK
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Tulloch JSP, Micocci M, Buckle P, Lawrenson K, Kierkegaard P, McLister A, Gordon AL, García-Fiñana M, Peddie S, Ashton M, Buchan I, Parvulescu P. Enhanced lateral flow testing strategies in care homes are associated with poor adherence and were insufficient to prevent COVID-19 outbreaks: results from a mixed methods implementation study. Age Ageing 2021; 50:1868-1875. [PMID: 34272866 PMCID: PMC8406873 DOI: 10.1093/ageing/afab162] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Indexed: 01/17/2023] Open
Abstract
Introduction Care homes have been severely affected by the SARS-CoV-2 pandemic. Rapid antigen testing could identify most SARS-CoV-2 infected staff and visitors before they enter homes. We explored implementation of staff and visitor testing protocols using lateral flow devices (LFDs). Methods An evaluation of a SARS-CoV-2 LFD-based testing protocol in 11 care homes in Liverpool, UK, including staff and visitor testing, plus a qualitative exploratory study in nine of these homes. The proportion of pilot homes with outbreaks, and outbreak size, were compared to non-pilot homes in Liverpool. Adherence to testing protocols was evaluated. Fifteen staff were interviewed, and transcript data were thematically coded using an iterative analysis to identify and categorize factors influencing testing implementation. Results In total, 1,638 LFD rapid tests were performed on 407 staff. Protocol adherence was poor with 8.6% of staff achieving >75% protocol adherence, and 25.3% achieving \documentclass[12pt]{minimal}
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}{}$\ge$\end{document}50%. Six care homes had outbreaks during the study. Compared to non-pilot care homes, there was no evidence of significant difference in the proportion of homes with outbreaks, or the size of outbreaks. Qualitative data showed difficulty implementing testing strategies due to excessive work burden. Factors influencing adherence related to test integration and procedural factors, socio-economic factors, cognitive overload and the emotional value of testing. Conclusion Implementation of staff and visitor care home LFD testing protocols was poorly adhered to and consequently did not reduce the number or scale of COVID-19 outbreaks. More focus is needed on the contextual and behavioural factors that influence protocol adherence.
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Affiliation(s)
- John S P Tulloch
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool CH64 7TE, UK
| | - Massimo Micocci
- NIHR London In Vitro Diagnostics Co-operative, Department of Surgery and Cancer, Imperial College London, London W2 1NY, UK
| | - Peter Buckle
- NIHR London In Vitro Diagnostics Co-operative, Department of Surgery and Cancer, Imperial College London, London W2 1NY, UK
| | - Karen Lawrenson
- Public Health Department, Liverpool City Council, Liverpool L3 1DS, UK
| | - Patrick Kierkegaard
- NIHR London In Vitro Diagnostics Co-operative, Department of Surgery and Cancer, Imperial College London, London W2 1NY, UK
- CRUK Convergence Science Centre, Institute for Cancer Research & Imperial College London, London SW7 2AZ, UK
| | - Anna McLister
- NIHR London In Vitro Diagnostics Co-operative, Department of Surgery and Cancer, Imperial College London, London W2 1NY, UK
| | - Adam L Gordon
- Division of Medical Sciences and Graduate Entry Medicine, University of Nottingham, Nottingham, UK
- NIHR Applied Research Collaboration East Midlands (ARC-EM), Nottingham, UK
| | | | - Steve Peddie
- Adults Social Care Department, Liverpool City Council, Liverpool L3 1DS, UK
| | - Matthew Ashton
- Public Health Department, Liverpool City Council, Liverpool L3 1DS, UK
| | - Iain Buchan
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Paula Parvulescu
- Public Health Department, Liverpool City Council, Liverpool L3 1DS, UK
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Stratil JM, Biallas RL, Burns J, Arnold L, Geffert K, Kunzler AM, Monsef I, Stadelmaier J, Wabnitz K, Litwin T, Kreutz C, Boger AH, Lindner S, Verboom B, Voss S, Movsisyan A. Non-pharmacological measures implemented in the setting of long-term care facilities to prevent SARS-CoV-2 infections and their consequences: a rapid review. Cochrane Database Syst Rev 2021; 9:CD015085. [PMID: 34523727 PMCID: PMC8442144 DOI: 10.1002/14651858.cd015085.pub2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Starting in late 2019, COVID-19, caused by the novel coronavirus SARS-CoV-2, spread around the world. Long-term care facilities are at particularly high risk of outbreaks, and the burden of morbidity and mortality is very high among residents living in these facilities. OBJECTIVES To assess the effects of non-pharmacological measures implemented in long-term care facilities to prevent or reduce the transmission of SARS-CoV-2 infection among residents, staff, and visitors. SEARCH METHODS On 22 January 2021, we searched the Cochrane COVID-19 Study Register, WHO COVID-19 Global literature on coronavirus disease, Web of Science, and CINAHL. We also conducted backward citation searches of existing reviews. SELECTION CRITERIA We considered experimental, quasi-experimental, observational and modelling studies that assessed the effects of the measures implemented in long-term care facilities to protect residents and staff against SARS-CoV-2 infection. Primary outcomes were infections, hospitalisations and deaths due to COVID-19, contaminations of and outbreaks in long-term care facilities, and adverse health effects. DATA COLLECTION AND ANALYSIS Two review authors independently screened titles, abstracts and full texts. One review author performed data extractions, risk of bias assessments and quality appraisals, and at least one other author checked their accuracy. Risk of bias and quality assessments were conducted using the ROBINS-I tool for cohort and interrupted-time-series studies, the Joanna Briggs Institute (JBI) checklist for case-control studies, and a bespoke tool for modelling studies. We synthesised findings narratively, focusing on the direction of effect. One review author assessed certainty of evidence with GRADE, with the author team critically discussing the ratings. MAIN RESULTS We included 11 observational studies and 11 modelling studies in the analysis. All studies were conducted in high-income countries. Most studies compared outcomes in long-term care facilities that implemented the measures with predicted or observed control scenarios without the measure (but often with baseline infection control measures also in place). Several modelling studies assessed additional comparator scenarios, such as comparing higher with lower rates of testing. There were serious concerns regarding risk of bias in almost all observational studies and major or critical concerns regarding the quality of many modelling studies. Most observational studies did not adequately control for confounding. Many modelling studies used inappropriate assumptions about the structure and input parameters of the models, and failed to adequately assess uncertainty. Overall, we identified five intervention domains, each including a number of specific measures. Entry regulation measures (4 observational studies; 4 modelling studies) Self-confinement of staff with residents may reduce the number of infections, probability of facility contamination, and number of deaths. Quarantine for new admissions may reduce the number of infections. Testing of new admissions and intensified testing of residents and of staff after holidays may reduce the number of infections, but the evidence is very uncertain. The evidence is very uncertain regarding whether restricting admissions of new residents reduces the number of infections, but the measure may reduce the probability of facility contamination. Visiting restrictions may reduce the number of infections and deaths. Furthermore, it may increase the probability of facility contamination, but the evidence is very uncertain. It is very uncertain how visiting restrictions may adversely affect the mental health of residents. Contact-regulating and transmission-reducing measures (6 observational studies; 2 modelling studies) Barrier nursing may increase the number of infections and the probability of outbreaks, but the evidence is very uncertain. Multicomponent cleaning and environmental hygiene measures may reduce the number of infections, but the evidence is very uncertain. It is unclear how contact reduction measures affect the probability of outbreaks. These measures may reduce the number of infections, but the evidence is very uncertain. Personal hygiene measures may reduce the probability of outbreaks, but the evidence is very uncertain. Mask and personal protective equipment usage may reduce the number of infections, the probability of outbreaks, and the number of deaths, but the evidence is very uncertain. Cohorting residents and staff may reduce the number of infections, although evidence is very uncertain. Multicomponent contact -regulating and transmission -reducing measures may reduce the probability of outbreaks, but the evidence is very uncertain. Surveillance measures (2 observational studies; 6 modelling studies) Routine testing of residents and staff independent of symptoms may reduce the number of infections. It may reduce the probability of outbreaks, but the evidence is very uncertain. Evidence from one observational study suggests that the measure may reduce, while the evidence from one modelling study suggests that it probably reduces hospitalisations. The measure may reduce the number of deaths among residents, but the evidence on deaths among staff is unclear. Symptom-based surveillance testing may reduce the number of infections and the probability of outbreaks, but the evidence is very uncertain. Outbreak control measures (4 observational studies; 3 modelling studies) Separating infected and non-infected residents or staff caring for them may reduce the number of infections. The measure may reduce the probability of outbreaks and may reduce the number of deaths, but the evidence for the latter is very uncertain. Isolation of cases may reduce the number of infections and the probability of outbreaks, but the evidence is very uncertain. Multicomponent measures (2 observational studies; 1 modelling study) A combination of multiple infection-control measures, including various combinations of the above categories, may reduce the number of infections and may reduce the number of deaths, but the evidence for the latter is very uncertain. AUTHORS' CONCLUSIONS This review provides a comprehensive framework and synthesis of a range of non-pharmacological measures implemented in long-term care facilities. These may prevent SARS-CoV-2 infections and their consequences. However, the certainty of evidence is predominantly low to very low, due to the limited availability of evidence and the design and quality of available studies. Therefore, true effects may be substantially different from those reported here. Overall, more studies producing stronger evidence on the effects of non-pharmacological measures are needed, especially in low- and middle-income countries and on possible unintended consequences of these measures. Future research should explore the reasons behind the paucity of evidence to guide pandemic research priority setting in the future.
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Affiliation(s)
- Jan M Stratil
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Renke L Biallas
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Laura Arnold
- Academy of Public Health Services, Duesseldorf, Germany
| | - Karin Geffert
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Angela M Kunzler
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Ina Monsef
- Cochrane Haematology, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Julia Stadelmaier
- Institute for Evidence in Medicine, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Katharina Wabnitz
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Tim Litwin
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Anna Helen Boger
- Institute of Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Saskia Lindner
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Ben Verboom
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
- Department of Social Policy and Intervention, University of Oxford, Oxford, UK
| | - Stephan Voss
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ani Movsisyan
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Chair of Public Health and Health Services Research, LMU Munich, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
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Sah P, Fitzpatrick MC, Zimmer CF, Abdollahi E, Juden-Kelly L, Moghadas SM, Singer BH, Galvani AP. Asymptomatic SARS-CoV-2 infection: A systematic review and meta-analysis. Proc Natl Acad Sci U S A 2021; 118:e2109229118. [PMID: 34376550 PMCID: PMC8403749 DOI: 10.1073/pnas.2109229118] [Citation(s) in RCA: 247] [Impact Index Per Article: 82.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Quantification of asymptomatic infections is fundamental for effective public health responses to the COVID-19 pandemic. Discrepancies regarding the extent of asymptomaticity have arisen from inconsistent terminology as well as conflation of index and secondary cases which biases toward lower asymptomaticity. We searched PubMed, Embase, Web of Science, and World Health Organization Global Research Database on COVID-19 between January 1, 2020 and April 2, 2021 to identify studies that reported silent infections at the time of testing, whether presymptomatic or asymptomatic. Index cases were removed to minimize representational bias that would result in overestimation of symptomaticity. By analyzing over 350 studies, we estimate that the percentage of infections that never developed clinical symptoms, and thus were truly asymptomatic, was 35.1% (95% CI: 30.7 to 39.9%). At the time of testing, 42.8% (95% prediction interval: 5.2 to 91.1%) of cases exhibited no symptoms, a group comprising both asymptomatic and presymptomatic infections. Asymptomaticity was significantly lower among the elderly, at 19.7% (95% CI: 12.7 to 29.4%) compared with children at 46.7% (95% CI: 32.0 to 62.0%). We also found that cases with comorbidities had significantly lower asymptomaticity compared to cases with no underlying medical conditions. Without proactive policies to detect asymptomatic infections, such as rapid contact tracing, prolonged efforts for pandemic control may be needed even in the presence of vaccination.
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Affiliation(s)
- Pratha Sah
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520
| | - Meagan C Fitzpatrick
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Charlotte F Zimmer
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520
| | - Elaheh Abdollahi
- Agent-Based Modelling Laboratory, York University, Toronto, ON M3J 1P3, Canada
| | - Lyndon Juden-Kelly
- Agent-Based Modelling Laboratory, York University, Toronto, ON M3J 1P3, Canada
| | - Seyed M Moghadas
- Agent-Based Modelling Laboratory, York University, Toronto, ON M3J 1P3, Canada
| | - Burton H Singer
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610
| | - Alison P Galvani
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520
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