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Review
. 2021 Sep 15;9(9):CD015085.
doi: 10.1002/14651858.CD015085.pub2.

Non-pharmacological measures implemented in the setting of long-term care facilities to prevent SARS-CoV-2 infections and their consequences: a rapid review

Jan M Stratil  1   2 , Renke L Biallas  1   2 , Jacob Burns  1   2 , Laura Arnold  3 , Karin Geffert  1   2 , Angela M Kunzler  4   5 , Ina Monsef  6 , Julia Stadelmaier  7 , Katharina Wabnitz  1   2 , Tim Litwin  8 , Clemens Kreutz  8 , Anna Helen Boger  8 , Saskia Lindner  4   5 , Ben Verboom  1   2   9 , Stephan Voss  1   2 , Ani Movsisyan  1   2
Affiliations
Review

Non-pharmacological measures implemented in the setting of long-term care facilities to prevent SARS-CoV-2 infections and their consequences: a rapid review

Jan M Stratil et al. Cochrane Database Syst Rev. .

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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Conflict of interest statement

Jan M Stratil: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Renke Lars Biallas: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Laura Arnold: none known

Jacob Burns: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Anna Boger: The Institute for Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), and Faculty of Medicine and Medical Center – University of Freiburg, received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Karin Geffert: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Angela M Kunzler: Department of Psychiatry and Psychotherapy at the University Medical Center of the Johannes Gutenberg University Mainz and Leibniz Institute for Resilience Research (LIR), Mainz, received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Clemens Kreutz: The Institute for Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), and Faculty of Medicine and Medical Center – University of Freiburg, received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Saskia Lindner: Department of Psychiatry and Psychotherapy at the University Medical Center of the Johannes Gutenberg University Mainz and Leibniz Institute for Resilience Research (LIR), Mainz, received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Tim Litwin: The Institute for Medical Biometry and Statistics (IMBI), Freiburg Center for Data Analysis and Modeling (FDM), and Faculty of Medicine and Medical Center – University of Freiburg, received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Ina Monsef: is funded by the Federal Ministry of Education and Research, Germany (NaFoUniMedCovid19, funding number: 01KX2021; part of the project "CEOSys", which was paid to the institution). No other conflicts of interest are known.

Julia Stadelmaier: The Institute for Evidence in Medicine at the University of Freiburg received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Stephan Voss: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Katharina Wabnitz: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Ani Movsisyan: the Chair for Public Health and Health Services Research at the Institute for Medical Information Processing, Biometry and Epidemiology received funding from the German Federal Ministry of Education and Research (BMBF) as part of the COVID‐19 evidence ecosystem (CEOsys) project. No other conflicts of interest are known.

Figures

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Figure 1: Process‐based logic model on the relation between intervention domains and outcomes (LTCF: long‐term care facility)
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Figure 2: System‐based logic model of the interventions and contextual factors as potential moderators (LTCF: long‐term care facility; Mgmt: management)
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PRISMA 2020 flow diagram for new systematic reviews, which included searches of databases, registers and other sources (Page 2021b)
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Depiction of the multiple confounders affecting the assessment of intervention effects in the observational studies included in this review. In assessing the relation between the intervention and the outcome, we need to account for a plurality of confounding factors
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Direction of effects plot for intervention domain 1: entry regulations
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Direction of effects plot for intervention domain 2: contact‐regulating and transmission‐reducing measures
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Direction of effects plot for intervention domain 3: surveillance measures
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Direction of effects plot for intervention domain 4: outbreak control measures
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Direction of effects plot for intervention domain 5: multicomponent measures across multiple intervention domains
See this image and copyright information in PMC

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