Key messages
• Non-medicinal measures (e.g. visiting restrictions or regular testing) may prevent SARS-CoV-2 infections (causing COVID-19 disease) in residents and staff in long term care facilities, but we have concerns about the reliability of the findings.
• More high-quality studies on real-world experiences are needed, in particular.
• More research is also needed on measures in facilities where most residents and staff are vaccinated, as well as regions other than North America and Europe.
What are non-medicinal measures?
Non-medicinal measures are ways of preventing or reducing disease without using medicine, such as vaccines. These include controlling people's movements and contacts, using personal protective equipment (PPE), or regular testing for infection.
SARS-CoV-2 is very infectious. Elderly or disabled people, who live in care homes (long-term care facilities), are vulnerable to infection because they live in close contact with other people, with carers and visitors entering and leaving the facility. Due to age and underlying health conditions, care home residents have an increased risk of becoming seriously ill with COVID-19 and dying from the disease.
What did we want to find out?
We wanted to find out how effective non-medicinal measures are in preventing residents and staff in long-term care facilities from becoming infected with SARS-CoV-2 and in reducing the spread of the infection. We focused on all types of long-term care facilities for adults, such as nursing homes for the elderly and skilled nursing facilities for people living with disabilities.
What did we do?
We searched for studies that investigated the effects of non-medicinal measures in long-term care facilities. To be included, studies had to report how many infections, hospitalisations or deaths the measures prevented in residents or staff, or whether the measures prevented the introduction of the virus into the facilities or prevented outbreaks within facilities. We included any type of study, including observational studies that used ‘real-world’ data, or modelling studies based on assumed data from computer-generated simulations.
What did we find?
We found 22 studies, 11 observational and 11 modelling studies. All studies were conducted in North America or Europe.
There were four main types of measures.
1.Entry regulation measures to prevent residents, staff or visitors introducing the virus into the facility. Measures included staff confining themselves with residents, quarantine for newly-admitted residents, testing new admissions, not allowing the admission of new residents, and preventing visitors from entering facilities.
2. Contact-regulating and transmission-reducing measures to prevent people passing on the virus. Measures included wearing masks or PPE, social distancing, extra cleaning, reducing contact between residents and among staff, and placing residents and staff in care groups and limiting contact between groups.
3. Surveillance measures designed to identify an outbreak early. Measures included regular testing of residents or staff regardless of symptoms, and symptom-based testing.
4. Outbreak control measures to reduce the consequences of an outbreak. Measures included isolation of infected residents, and separating infected and non-infected residents or staff caring for them.
Some studies used a combination of these measures.
Main results
Entry regulation measures (4 observational studies; 4 modelling studies)
Most studies showed that such measures were beneficial, but some studies found no effects or unwanted effects, such as depression and delirium among residents in the context of visiting restrictions.
Contact-regulating and transmission-reducing measures (6 observational studies; 2 modelling studies)
Some measures may be beneficial, but often the evidence is very uncertain.
Surveillance measures (2 observational studies; 6 modelling studies)
Routine testing of residents and staff may reduce the number of infections, hospitalisations and deaths among residents, although the evidence on the number of deaths among staff was less clear. Testing more often, getting test results faster, and using more accurate tests were predicted to have more beneficial effects.
Outbreak control measures (4 observational studies; 3 modelling studies)
These measures may reduce the number of infections and the risk of outbreaks in facilities, but often the evidence is very uncertain.
Combination measures (2 observational studies; 1 modelling study)
A combination of different measures may be effective in reducing the number of infections and deaths.
What are the limitations of the evidence?
Our confidence in these results is limited. Many studies used mathematical prediction rather than real-world data, and we cannot be confident that the model assumptions are accurate. Most observational studies did not use the most reliable methods. This means we cannot be confident that the measure caused the effect, for example, that testing of residents reduced the number of deaths.
How up to date is this evidence?
This review includes studies published up to 22 January 2021.
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.
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.
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.
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.
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.
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.
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.