Table 5 Sub-group analyses of type of measures with unfavorable/favorable/null effects on COVID-19 transmission

Vijh et al., 2021 [41]

A mixed-effect segmented Poisson regression was fitted to our facility-specific COVID-19 case data against time to assess the association between the intervention and the COVID-19 incidence rate. The model was built using a standard approach for segmented regression of time series data

Strategic

A series of outbreak measures are classified into four categories: case and contact management, proactive case detection, rigorous infection control practices, and resource prioritization and stewardship

Tactic

Operational

Aghili et al. 2022 [34]

Bivariate logistic regression models were performed amongst the variables of infection prevention and control principles as predictor variables, and get COVID-19 as the dependent variable (yes/no). A multivariate logistic regression model was used to identify independent predictors of getting COVID-19 using variables with p values less than.05. The final model was obtained after removing all non-statistically significant variables (enter the selection procedure)

Strategic

Number of beds in the room

Meals places

Surface disinfection

Tactic

Longer staff shifts (vs. No)

3.02 (1.68–5.43), p < 0.001

Education for COVID-19

Operational

Not using a mask outside the room (vs. Yes)

3.37 (1.74–6.53), p < 0.001

Glass in visitors’ space (vs. No)

1.95 (1.11– 3.50), p = 0.025

Flu vaccine, Flu history

Using a mask inside the room

Mask wearing method

Mask change time (daily)

Physical distance with no roommate

Number of hand washing (daily)

Hand washing time (seconds)

Travel history in the last month, Take vitamin D

Cloth mask or nothing (vs. Simple surgical mask)

2.47(1.13–5.42), p = 0.024

Telford et al., 2021 [35]

Frequency distribution (counts and percentages) was used to describe the overall LTCF adherence to each key indicator. The implementation of key indicators within each category was also calculated as a composite proportion of all possible indicators that could be adhered to within the category

A chi-squared test of proportions was used to test for differences between the higher and lower prevalence groups, with P.05 set as the cutoff for statistical significance

A two-tailed t-test for two independent means was used to determine statistical differences between groups for continuous variables, with p.05 selected as the level of significance

Strategic

Bathroom and sink inside the bedroom (HPG 73% vs. LPG 100%, p = 0.04)

Disinfection

Tactic

Training and frequent audits are conducted to ensure proper mask use by staff members (HPG 36% vs. LPG 85%, p = 0.02)

Staff is trained, and audits take place to ensure proper donning and doffing of PPE (HPG 55% vs. LPG 92%, p = 0.03)

Operational

Distancing from others (HPG 54% vs LPG 74%, p = 0.01)

Symptom Screening

Hand Hygiene

PPE category (HPG 41% vs. LPG 72%, p = 0.01)

Masks are used properly by staff inside the COVID unit (HPG 45% vs. LPG 100%,p < 0.01)

Wang et al., 2021 [36]

Separate logistic regression models were built to estimate the association between infection prevention and control deficiencies, the overall five-star rating, the presence of COVID-19 resident cases, and, among nursing homes with one or more cases, the presence of an outbreak

The multicollinearity of independent variables was examined using the variance inflation factor. Odds ratios (ORs), 95% confidence intervals (CIs), and significant levels were reported

Strategic

Higher average occupancy rate (vs. lower)

21.24 (1.08, 418.13)–31.19 (3.82, 254.54), p = 0.05–0.01, for COVID + cases

Facilities ≤ 60 beds (vs. > 121 beds)

0.13 (0.03, 0.52)–

0.20 (0.10, 0.40), p. < 0.001–0.1, for Covid + cases

Prevention and control efficiency > 1 citation (vs. ≤ 1 citation)

2.09 (0.95, 4.60, p = 0.068 for Covid + cases)

Facilities between 61–120 beds

0.27(0.08, 0.94) –0.53(0.32, 0.87), p < 0.05, for Covid + cases

Tactic

Low nurse staffing hours per resident per day (vs. higher)

0.67 (0.44, 1.04), p = 0.1 for the COVID + outbreak

Operational

Ohta et al. 2021 [37]

The differences in participant characteristics, frequency of patients’ medical care visits

to the outpatient and emergency departments, and the number of days off taken by the staff between the pre and post-COVID-19 control groups were analyzed using t-tests and chi-squared tests. CCI was categorized binomially (5 or > 5) to assess the severity of medical conditions. For all comparisons, the level of statistical significance was set at p 0.05

Strategic

Usage of information and communication

technology. An ICT system called “Mame-net” was used to share patient information between the clinic and the nursing home

Tactic

Daily Monitoring of the Staff’s Health Conditions

The staff and clinic physicians monitored their fever and symptoms

daily and note down their conditions on a checklist. The checklists were monitored, and if they had mild symptoms or fever > 37 ◦C, they were not permitted to work in the nursing home

Operational

Contact Limitation

To reduce the risk of infection transmission, care workers wore facemasks, plastic

gloves, and face shields, and used hand sanitizers every time they cared for their patients

The frequency of care was reduced from three times/day to two times/day. Regarding mealtimes, the patients usually ate their food in the lounges; however, they were now required to eat in their respective rooms during the pandemic. Further, the patients’ families were restricted from meeting the patients, except in emergency situations

Orlando 2022 [38]

A binary logistic regression model evaluated the association between potential risk factors with the probability of reporting at least two positive cases among residents

Strategic

 > 15 beds (vs < 15 beds)

5.37 (1.58 to 22.8), p < 0.05, for COVID-19 outbreak

Presence of multiple rooms

The proportion of single rooms

Separate entrances

Presence of isolation environment

Active surveillance of staff

Presence of written operational procedure for the management of cases

External cleaning company

Presence of a written operational procedure for new admissions

Access by external suppliers

Opened to visitors post-first lockdown

Tactic

Frequency of shifts staff

Presence of a grey area for healthcare and non-healthcare staff

Dressing rooms for staff

Trained staff on procedures to contain COVID-19

Trained residents on procedures to contain COVID-19

Operational

Use of personal protective equipment

Active surveillance for guests

Stemler et al., 2022 [42]

We evaluated the occurrence of symptomatic SARSCoV-2 incidence among residents in both INH and CNH as the primary endpoint (with an outbreak defined as the occurrence of ≥ 1 SARS-CoV-2-infected resident in a timely and situational context). Qualitative data were summarized by absolute and relative (%) frequency and quantitative data by a median and interquartile range (IQR)

Differences in categorical frequency distributions were only tentatively evaluated using the Chi-square test since the assumption of independent observations is untenable, and more adequate methods require more data

Strategic

Regular and voluntary RT-PCR SARS-CoV-2 testing of HCWs and visitors

Tactic

Operational

Shallcross et al., 2021 [46]

A multivariable logistic regression model was performed to identify factors associated with infection in staff and residents

Strategic

Increase in the number of new admissions (vs baseline)

Infection in residents (1·01 [1·01–1·01],

p < 0·0001) and staff (1·00 [1·00–1·01], p = 0·0005), and of outbreaks (1·08 [1·05–1·10], p < 0·0001)

Tactic

Staff often or always cared for both infected or uninfected residents (vs. cohorted staff

with either infected or uninfected residents)

The odds of infection in residents (1·30 [1·23–1·37], p < 0·0001) and

staff (1·20 [1·13–1·29], p < 0·0001), and of outbreaks (2·56 [1·94–3·49], p < 0·0001) were significantly higher

Paid staff statutory sick pay (vs No)

The odds of infection in residents 0·80 [

0·75–0·86], p < 0·0001),

staff 0·70 [0·65–0·77], p < 0·0001),

Large outbreaks 0·59 [0·38–0·93], p = 0·024)

were significantly lower

Frequent employment of agency nurses or carers (vs

no employment of agency

nurses or carers)

Significantly increased odds of infection in residents (1·65 [1·56–1·74], p < 0·0001) and staff (1·85 [1·72–1·98], p < 0·0001), and of outbreaks (2·33

[1·72–3·16], p < 0·0001) and large outbreaks (2·42 [1·67–3·51], p < 0·0001)

Increase in the staff-to-bed ratio (vs No)

Reduced odds of infection in residents (0·82 [0·78–0·87],

p < 0·0001) and staff (0·63 [0·59–0·68], p < 0·0001

Operational

Difficulties in isolating residents (vs. No)

Significantly higher odds of infection in residents (1·33 [1·28–1·38], p < 0·0001) and staff (1·48 [1·41–1·56], p < 0·0001),

and of outbreaks (1·84 [1·48–2·30], p < 0·0001) and large outbreaks (1·62 [1·24–2·11], p = 0·0004)

Lombardo et a., 2021 [43]

An univariate and a multivariate regression logistic model were performed to assess whether critical aspects and characteristics of the NHS, adjusted for a geographical area, were associated to COVID‐19 outbreaks defined as the presence of laboratory‐confirmed cases among deceased and hospitalized residents or staff members, and among residents

currently living in the facility

Strategic

Lack of laboratory tests

Lack of PPE

Median number of beds > 60 (vs. < 60 beds)

1.50 (1.09–2.07), p = 0.013

Tactic

Lack of personnel (vs. No)

3.22 (2.38–4.36), p < 0.001

Operational

Difficulty in isolating (vs. N)

1.97 (142–2.73), p < 0.001

Difficulty in transferring (vs. N)

4.66 (2.98–7.31), p < 0.001

Green et al., 2021 [39]

Where the prevalence of positive residents was high enough, a Poisson regression model was created to explore the above variables while accounting for care home differences

Where the prevalence was too low to allow appropriate stratification, univariable analysis was undertaken using Fisher’s exact test dependent on the numbers within

the contingency tables

Strategic

Closing residents shared space

Tactic

Employing agency staff was more likely to contain test-positive residents

8.4 (1.2–60.8)

Operational

Cazzoletti et al., 2021 [44]

The association between median cumulative incidence of COVID-19 cases among residents and characteristics of nursing homes was assessed by Mann–Whitney U test, Kruskal–Wallis test or Spearman rho. To evaluate the potential confounding of geographical area, a 2-level random intercept logistic model was fitted, with level 1 units (patients in nursing homes) nested into level 2 units (nursing homes), and “being a COVID-19 case” as the dependent variable

Strategic

The nursing homes with no cases of COVID-19 were those that were more likely to implement outbreak management procedures compared to homes with at least 1 case of COVID-19

Nursing homes with implement outbreak management procedures (23.5%), vs nursing homes with at least 1 case of COVID-19 (3.6%), p = 0.060

Facility size

Single-occupancy rooms

Policies for the management of personnel at risk of infection

Official protocols/procedures on Infection control and prevention

Established an infection surveillance program

Procedure on standard and additional precautions

Tactic

Full-time equivalent nurses, physicians,

aid staff

Training of staff on the management of occupational exposures to biohazards

Training of staff on the correct hand hygiene procedure

Training of staff on how to prevent the spread of respiratory infections

Training of staff on the correct use of PPE

Operational

Conformity to quality standards

Compliance with operations of routine and terminal cleaning/sanitation/disinfection

Availability of hand hygiene supplies

Regular checks of the quality of the cleaning/sanitation/disinfection

Hand hygiene

Use of PPE

Isolation measures

Sanitation

Procedure for management of residents with suspected communicable diseases

Zimmerman et al., 2021 [40]

For each COVID-19 outcome (cases, admission/readmission, mortality), a log-rank test, which is a nonparametric test that emphasizes detection of group differences among higher values, was applied to compare rates among the 3 Nursing Home types. Multiple comparisons between 2 groups (Green House/small Nursing Homes vs traditional Nursing Homes < 50 beds; Green House/small Nursing Homes vs traditional Nursing Homes ≥ 50 beds) were subsequently performed using log-rank tests if the omnibus test was statistically significant at p < 0,05. Statistical significance for the pairwise comparisons was set at p < 0,025, per Bonferroni adjustment

Strategic

Non-traditional small house Nursing Homes model: 10 to 12 residents and have consistent and universal staff assignment (thereby limiting ancillary staff), private rooms and bathrooms, smaller overall space, and a central entry

COVID-19 cases are lower in Non-traditional small house Nursing Homes (Compared with < 50 Beds – p = 0,014; Compared with ≥ 50 Beds, p < 0,001)

COVID-19 admissions/ readmissions are lower in Non-traditional small house Nursing Homes (Compared with < 50 Beds – p = 0,007; Compared with ≥ 50 Beds, p = 0,007)

COVID-19 mortality are lower in Non-traditional small house Nursing Homes (Compared with < 50 Beds – p < 0,001; Compared with ≥ 50 Beds, p < 0,001)

Tactic

Operational

Brown et al., 2021 [45]

All analyses were conducted using SAS, version 9.4 (SAS Institute), and all reported P values were based on 2-sided testing. P values less than .05 were considered statistically significant. A quasi-Poisson regression was used to model cases and deaths using the logarithm of the number of beds in the home as an offset, while logistic regression was used to model introduction of COVID-19 into the home

The nursing home crowding index was defined as the mean number of occupants per room and bathroom across an entire home according to the following equation: Nresidents ÷ (½Nbedrooms + ½Nbathrooms). This translated to weights per resident according to the room they occupied: single-occupancy room with private bathroom (1); single-occupancy room with a shared bathroom (1.5); double-occupancy room (with shared bathroom) (2); and quadruple-occupancy room (4)

Strategic

Hight Crowding Index

[double-occupancy room (with shared bathroom); or quadruple-occupancy room]

Compared with a home with low Crowding index, homes with hight Crowding Index

had the double of the COVID-19 incidence (relative risk [RR], 2.05; 95% CI, 1.49–2.70) and is associated with COVID-19 mortality (RR, 1.97; 95% CI, 1.36–2.84)

Tactic

Operational