Table 3.
Risk-Based Studies of Association
| Citation | Country | Study Design | Subjects and Setting | Study Aim | Outcomes Assessed | Relevant Findings |
|---|---|---|---|---|---|---|
| Abrams et al (2020)77 | USA | Cross-sectional study using linked data sets | 9395 nursing homes in 30 US states | Examine characteristics of nursing homes with documented COVID-19 cases | Facilities with COVID-19 cases, number of cases | Background factors affecting community transmission seem especially influential in whether an outbreak occurs; other factors may be more influential around internal spread |
| Brown et al (2020)78 | Canada | Population-based retrospective cohort study | Nursing homes in Ontario, Canada, N = 600 nursing homes, N = 78,000 residents | Develop reproducible index of nursing home crowding and determine whether crowding was associated with COVID-19 cases and mortality in the first months of the pandemic | Cumulative incidence of COVID-19 cases confirmed by validated nucleic acid amplification assay, and mortality per 100 residents; the introduction of COVID-19 into a home (≥1 resident case) as a negative tracer outcome | Crowding associated with an increased incidence of infection and mortality and highly crowded LTCFs more likely to experience larger and deadlier outbreaks; no difference in probability of introduction of COVID-19 into a facility according to level of crowding; need for interventions targeting crowding including reducing room occupancy to reduce risk of transmission; reinforcement of other IPC measures also essential |
| Bui et al (2020)79 | USA | Cross-sectional study using linked facility-level data | 123 nursing homes in West Virginia | Examine associations between CMS star (quality) ratings and COVID-19 outbreaks in nursing homes | Outbreak vs no outbreak | Odds of a COVID-19 outbreak in 1-star–rated nursing homes were approximately 7 times higher than 2-3-star–rated and 17 times higher than 4-5-star–rated facilities; lower-rated homes might struggle to implement effective IPC and require assistance |
| Dean et al (2020)80 | USA | Cross-sectional study | New York State nursing homes with confirmed COVID-19 deaths, N = 355 | Assess relation between COVID-19 mortality rate risk factors | Percentage of COVID-19 deaths, access to PPE and COVID-19 infection rates | Presence of labor unions in LTCFs associated with lower COVID-19 infection and mortality, and greater access to PPE (N95 respirators and eye shields), suggesting unions improve safety and health standards for workers and improved patient outcomes; unionization may play an important role in mitigating and preventing outbreaks in this setting |
| Figueroa et al (2020)81 | USA | Cross-sectional study using linked facility-level data | 4254 nursing homes across 8 US states | Evaluate relationship between CMS quality measures (star ratings) and COVID-19 cases in nursing homes | Ordinal categories of case numbers | High-performing facilities less likely to have had >30 cases across each domain, but also had fewer beds |
| Fisman et al (2020)82 | Canada | Cohort study | 627 long-term care facilities in Ontario | Understand risk factors associated with COVID-19 death in long-term care | Mortality rates | Documented infection in facility staff is a strong identifiable risk factor for mortality in residents, with temporality suggesting residents are infected by staff and not vice versa |
| Greene and Gibson (2020)83 | USA | Retrospective population-based cohort study from a national survey | Workers in long-term care facilities (N = 552) | Quantify risk for severe COVID-19 illness among workers at LTCFs | Demographic features; supply of PPEs; comorbidities | Working in LTCFs associated with an increased risk of severe illness from COVID-19 (50% of staff affected). Black, female, low-income employees and those with lower educational attainment highly vulnerable to infection; access to adequate PPE crucial along with testing and paid sick leave |
| Gorges and Konetzka RT (2020)84 | USA | Cross-sectional study using linked facility-level data | 13,167 nursing homes reporting COVID-19 data | Explore role of staffing in COVID-19 cases and deaths using national data | Outbreak occurrence (any cases) and outbreak size (no. of cases) | Among facilities with at least 1 case, higher nurse aide hours and total nursing hours are associated with lower probability of an outbreak and with fewer deaths. |
| Harrington et al (2020)85 | USA | Cross-sectional study using linked facility-level data | 1091 licensed Medicare/Medicaid certified nursing homes in California: 819 with no reported COIVD-19 cases; 272 with 1 or more COVID-19 cases | Comparative analysis of the association between nurse staffing and COVID-19 infection | Facilities with COVID-19 cases, number of cases | Nursing homes with low RN and total staffing levels appear to leave residents vulnerable to COVID19 infections; establishing minimum staffing standards at the federal and state levels could prevent this in the future |
| He et al (2020)86 | USA | Cross-sectional study using linked facility-level data | 1223 California skilled nursing facilities with reported quality metrics and longitudinal data on COVID-19 cases | Examine the relationship between nursing home reported quality and COVID-19 cases and deaths; other independent variables included nursing home ownership, size, years of operation, and patient race composition | COVID-19 resident cases and deaths | Nursing homes with 5-star ratings were less likely to have COVID-19 cases and deaths after adjusting for nursing home size and patient race proportion |
| Hoxha et al (2020)87 | Belgium | Cross-sectional analysis of laboratory data from mass testing campaign | 2074/2500 long-term care facilities, with N = 280,427 people tested, including 142,100 residents (51%) and 138,327 staff (49%) | Ascertain infection rate among symptomatic vs asymptomatic residents and staff of LTCFs | COVID-19-–positive test rates for residents and staff; symptomatic vs asymptomatic positive tests | In LTCFs, asymptomatic carriers represent an important driver of transmission; to limit the spread of SARS-CoV-2 in closed residential facilities; extensive IPC measures should be widely applied while the epidemic is ongoing |
| Ladhani et al (2020)88 | England | Observational study | 254 staff in 6 London care homes reporting a suspected outbreak (≥2 suspected cases) of COVID-19 | Assess occupational risk factors for SARS-CoV-2 infection among staff in care homes experiencing a COVID-19 outbreak | COVID-19 positive vs negative; symptomatic vs asymptomatic at time of testing; working in a single care home vs across different care homes; regular contact with residents vs no contact with residents | Working across different care homes significantly increases the risk of COVID-19 infection. Infection control measures should be extended for all contact, including those between staff, while on care home premises |
| Li et al (2020)89 | USA | Cross-sectional analysis of linked data sets | All Connecticut nursing homes (n = 215) | Determine association of nursing home registered nurse (RN) staffing, overall quality of care, and concentration of Medicaid or racial and ethnic minority residents with COVID-19 cases and mortality, using multivariable 2-part models | Confirmed COVID-19 cases and deaths among residents | Nursing homes with higher RN staffing and quality ratings have the potential to better control the spread of the novel coronavirus and reduce deaths; Nursing homes caring predominantly for Medicaid or racial and ethnic minority residents tend to have more confirmed cases |
| Lindahl et al (2020)90 | Sweden | Observational study using secondary analysis of data from a rapid antibody screening test for detection of SARS-CoV-2 | 1005 employees of 22 older care homes in Stockholm, Sweden, were analyzed. | Ascertain the time point for a safe return to the workplace after COVID-19 infection. | Positive vs negative SARS-CoV-2 antibody tests; symptom status at time of testing | Results suggest that antibody testing of employees in older care homes is valuable for surveillance of disease development and a crucial screening tool |
| Shallcross et al (2021)1 | England | Cross-sectional, national survey | LTCFs (n = 5126) providing care to residents with dementia or aged ≥65 y | Identify factors associated with SARS-CoV-2 infection and outbreaks among LTCF staff and residents | Outbreaks, defined as at least 1 case of COVID-19 in a resident or staff member | Reduced transmission associated with adequate sick pay, minimal use of agency staff, increased staff-to-bed ratio, and staff cohorting with residents; increased transmission associated with a higher number of new admissions and poor compliance with isolation procedures |
| Shi et al (2020)91 | USA | Retrospective cohort study | An academic long-term care facility (398 residents tested for SARS-CoV-2) | Describe clinical characteristics and risk factors associated with COVID-19 in long-stay nursing home residents | COVID-19 infection rates, and mortality rates | COVID-19 prevalence in many LTCFs associated with high asymptomatic transmission; significant predictors of infection include male sex, non-white, bowel incontinence, dementia, and staff residence in communities with high burden of disease; frailty was a risk factor for death with mortality increasing with frailty; need for strategies to identify and mitigate spread of COVID-19 including early, universal testing of residents and staff, and alternative housing for health care workers to reduce community exposure and potential introduction into LTCFs |
| Stall et al (2020)92 | Canada | Retrospective cohort study using administrative data set | Long-term care facilities in Ontario, N = 623 | Examine association between for-profit status and risk of COVID-19 outbreaks | Outbreaks in the home (at least 1 resident case), extent of outbreak, number of resident deaths | Risk of an outbreak related to community transmission plus facility size (no. of beds) and older design standards; for-profit homes have larger outbreaks with more deaths than nonprofit and municipal (government) homes, mediated by older design standards and chain ownership; long-standing issues in financing, operation, and regulation of LTC homes exposed |
| Stivanello et al (2020)93 | Italy | Retrospective cohort study | Confirmed cases of COVID-19 in Bologna based on community testing criteria (epidemiologic link to another case or relevant symptoms) | Describe sociodemographic and transmission profile of COVID-19 after introduction of a stay at home order | New confirmed cases of COVID-19 before and after specified date | In this study, visits to facilities already restricted prior to the decree; residential care facilities unlikely to be protected by such measures if transmission has already occurred; highlights vulnerability rather than strategy |
| Sun et al (2020)94 | USA | Retrospective cohort study; predictive model using machine learning algorithm | 1146 nursing homes | Assess risk of COVID-19 outbreaks in nursing homes, associated risk factors, and possible vectors of infection using a machine-learning approach (model) trained on nursing home COVID-19 outcome data | Predictors of COVID-19 infection, sensitivity and specificity of model | Increased risk associated with county infection rate and population density, number of separate units in LTCF, health deficiencies, facility density of residents and staff; non-Hispanic white ethnicity a protective factor; possible primary vectors of infection included introduction from the outside community through presymptomatic and asymptomatic individuals and intrafacility transmission through close staff contact with residents. |
| Sugg et al (2020)95 | USA | Retrospective cohort study using linked data sets; 2-stage regression with multilevel modeling | US nursing homes, n = 13,709 | Determine association between facility characteristics, geographic variables, and confirmed cases in nursing homes | Cumulative cases (rate) | Increased risk associated with: LPN staffing level; county transmission rate; no. of fines in 2020; unemployment rate; ethnicity; population density; household size, and per capita income; reduced risk associated with total staff |
| White et al (2020)96 | USA | Cross-sectional study using linked data sets | 341 skilled nursing facilities in 25 US states | Identify county and facility factors associated with SARS-CoV-2 outbreaks in skilled nursing facilities | Any cases, number of confirmed cases, facility-level case fatality rate, case positive rate in facilities with universal testing | Outbreak risk (probability and severity) associated with facility size and community transmission; no evidence of relationship with SNF quality or staffing indices; larger size = more staff, visitors, and opportunities for transmission |
CDC, Centers for Disease Control and Prevention; CMS, Centers for Medicare & Medicaid Services; LPN, licensed practical nurse; RN, registered nurse.