Risk factors for retirement home COVID-19 outbreaks in Ontario, Canada: A population-level cohort study

BackgroundThe epidemiology of COVID-19 in retirement homes (also known as assisted living facilities) is largely unknown. We examined the association between retirement home and community level characteristics and the risk of COVID-19 outbreaks in retirement homes during the first wave of the COVID-19 epidemic. MethodsWe conducted a population-based retrospective cohort study of licensed retirement homes in Ontario, Canada, from March 1st - September 24th, 2020. Our primary outcome was a COVID-19 outbreak ([≥]1 resident or staff confirmed case by validated nucleic acid amplification assay). We used time-dependent proportional hazards methods to model the associations between retirement home and community level characteristics and COVID-19 outbreaks. ResultsOur cohort included all 770 licensed retirement homes in Ontario, which housed 56,491 residents. There were 172 (22.3%) COVID-19 retirement home outbreaks involving 1,045 (1.9%) residents and 548 staff (1.5%). COVID-19 cases were distributed unevenly across retirement homes, with 1,593 (92.2%) resident and staff cases occurring in 77 (10%) of homes. The adjusted hazard of a COVID-19 outbreak in a retirement home was positively associated with homes that had a large resident capacity, homes that were co-located with a long-term care facility, large corporate owned chains, homes that offered many services onsite, increases in regional COVID-19 incidence, and a higher community-level ethnic concentration. InterpretationReadily identifiable retirement home-level characteristics are independently associated with COVID-19 outbreaks and may support risk identification. A higher ethnic concentration of the community surrounding a retirement home is associated COVID-19 outbreaks, with an uncertain mechanism.

Increased Prescribing of Psychotropic Medications to Ontario Nursing Home Residents during the COVID-19 Pandemic

In this population-based study of all Ontario nursing home residents, we found increased prescribing of psychotropic drugs at the onset of the COVID-19 pandemic that persisted through September 2020. Increases in prescribing were out of proportion to expected secular trends, and distinct from observed prescribing changes in other drugs during the pandemic. Our findings underscore the urgency of balancing infection prevention and control measures in nursing homes with the mental wellbeing of residents.

Impact of a public policy restricting staff mobility between long-term care homes in Ontario, Canada during the COVID-19 pandemic

ObjectivesTo assess changes in the mobility of staff between long-term care homes in Ontario, Canada before and after enactment of public policy restricting staff from working at multiple homes. DesignPre-post observational study. Setting and Participants623 long-term cares homes in Ontario, Canada between March 2020 and June 2020. MethodsWe used anonymized mobile device location data to approximate connectivity between all 623 long-term care homes in Ontario during the 7 weeks before (March 1 - April 21) and after (April 22 - June 13) the policy restricting staff movement was implemented. We visualized connectivity between long-term care homes in Ontario using an undirected network and calculated the number of homes that had a connection with another long-term care home and the average number of connections per home in each period. We calculated the relative difference in these mobility metrics between the two time periods and compared within-home changes using McNemars test and the Wilcoxon rank-sum test. ResultsIn the period preceding restrictions, 266 (42.7%) long-term care homes had a connection with at least one other home, compared to 79 (12.7%) homes during the period after restrictions, a drop of 70.3% (p <0.001). The average number of connections in the before period was 3.90 compared to 0.77 in after period, a drop of 80.3% (p < 0.001). In both periods, mobility between long-term care homes was higher in homes located in larger communities, those with higher bed counts, and those part of a large chain. Conclusions and ImplicationsMobility between long-term care homes in Ontario fell sharply after an emergency order by the Ontario government limiting long-term care staff to a single home, though some mobility persisted. Reducing this residual mobility should be a focus of efforts to reduce risk within the long-term care sector during the COVID-19 pandemic.

Association Between Nursing Home Crowding and COVID-19 Infection and Mortality in Ontario, Canada

ImportanceNursing home residents have been disproportionately impacted by the COVID-19 epidemic. Prevention recommendations have emphasized frequent testing of healthcare personnel and residents, but additional strategies are needed to protect nursing home residents. ObjectiveWe developed a reproducible index of nursing home crowding and determined whether crowding was associated with incidence of COVID-19 in the first months of the COVID-19 epidemic. Design, Setting, and ParticipantsPopulation-based retrospective cohort study of over 78,000 residents of 618 distinct nursing homes in Ontario, Canada from March 29 to May 20, 2020. ExposureThe nursing home crowding index equalled the average number of residents per bedroom and bathroom. OutcomesPrimary outcomes included the cumulative incidence of COVID-19 infection and mortality, per 100 residents; introduction of COVID-19 into a home ([≥]1 resident case) was a negative tracer. ResultsOf 623 homes in Ontario, we obtained complete information on 618 homes (99%) housing 78,607 residents. A total of 5,218 residents (6.6%) developed COVID-19 infection, and 1,452 (1.8%) died with COVID-19 infection as of May 20, 2020. COVID-19 infection was distributed unevenly across nursing homes: 4,496 (86%) of infections occurred in just 63 (10%) of homes. The crowding index ranged across homes from 1.3 (mainly single-occupancy rooms) to 4.0 (exclusively quadruple occupancy rooms); 308 (50%) homes had high crowding index ([≥]2). Incidence in high crowding index homes was 9.7%, versus 4.5% in low crowding index homes (p<0.001), while COVID-19 mortality was 2.7%, versus 1.3%. The likelihood of COVID-19 introduction did not differ (31.3% vs 30.2%, p=0.79). After adjustment for regional, nursing home, and resident covariates, the crowding index remained associated with increased risk of infection (RR=1.72, 95% Confidence Interval [CI]: 1.11-2.65) and mortality (RR=1.72, 95%CI: 1.03-2.86). Propensity score analysis yielded similar conclusions for infection (RR=2.06, 95%CI: 1.34-3.17) and mortality (RR=2.09, 95%CI: 1.30-3.38). Simulations suggested that converting all 4-bed rooms to 2-bed rooms would have averted 988 (18.9%) infections of COVID-19 and 271 (18.7%) deaths. Conclusions and RelevanceCrowding was associated with higher incidence of COVID-19 infection and mortality. Reducing crowding in nursing homes could prevent future COVID-19 mortality.

For-profit nursing homes and the risk of COVID-19 outbreaks and resident deaths in Ontario, Canada

BackgroundNursing homes have become the epicentre of the coronavirus disease 2019 (COVID-19) pandemic in Canada. Previous research demonstrates that for-profit nursing homes deliver inferior care across a variety of outcome and process measures, raising the question of whether for-profit homes have had worse COVID-19 outcomes than non-profit homes. MethodsWe conducted a retrospective cohort study of all nursing homes in Ontario, Canada from March 29-May 20, 2020 using a COVID-19 outbreak database maintained by the Ontario Ministry of Long-Term Care. We used hierarchical logistic and count-based methods to model the associations between nursing home profit status (for-profit, non-profit or municipal) and nursing home COVID-19 outbreaks, COVID-19 outbreak sizes, and COVID-19 resident deaths. ResultsThe analysis included all 623 Ontario nursing homes, of which 360 (57.7%) were for-profit, 162 (26.0%) were non-profit, and 101 (16.2%) were municipal homes. There were 190 (30.5%) COVID-19 nursing home outbreaks involving 5218 residents (mean of 27.5 {+/-} 41.3 residents per home), resulting in 1452 deaths (mean of 7.6 {+/-} 12.7 residents per home) with an overall case fatality rate of 27.8%. The odds of a COVID-19 outbreak was associated with the incidence of COVID-19 in the health region surrounding a nursing home (adjusted odds ratio [aOR], 1.94; 95% confidence interval [CI] 1.23-3.09) and number of beds (aOR, 1.40; 95% CI 1.20-1.63), but not profit status. For-profit status was associated with both the size of a nursing home outbreak (adjusted risk ratio [aRR], 1.96; 95% CI 1.26-3.05) and the number of resident deaths (aRR, 1.78; 95% CI 1.03-3.07), compared to non-profit homes. These associations were mediated by a higher prevalence of older nursing home design standards in for-profit homes. Interpretation: For-profit status is associated with the size of a COVID-19 nursing home outbreak and the number of resident deaths, but not the likelihood of outbreaks. Differences between for profit and non-profit homes are largely explained by older design standards, which should be a focus of infection control efforts and future policy.

Virtual health care for community management of patients with COVID-19.

ObjectivesTo describe the implementation and early experience of virtual health care for community management of patients with COVID-19. Designobservational cohort study. Settinglarge Australian metropolitan health service with established virtual health care program and remote patient monitoring capability. Participantspatients with COVID-19 living within the health service who can self-isolate safely, do not require immediate admission to an inpatient setting, have no major active comorbid illness and can be managed at home or other suitable accommodation. Main outcome measurescare escalation rates, including hospital admission. Resultsbetween 11-29 March 2020, 162/173 (93.6%) locally diagnosed patients with COVID-19 were accepted to the virtual health care program, median age 38y (range 11-79). For the 62 patients discharged during this period the median length of stay was 8 days (range 1-17). The peak of 100 prevalent patients equated to approximately 25 patients per Registered Nurse per shift. Patients were contacted a median of 16 times (range 1-30) during this period, with video consultations used 66.3% of the time; 132/162 (81.5%) patients were monitored remotely. Care escalation rates were low: ambulance attendance, 5 (3%); ED attendance, 4 (2.5%); hospital admission, 3 (1.9%). There were no deaths. Conclusions: community-based virtual health care is feasible for managing most patients with COVID-19 and can be rapidly implemented in an urban Australian context for pandemic management. Health services implementing virtual health care should anticipate challenges with rapid technology deployments and provide adequate support to resolve them including strategies supporting consumer use of health information technologies.