Association between nursing home crowding and outbreak-associated respiratory infection and death prior to the COVID-19 pandemic between 2014 and 2019 in Ontario, Canada

ImportanceResident crowding in nursing homes is associated with larger SARS-CoV-2 outbreaks. However, this association has not been previously documented for non-SARS-CoV-2 respiratory infections. ObjectiveWe sought to measure the association between nursing home crowding and respiratory infections in Ontario nursing homes prior to the COVID-19 pandemic. Design, Setting, and ParticipantsWe conducted a retrospective cohort study of nursing home residents in Ontario, Canada over a five-year period prior to the COVID-19 pandemic, between September 2014 and August 2019. ExposureUsing administrative data, we estimated the crowding index equal to the mean number of residents per bedroom and bathroom (residents / [0.5*bedrooms+0.5*bathrooms]). OutcomesThe incidence of outbreak-associated infections and mortality per 100 nursing home residents per year. We also examined infection and mortality outcomes for outbreaks due to 7 specific pathogens: coronaviruses (OC43, 229E, NL63, HKU1), influenza A, influenza B, human metapneumovirus, parainfluenza virus, respiratory syncytial virus, rhinovirus/enterovirus. ResultsThere was one or more respiratory outbreak in 93.9% (588/626) nursing homes in Ontario. There were 4,921 outbreaks involving 64,829 cases of respiratory infection, and 1,969 deaths. Outbreaks attributable to a single identified pathogen were principally caused by influenza A (29%), rhinovirus (11.7%), influenza B (8.1%), and respiratory syncytial virus (6.1%). Among homes, 42.7% (251/588) homes had a high crowding index ([≥] 2.0). After adjustment, more crowded homes had higher outbreak-associated respiratory infection incidence (aRR 1.89; 95% 1.64-2.18) and mortality incidence (aRR 2.28; 95% 1.84-2.84). More crowded homes had higher adjusted estimates of the incidence of infection and mortality for each of the 7 respiratory pathogens examined. Conclusions and RelevanceResidents of crowded nursing homes experienced more respiratory-outbreak infections and mortality due to influenza and other non-SARS-CoV-2 respiratory pathogens. Decreasing crowding in nursing homes is an important patient safety target beyond the COVID-19 pandemic.

Early estimates of SARS-CoV-2 Omicron variant severity based on a matched cohort study, Ontario, Canada

While it is now evident that Omicron is rapidly replacing Delta, largely due to immune escape, it is less clear how the severity of Omicron compares to Delta. In Ontario, we sought to examine hospitalization and death associated with Omicron, as compared to cases infected with Delta. We conducted a matched cohort study, considering time to hospitalization or death as the outcome. Cases were matched on gender, age, vaccination status, health region and onset date. We identified 29,594 Omicron cases that met eligibility criteria, of which 11,622 could be matched with at least one Delta case (N=14,181). There were 59 (0.51%) hospitalizations and 3 (0.03%) deaths among matched Omicron cases, compared to 221 (1.6%) hospitalizations and 17 (0.12%) deaths among matched Delta cases. The risk of hospitalization or death was 65% lower (hazard ratio, HR=0.35, 95%CI: 0.26, 0.46) among Omicron cases compared to Delta cases, while risk of intensive care unit admission or death was 83% lower (HR=0.17, 95%CI: 0.08, 0.37). While severity is likely to be reduced, the absolute number of hospitalizations and impact on the healthcare system may nevertheless be significant due to the increased transmissibility of Omicron.

Incidence of outbreak-associated COVID-19 cases by industry in Ontario, Canada, April 1, 2020- March 31, 2021

ObjectivesThe objective of our study was to estimate the rate of workplace outbreak-associated cases of COVID-19 by industry in labour market participants aged 15-69 years who reported working the majority of hours outside the home in Ontario, Canada. MethodsWe conducted a population based cross-sectional study of COVID-19 workplace outbreaks and associated-cases reported in Ontario between April 1, 2020 and March 31, 2021. All outbreaks were manually classified into two digit North American Industry Classification System (NAICS) codes. We obtained denominator data from the Statistics Canada Labour Force Survey in order to estimate the incidence of outbreak-associated cases per 100,000,000 hours amongst individuals who reported the majority of hours were worked outside the home. We performed this analysis across industries and in three distinct time periods. ResultsOverall, 12% of cases were attributed to workplace outbreaks among working age adults across our study period. While incidence varied across the time periods, the five industries with the highest incidence rates across our study period were agriculture; healthcare and social assistance; food manufacturing; educational services; and, transportation and warehousing. ConclusionsCertain industries have consistently increased incidence of COVID-19 over the course of the pandemic. These results may assist in ongoing efforts to reduce transmission of COVID-19, by prioritizing resources, as well as industry-specific guidance, vaccination, and public health messaging.

Inflection in prevalence of SARS-CoV-2 infections missing the N501Y mutation as a marker of rapid Delta (B.1.617.2) lineage expansion in Ontario, Canada

BackgroundThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta lineage (B.1.617.2) was implicated in the SARS-CoV-2 surge in India. We sought to describe the rapid expansion of the Delta lineage in Ontario, Canada (population 15 million) using mutation profile information and confirmatory whole genome sequencing. MethodsAll laboratory-confirmed SARS-CoV-2 cases reported to Public Health Ontario between April 1st and June 12th 2021, with cycle threshold values [≤]35, were eligible for screening for the N501Y and the E484K mutations. We classified cases via mutation screening as: (1) N501Y-/E484K- (wild-type/Delta), (2) Alpha (N501Y+/E484K-), (3) Beta/Gamma (N501Y+/E484K+), or (4) N501Y-/E484K+ (predominantly B.1.525, and B.1.1.318). ResultsThe N501Y-/E484K- mutation profile went from having a 29% transmission deficit relative to Alpha (relative Re = 0.71, 95%CI: 0.64, 0.77) on April 1st to having a 50% transmission advantage on June 12th (relative Re = 1.50, 95%CI: 1.31, 1.71). Whole genome sequencing of N501Y-/E484K-cases (N=583) confirmed that the pattern of increasing relative reproduction number coincided with the replacement of wild-type with Delta variant (from 2.2% in early April, to 83% in late May). DiscussionDelta is rapidly overtaking other SARS-CoV-2 variants in Ontario, and has a substantial transmission advantage. An inflection in the proportion of cases missing the N501Y mutation from rapidly decreasing to rapidly increasing,3 may be an early warning signal for Delta lineage expansion.

Impact of a nighttime curfew on overnight mobility

BackgroundAmong non-pharmaceutical interventions, individual movement restrictions have been among the most impactful methods for controlling COVID-19 case growth. While nighttime curfews to control COVID-19 case growth have been implemented in certain regions and cities, few studies have examined their impacts on mobility or COVID-19 incidence. In the second wave of COVID-19, Canadas two largest and adjacent provinces implemented lockdown restrictions with (Quebec) and without (Ontario) a nighttime curfew, providing a natural experiment to study the association between curfews and mobility. MethodsThis study spanned from December 1, 2020 to January 23, 2021 and included the populations of Ontario (including Toronto) and Quebec (including Montreal). The intervention of interest was a nighttime curfew implemented across Quebec on January 9, 2021. Unadjusted and adjusted difference-in-differences models (DID) were used to measure the incremental impact of the curfew on nighttime mobility in Quebec as compared to Ontario. ResultsThe implementation of the curfew was associated with an immediate reduction in nighttime mobility. The adjusted DID analysis indicated that Quebec experienced a 31% relative reduction in nighttime mobility (95%CI: -36% to -25%) compared to Ontario, and that Montreal experienced a 39% relative reduction compared to Toronto (95%CI: -43, -34). DiscussionHowever, this natural experiment among two neighbouring provinces provides useful evidence that curfews lead to an immediate and substantial decrease nighttime mobility, particularly in these provinces largest urban areas hardest hit by COVID-19.

The mobility gap: estimating mobility levels required to control Canada's winter COVID-19 surge

BackgroundNon-pharmaceutical interventions remain a primary means of suppressing COVID-19 until vaccination coverage is sufficient to achieve herd immunity. We used anonymized smartphone mobility measures in seven Canadian provinces to quantify the mobility level needed to suppress COVID-19 (mobility threshold), and the difference relative to current mobility levels (mobility gap). MethodsWe conducted a longitudinal study of weekly COVID-19 incidence from March 15, 2020 to January 16, 2021, among provinces with 20 COVID-19 cases in at least 10 weeks. The outcome was weekly growth rate defined as the ratio of current cases compared to the previous week. We examined the effects of average time spent outside the home (non-residential mobility) in the prior three weeks using a lognormal regression model accounting for province, season, and mean temperature. We calculated the COVID-19 mobility threshold and gap. ResultsAcross the 44-week study period, a total of 704,294 persons were infected with COVID-19. Non-residential mobility dropped rapidly in the spring and reached a median of 36% (IQR: 31,40) in April 2020. After adjustment, each 5% increase in non-residential mobility was associated with a 9% increase in the COVID-19 weekly growth rate (ratio=1.09, 95%CI: 1.07,1.12). The mobility gap increased through the fall months, which was associated with increasing case growth. InterpretationMobility strongly and consistently predicts weekly case growth, and low levels of mobility are needed to control COVID-19 through winter 2021. Mobility measures from anonymized smartphone data can be used to guide the provincial and regional implementation and loosening of physical distancing measures.