COVID-19 Vaccination is Associated with Reduced Outpatient Antibiotic Prescribing In Older Adults with Confirmed SARS-CoV-2: A Population Wide Cohort Study.

BACKGROUND: Antibiotics are frequently prescribed unnecessarily in outpatients with COVID-19. We sought to evaluate factors associated with antibiotic prescribing in those with SARS-CoV-2 infection. METHODS: We performed a population-wide cohort study of outpatients 66 years or older with PCR-confirmed SARS-CoV-2 from January 1st 2020 to December 31st 2021 in Ontario, Canada. We determined rates of antibiotic prescribing within 1-week before (pre-diagnosis) and 1-week after (post-diagnosis) reporting of the positive SARS-CoV-2 result, compared to a self-controlled period (baseline). We evaluated predictors of prescribing, including a primary series COVID-19 vaccination, in univariate and multivariable analyses. RESULTS: We identified 13,529 eligible nursing home residents and 50,885 eligible community dwelling adults with SARS-CoV-2 infection. Of the nursing home and community residents, 3,020 (22%) and 6,372 (13%) received at least one antibiotic prescription within 1 week of a SARS-CoV-2 positive result, respectively. Antibiotic prescribing in nursing home and community residents occurred at 15.0 and 10.5 prescriptions per 1000 person-days pre-diagnosis and 20.9 and 9.8 per 1000 person-days post-diagnosis, higher than the baseline rates of 4.3 and 2.5 prescriptions per 1000 person-days. COVID-19 vaccination was associated with reduced prescribing in nursing home and community residents, with adjusted post-diagnosis IRRs of 0.7 (95%CI 0.4-1) and 0.3 (95%CI 0.3-0.4) respectively. CONCLUSIONS: Antibiotic prescribing was high and with little or no decline following SARS-CoV-2 diagnosis, though was reduced in COVID-19 vaccinated individuals, highlighting the importance of vaccination and antibiotic stewardship in older adults with COVID-19.

Incidence of SARS-CoV-2 Infection Among People Experiencing Homelessness in Toronto, Canada.

Importance: People experiencing homelessness are at high risk of SARS-CoV-2 infection. Incident infection rates have yet to be established in these communities and are needed to inform infection prevention guidance and related interventions. Objective: To quantify the SARS-CoV-2 incident infection rate among people experiencing homelessness in Toronto, Canada, in 2021 and 2022 and to assess factors associated with incident infection. Design, Setting, and Participants: This prospective cohort study was conducted among individuals aged 16 years and older who were randomly selected between June and September 2021 from 61 homeless shelters, temporary distancing hotels, and encampments in Toronto, Canada. Exposures: Self-reported housing characteristics, such as number sharing living space. Main Outcomes and Measures: Prevalence of prior SARS-CoV-2 infection in summer 2021, defined as self-reported or polymerase chain reaction (PCR)- or serology-confirmed evidence of infection at or before the baseline interview, and SARS-CoV-2 incident infection, defined as self-reported or PCR- or serology-confirmed infection among participants without history of infection at baseline. Factors associated with infection were assessed using modified Poisson regression with generalized estimating equations. Results: The 736 participants (415 of whom did not have SARS-CoV-2 infection at baseline and were included in the primary analysis) had a mean (SD) age of 46.1 (14.6) years; 486 (66.0%) self-identified as male. Of these, 224 (30.4% [95% CI, 27.4%-34.0%]) had a history of SARS-CoV-2 infection by summer 2021. Of the remaining 415 participants with follow-up, 124 experienced infection within 6 months, representing an incident infection rate of 29.9% (95% CI, 25.7%-34.4%), or 5.8% (95% CI, 4.8%-6.8%) per person-month. Report after onset of the SARS-CoV-2 Omicron variant was associated with incident infection, with an adjusted rate ratio (aRR) of 6.28 (95% CI, 3.94-9.99). Other factors associated with incident infection included recent immigration to Canada (aRR, 2.74 [95% CI, 1.64-4.58]) and alcohol consumption over the past interval (aRR, 1.67 [95% CI, 1.12-2.48]). Self-reported housing characteristics were not significantly associated with incident infection. Conclusions and Relevance: In this longitudinal study of people experiencing homelessness in Toronto, SARS-CoV-2 incident infection rates were high in 2021 and 2022, particularly once the Omicron variant became dominant in the region. Increased focus on homelessness prevention is needed to more effectively and equitably protect these communities.

Testing if higher contact among vaccinated can be a mechanism for observed negative vaccine effectiveness.

Evidence from early observational studies suggested negative vaccine effectiveness (${V}_{Eff}$) for the SARS-CoV-2 Omicron variant. Since true ${V}_{Eff}$ is unlikely to be negative, we explored how differences in contact among vaccinated persons (e.g. potentially from the implementation of vaccine mandates), could lead to observed negative ${V}_{Eff}$. Using an $SEIR$transmission model, we examined how vaccinated contact heterogeneity, defined as an increase in the contact rate only between vaccinated individuals, interacted with two mechanisms of vaccine efficacy: vaccine efficacy against susceptibility (${VE}_S$) and vaccine efficacy against infectiousness (${VE}_I$), to produce underestimated and in some cases, negative measurements of ${V}_{Eff}$. We found that vaccinated contact heterogeneity led to negative estimates when ${VE}_I$, and especially ${VE}_S,$ were low. Moreover, we determined that when contact heterogeneity was very high, ${V}_{Eff}$ could still be underestimated given relatively high vaccine efficacies (0.7) although its effect on ${V}_{Eff}$ was strongly reduced. We also found that this contact heterogeneity mechanism generated a signature temporal pattern: the largest underestimates and negative measurements of ${V}_{Eff}$ occurred during epidemic growth. Overall, our research illustrates how vaccinated contact heterogeneity could have feasibly produced negative measurements during the Omicron period and highlights its general ability to bias observational studies of ${V}_{Eff}$.

Observed negative vaccine effectiveness could be the canary in the coal mine for biases in observational COVID-19 studies.

Since the emergence of the SARS-CoV-2 Omicron variant, multiple observational studies have reported negative vaccine effectiveness (VE) against infection, symptomatic infection, and even severity (hospitalization), potentially leading to an interpretation that vaccines were facilitating infection and disease. However, current observations of negative VE likely stem from the presence of various biases (e.g., exposure differences, testing differences). Although negative VE is more likely to arise when true biological efficacy is generally low and biases are large, positive VE measurements can also be subject to the same mechanisms of bias. In this perspective, we first outline the different mechanisms of bias that could lead to false-negative VE measurements and then discuss their ability to potentially influence other protection measurements. We conclude by discussing the use of suspected false-negative VE measurements as a signal to interrogate the estimates (quantitative bias analysis) and to discuss potential biases when communicating real-world immunity research.

Differential patterns by area-level social determinants of health in COVID-19 related mortality and non-COVID-19 mortality: a population-based study of 11.8 million people in Ontario, Canada.

BACKGROUND: Social determinants of health (SDOH) have been associated with COVID-19 outcomes. We examined differential patterns in COVID-19-related mortality by SDOH accounting for confounders and compared these patterns to those for non-COVID-19 mortality. METHODS: Residents of Ontario, Canada aged ≥20 years were followed from March-01-2020 to March-02-2021. COVID-19-related death was defined as death within [-7,30] days of a positive COVID-19 test. Area-level SDOH from 2016 Census included: median household income; proportion with diploma or higher educational-attainment; proportion essential workers, racially-minoritised groups, recent immigrants, apartment buildings, and high-density housing; and average household size. We examined associations between SDOH and COVID-19-related mortality using cause-specific hazard models, treating non-COVID-19 mortality as competing risks, and vice-versa. RESULTS: Of 11,810,255 individuals, we observed 3,880(0.03%) COVID-19-related deaths and 88,107(0.75%) non-COVID-19 deaths. After accounting for individual-level demographics, baseline health, and other area-level SDOH, the following area-level SDOH were associated with increased hazards of COVID-19-related death (hazard ratios[95% confidence intervals]: lower income (1.30[1.04-1.62]), lower educational-attainment (1.27[1.07-1.52]), higher proportions essential workers (1.28[1.05-1.57]), racially-minoritised groups (1.42[1.08-1.87]), apartment buildings (1.25[1.07-1.46]), and large vs. medium household size (1.30[1.12-1.50]). In comparison, areas with higher proportion racially-minoritised groups were associated with a lower hazard of non-COVID-19 mortality (0.88[0.84-0.92]). CONCLUSIONS: Area-level SDOH are associated with COVID-19-related mortality after accounting for demographic and clinical factors. COVID-19 has reversed patterns of lower non-COVID-19 mortality among racially-minoritised groups vs. their counterparts. Pandemic responses should include strategies (e.g., 'hotspot' and risk-group tailored vaccination) to address disproportionate risks and inequitable reach of, and access to, preventive interventions associated with SDOH.

Effect of the incremental protection of previous infection against Omicron infection among individuals with a hybrid of infection- and vaccine-induced immunity: a population-based cohort study in Canada.

OBJECTIVES: We examined the incremental protection and durability of infection-acquired immunity against Omicron infection in individuals with hybrid immunity in Ontario, Canada. METHODS: We followed up six million Individuals with at least one RT-PCR test before November 21, 2021 until an Omicron infection. Protection via infection-acquired immunity was assessed by comparing Omicron infection risk between previously infected individuals and those without documented infection under different vaccination scenarios and stratified by time since last infection or vaccination. RESULTS: A prior infection was associated with 68% (95%CI 61-73) and 43% (95%CI 27-56) increased protection against Omicron infection in individuals with two and three doses, respectively. Among individuals with two-dose vaccination, the incremental protection of infection-induced immunity decreased from 79% (95%CI 75-81) within 3 months after vaccination or infection to 27% (95%CI 14-37) at 9-11 months. In individuals with three-dose vaccination, it decreased from 57% (95%CI 50-63) within 3 months to 37% (95%CI 19-51) at 3-5 months after vaccination or infection. CONCLUSION: Previous SARS-CovV-2 infections provide added cross-variant immunity to vaccination. Given the limited durability of infection-acquired protection in individuals with hybrid immunity, its influence on shield-effects at population level and reinfection risks at individual level may be limited.

Clinical severity of Omicron SARS-CoV-2 variant relative to Delta in British Columbia, Canada: A retrospective analysis of whole genome sequenced cases.

BACKGROUND: In late 2021, the Omicron SARS-CoV-2 variant emerged and rapidly replaced Delta as the dominant variant globally. The increased transmissibility of the variant led to surges in case rates as well as increases in hospitalizations, however, the true severity of the variant remained unclear. We aimed to provide robust estimates of Omicron severity relative to Delta. METHODS: This study was conducted using a retrospective cohort design with data from the British Columbia COVID-19 Cohort - a large provincial surveillance platform with linkage to administrative datasets. To capture the time of co-circulation with Omicron and Delta, December 2021 was chosen as the study period. We included individuals diagnosed with Omicron or Delta infection, as determined by whole genome sequencing (WGS). To assess the severity (hospitalization, ICU admission, length of stay), we conducted adjusted Cox proportional hazard models, weighted by inverse probability of treatment weights (IPTW), accounting for age, sex, underlying comorbidities, vaccination, sociodemographic status, and geographical variation. RESULTS: The cohort was composed of 13,128 individuals (7,729 Omicron and 5,399 Delta). There were 419 COVID-19 hospitalizations, with 118 (22%) among people diagnosed with Omicron (crude rate = 1.5% Omicron, 5.6% Delta). In multivariable IPTW analysis, Omicron was associated with a 50% lower risk of hospitalization compared to Delta (aHR = 0.50; 95%CI = 0·43-0.59), a 73% lower risk of ICU admission (aHR = 0.27; 95%CI = 0.19-0.38), and a 5 days shorter hospital stay on average (aß=-5.03; 95% CI=-8.01, -2.05). CONCLUSIONS: Our analysis supports findings from other studies demonstrating lower risk of severe outcomes in Omicron-infected individuals relative to Delta.

Which Curve Are We Flattening? The Disproportionate Impact of COVID-19 Among Economically Marginalized Communities in Ontario, Canada, Was Unchanged From Wild-Type to Omicron.

Person-level surveillance (N = 14 million) and neighborhood-level income data were used to explore magnitude of inequalities in COVID-19 hospitalizations and deaths over 5 waves in Ontario, Canada. Despite attempts at equity-informed policies alongside fluctuating levels of public health measures, the magnitude of inequalities in hospitalizations and deaths remained unchanged across waves.

COVID-19 Cases Among Congregate Care Facility Staff by Neighborhood of Residence and Social and Structural Determinants: Observational Study.

BACKGROUND: Disproportionate risks of COVID-19 in congregate care facilities including long-term care homes, retirement homes, and shelters both affect and are affected by SARS-CoV-2 infections among facility staff. In cities across Canada, there has been a consistent trend of geographic clustering of COVID-19 cases. However, there is limited information on how COVID-19 among facility staff reflects urban neighborhood disparities, particularly when stratified by the social and structural determinants of community-level transmission. OBJECTIVE: This study aimed to compare the concentration of cumulative cases by geography and social and structural determinants across 3 mutually exclusive subgroups in the Greater Toronto Area (population: 7.1 million): community, facility staff, and health care workers (HCWs) in other settings. METHODS: We conducted a retrospective, observational study using surveillance data on laboratory-confirmed COVID-19 cases (January 23 to December 13, 2020; prior to vaccination rollout). We derived neighborhood-level social and structural determinants from census data and generated Lorenz curves, Gini coefficients, and the Hoover index to visualize and quantify inequalities in cases. RESULTS: The hardest-hit neighborhoods (comprising 20% of the population) accounted for 53.87% (44,937/83,419) of community cases, 48.59% (2356/4849) of facility staff cases, and 42.34% (1669/3942) of other HCW cases. Compared with other HCWs, cases among facility staff reflected the distribution of community cases more closely. Cases among facility staff reflected greater social and structural inequalities (larger Gini coefficients) than those of other HCWs across all determinants. Facility staff cases were also more likely than community cases to be concentrated in lower-income neighborhoods (Gini 0.24, 95% CI 0.15-0.38 vs 0.14, 95% CI 0.08-0.21) with a higher household density (Gini 0.23, 95% CI 0.17-0.29 vs 0.17, 95% CI 0.12-0.22) and with a greater proportion working in other essential services (Gini 0.29, 95% CI 0.21-0.40 vs 0.22, 95% CI 0.17-0.28). CONCLUSIONS: COVID-19 cases among facility staff largely reflect neighborhood-level heterogeneity and disparities, even more so than cases among other HCWs. The findings signal the importance of interventions prioritized and tailored to the home geographies of facility staff in addition to workplace measures, including prioritization and reach of vaccination at home (neighborhood level) and at work.

An Elastic Net Regression Model for Identifying Long COVID Patients Using Health Administrative Data: A Population-Based Study.

Background: Long coronavirus disease (COVID) patients experience persistent symptoms after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Healthcare utilization data could provide critical information on the disease burden of long COVID for service planning; however, not all patients are diagnosed or assigned long COVID diagnostic codes. We developed an algorithm to identify individuals with long COVID using population-level health administrative data from British Columbia (BC), Canada. Methods: An elastic net penalized logistic regression model was developed to identify long COVID patients based on demographic characteristics, pre-existing conditions, COVID-19-related data, and all symptoms/conditions recorded >28-183 days after the COVID-19 symptom onset/reported (index) date of known long COVID patients (n = 2430) and a control group (n = 24 300), selected from all adult COVID-19 cases in BC with an index date on/before October 31, 2021 (n = 168 111). Known long COVID cases were diagnosed in a clinic and/or had the International Classification of Diseases, Tenth Revision, Canada (ICD-10-CA) code for "post COVID-19 condition" in their records. Results: The algorithm retained known symptoms/conditions associated with long COVID, demonstrating high sensitivity (86%), specificity (86%), and area under the receiver operator curve (93%). It identified 25 220 (18%) long COVID patients among the remaining 141 381 adult COVID-19 cases, >10 times the number of known cases. Known and predicted long COVID patients had comparable demographic and health-related characteristics. Conclusions: Our algorithm identified long COVID patients with a high level of accuracy. This large cohort of long COVID patients will serve as a platform for robust assessments on the clinical course of long COVID, and provide much needed concrete information for decision-making.

Clinical severity of Omicron subvariants BA.1, BA.2, and BA.5 in a population-based cohort study in British Columbia, Canada.

The SARS-CoV-2 variant Omicron emerged in late 2021. In British Columbia (BC), Canada, and globally, three genetically distinct subvariants of Omicron, BA.1, BA.2, and BA.5, emerged and became dominant successively within an 8-month period. SARS-CoV-2 subvariants continue to circulate in the population, acquiring new mutations that have the potential to alter infectivity, immunity, and disease severity. Here, we report a propensity-matched severity analysis from residents of BC over the course of the Omicron wave, including 39,237 individuals infected with BA.1, BA.2, or BA.5 based on paired high-quality sequence data and linked to comprehensive clinical outcomes data between December 23, 2021 and August 31, 2022. Relative to BA.1, BA.2 cases were associated with a 15% and 28% lower risk of hospitalization and intensive care unit (ICU) admission (aHRhospital = 1.17; 95% confidence interval [CI] = 1.096-1.252; aHRICU = 1.368; 95% CI = 1.152-1.624), whereas BA.5 infections were associated with an 18% higher risk of hospitalization (aHRhospital = 1.18; 95% CI = 1.133-1.224) after accounting for age, sex, comorbidities, vaccination status, geography, and social determinants of health. Phylogenetic analysis revealed no specific subclades associated with more severe clinical outcomes for any Omicron subvariant. In summary, BA.1, BA.2, and BA.5 subvariants were associated with differences in clinical severity, emphasizing how variant-specific monitoring programs remain critical components of patient and population-level public health responses as the pandemic continues.

Corrigendum to "Estimating effective reproduction number using generation time versus serial interval, with application to covid-19 in the Greater Toronto Area, Canada" [Infectious Disease Modelling 5 (2020) 889-896].

[This corrects the article DOI: 10.1016/j.idm.2020.10.009.].

Measuring progress towards reaching zero new HIV acquisitions among key populations in Québec (Canada) using routine surveillance data: a mathematical modelling study.

INTRODUCTION: Men who have sex with men (MSM) and people who inject drugs (PWID) are disproportionately impacted by the HIV epidemic in Canada. Having the second-highest provincial diagnosis rate, an improved understanding of the epidemic among these populations in Québec could aid ongoing elimination efforts. We estimated HIV incidence and other epidemic indicators among MSM and PWID in Montréal and across Québec using a back-calculation model synthesizing surveillance data. METHODS: We developed a deterministic, compartmental mathematical model stratified by age, HIV status and disease progression, and clinical care stages. Using AIDS and HIV diagnoses data, including self-reported time since the last negative test and laboratory results of CD4 cell count at diagnosis, we estimated HIV incidence in each population over 1975-2020 by modelling a cubic M-spline. The prevalence, undiagnosed fraction, fraction diagnosed that started antiretroviral treatment (ART) and median time to diagnosis were also estimated. Since the COVID-19 pandemic disrupted testing, we excluded 2020 data and explored this in sensitivity analyses. RESULTS: HIV incidence in all populations peaked early in the epidemic. In 2020, an estimated 97 (95% CrI: 33-227) and 266 (95% CrI: 103-508) HIV acquisitions occurred among MSM in Montréal and Québec, respectively. Among PWID, we estimated 2 (95% CrI: 0-14) and 6 (95% CrI: 1-26) HIV acquisitions in those same regions. With 2020 data, unless testing rates were reduced by 50%, these estimates decreased, except among Québec PWID, whose increased. Among all, the median time to diagnosis shortened to <2 years before 2020 and the undiagnosed fraction decreased to <10%. This fraction was higher in younger MSM, with 22% of 15-24 year-olds living with HIV in Montréal (95% CrI: 9-39%) and 31% in Québec (95% CrI: 17-48%) undiagnosed by 2020 year-end. Finally, ART access neared 100% in all diagnosed populations. CONCLUSIONS: HIV incidence has drastically decreased in MSM and PWID across Québec, alongside significant improvements in diagnosis and treatment coverage-and the 2013 introduction of pre-exposure prophylaxis. Despite this, HIV transmission continued. Effective efforts to halt this transmission and rapidly diagnose people who acquired HIV, especially among younger MSM, are needed to achieve elimination. Further, as the impacts of the COVID-19 pandemic on HIV transmission are understood, increased efforts may be needed to overcome these.

Competing health risks associated with the COVID-19 pandemic and early response: A scoping review.

BACKGROUND: COVID-19 has rapidly emerged as a global public health threat with infections recorded in nearly every country. Responses to COVID-19 have varied in intensity and breadth, but generally have included domestic and international travel limitations, closure of non-essential businesses, and repurposing of health services. While these interventions have focused on testing, treatment, and mitigation of COVID-19, there have been reports of interruptions to diagnostic, prevention, and treatment services for other public health threats. OBJECTIVES: We conducted a scoping review to characterize the early impact of COVID-19 on HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition. METHODS: A scoping literature review was completed using searches of PubMed and preprint servers (medRxiv/bioRxiv) from November 1st, 2019 to October 31st, 2020, using Medical Subject Headings (MeSH) terms related to SARS-CoV-2 or COVID-19 and HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition. Empiric studies reporting original data collection or mathematical models were included, and available data synthesized by region. Studies were excluded if they were not written in English. RESULTS: A total of 1604 published papers and 205 preprints were retrieved in the search. Overall, 8.0% (129/1604) of published studies and 10.2% (21/205) of preprints met the inclusion criteria and were included in this review: 7.3% (68/931) on HIV, 7.1% (24/339) on tuberculosis, 11.6% (26/224) on malaria, 7.8% (19/183) on sexual and reproductive health, and 9.8% (13/132) on malnutrition. Thematic results were similar across competing health risks, with substantial indirect effects of the COVID-19 pandemic and response on diagnostic, prevention, and treatment services for HIV, tuberculosis, malaria, sexual and reproductive health, and malnutrition. DISCUSSION: COVID-19 emerged in the context of existing public health threats that result in millions of deaths every year. Thus, effectively responding to COVID-19 while minimizing the negative impacts of COVID-19 necessitates innovation and integration of existing programs that are often siloed across health systems. Inequities have been a consistent driver of existing health threats; COVID-19 has worsened disparities, reinforcing the need for programs that address structural risks. The data reviewed here suggest that effective strengthening of health systems should include investment and planning focused on ensuring the continuity of care for both rapidly emergent and existing public health threats.

Ku-gaa-gii pimitizi-win, the COVID-19 cohort study of people experiencing homelessness in Toronto, Canada: a study protocol

Introduction Initial reports suggest people experiencing homelessness (PEH) are at high risk for SARS-CoV-2 infection and associated morbidity and mortality. However, there have been few longitudinal evaluations of the spread and impact of COVID-19 among PEH. This study will estimate the prevalence and incidence of COVID-19 infections in a cohort of PEH followed prospectively in Toronto, Canada. It will also examine associations between individual-level and shelter-level characteristics with COVID-19 infection, adverse health outcomes related to infection and vaccination. Finally, the data will be used to develop and parameterise a mathematical model to characterise SARS-CoV-2 transmission dynamics, and the transmission impact of interventions serving PEH. Design, methods and analysis Ku-gaa-gii pimitizi-win will follow a random sample of PEH from across Toronto (Canada) for 12 months. 736 participants were enrolled between June and September 2021, and will be followed up at 3-month intervals. At each interval, specimens (saliva, capillary blood) will be collected to determine active SARS-CoV-2 infection and serologic evidence of past infection and/or vaccination, and a detailed survey will gather self-reported information, including a detailed housing history. To examine the association between individual-level and shelter-level characteristics on COVID-19-related infection, adverse outcomes, and vaccination, shelter and healthcare administrative data will be linked to participant study data. Healthcare administrative data will also be used to examine long-term (up to 5 years) COVID-19-related outcomes among participants. Ethics and dissemination Ethical approval was obtained from the Unity Health Toronto and University of Toronto Health Sciences Research Ethics Boards (# 20-272). Ku-gaa-gii pimitizi-win was designed in collaboration with community and service provider partners and people having lived experience of homelessness. Findings will be reported to groups supporting Ku-gaa-gii pimitizi-win, Indigenous and other community partners and service providers, funding bodies, public health agencies and all levels of government to inform policy and public health programs.

Mortality trends and length of stays among hospitalized patients with COVID-19 in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves.

BACKGROUND: Epidemics of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stays in hospital and intensive care units (ICUs) among patients with COVID-19 hospitalized through the first three epidemic waves in Canada. METHODS: We used population-based provincial hospitalization data from the epicenters of Canada's epidemics (Ontario and Québec). Adjusted estimates were obtained using marginal standardization of logistic regression models, accounting for patient-level and hospital-level determinants. RESULTS: Using all hospitalizations from Ontario (N = 26,538) and Québec (N = 23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6-7%. This general trend remained after adjustments. The odds of in-hospital mortality in the highest patient load quintile were 1.2-fold (95% CI: 1.0-1.4; Ontario) and 1.6-fold (95% CI: 1.3-1.9; Québec) that of the lowest quintile. Mean hospital and ICU length of stays decreased over time but ICU stays were consistently higher in Ontario than Québec. CONCLUSIONS: In-hospital mortality risks and length of ICU stays declined over time despite changing patient demographics. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.

Predictors of all-cause mortality among patients hospitalized with influenza, respiratory syncytial virus, or SARS-CoV-2.

BACKGROUND: Shared and divergent predictors of clinical severity across respiratory viruses may support clinical and community responses in the context of a novel respiratory pathogen. METHODS: We conducted a retrospective cohort study to identify predictors of 30-day all-cause mortality following hospitalization with influenza (N = 45,749; 2010-09 to 2019-05), respiratory syncytial virus (RSV; N = 24 345; 2010-09 to 2019-04), or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; N = 8988; 2020-03 to 2020-12; pre-vaccine) using population-based health administrative data from Ontario, Canada. Multivariable modified Poisson regression was used to assess associations between potential predictors and mortality. We compared the direction, magnitude, and confidence intervals of risk ratios to identify shared and divergent predictors of mortality. RESULTS: A total of 3186 (7.0%), 697 (2.9%), and 1880 (20.9%) patients died within 30 days of hospital admission with influenza, RSV, and SARS-CoV-2, respectively. Shared predictors of increased mortality included older age, male sex, residence in a long-term care home, and chronic kidney disease. Positive associations between age and mortality were largest for patients with SARS-CoV-2. Few comorbidities were associated with mortality among patients with SARS-CoV-2 as compared with those with influenza or RSV. CONCLUSIONS: Our findings may help identify patients at greatest risk of illness secondary to a respiratory virus, anticipate hospital resource needs, and prioritize local prevention and therapeutic strategies to communities with higher prevalence of risk factors.

Adaptive data-driven age and patch mixing in contact networks with recurrent mobility.

Infectious disease transmission models often stratify populations by age and geographic patches. Contact patterns between age groups and patches are key parameters in such models. Arenas et al. (2020) develop an approach to simulate contact patterns associated with recurrent mobility between patches, such as due to work, school, and other regular travel. Using their approach, mixing between patches is greater than mobility data alone would suggest, because individuals from patches A and B can form contacts if they meet in patch C. We build upon their approach to address three potential gaps that remain, outlined in the bullets below. We describe the steps required to implement our approach in detail, and present step-wise results of an example application to generate contact matrices for SARS-CoV-2 transmission modelling in Ontario, Canada. We also provide methods for deriving the mobility matrix based on GPS mobility data (appendix).•Our approach includes a distribution of contacts by age that is responsive to the underlying age distributions of the mixing populations.•Our approach maintains different age mixing patterns by contact type, such that changes to the numbers of different types of contacts are appropriately reflected in changes to overall age mixing patterns.•Our approach distinguishes between two mixing pools associated with each patch, with possible implications for the overall connectivity of the population: the home pool, in which contacts can only be formed with other individuals residing in the same patch, and the travel pool, in which contacts can be formed with some residents of, and any other visitors to the patch.

Stringency of containment and closures on the growth of SARS-CoV-2 in Canada prior to accelerated vaccine roll-out.

BACKGROUND: Many studies have examined the effectiveness of non-pharmaceutical interventions (NPIs) on SARS-CoV-2 transmission worldwide. However, less attention has been devoted to understanding the limits of NPIs across the course of the pandemic and along a continuum of their stringency. In this study, we explore the relationship between the growth of SARS-CoV-2 cases and an NPI stringency index across Canada before the accelerated vaccine roll-out. METHODS: We conducted an ecological time-series study of daily SARS-CoV-2 case growth in Canada from February 2020 to February 2021. Our outcome was a back-projected version of the daily growth ratio in a stringency period (i.e., a 10-point range of the stringency index) relative to the last day of the previous period. We examined the trends in case growth using a linear mixed-effects model accounting for stringency period, province, and mobility in public domains. RESULTS: Case growth declined rapidly by 20-60% and plateaued within the first month of the first wave, irrespective of the starting values of the stringency index. When stringency periods increased, changes in case growth were not immediate and were faster in the first wave than in the second. In the first wave, the largest decreasing trends from our mixed effects model occurred in both early and late stringency periods, depending on the province, at a geometric mean index value of 30⋅1 out of 100. When compared with the first wave, the stringency periods in the second wave possessed little association with case growth. CONCLUSIONS: The minimal association in the first wave, and the lack thereof in the second, is compatible with the hypothesis that NPIs do not, per se, lead to a decline in case growth. Instead, the correlations we observed might be better explained by a combination of underlying behaviors of the populations in each province and the natural dynamics of SARS-CoV-2. Although there exist alternative explanations for the equivocal relationship between NPIs and case growth, the onus of providing evidence shifts to demonstrating how NPIs can consistently have flat association, despite incrementally high stringency.