Use of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study.

OBJECTIVES: We sought to 1) identify long COVID phenotypes based on patient reported outcome measures (PROMs) and 2) determine whether the phenotypes were associated with quality of life (QoL) and/or lung function. METHODS: This was a longitudinal cohort study of hospitalized and non-hospitalized patients from March 2020 to January 2022 that was conducted across 4 Post-COVID Recovery Clinics in British Columbia, Canada. Latent class analysis was used to identify long COVID phenotypes using baseline PROMs (fatigue, dyspnea, cough, anxiety, depression, and post-traumatic stress disorder). We then explored the association between the phenotypes and QoL (using the EuroQoL 5 dimensions visual analogue scale [EQ5D VAS]) and lung function (using the diffusing capacity of the lung for carbon monoxide [DLCO]). RESULTS: There were 1,344 patients enrolled in the study (mean age 51 ±15 years; 780 [58%] were females; 769 (57%) were of a non-White race). Three distinct long COVID phenotypes were identified: Class 1) fatigue and dyspnea, Class 2) anxiety and depression, and Class 3) fatigue, dyspnea, anxiety, and depression. Class 3 had a significantly lower EQ5D VAS at 3 (50±19) and 6 months (54 ± 22) compared to Classes 1 and 2 (p<0.001). The EQ5D VAS significantly improved between 3 and 6 months for Class 1 (median difference of 6.0 [95% CI, 4.0 to 8.0]) and Class 3 (median difference of 5.0 [95% CI, 0 to 8.5]). There were no differences in DLCO between the classes. CONCLUSIONS: There were 3 distinct long COVID phenotypes with different outcomes in QoL between 3 and 6 months after symptom onset. These phenotypes suggest that long COVID is a heterogeneous condition with distinct subpopulations who may have different outcomes and warrant tailored therapeutic approaches.

Understanding the impact of mobility on COVID-19 spread: A hybrid gravity-metapopulation model of COVID-19.

The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.

Association of COVID-19 Infection With Incident Diabetes.

Importance: SARS-CoV-2 infection may lead to acute and chronic sequelae. Emerging evidence suggests a higher risk of diabetes after infection, but population-based evidence is still sparse. Objective: To evaluate the association between COVID-19 infection, including severity of infection, and risk of diabetes. Design, Setting, and Participants: This population-based cohort study was conducted in British Columbia, Canada, from January 1, 2020, to December 31, 2021, using the British Columbia COVID-19 Cohort, a surveillance platform that integrates COVID-19 data with population-based registries and administrative data sets. Individuals tested for SARS-CoV-2 by real-time reverse transcription-polymerase chain reaction (RT-PCR) were included. Those who tested positive for SARS-CoV-2 (ie, those who were exposed) were matched on sex, age, and collection date of RT-PCR test at a 1:4 ratio to those who tested negative (ie, those who were unexposed). Analysis was conducted January 14, 2022, to January 19, 2023. Exposure: SARS-CoV-2 infection. Main Outcomes and Measures: The primary outcome was incident diabetes (insulin dependent or not insulin dependent) identified more than 30 days after the specimen collection date for the SARS-CoV-2 test with a validated algorithm based on medical visits, hospitalization records, chronic disease registry, and prescription drugs for diabetes management. Multivariable Cox proportional hazard modeling was performed to evaluate the association between SARS-CoV-2 infection and diabetes risk. Stratified analyses were performed to assess the interaction of SARS-CoV-2 infection with diabetes risk by sex, age, and vaccination status. Results: Among 629 935 individuals (median [IQR] age, 32 [25.0-42.0] years; 322 565 females [51.2%]) tested for SARS-CoV-2 in the analytic sample, 125 987 individuals were exposed and 503 948 individuals were unexposed. During the median (IQR) follow-up of 257 (102-356) days, events of incident diabetes were observed among 608 individuals who were exposed (0.5%) and 1864 individuals who were not exposed (0.4%). The incident diabetes rate per 100 000 person-years was significantly higher in the exposed vs nonexposed group (672.2 incidents; 95% CI, 618.7-725.6 incidents vs 508.7 incidents; 95% CI, 485.6-531.8 incidents; P < .001). The risk of incident diabetes was also higher in the exposed group (hazard ratio [HR], 1.17; 95% CI, 1.06-1.28) and among males (adjusted HR, 1.22; 95% CI, 1.06-1.40). The risk of diabetes was higher among people with severe disease vs those without COVID-19, including individuals admitted to the intensive care unit (HR, 3.29; 95% CI, 1.98-5.48) or hospital (HR, 2.42; 95% CI, 1.87-3.15). The fraction of incident diabetes cases attributable to SARS-CoV-2 infection was 3.41% (95% CI, 1.20%-5.61%) overall and 4.75% (95% CI, 1.30%-8.20%) among males. Conclusions and Relevance: In this cohort study, SARS-CoV-2 infection was associated with a higher risk of diabetes and may have contributed to a 3% to 5% excess burden of diabetes at a population level.

A population-based assessment of myocarditis after messenger RNA COVID-19 booster vaccination among adult recipients.

OBJECTIVES: We aimed to estimate the rate of myocarditis after the messenger RNA (mRNA) COVID-19 booster vaccination by vaccine type, age, and sex. METHODS: We used data from the British Columbia COVID-19 Cohort, a population-based cohort surveillance platform. The exposure was a booster dose of an mRNA vaccine. The outcome was diagnosis of myocarditis during hospitalization or an emergency department visit within 7-21 days of booster vaccination. RESULTS: The overall rate of myocarditis was lower for the booster dose (6.41, 95% confidence interval [CI]: 3.50-10.75) than the second dose (17.97, 95% CI: 13.78-23.04); (Rate ratiobooster vs dose-2 = 0.34, 95% CI: 0.17-0.61). This difference was more apparent for the mRNA-1273 vaccine type. After the second dose, the myocarditis rate in males was significantly lower for BNT162b2 than mRNA-1273 overall and among those aged 18-39 years. In contrast, after the booster dose, no significant differences between myocarditis and vaccine type was observed overall or within the specific age groups among males or females. CONCLUSION: Myocarditis after mRNA COVID-19 vaccines is a rare event. A lower absolute risk of myocarditis was observed after a booster dose of mRNA vaccine than the primary series second dose.

Risk factors for COVID-19 hospitalization after COVID-19 vaccination: a population-based cohort study in Canada.

OBJECTIVES: With the uptake of COVID-19 vaccines, there is a need for population-based studies to assess risk factors for COVID-19-related hospitalization after vaccination and how they differ from unvaccinated individuals. METHODS: We used data from the British Columbia COVID-19 Cohort, a population-based cohort that includes all individuals (aged ≥18 years) who tested positive for SARS-CoV-2 by real-time reverse transcription-polymerase chain reaction from January 1, 2021 (after the start of vaccination program) to December 31, 2021. We used multivariable logistic regression models to assess COVID-19-related hospitalization risk by vaccination status and age group among confirmed COVID-19 cases. RESULTS: Of the 162,509 COVID-19 cases included in the analysis, 8,546 (5.3%) required hospitalization. Among vaccinated individuals, an increased odds of hospitalization with increasing age was observed for older age groups, namely those aged 50-59 years (odds ratio [OR] = 2.95, 95% confidence interval [CI]: 2.01-4.33), 60-69 years (OR = 4.82, 95% CI: 3.29, 7.07), 70-79 years (OR = 11.92, 95% CI: 8.02, 17.71), and ≥80 years (OR = 24.25, 95% CI: 16.02, 36.71). However, among unvaccinated individuals, there was a graded increase in odds of hospitalization with increasing age, starting at age group 30-39 years (OR = 2.14, 95% CI: 1.90, 2.41) to ≥80 years (OR = 41.95, 95% CI: 35.43, 49.67). Also, comparing all the age groups to the youngest, the observed magnitude of association was much higher among unvaccinated individuals than vaccinated ones. CONCLUSION: Alongside a number of comorbidities, our findings showed a strong association between age and COVID-19-related hospitalization, regardless of vaccination status. However, age-related hospitalization risk was reduced two-fold by vaccination, highlighting the need for vaccination in reducing the risk of severe disease and subsequent COVID-19-related hospitalization across all population groups.

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.

Cross-Canada Variability in Blood Donor SARS-CoV-2 Seroprevalence by Social Determinants of Health.

We compared the seroprevalence of SARS-CoV-2 anti-nucleocapsid antibodies in blood donors across Canadian regions in 2021. The seroprevalence was the highest in Alberta and the Prairies, and it was so low in Atlantic Canada that few correlates were observed. Being male and of young age were predictive of seropositivity. Racialization was associated with higher seroprevalence in British Columbia and Ontario but not in Alberta and the Prairies. Living in a materially deprived neighborhood predicted higher seroprevalence, but it was more linear across quintiles in Alberta and the Prairies, whereas in British Columbia and Ontario, the most affluent 60% were similarly low and the most deprived 40% similarly elevated. Living in a more socially deprived neighborhood (more single individuals and one parent families) was associated with lower seroprevalence in British Columbia and Ontario but not in Alberta and the Prairies. These data show striking variability in SARS-CoV-2 seroprevalence across regions by social determinants of health. IMPORTANCE Canadian blood donors are a healthy adult population that shows clear disparities associated with racialization and material deprivation. This underscores the pervasiveness of the socioeconomic gradient on SARS-CoV-2 infections in Canada. We identify regional differences in the relationship between SARS-CoV-2 seroprevalence and social determinants of health. Cross-Canada studies, such as ours, are rare because health information is under provincial jurisdiction and is not available in sufficient detail in national data sets, whereas other national seroprevalence studies have insufficient sample sizes for regional comparisons. Ours is the largest seroprevalence study in Canada. An important strength of our study is the interpretation input from a public health team that represented multiple Canadian provinces. Our blood donor seroprevalence study has informed Canadian public health policy at national and provincial levels since the start of the SARS-CoV-2 pandemic.

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.

Coronavirus disease 2019 vaccine effectiveness among a population-based cohort of people living with HIV.

OBJECTIVE: People with HIV were underrepresented in coronavirus disease 2019 (COVID-19) vaccine clinical trials. We estimated vaccine effectiveness (VE) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection for the BNT162b2, mRNA-1273, and ChAdOx1 vaccines among a population-based cohort of people with HIV in Ontario, Canada. DESIGN: Test-negative design. METHODS: We identified people with HIV aged ≥19 years who were tested for SARS-CoV-2 by RT-PCR between December 14, 2020 (first availability of COVID-19 vaccines) and November 21, 2021 (pre-Omicron circulation). Outcomes included any infection, symptomatic infection, and COVID-19-related hospitalization/death. We compared the odds of vaccination between test-positive cases and test-negative controls using multivariable logistic regression with adjustment for age, sex, region, calendar time, SARS-CoV-2 test histories, influenza vaccination, comorbidities, and neighborhood-level socio-economic status. VE was derived as (1 - adjusted odds ratio) × 100%. RESULTS: Among 21 023 adults living with HIV, there were 801 (8.3%) test-positive cases and 8,879 (91.7%) test-negative controls. 20.1% cases and 47.8% of controls received ≥1 COVID-19 vaccine dose; among two-dose recipients, 93.4% received ≥1 mRNA dose. Two-dose VE ≥7 days before specimen collection was 82% (95% confidence interval [CI] = 74-87%) against any infection, 94% (95% CI = 82-98%) against symptomatic infection, and 97% (95% CI = 85-100%) against hospitalization/death. Against any infection, VE declined from 86% (95% CI = 77-92%) within 7-59 days after the second dose to 66% (95% CI = -15-90%) after ≥180 days; we did not observe evidence of waning protection for other outcomes. CONCLUSION: Two doses of COVID-19 vaccine offered substantial protection against symptomatic illness and hospitalization/death in people with HIV prior to the emergence of the Omicron variant. Our findings do not support a broad conclusion that COVID-19 VE is lower among people with HIV in populations that, for the most part, are attending HIV care, taking antiretroviral medication, and are virally suppressed.

Comparative Risk of Myocarditis/Pericarditis Following Second Doses of BNT162b2 and mRNA-1273 Coronavirus Vaccines.

BACKGROUND: Postmarketing evaluations have linked myocarditis to COVID-19 mRNA vaccines. However, few population-based analyses have directly compared the safety of the 2 mRNA COVID-19 vaccines. OBJECTIVES: This study aimed to compare the risk of myocarditis, pericarditis, and myopericarditis between BNT162b2 and mRNA-1273. METHODS: We used data from the British Columbia COVID-19 Cohort (BCC19C), a population-based cohort study. The exposure was the second dose of an mRNA vaccine. The outcome was diagnosis of myocarditis, pericarditis, or myopericarditis during a hospitalization or an emergency department visit within 21 days of the second vaccination dose. We performed multivariable logistic regression to assess the association between vaccine product and the outcomes of interest. RESULTS: The rates of myocarditis and pericarditis per million second doses were higher for mRNA-1273 (n = 31, rate 35.6; 95% CI: 24.1-50.5; and n = 20, rate 22.9; 95% CI: 14.0-35.4, respectively) than BNT162b2 (n = 28, rate 12.6; 95% CI: 8.4-18.2 and n = 21, rate 9.4; 95% CI: 5.8-14.4, respectively). mRNA-1273 vs BNT162b2 had significantly higher odds of myocarditis (adjusted OR [aOR]: 2.78; 95% CI: 1.67-4.62), pericarditis (aOR: 2.42; 95% CI: 1.31-4.46) and myopericarditis (aOR: 2.63; 95% CI: 1.76-3.93). The association between mRNA-1273 and myocarditis was stronger for men (aOR: 3.21; 95% CI: 1.77-5.83) and younger age group (18-39 years; aOR: 5.09; 95% CI: 2.68-9.66). CONCLUSIONS: Myocarditis/pericarditis following mRNA COVID-19 vaccines is rare, but we observed a 2- to 3-fold higher odds among individuals who received mRNA-1273 vs BNT162b2. The rate of myocarditis following mRNA-1273 receipt is highest among younger men (age 18-39 years) and does not seem to be present at older ages. Our findings may have policy implications regarding the choice of vaccine offered.

Association between close interpersonal contact and vaccine hesitancy: Findings from a population-based survey in Canada

Background Vaccine hesitancy threatens efforts to bring the coronavirus disease 2019 (COVID-19) pandemic to an end. Given that social or interpersonal contact is an important driver for COVID-19 transmission, understanding the relationship between contact rates and vaccine hesitancy may help identify appropriate targets for strategic intervention. The purpose of this study was to assess the association between interpersonal contact and COVID-19 vaccine hesitancy among a sample of unvaccinated adults in the Canadian province of British Columbia (BC). Methods Unvaccinated individuals participating in the BC COVID-19 Population Mixing Patterns Survey (BC-Mix) were asked to indicate their level of agreement to the statement, “I plan to get the COVID-19 vaccine.” Multivariable multinomial logistic regression was used to assess the association between self-reported interpersonal contact and vaccine hesitancy, adjusting for age, sex, ethnicity, educational attainment, occupation, household size and region of residence. All analyses incorporated survey sampling weights based on age, sex, geography, and ethnicity. Results Results were based on survey responses collected between March 8, 2021 and December 6, 2021, by a total of 4,515 adults aged 18 years and older. Overall, 56.7% of respondents reported that they were willing to get the COVID-19 vaccine, 27.0% were unwilling and 16.3% were undecided. We found a dose-response association between interpersonal contact and vaccine hesitancy. Compared to individuals in the lowest quartile (least contact), those in the fourth quartile (highest contact), third quartile and second quartile groups were more likely to be vaccine hesitant, with adjusted odd ratios (aORs) of 2.85 (95% CI: 2.02, 4.00), 1.91(95% CI: 1.38, 2.64), 1.78 (95% CI: 1.13, 2.82), respectively. Conclusion Study findings show that among unvaccinated people in BC, vaccine hesitancy is greater among those who have high contact rates, and hence potentially at higher risk of acquiring and transmitting infection. This may also impact future uptake of booster doses.

Impact of COVID-19 pandemic on HCV care cascade in Rwanda: Ecological study from July 2019 to June 2021.

Content available: Author Interview and Audio Recording.

Spike Mutation Profiles Associated With SARS-CoV-2 Breakthrough Infections in Delta Emerging and Predominant Time Periods in British Columbia, Canada

Background COVID-19 vaccination is a key public health measure in the pandemic response. The rapid evolution of SARS-CoV-2 variants introduce new groups of spike protein mutations. These new mutations are thought to aid in the evasion of vaccine-induced immunity and render vaccines less effective. However, not all spike mutations contribute equally to vaccine escape. Previous studies associate mutations with vaccine breakthrough infections (BTI), but information at the population level remains scarce. We aimed to identify spike mutations associated with SARS-CoV-2 vaccine BTI in a community setting during the emergence and predominance of the Delta-variant. Methods This case-control study used both genomic, and epidemiological data from a provincial COVID-19 surveillance program. Analyses were stratified into two periods approximating the emergence and predominance of the Delta-variant, and restricted to primary SARS-CoV-2 infections from either unvaccinated individuals, or those infected ≥14 days after their second vaccination dose in a community setting. Each sample's spike mutations were concatenated into a unique spike mutation profile (SMP). Penalized logistic regression was used to identify spike mutations and SMPs associated with SARS-CoV-2 vaccine BTI in both time periods. Results and Discussion This study reports population level relative risk estimates, between 2 and 4-folds, of spike mutation profiles associated with BTI during the emergence and predominance of the Delta-variant, which comprised 19,624 and 17,331 observations, respectively. The identified mutations cover multiple spike domains including the N-terminal domain (NTD), receptor binding domain (RBD), S1/S2 cleavage region, fusion peptide and heptad regions. Mutations in these different regions imply various mechanisms contribute to vaccine escape. Our profiling method identifies naturally occurring spike mutations associated with BTI, and can be applied to emerging SARS-CoV-2 variants with novel groups of spike mutations.

Social Contacts and Transmission of COVID-19 in British Columbia, Canada.

Background: Close-contact rates are thought to be a driving force behind the transmission of many infectious respiratory diseases. Yet, contact rates and their relation to transmission and the impact of control measures, are seldom quantified. We quantify the response of contact rates, reported cases and transmission of COVID-19, to public health contact-restriction orders, and examine the associations among these three variables in the province of British Columbia, Canada. Methods: We derived time series data for contact rates, daily cases and transmission of COVID-19 from a social contacts survey, reported case counts and by fitting a transmission model to reported cases, respectively. We used segmented regression to investigate impacts of public health orders; Pearson correlation to determine associations between contact rates and transmission; and vector autoregressive modeling to quantify lagged associations between contacts rates, daily cases, and transmission. Results: Declines in contact rates and transmission occurred concurrently with the announcement of public health orders, whereas declines in cases showed a reporting delay of about 2 weeks. Contact rates were a significant driver of COVID-19 and explained roughly 19 and 20% of the variation in new cases and transmission, respectively. Interestingly, increases in COVID-19 transmission and cases were followed by reduced contact rates: overall, daily cases explained about 10% of the variation in subsequent contact rates. Conclusion: We showed that close-contact rates were a significant time-series driver of transmission and ultimately of reported cases of COVID-19 in British Columbia, Canada and that they varied in response to public health orders. Our results also suggest possible behavioral feedback, by which increased reported cases lead to reduced subsequent contact rates. Our findings help to explain and validate the commonly assumed, but rarely measured, response of close contact rates to public health guidelines and their impact on the dynamics of infectious diseases.

Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates.

Following the emergence of COVID-19 at the end of 2019, several mathematical models have been developed to study the transmission dynamics of this disease. Many of these models assume homogeneous mixing in the underlying population. However, contact rates and mixing patterns can vary dramatically among individuals depending on their age and activity level. Variation in contact rates among age groups and over time can significantly impact how well a model captures observed trends. To properly model the age-dependent dynamics of COVID-19 and understand the impacts of interventions, it is essential to consider heterogeneity arising from contact rates and mixing patterns. We developed an age-structured model that incorporates time-varying contact rates and population mixing computed from the ongoing BC Mix COVID-19 survey to study transmission dynamics of COVID-19 in British Columbia (BC), Canada. Using a Bayesian inference framework, we fit four versions of our model to weekly reported cases of COVID-19 in BC, with each version allowing different assumptions of contact rates. We show that in addition to incorporating age-specific contact rates and mixing patterns, time-dependent (weekly) contact rates are needed to adequately capture the observed transmission dynamics of COVID-19. Our approach provides a framework for explicitly including empirical contact rates in a transmission model, which removes the need to otherwise model the impact of many non-pharmaceutical interventions. Further, this approach allows projection of future cases based on clear assumptions of age-specific contact rates, as opposed to less tractable assumptions regarding transmission rates.

Geographic concentration of SARS-CoV-2 cases by social determinants of health in metropolitan areas in Canada: a cross-sectional study.

BACKGROUND: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. METHODS: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARS-CoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. RESULTS: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. INTERPRETATION: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2.

Quantifying transmissibility of SARS-CoV-2 and impact of intervention within long-term healthcare facilities.

Estimates of the basic reproduction number (R 0) for COVID-19 are particularly variable in the context of transmission within locations such as long-term healthcare (LTHC) facilities. We sought to characterize the heterogeneity of R 0 across known outbreaks within these facilities. We used a unique comprehensive dataset of all outbreaks that occurred within LTHC facilities in British Columbia, Canada as of 21 September 2020. We estimated R 0 in 18 LTHC outbreaks with a novel Bayesian hierarchical dynamic model of susceptible, exposed, infected and recovered individuals, incorporating heterogeneity of R 0 between facilities. We further compared these estimates to those obtained with standard methods that use the exponential growth rate and maximum likelihood. The total size of outbreaks varied dramatically, with range of attack rates 2%-86%. The Bayesian analysis provided an overall estimate of R 0 = 2.51 (90% credible interval 0.47-9.0), with individual facility estimates ranging between 0.56 and 9.17. Uncertainty in these estimates was more constrained than standard methods, particularly for smaller outbreaks informed by the population-level model. We further estimated that intervention led to 61% (52%-69%) of all potential cases being averted within the LTHC facilities, or 75% (68%-79%) when using a model with multi-level intervention effect. Understanding of transmission risks and impact of intervention are essential in planning during the ongoing global pandemic, particularly in high-risk environments such as LTHC facilities.

Impact of COVID-19-related public health measures on HCV testing in British Columbia, Canada: An interrupted time series analysis.

BACKGROUND & AIMS: Public health measures introduced to limit transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), also disrupted various healthcare services in many regions worldwide, including British Columbia (BC), Canada. We assessed the impact of these measures, first introduced in BC in March 2020, on hepatitis C (HCV) testing and first-time HCV-positive diagnoses within the province. METHODS: De-identified HCV testing data for BC residents were obtained from the provincial Public Health Laboratory. Weekly changes in anti-HCV, HCV RNA and genotype testing episodes and first-time HCV-positive (anti-HCV/RNA/genotype) diagnoses from January 2018 to December 2020 were assessed and associations were determined using segmented regression models examining rates before vs after calendar week 12 of 2020, when measures were introduced. RESULTS: Average weekly HCV testing and first-time HCV-positive diagnosis rates fell immediately following the imposition of public health measures by 62.3 per 100 000 population and 2.9 episodes per 1 000 000 population, respectively (P < .0001 for both), and recovered in subsequent weeks to near pre-March 2020 levels. Average weekly anti-HCV positivity rates decreased steadily pre-restrictions and this trend remained unchanged afterwards. CONCLUSIONS: Reductions in HCV testing and first-time HCV-positive diagnosis rates, key drivers of progression along the HCV care cascade, occurred following the introduction of COVID-19-related public health measures. Further assessment will be required to better understand the full impact of these service disruptions on the HCV care cascade and to inform strategies for the re-engagement of people who may have been lost to care because of these measures.

Effectiveness of BNT162b2 and mRNA-1273 covid-19 vaccines against symptomatic SARS-CoV-2 infection and severe covid-19 outcomes in Ontario, Canada: test negative design study.

OBJECTIVE: To estimate the effectiveness of mRNA covid-19 vaccines against symptomatic infection and severe outcomes (hospital admission or death). DESIGN: Test negative design study. SETTING: Ontario, Canada between 14 December 2020 and 19 April 2021. PARTICIPANTS: 324 033 community dwelling people aged ≥16 years who had symptoms of covid-19 and were tested for SARS-CoV-2. INTERVENTIONS: BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccine. MAIN OUTCOME MEASURES: Laboratory confirmed SARS-CoV-2 by reverse transcription polymerase chain reaction (RT-PCR) and hospital admissions and deaths associated with SARS-CoV-2 infection. Multivariable logistic regression was adjusted for personal and clinical characteristics associated with SARS-CoV-2 and vaccine receipt to estimate vaccine effectiveness against symptomatic infection and severe outcomes. RESULTS: Of 324 033 people with symptoms, 53 270 (16.4%) were positive for SARS-CoV-2 and 21 272 (6.6%) received at least one dose of vaccine. Among participants who tested positive, 2479 (4.7%) were admitted to hospital or died. Vaccine effectiveness against symptomatic infection observed ≥14 days after one dose was 60% (95% confidence interval 57% to 64%), increasing from 48% (41% to 54%) at 14-20 days after one dose to 71% (63% to 78%) at 35-41 days. Vaccine effectiveness observed ≥7 days after two doses was 91% (89% to 93%). Vaccine effectiveness against hospital admission or death observed ≥14 days after one dose was 70% (60% to 77%), increasing from 62% (44% to 75%) at 14-20 days to 91% (73% to 97%) at ≥35 days, whereas vaccine effectiveness observed ≥7 days after two doses was 98% (88% to 100%). For adults aged ≥70 years, vaccine effectiveness estimates were observed to be lower for intervals shortly after one dose but were comparable to those for younger people for all intervals after 28 days. After two doses, high vaccine effectiveness was observed against variants with the E484K mutation. CONCLUSIONS: Two doses of mRNA covid-19 vaccines were observed to be highly effective against symptomatic infection and severe outcomes. Vaccine effectiveness of one dose was observed to be lower, particularly for older adults shortly after the first dose.