Effectiveness of Influenza Vaccination During Pregnancy Against Laboratory-Confirmed Seasonal Influenza Among Infants Under 6 Months of Age in Ontario, Canada.

BACKGROUND: Randomized trials conducted in low- and middle-income settings demonstrated efficacy of influenza vaccination during pregnancy against influenza infection among infants <6 months of age. However, vaccine effectiveness (VE) estimates from settings with different population characteristics and influenza seasonality remain limited. METHODS: We conducted a test-negative study in Ontario, Canada. All influenza virus tests among infants <6 months from 2010 to 2019 were identified and linked with health databases to ascertain information on maternal-infant dyads. VE was estimated from the odds ratio for influenza vaccination during pregnancy among cases versus controls, computed using logistic regression with adjustment for potential confounders. RESULTS: Among 23 806 infants tested for influenza, 1783 (7.5%) were positive and 1708 (7.2%) were born to mothers vaccinated against influenza during pregnancy. VE against laboratory-confirmed infant influenza infection was 64% (95% confidence interval [CI], 50%-74%). VE was similar by trimester of vaccination (first/second, 66% [95% CI, 40%-80%]; third, 63% [95% CI, 46%-74%]), infant age at testing (0 to <2 months, 63% [95% CI, 46%-75%]; 2 to <6 months, 64% [95% CI, 36%-79%]), and gestational age at birth (≥37 weeks, 64% [95% CI, 50%-75%]; < 37 weeks, 61% [95% CI, 4%-86%]). VE against influenza hospitalization was 67% (95% CI, 50%-78%). CONCLUSIONS: Influenza vaccination during pregnancy offers effective protection to infants <6 months, for whom vaccines are not currently available.

Early influenza vaccine effectiveness estimates using routinely collected data, Alberta, Canada, 2023/24 season.

Timely and precise influenza vaccine effectiveness (VE) estimates are needed to guide public health messaging and impact vaccine uptake immediately. Using routinely collected laboratory, vaccination and health administrative data from Alberta, Canada, we estimated influenza VE against infection for the 2023/24 season on a near real-time basis, to late December, at 61% (95% CI: 58-64) against influenza A(H1N1), 49% (95% CI: 28-63) against influenza A(H3N2) and 75% (95% CI: 58-85) against influenza B.

Measuring waning protection from seasonal influenza vaccination during nine influenza seasons, Ontario, Canada, 2010/11 to 2018/19.

BackgroundWaning immunity from seasonal influenza vaccination can cause suboptimal protection during peak influenza activity. However, vaccine effectiveness studies assessing waning immunity using vaccinated and unvaccinated individuals are subject to biases.AimWe examined the association between time since vaccination and laboratory-confirmed influenza to assess the change in influenza vaccine protection over time.MethodsUsing linked laboratory and health administrative databases in Ontario, Canada, we identified community-dwelling individuals aged ≥ 6 months who received an influenza vaccine before being tested for influenza by RT-PCR during the 2010/11 to 2018/19 influenza seasons. We estimated the adjusted odds ratio (aOR) for laboratory-confirmed influenza by time since vaccination (categorised into intervals) and for every 28 days.ResultsThere were 53,065 individuals who were vaccinated before testing for influenza, with 10,264 (19%) influenza-positive cases. The odds of influenza increased from 1.05 (95% CI: 0.91-1.22) at 42-69 days after vaccination and peaked at 1.27 (95% CI: 1.04-1.55) at 126-153 days when compared with the reference interval (14-41 days). This corresponded to 1.09-times increased odds of influenza every 28 days (aOR = 1.09; 95% CI: 1.04-1.15). Individuals aged 18-64 years showed the greatest decline in protection against influenza A(H1N1) (aORper 28 days = 1.26; 95% CI: 0.97-1.64), whereas for individuals aged ≥ 65 years, it was against influenza A(H3N2) (aORper 28 days = 1.20; 95% CI: 1.08-1.33). We did not observe evidence of waning vaccine protection for individuals aged < 18 years.ConclusionsInfluenza vaccine protection wanes during an influenza season. Understanding the optimal timing of vaccination could ensure robust protection during seasonal influenza activity.

Pediatric Respiratory Syncytial Virus Hospitalizations, 2017-2023.

Importance: Respiratory syncytial virus (RSV) transmission was disrupted worldwide following the COVID-19 pandemic, and further study is required to better understand these changes. Objective: To compare observed and expected RSV hospital and intensive care unit (ICU) admission rates and characteristics of admitted children during the 2021-2022 and 2022-2023 seasons. Design, Setting, and Participants: A population-based cohort study of all children aged younger than 5 years in Ontario, Canada, July 1, 2017, through March 31, 2023, was conducted. Exposures: Individual and neighborhood-level sociodemographic and clinical characteristics were identified from administrative data, including age, palivizumab eligibility, complex medical conditions, rurality, and living in a marginalized neighborhood. Main Outcomes and Measures: The main outcome was RSV-associated hospitalization. Secondary outcomes included ICU admissions, mechanical ventilation, extracorporeal membrane oxygenation, and in-hospital death. Poisson generalized estimating equations were used to model weekly age- and sex-specific hospitalization rates and estimate expected rates in the postpandemic era; adjusted rate ratios (RRs) and 95% CIs are reported. Results: This cohort study included approximately 700 000 children per study year. Compared with prepandemic years (2017-2018, 2018-2019, and 2019-2020), the 2021-2022 RSV season peaked slightly earlier, but overall admission rates were comparable (289.1 vs 281.4-334.6 per 100 000, or approximately 2000 admissions). The 2022-2023 season peaked a month earlier and resulted in more than twice as many hospitalizations (770.0 per 100 000; n = 4977 admissions). The proportion of children admitted to an ICU in 2022-2023 (13.9%) was slightly higher than prepandemic (9.6%-11.4%); however, the population-based rate was triple the prepandemic levels (106.9 vs 27.6-36.6 per 100 000 children in Ontario). With the exception of palivizumab-eligible children, all sociodemographic and health status characteristics were associated with lower-than-expected RSV hospitalization rates in 2021-2022. In contrast, older age of patients was associated with higher-than-expected rates in 2022-2023 (ie, 24-59 months: RR, 1.90; 95% CI, 1.35-2.66). Conclusions and Relevance: There were notable differences in RSV epidemiologic characteristics in Ontario following the COVID-19 pandemic. It is not yet clear whether and how long atypical RSV epidemics may persist. Clinicians and program planners should consider the potential for ongoing impacts to health care capacity and RSV immunization programs.

Invasive Pneumococcal Disease Epidemiology and Serotype Replacement After the Introduction of the 13-Valent Pneumococcal Conjugate Vaccine in Ontario, Canada, 2007-2022.

Background: New vaccine products were recently authorized for protection against invasive pneumococcal disease (IPD) in Canada. Our aim was to determine age- and serotype-specific trends in IPD incidence and severity in Canada's largest province, Ontario. Methods: We included all confirmed IPD cases reported in Ontario and defined the pre-pneumococcal 13-valent conjugate vaccine (PCV13) era (01/2007 to 12/2010), post-PCV13 era (01/2011 to 12/2019), and coronavirus disease 2019 (COVID-19) pandemic era (01/2020 to 12/2022). We estimated incidence, hospitalization, and case fatality rate (CFR) by age. We grouped IPD cases by vaccine-specific serotypes (PCV13; PCV15-non-PCV13; PCV20-non-PCV13; PCV20-non-PCV15; polysaccharide 23-valent vaccine-non-PCV20; and non-vaccine-preventable [NVP]). We then compared incidence rates by age and serotype group in the pre- and post-PCV13 eras by calculating rate ratios (RRs) and their 95% CIs. Results: Incidence and hospitalizations declined from the pre- to post-PCV13 era in children aged <5 years (RR, 0.7; 95% CI, 0.6-0.8; and RR, 0.8; 95% CI, 0.7-0.9, respectively), but the CFR increased (1.4% to 2.3%). Other age groups saw smaller declines or more stable incidence rates across the years; hospitalizations increased in adults aged 50-64 years (RR, 1.2; 95% CI, 1.1-1.4) and ≥65 years (RR, 1.1; 95% CI, 1.0-1.1). For all ages, IPD cases and hospitalizations attributable to PCV13 serotypes declined, and those attributable to PCV15-non-PCV13, PCV20-non-PCV13, and NVP serotypes increased. IPD incidence declined during the COVID-19 era. Conclusions: IPD incidence and hospitalizations due to PCV13 serotypes decreased after PCV13 introduction but increased for other serotypes. Continued surveillance is required to evaluate changes to pneumococcal vaccination programs and ongoing changes to the distribution of IPD-causing serotypes.

Neighborhood-Level Burden of Social Risk Factors on Respiratory Syncytial Virus Hospitalization in Ontario, Canada, 2016-2019.

Background: Beyond clinical risk factors, little is known about the impact of social determinants on respiratory syncytial virus (RSV) burden. Our study aimed to estimate RSV-related hospitalization rates across sociodemographic and housing characteristics. Methods: We conducted a population-based study of all RSV-related hospitalizations in Ontario, Canada, between September 1, 2016, and August 31, 2019, using validated hospital discharge codes and census data. Crude and age-standardized annualized RSV incidence rates and rate ratios (RRs) were estimated for a range of individual-level demographics and neighborhood-level measures of marginalization and housing characteristics. Results: Overall, the annual RSV-related hospitalization rate was 27 per 100 000, with the highest rates observed in children age <12 months (1049 per 100 000) and 12-23 months (294 per 100 000) and adults age ≥85 years (155 per 100 000). Higher RSV-related hospitalization rates were associated with increasing marginalization quintile (Q) of material resources (RR, 1.4; Q5: 33 per 100 000 vs Q1: 24 per 100 000) and household instability (RR, 1.5; Q5: 31 per 100 000 vs Q1: 22 per 100 000). Conclusions: The burden of RSV-related hospitalization was greatest in young children and older adults, with variation by sociodemographic and housing factors. Understanding the role of these social factors is crucial for informing equitable preventive program delivery.

A multiprovincial retrospective analysis of the incidence of myocarditis or pericarditis after mRNA vaccination compared to the incidence after SARS-CoV-2 infection.

Objective: To compare myocarditis/pericarditis risk after COVID-19 mRNA vaccination versus SARS-CoV-2 infection, and to assess if myocarditis/pericarditis risk varies by vaccine dosing interval. Methods: In this retrospective cohort study, we used linked databases in Quebec, Ontario, and British Columbia between January 26, 2020, and September 9, 2021. We included individuals aged 12 or above who received an mRNA vaccine as the second dose or were SARS-CoV-2-positive by RT-PCR. The outcome was hospitalization/emergency department visit for myocarditis/pericarditis within 21 days of exposure. We calculated age- and sex-stratified incidence ratios (IRs) of myocarditis/pericarditis following mRNA vaccination versus SARS-CoV-2 infection. We also calculated myocarditis/pericarditis incidence by vaccine type, homologous/heterologous schedule, and dosing interval. We pooled province-specific estimates using meta-analysis. Results: Following 18,860,817 mRNA vaccinations and 860,335 SARS-CoV-2 infections, we observed 686 and 160 myocarditis/pericarditis cases, respectively. Myocarditis/pericarditis incidence was lower after vaccination than infection (IR [BNT162b2/SARS-CoV-2], 0.14; 95%CI, 0.07-0.29; IR [mRNA-1273/SARS-CoV-2], 0.28; 95%CI, 0.20-0.39). Within the vaccinated cohort, myocarditis/pericarditis incidence was lower with longer dosing intervals; IR (56 or more days/15-30 days) was 0.28 (95%CI, 0.19-0.41) for BNT162b2 and 0.26 (95%CI, 0.18-0.38) for mRNA-1273. Conclusion: Myocarditis/pericarditis risk was lower after mRNA vaccination than SARS-CoV-2 infection, and with longer intervals between primary vaccine doses.

Population immunity to varicella in Canada: A Canadian Immunization Research Network (CIRN) study.

INTRODUCTION: The incidence of varicella in Canada has decreased by almost 99% since vaccination was introduced. However, variation in the timing and eligibility of vaccination programs across the country has resulted in some cohorts being under-vaccinated and therefore potentially susceptible to infection. METHODS: We used nationally representative specimens from the Biobank of Statistics Canada's Canadian Health Measures Survey (CHMS) as well as residual specimens from Ontario collected between 2009-2014 to estimate population immunity across age-groups and geography, and identify any groups at increased risk of varicella infection. RESULTS: The weighted proportion of specimens with antibody levels above the threshold of protection was 93.6% (95% CI: 92.4, 95.0). Protection was lowest among those aged 3-5 years (54.3%; 95% CI: 47.3, 61.4), but increased with age. Individuals born outside Canada had more than twice the odds of varicella susceptibility than those born in Canada (aOR: 2.7; 95% CI: 1.4, 5.0; p = 0.004). There were no differences by sex or geography within Canada, and there were no statistically significant differences when Ontario CHMS sera were compared to Ontario residual sera, apart from in participants aged 12-19 year age-group, for whom the CHMS estimate (91.2%; 95% CI: 86.7, 95.7) was significantly higher (p = 0.03) than that from residual specimens (85.9%, 95% CI: 81.1, 90.8). DISCUSSION: Varicella immunity in Canada is changing. Children appear to have low population immunity, placing them at greater risk of infection and at increased risk of severe disease as they age. Our results underscore the importance of performing periodic serosurveys to monitor further population immunity changes as the proportion of vaccine-eligible birth-cohorts increases, and to continually assess the risk of outbreaks.

COVID-19 farm outbreaks in Ontario, January-December 2020.

Background: Farm workers are critical to Ontario's food supply chain as they grow and harvest the food that Ontario relies on; however, they are subject to several occupation-related coronavirus disease 2019 (COVID-19) transmission risk factors. We describe the epidemiology of farm outbreaks in Ontario over the first calendar year of the pandemic and explore trends in outbreaks by season and type of farm. Methods: Data pertaining to farm outbreaks in Ontario from January 1 to December 31, 2020, and their associated laboratory-confirmed cases were extracted from the provincial database. Outbreaks were characterized by size, season, farm type and duration. Cases were characterized by age, gender, medical risk factors, clinical presentation and outcomes. Results: There were 64 farm outbreaks associated with 2,202 confirmed cases of COVID-19 in Ontario during 2020. The majority of outbreaks occurred in spring (n=25, 39.1%) and fall (n=25, 39.1%). The fewest outbreaks occurred in the summer (n=6, 9.4%), corresponding with low community rates during that time, and the majority of these were in greenhouse farms (n=5, 83.3%). The median outbreak size was 14.5 cases (range: 1-240), and the median duration was 23 days (range: 0-128). Among cases, most were male (83.2%), the median age was 35 years, 10.0% had one or more comorbidities, 31.2% were asymptomatic, 16 required hospitalization and three died. Conclusion: Farm outbreaks were a source of COVID-19 transmission and illness in 2020, particularly in the spring and fall. Outbreaks continued in greenhouse farms despite lower summer community transmission.

Natural Language Processing for Clinical Laboratory Data Repository Systems: Implementation and Evaluation for Respiratory Viruses.

BACKGROUND: With the growing volume and complexity of laboratory repositories, it has become tedious to parse unstructured data into structured and tabulated formats for secondary uses such as decision support, quality assurance, and outcome analysis. However, advances in natural language processing (NLP) approaches have enabled efficient and automated extraction of clinically meaningful medical concepts from unstructured reports. OBJECTIVE: In this study, we aimed to determine the feasibility of using the NLP model for information extraction as an alternative approach to a time-consuming and operationally resource-intensive handcrafted rule-based tool. Therefore, we sought to develop and evaluate a deep learning-based NLP model to derive knowledge and extract information from text-based laboratory reports sourced from a provincial laboratory repository system. METHODS: The NLP model, a hierarchical multilabel classifier, was trained on a corpus of laboratory reports covering testing for 14 different respiratory viruses and viral subtypes. The corpus includes 87,500 unique laboratory reports annotated by 8 subject matter experts (SMEs). The classification task involved assigning the laboratory reports to labels at 2 levels: 24 fine-grained labels in level 1 and 6 coarse-grained labels in level 2. A "label" also refers to the status of a specific virus or strain being tested or detected (eg, influenza A is detected). The model's performance stability and variation were analyzed across all labels in the classification task. Additionally, the model's generalizability was evaluated internally and externally on various test sets. RESULTS: Overall, the NLP model performed well on internal, out-of-time (pre-COVID-19), and external (different laboratories) test sets with microaveraged F1-scores >94% across all classes. Higher precision and recall scores with less variability were observed for the internal and pre-COVID-19 test sets. As expected, the model's performance varied across categories and virus types due to the imbalanced nature of the corpus and sample sizes per class. There were intrinsically fewer classes of viruses being detected than those tested; therefore, the model's performance (lowest F1-score of 57%) was noticeably lower in the detected cases. CONCLUSIONS: We demonstrated that deep learning-based NLP models are promising solutions for information extraction from text-based laboratory reports. These approaches enable scalable, timely, and practical access to high-quality and encoded laboratory data if integrated into laboratory information system repositories.