Emerging studies across the globe are reporting the impact of COVID-19 and its related virus containment measures, such as school closures and social distancing, on the mental health presentations and service utilization of children and youth during the early stages of lockdowns in their respective countries. However, there remains a need for studies which examine the impact of COVID-19 on children and youth's mental health needs and service utilization across multiple waves of the pandemic. The present study used data from 35,162 interRAI Child and Youth Mental Health (ChYMH) assessments across 53 participating mental health agencies in Ontario, Canada, to assess the mental health presentations and referral trends of children and youth across the first two waves of the COVID-19 pandemic in the province. Wave 1 consisted of data from March to June 2020, with Wave 2 consisting of data from September 2020 to January 2021. Data from each wave were compared to each other and to the equivalent period one year prior. While assessment volumes declined during both pandemic waves, during the second wave, child and youth assessments in low-income neighborhoods declined more than those within high-income neighborhoods. There were changes in family stressors noted in both waves. Notably, the proportion of children exposed to domestic violence and recent parental stressors increased in both waves of the pandemic, whereas there were decreases noted in the proportion of parents expressing feelings of distress, anger, or depression and reporting recent family involvement with child protection services. When comparing the two waves, while depressive symptoms and recent self-injurious attempts were more prevalent in the second wave of the pandemic when compared to the first, a decrease was noted in the prevalence of disruptive/aggressive behaviors and risk of injury to others from Wave 1 to Wave 2. These findings highlight the multifaceted impact of multiple pandemic waves on children and youth's mental health needs and underscore the need for future research into factors impacting children and youth's access to mental health agencies during this time.

This dataset compiles daily counts of patients (both COVID-related and non-COVID-related) in adult and pediatric ICU beds and the number of adult and pediatric ICU beds that are unoccupied.

**Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool **

Data includes:

• date
• number of adults in ICU for COVID-related critical illness (CRCI)_**_
• number of adults in ICU for non-CRCI reasons
• number of adult ICU beds that are unoccupied
• total number of adults in ICU for any reason
• number of patients in pediatric ICU for COVID-related critical illness (CRCI)_**_
• number of patients in pediatric ICU beds for non-CRCI reasons
• number of pediatric ICU beds that are unoccupied
• total number of patients in pediatric ICU beds for any reason

**These results may not match the CRCI cases in ICU reported elsewhere (on Ontario.ca) as they are restricted to either adults only or pediatric patients only and do not include cases in other ICU bed types.

• ICU data includes patients in levels 2 and 3 adult or pediatric ICU beds. The reported numbers reflect the previous day’s values. Patients are counted at a single point in time (11:59 pm) to ensure that each person is only counted once, and their COVID status is updated at 6 am, prior to posting. This may vary slightly from similar sources who update at different times.
• COVID-related critical illness (CRCI) includes patients currently testing positive for COVID and patients in ICU due to COVID who are no longer testing positive for COVID.
• Since the start of the pandemic, the province has invested in “incremental” ICU beds to accommodate potential surges in ICU demand due to COVID. These beds were added at various points in time (i.e., October 2020, February 2021, April 2021) to ensure system preparedness and meet operational needs. Aligned with the decline of Wave 3 and COVID-related pressures and at the direction of Ontario Health, a number of these beds were brought offline in July 2021. These events account for the sudden increases and/or decreases in ICU beds seen in the data. The number of ICU beds continues to fluctuate slightly as beds are brought on and offline to meet localized demands/need.

Modifications to this data

Data for the period of October 24, 2023 to March 24, 2024 excludes hospitals in the West region who were experiencing data availability issues.

Daily adult, pediatric, and neonatal patient ICU census data were impacted by technical issues between September 9 and October 20, 2023. As a result, when public reporting resumes on November 16, 2023, historical ICU data for this time period will be excluded.

January 18, 2022: Information on pediatric ICU beds was added to the file for the period of May 2020 to present.

January 7, 2022: Due to some methodology changes, historical data were impacted during the following timeframes:

• May 1, 2020 to October 22, 2020.
• February 19, 2021 to July 26, 2021.

How the data was impacted

To ensure system preparedness throughout the pandemic, hospitals were asked to identify the number of beds (i.e., non-ICU beds) and related resources that could be made available within 24 hours for use as an ICU bed in case of a surge in COVID patients. These beds were considered expanded ICU capacity and were not used to calculate hospitals’ ICU occupancy. These beds were previously included in this data.

The current numbers include only funded ICU beds based on data from the Critical Care Information System (CCIS).

BackgroundDuring the COVID-19 pandemic, non-urgent surgeries were delayed in order to increase the capacity to care for patients with COVID-19. To shed light on the effect of pandemic-related surgical ramp down on the quality of surgical care, this study compared Ontario with Alberta on (1) changes in the proportion of completion and wait time of surgeries with decision-to-treat in a pre-pandemic period compared to those with decision-to-treat in each of the four COVID-19 waves and (2) shifts in healthcare utilization and safety of surgical patients for the same time periods.MethodsA retrospective population-based cohort study was conducted in Ontario on scheduled non-urgent surgeries among adults with decision-to-treat (index dates) between January 1, 2018 and December 31, 2021. Logistic regression was used to examine surgery completion (observed up to December 31, 2021) on the index date period (each COVID-19 wave vs. pre-pandemic). For completed surgeries, median regression was used to assess wait time on the index date period. Descriptive statistics were provided on healthcare utilization and safety indicators among the cohort. Results from regression models and descriptive statistics were then compared with published data from Alberta.ResultsThere were 2,073,688 non-urgent surgeries scheduled for 1,560,265 unique adults in Ontario. Surgeries with an index date in each COVID-19 wave were associated with lower odds of completion compared to the pre-pandemic period, which is in contrast to Alberta where the odds of having surgery completed was not lower during the pandemic than pre-pandemic. Among completed surgeries (91.7%) in Ontario, the median wait time was shorter for surgeries with an index date in waves 2 and 4 than in the pre-pandemic period, while in Alberta the median wait time was shorter for surgeries with index dates in waves 2–4 than pre-pandemic. During the pandemic, Alberta reported a decrease in median intensive care unit (ICU) hours and hospital length of stay for patients relative to pre-pandemic, while Ontario reported an increase in median ICU hours of these patients.ConclusionsThese findings highlight interprovincial differences in surgical care which might be related to COVID-19 policies in each province, healthcare system capacity and patient demographics.

Daily number of Ottawa residents tested for COVID-19 and the percentage of residents tested with laboratory-confirmed COVID-19. Data are based on information extracted from the Ontario Laboratories Information System (OLIS) on Thursdays. Accuracy: Points of consideration for interpretation of the data: Not all labs report to OLIS and only patients with health card numbers are included in the OLIS dataset.Once an individual is confirmed positive, subsequent tests for that individual are excluded from the daily totals.Duplicate tests are excluded from the total number of positive tests, including those that arose from multiple tests of cure. Results for patients who placed an OLIS consent block (~50 records province-wide) are excluded. Staff working in long-term care homes are not captured in OLIS.City assignment in OLIS is based on the patient's health card address. Patients living in long-term care homes may not have the correct address associated with their health cards; therefore, not all tests in long-term care homes may be captured.Confirmed cases are those with a confirmed COVID-19 laboratory result as per the Ministry of Health Public health management of cases and contacts of COVID-19 in Ontario. March 25, 2020 version 6.0.The province has had to limit testing to priority groups in the early stages of the pandemic. Since only a small fraction of all the persons who were infected with the COVID-19 virus were tested, the number of reported confirmed community cases underestimates the actual number of infections. Information on overall infection rates in Canada will not be available until large studies on COVID-19 antibody presence in blood serum are conducted. Based on available information, the actual number of infections may lie from 5 to 30 times or more than the reported number of cases (1).Surveillance testing for COVID-19 began in long term care facilities on April 25, 2020.Reference: Richterich P. Severe underestimation of COVID-19 case numbers: Effect of epidemic growth rate and test restrictions. medRxiv. April 2020: 2020.04.13. doi.org/10.1101/2020.04.13.20064220Update Frequency: ThursdaysAttributes: Data fields: Date – date of the test (YYYY-MM-DD).Number of tests – number of Ottawa residents tested for COVID-19Daily % Positivity – number of Ottawa residents tested on that day that received a positive test result for COVID-19 divided by the total number of Ottawa residents tested on that dayNumber of tests in LTCH– number of Ottawa residents in long-term care homes who were tested for COVID-19LTCH Daily % Positivity – number of Ottawa residents in long-term care homes tested on that day that received a positive test result for COVID-19 divided by the total number of Ottawa residents in long-term care homes tested on that dayContact: OPH Epidemiology Team | Epidemiology & Evidence, Ottawa Public Health

Data and Stata code for recreation of "Impact of Adjustment for Differential Testing by Age and Sex on Apparent Epidemiology of SARS-CoV-2 Infection in Ontario, Canada". For questions about analysis please contact me directly at david.fisman@gmail.com or david.fisman@utoronto.ca.Paper abstract: Surveillance of communicable diseases typically relies on case counts for estimates of risk, and counts can be strongly influenced by testing rates. In the Canadian province of Ontario, testing rates varied markedly by age, sex, geography and time over the course of the SARS-CoV-2 pandemic. We applied a standardization-based approach to test-adjustment to better understand pandemic dynamics from 2020 to 2022, and to better understand when test-adjustment is necessary for accurate estimation of risk. SARS-CoV-2 case counts by age, sex, public health unit and week were obtained from Ontario’s Case and Contact Management system (CCM), which includes all SARS-CoV-2 cases from March 2020 to August 2022. Complete data on testing volumes was obtained from the Ontario Laboratory Information System (OLIS). Case counts were adjusted for under-testing using a previously published standardization-based approach that estimates case numbers that would have been expected if the entire population was tested at the same rate as most-tested age and sex groups. Logistic regression was used to identify threshold testing rates beyond which test-adjustment was unnecessary. Testing rates varied markedly by age, sex, public health unit and pandemic wave. After adjustment for under-testing, overall case counts increased threefold. Adjusted epidemic curves suggested, in contrast to reported case counts, that the first two pandemic waves were equivalent in size, and that there were three distinct pandemic waves in 2022, due to the emergence of Omicron variants. Under-reporting was greatest in children and young males, and varied significantly across public health units, with variation explained partly by testing rates and prevalence of multigenerational households. Test adjustment resulted in little change in the epidemic curve during pandemic waves when testing rates were highest; we found that test-adjustment did not increase case counts once weekly per capita testing rates exceeded 6.3%. We conclude that standardization-based adjustment for differential testing by age and sex, and for dynamic changes in testing over time, results in a different picture of infection risk during the SARS-CoV-2 pandemic in Ontario; test-adjusted epidemic curves are concordant with observed patterns of mortality during the pandemic and have face validity. This methodology offers an alternative to sero-epidemiology for identification of true burden of infection when reinfection, sero-reversion, and non-specificity of serological assays make sero-epidemiology challenging.Update, December 15, 2023This data source is being updated in relation to work in progress showing the importance of test-adjusting for accurate estimation of the impacts of community masking mandates, as were introduced in Ontario in summer 2020 (see https://www.medrxiv.org/content/10.1101/2023.07.26.23293155v1). New files include a dataset that can be used to run updated analyses, Stata macros that create test-adjusted case counts by public health unit, age group, gender and week, and a spreadsheet that shows the estimated impact of mask mandates as compared to a counterfactual where they were not introduced.

Objective: This study aimed to explore topics and sentiments using tweets from Ontario, Canada, during the second wave of the COVID-19 pandemic.Methods: Tweets were collected from December 5, 2020, to March 6, 2021, excluding non-individual accounts. Dates of vaccine-related events and policy changes were collected from public health units in Ontario. The daily number of COVID-19 cases was retrieved from the Ontario provincial government’s public health database. Latent Dirichlet Allocation was used for unsupervised topic modelling. VADER was used to calculate daily and average sentiment compound scores for topics identified.Results: Vaccine, pandemic, business, lockdown, mask, and Ontario were six topics identified from the unsupervised topic modelling. The average sentiment compound score for each topic appeared to be slightly positive, yet the daily sentiment compound scores varied greatly between positive and negative emotions for each topic.Conclusion: Our study results have shown a slightly positive sentiment on average during the second wave of the COVID-19 pandemic in Ontario, along with six topics. Our research has also demonstrated a social listening approach to identify what the public sentiments and opinions are in a timely manner.

Tracking parents’ mental health symptoms and understanding barriers to seeking professional help are critical for determining policies and services to support families’ well-being. The COVID-19 pandemic has posed enormous challenges to parents’ mental health and the access to professional help, and there are important public health lessons that must be learned from the past 2 years’ experiences to inform future mental health responses to social- and family-level stressful events. This study examines the trajectories of parents’ depressive and anxiety symptoms over a year during the pandemic as related to their mental health help-seeking. Data were collected from a sample of parents residing in Ontario, Canada at baseline (May–June, 2020; Wave 1) and again 1 year later (Wave 2; referred to as W1 and W2 below). Parents (n = 2,439; Mage = 39.47, SD = 6.65; 95.0% females) reported their depressive and anxiety symptoms at both waves. Mental health help-seeking, including self-reported contact with professional help and perceived unmet mental health needs, was measured at W2. Parents were classified into four groups by mental health help-seeking. Inconsistent seekers and non-seeking needers, both reporting perceived unmet needs for professional help, showed greater increases in depressive and anxiety symptoms, whereas parents with no need or needs met showed smaller increases in depressive symptoms and decreases in anxiety symptoms. Belief in self-reliance and time constraints were the leading reasons for not seeking help. These findings suggest that over a year into the pandemic, parents with perceived unmet mental health needs were at greater risk for worsening depressive and anxiety symptoms. Recognizing the demands for mental health services when families experience chronic stressors and targeting the identified barriers may promote family well-being during and beyond this pandemic.

Comparison of patients with a delayed discharged by time period (pre vs onset of COVID-19), Ontario, Canadaa.

Healthcare utilization and mortality following a delayed discharged in acute care, stratified by discharge destination and time period (pre vs onset of COVID-19), Ontario, Canada.

ObjectiveThis cross-sectional study examined the self-perceived impact of the COVID-19 pandemic on 2,378 education workers in Ontario, Canada, during the second wave.MethodsWe examined six domains of functioning as per the short version of the World Health Organization Disability Assessment Schedule-2.0. Participants selected if their functioning had improved, remained unchanged or worsened during the pandemic for each item.ResultsEducational workers described a general worsening of functional activities since the beginning of the pandemic. Moderate-to-extreme challenges were reported for all six functional domains. These challenges appeared to aggravate functional challenges for workers with disability, as indicated by pre-existing work accommodations. Older participants reported worse mobility than younger participants; however, they appeared to have better coping skills in learning new tasks and maintaining friendships. Women were more likely to report difficulties in maintaining household responsibilities.ConclusionsWe consider the role of mental health challenges and pre-existing inequality as predictors of pandemic-related difficulties. Recommendations include more longitudinal research in this population and policymakers to incorporate a health promotion lens to support their education workers more proactively.