An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa, Canada

Monitoring of viral signal in wastewater is considered a useful tool for monitoring the burden of COVID-19, especially during times of limited availability in testing. Studies have shown that COVID-19 hospitalizations are highly correlated with wastewater viral signals and the increases in wastewater viral signals can provide an early warning for increasing hospital admissions. The association is likely nonlinear and time-varying. This project employs a distributed lag nonlinear model (DLNM) (Gasparrini et al., 2010) to study the nonlinear exposure-response delayed association of the COVID-19 hospitalizations and SARS-CoV-2 wastewater viral signals using relevant data from Ottawa, Canada. We consider up to a 15-day time lag from the average of SARS-CoV N1 and N2 gene concentrations to COVID-19 hospitalizations. The expected reduction in hospitalization is adjusted for vaccination efforts. A correlation analysis of the data verifies that COVID-19 hospitalizations are highly correlated with wastewater viral signals with a time-varying relationship. Our DLNM based analysis yields a reasonable estimate of COVID-19 hospitalizations and enhances our understanding of the association of COVID-19 hospitalizations with wastewater viral signals.

Population-Level Trends in Emergency Department Encounters for Sexual Assault Preceding and During the COVID-19 Pandemic Across Ontario, Canada.

Importance: Lockdown measures and the stress of the COVID-19 pandemic are factors associated with increased risk of violence, yet there is limited information on trends in emergency department (ED) encounters for sexual assault. Objective: To compare changes in ED encounters for sexual assault during the COVID-19 pandemic vs prepandemic estimates. Design, Setting, and Participants: This retrospective, population-based cohort study used linked health administrative data from 197 EDs across Ontario, Canada, representing more than 15 million residents. Participants included all patients who presented to an ED in Ontario from January 11, 2019, to September 10, 2021. Male and female individuals of all ages were included. Data analysis was performed from March to October 2022. Exposures: Sexual assault, defined through 27 International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, procedure and diagnoses codes. Main Outcomes and Measures: Ten bimonthly time periods were used to compare differences in the frequency and rates of ED encounters for sexual assault between 2020 to 2021 (during the pandemic) compared with baseline prepandemic rates in 2019. Rate differences (RDs) and age adjusted rate ratios (aRRs) and Wald 95% CIs were calculated using Poisson regression. Results: From January 11, 2019, to September 10, 2021, there were 14 476 656 ED encounters, including 10 523 for sexual assault (9304 [88.4%] among female individuals). The median (IQR) age was 23 (17-33) years for female individuals and 15 (4-29) years for male individuals. Two months before the pandemic, ED encounters increased for sexual assault among female individuals (8.4 vs 6.9 cases per 100 000; RD, 1.51 [95% CI, 1.06 to 1.96]; aRR, 1.22 [95% CI, 1.09 to 1.38]) and male individuals (1.2 vs 1.0 cases per 100 000; RD, 0.19 [95% CI, 0.05 to 0.36]; aRR, 1.19 [95% CI, 0.87 to 1.64]). During the first 2 months of the pandemic, the rates decreased for female individuals (4.2 vs 8.3 cases per 100 000; RD, -4.07 [95% CI, -4.48 to -3.67]; aRR, 0.51 [95% CI, 0.44 to 0.58]) and male individuals (0.5 vs 1.2 cases per 100 000; RD, -0.72 [95% CI, -0.86 to -0.57]; aRR, 0.39 [95% CI, 0.26 to 0.58]). For the remainder of the study period, the rates of sexual assault oscillated, returning to prepandemic levels during the summer months and between COVID-19 waves. Conclusions and Relevance: These findings suggest that lockdown protocols should evaluate the impact of limited care for sexual assault. Survivors should still present to EDs, especially when clinical care or legal interventions are needed.

Trends in all-cause mortality and inpatient and outpatient visits for ambulatory care sensitive conditions during the first year of the COVID-19 pandemic: A population-based study.

BACKGROUND: The impact of the COVID-19 pandemic on the management of ambulatory care sensitive conditions (ACSCs) remains unknown. OBJECTIVES: To compare observed and expected (projected based on previous years) trends in all-cause mortality and healthcare use for ACSCs in the first year of the pandemic (March 2020 to March 2021). DESIGN, SETTING AND PARTICIPANTS: We conducted a population-based study using provincial health administrative data on general adul population (Ontario, Canada). OUTCOMES AND MEASURES: Monthly all-cause mortality, and hospitalizations, emergency department (ED) and outpatient visit rates (per 100,000 people at-risk) for seven combined ACSCs (asthma, chronic obstructive pulmonary disease, angina, congestive heart failure, hypertension, diabetes, and epilepsy) during the first year were compared with similar periods in previous years (2016-2019) by fitting monthly time series autoregressive integrated moving-average models. RESULTS: Compared to previous years, all-cause mortality rates increased at the beginning of the pandemic (observed rate in March to May 2020 of 79.98 vs. projected of 71.24 [66.35-76.50]) and then returned to expected in June 2020-except among immigrants and people with mental health conditions where they remained elevated. Hospitalization and ED visit rates for ACSCs remained lower than projected throughout the first year: observed hospitalization rate of 37.29 versus projected of 52.07 (47.84-56.68); observed ED visit rate of 92.55 versus projected of 134.72 (124.89-145.33). ACSC outpatient visit rates decreased initially (observed rate of 4299.57 vs. projected of 5060.23 [4712.64-5433.46]) and then returned to expected in June 2020.

The Collapse of Infectious Disease Diagnoses Commonly Due to Communicable Respiratory Pathogens During the Coronavirus Disease 2019 Pandemic: A Time Series and Hierarchical Clustering Analysis.

Background: Nonpharmaceutical interventions such as physical distancing and mandatory masking were adopted in many jurisdictions during the coronavirus disease 2019 pandemic to decrease spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We determined the effects of these interventions on incidence of healthcare utilization for other infectious diseases. Methods: Using a healthcare administrative dataset, we employed an interrupted time series analysis to measure changes in healthcare visits for various infectious diseases across the province of Ontario, Canada, from January 2017 to December 2020. We used a hierarchical clustering algorithm to group diagnoses that demonstrated similar patterns of change through the pandemic months. Results: We found that visits for infectious diseases commonly caused by communicable respiratory pathogens (eg, acute bronchitis, acute sinusitis) formed distinct clusters from diagnoses that often originate from pathogens derived from the patient's own flora (eg, urinary tract infection, cellulitis). Moreover, infectious diagnoses commonly arising from communicable respiratory pathogens (hierarchical cluster 1: highly impacted diagnoses) were significantly decreased, with a rate ratio (RR) of 0.35 (95% confidence interval [CI], .30-.40; P < .001) after the introduction of public health interventions in April-December 2020, whereas infections typically arising from the patient's own flora (hierarchical cluster 3: minimally impacted diagnoses) did not demonstrate a sustained change in incidence (RR, 0.95 [95% CI, .90-1.01]; P = .085). Conclusions: Public health measures to curtail the incidence of SARS-CoV-2 were widely effective against other communicable respiratory infectious diseases with similar modes of transmission but had little effect on infectious diseases not strongly dependent on person-to-person transmission.

A commentary on the value of hospital data for covid-19 pandemic surveillance and planning.

Hospital data for covid-19 surveillance, planning and modelling are challenging to find worldwide in public aggregation portals. Detailed covid-19 hospital data provides insights into covid-19's health burden including identifying which sociodemographic groups are at greatest risk of covid-19 morbidity and mortality. Timely hospital data is the best source of information for actionable forecasts and projection models of hospital capacity, including critical resources such as intensive care unit beds and ventilators that take time to plan or procure. A challenge to generate timely and detailed hospital data is the reliance on separation or discharge abstracts and census counts. What are needed are well-maintained lists of patients hospitalized with covid-19. From the standpoint of public health and health services researchers and practitioners, we describe the role of hospital data for studying covid-19, why admission data are hard to find, and how improved data infrastructure can meet surveillance and planning needs in the near future. Modern hospital electronic health records can create covid-19 patient lists and these decision support tools are increasingly used for research. These tools can generate patient lists that are transmitted and combined with public health data systems.

The Impact of COVID-19 on Outpatient Antibiotic Prescriptions in Ontario, Canada; An Interrupted Time Series Analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has potentially impacted outpatient antibiotic prescribing. Investigating this impact may identify stewardship opportunities in the ongoing COVID-19 period and beyond. METHODS: We conducted an interrupted time series analysis on outpatient antibiotic prescriptions and antibiotic prescriptions/patient visits in Ontario, Canada, between January 2017 and December 2020 to evaluate the impact of the COVID-19 pandemic on population-level antibiotic prescribing by prescriber specialty, patient demographics, and conditions. RESULTS: In the evaluated COVID-19 period (March-December 2020), there was a 31.2% (95% CI, 27.0% to 35.1%) relative reduction in total antibiotic prescriptions. Total outpatient antibiotic prescriptions decreased during the COVID-19 period by 37.1% (95% CI, 32.5% to 41.3%) among family physicians, 30.7% (95% CI, 25.8% to 35.2%) among subspecialist physicians, 12.1% (95% CI, 4.4% to 19.2%) among dentists, and 25.7% (95% CI, 21.4% to 29.8%) among other prescribers. Antibiotics indicated for respiratory infections decreased by 43.7% (95% CI, 38.4% to 48.6%). Total patient visits and visits for respiratory infections decreased by 10.7% (95% CI, 5.4% to 15.6%) and 49.9% (95% CI, 43.1% to 55.9%). Total antibiotic prescriptions/1000 visits decreased by 27.5% (95% CI, 21.5% to 33.0%), while antibiotics indicated for respiratory infections/1000 visits with respiratory infections only decreased by 6.8% (95% CI, 2.7% to 10.8%). CONCLUSIONS: The reduction in outpatient antibiotic prescribing during the COVID-19 pandemic was driven by less antibiotic prescribing for respiratory indications and largely explained by decreased visits for respiratory infections.

Catching a resurgence: Increase in SARS-CoV-2 viral RNA identified in wastewater 48 h before COVID-19 clinical tests and 96 h before hospitalizations.

Curtailing the Spring 2020 COVID-19 surge required sweeping and stringent interventions by governments across the world. Wastewater-based COVID-19 epidemiology programs have been initiated in many countries to provide public health agencies with a complementary disease tracking metric and non-discriminating surveillance tool. However, their efficacy in prospectively capturing resurgences following a period of low prevalence is unclear. In this study, the SARS-CoV-2 viral signal was measured in primary clarified sludge harvested every two days at the City of Ottawa's water resource recovery facility during the summer of 2020, when clinical testing recorded daily percent positivity below 1%. In late July, increases of >400% in normalized SARS-CoV-2 RNA signal in wastewater were identified 48 h prior to reported >300% increases in positive cases that were retrospectively attributed to community-acquired infections. During this resurgence period, SARS-CoV-2 RNA signal in wastewater preceded the reported >160% increase in community hospitalizations by approximately 96 h. This study supports wastewater-based COVID-19 surveillance of populations in augmenting the efficacy of diagnostic testing, which can suffer from sampling biases or timely reporting as in the case of hospitalization census.

Derivation and Internal Validation of a Model to Predict the Probability of Severe Acute Respiratory Syndrome Coronavirus-2 Infection in Community People.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease. There are concerns regarding limited testing capacity and the exclusion of cases from unproven screening criteria. Knowing COVID-19 risks can inform testing. This study derived and assessed a model to predict risk of SARS-CoV-2 in community-based people. METHODS: All people presenting to a community-based COVID-19 screening center answered questions regarding symptoms, possible exposure, travel, and occupation. These data were anonymously linked to SARS-CoV-2 testing results. Logistic regression was used to derive a model to predict SARS-CoV-2 infection. Bootstrap sampling evaluated the model. RESULTS: A total of 9172 consecutive people were studied. Overall infection rate was 6.2% but this varied during the study period. SARS-CoV-2 infection likelihood was primarily influenced by contact with a COVID-19 case, fever symptoms, and recent case detection rates. Internal validation found that the SARS-CoV-2 Risk Prediction Score (SCRiPS) performed well with good discrimination (c-statistic 0.736, 95%CI 0.715-0.757) and very good calibration (integrated calibration index 0.0083, 95%CI 0.0048-0.0131). Focusing testing on people whose expected SARS-CoV-2 risk equaled or exceeded the recent case detection rate would increase the number of identified SARS-CoV-2 cases by 63.1% (95%CI 54.5-72.3). CONCLUSION: The SCRiPS model accurately estimates the risk of SARS-CoV-2 infection in community-based people undergoing testing. Using SCRiPS can importantly increase SARS-CoV-2 infection identification when testing capacity is limited.