IntroductionThe unprecedented COVID-19 pandemic unveiled a strong need for advanced and informative surveillance tools. The Centre for Health Informatics (CHI) at the University of Calgary took action to develop a surveillance dashboard, which would facilitate the education of the public, and answer critical questions posed by local and national government. ObjectivesThe objective of this study was to create an interactive method of surveillance, or a “COVID-19 Tracker” for Canadian use. The Tracker offers user-friendly graphics characterizing various aspects of the current pandemic (e.g. case count, testing, hospitalizations, and policy interventions). MethodsSix publicly available data sources were used, and were selected based on the frequency of updates, accuracy and types of data, and data presentation. The datasets have different levels of granularity for different provinces, which limits the information that we are able to show. Additionally, some datasets have missing entries, for which the “last observation carried forward” method was used. The website was created and hosted online, with a backend server, which is updated on a daily basis. The Tracker development followed an iterative process, as new figures were added to meet the changing needs of policy-makers. ResultsThe resulting Tracker is a dashboard that visualizes real-time data, along with policy interventions from various countries, via user-friendly graphs with a hover option that reveals detailed information. The interactive features allow the user to customize the figures by jurisdiction, country/region, and the type of data shown. Data is displayed at the national and provincial level, as well as by health regions. ConclusionsThe COVID-19 Tracker offers real-time, detailed, and interactive visualizations that have the potential to shape crucial decision-making and inform Albertans and Canadians of the current pandemic.
Wastewater-based surveillance (WBS) is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19 impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with local workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.3 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5% (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4,524 unrelated absences COVID-19 cases were recorded. Employee absences significantly increased as wastewater signal increased through pandemic waves. Strong correlations between COVID-19-confirmed absences and wastewater SARS-CoV-2 signals (N1 gene: r=0.824, p<0.0001 and N2 gene: r=0.826, p<0.0001) were observed. Linear regression with adjusted R2-value demonstrated a robust association (adjusted R2=0.783), when adjusted by 7 days, indicating wastewater provides a one-week leading signal. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P<0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.
ObjectiveCoding of obesity using the International Classification of Diseases (ICD) in healthcare administrative databases is under‐reported and thus, unreliable for measuring prevalence or incidence. This study aimed to develop and test a rule‐based algorithm for automating detection and severity of obesity using height and weight collected in several sections of the Electronic Medical Records (EMR).MethodsIn this cross‐sectional study, 1904 inpatient charts randomly selected in three hospitals in Calgary, Canada between January and June 2015 were reviewed and linked with AllScripts Sunrise Clinical Manager EMRs. A rule‐based algorithm was created which looks for patients' height and weight values recorded in EMRs. Clinical notes were split into sentences and searched for height and weight, and BMI was computed.ResultsThe study cohort consisted of 1904 patients with 50.8% female and 43.3% > 64 years of age. Final model to identify obesity within EMRs resulted in a sensitivity of 92.9%, specificity of 98.4%, positive predictive value of 96.7%, negative predictive value of 96.6%, and an F1 score of 94.8%.ConclusionsThis study developed a highly valid rule‐based EMR algorithm that detects height and weight. This could allow large scale analyses using obesity that were previously not possible.This article is protected by copyright. All rights reserved.
Wastewater-based epidemiology (WBE) is an emerging surveillance tool that has been used to monitor the ongoing COVID-19 pandemic by tracking SARS-CoV-2 RNA shed into wastewater. WBE was performed to monitor the occurrence and spread of SARS-CoV-2 from three wastewater treatment plants (WWTP) and six neighborhoods in the city of Calgary, Canada (population 1.3 million). A total of 222 WWTP and 192 neighborhood samples were collected from June 2020 to May 2021, encompassing the end of the first-wave (June 2020), the second-wave (November end to December, 2020) and the third-wave of the COVID-19 pandemic (mid-April to May, 2021). Flow-weighted 24-hour composite samples were processed to extract RNA that was then analyzed for two SARS-CoV-2-specific regions of the nucleocapsid gene, N1 and N2, using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Using this approach SARS-CoV-2 RNA was detected in 98.06 % (406/414) of wastewater samples. SARS-CoV-2 RNA abundance was compared to clinically diagnosed COVID-19 cases organized by the three-digit postal code of affected individuals’ primary residences, enabling correlation analysis at neighborhood, WWTP and city-wide scales. Strong correlations were observed between N1 & N2 gene signals in wastewater and new daily cases for WWTPs and neighborhoods. Similarly, when flow rates at Calgary’s three WWTPs were used to normalize observed concentrations of SARS-CoV-2 RNA and combine them into a city-wide signal, this was strongly correlated with regionally diagnosed COVID-19 cases and clinical test percent positivity rate. Linked census data demonstrated disproportionate SARS-CoV-2 in wastewater from areas of the city with lower socioeconomic status and more racialized communities. WBE across a range of urban scales was demonstrated to be an effective mechanism of COVID-19 surveillance.
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