[Healthcare Policy, August 2023]

Background: This paper aims to assess the extent to which the COVID-19 vaccine's speed to market affected Canadian residents' decision to remain unvaccinated.

Method: A cross-sectional survey conducted in late 2021 asked participants whether they had received the vaccine and their reasons for abstaining.

Results: Of the 2,712 participants who completed the survey, 8.9% remained unvaccinated. Unvaccinated respondents who selected “They made the vaccine too fast” (59.8%), were significantly more likely to identify as white, believe that the COVID-19 pandemic was not serious and have an unvaccinated social circle.

Conclusion: Should the COVID-19 vaccine rapid regulatory process be expanded, more patients may refuse treatment than if traditional timelines are followed.

[International Journal of Population Data Science, 26 October 2023]

Alberta has rich clinical and health services data held under the custodianship of Alberta Health and Alberta Health Services (AHS), which is not only used for clinical and administrative purposes but also disease surveillance and epidemiological research. Alberta is the largest province in Canada with a single payer centralised health system, AHS, and a consolidated data and analytics team supporting researchers across the province.

This paper describes Alberta's data custodians, data governance mechanisms, and streamlined processes followed for research data access. AHS has created a centralised data repository from multiple sources, including practitioner claims data, hospital discharge data, and medications dispensed, available for research use through the provincial Data and Research Services (DRS) team. The DRS team is integrated within AHS to support researchers across the province with their data extraction and linkage requests. Furthermore, streamlined processes have been established, including: 1) ethics approval from a research ethics board, 2) any necessary operational approvals from AHS, and 3) a tripartite legal agreement dictating terms and conditions for data use, disclosure, and retention. This allows researchers to gain timely access to data. To meet the evolving and ever-expanding big-data needs, the University of Calgary, in partnership with AHS, has built high-performance computing (HPC) infrastructure to facilitate storage and processing of large datasets. When releasing data to researchers, the analytics team ensures that Alberta's Health Information Act's guiding principles are followed. The principal investigator also ensures data retention and disposition are according to the plan specified in ethics and per the terms set out by funding agencies.

Even though there are disparities and variations in the data protection laws across the different provinces in Canada, the streamlined processes for research data access in Alberta are highly efficient.

[Water Research, 8 August 2023]

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites – regardless of several normalization strategies – with certain catchments consistently demonstrating values 1–2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.

[Science of The Total Environment, 26 June 2023]

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's 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 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 (1.4 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 4524 unrelated absences COVID-19 cases were recorded. 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). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (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.

[Vaccine, 6 February 2024]

Background: Social networks have an important impact on our health behaviours, including vaccination. People’s vaccination beliefs tend to mirror those of their social network. As social networks are homogenous in many ways, we sought to determine in the context of COVID-19 which factors were most predictive of belonging to a mostly vaccinated or unvaccinated social group.

Methods: We conducted a cross-sectional survey among Canadian residents in November and December 2021. Participants were asked about the vaccination status of their social networks their beliefs relating to COVID-19, and various sociodemographic factors. Respondents were split into three groups based on social network vaccination: low-, medium-, and high-risk. Chi-squared tests tested associations between factors and risk groups, and an ordinal logistic model was created to determine their direction and strength.

Results: Most respondents (81.1 %) were classified as low risk (i.e., a mostly vaccinated social network) and few respondents (3.7 %) were classified as high-risk (i.e., an unvaccinated social group). Both the chi-square test (29.2 % difference between the low- and high- risk groups [1.8 % vs. 31.0 %], p < 0.001) and the ordinal logistic model (odds ratio between the low- and high-risk groups: 14.45, p < 0.01) found that respondents’ perceptions of COVID-19 as a “not at all serious” risk to Canadians was the most powerful predictor of belonging to a predominantly unvaccinated social circle. The model also found that those in mostly unvaccinated social circles also more often reported severe COVID-19 symptoms (odds ratio between the low- and high-risk groups: 2.26, p < 0.05).

Conclusion: Perception of COVID-19 as a threat to others may signal communities with lower vaccination coverage and higher risk of severe outcomes. This may have implications for strategies to improve public outreach, messaging, and planning for downstream consequences of low intervention uptake.

[Statistics in Medicine, 14 January 2024]

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.