Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism.

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.

Dark clouds and silver linings: impact of COVID-19 on internet users' privacy.

OBJECTIVES: To examine the impact of coronavirus disease 2019 (COVID-19) pandemic on the extent of potential violations of Internet users' privacy. MATERIALS AND METHODS: We conducted a longitudinal study of the data sharing practices of the top 1000 websites in the United States between April 9 and August 27, 2020. We fitted a conditional latent growth curve model on the data to examine the longitudinal trajectory of the third-party data sharing over the 21 weeks period of the study and examine how website characteristics affect this trajectory. We denote websites that asked for permission before placing cookies on users' browsers as "privacy-respecting." RESULTS: As the weekly number of COVID-19 deaths increased by 1000, the average number of third parties increased by 0.26 (95% confidence interval [CI] 0.15-0.37) P < 0.001 units in the next week. This effect was more pronounced for websites with higher traffic as they increased their third parties by an additional 0.41 (95% CI 0.18-0.64); P < 0.001 units per week. However, privacy respecting websites that experienced a surge in traffic reduced their third parties by 1.01 (95% CI -2.01 to 0); P = 0.05 units per week in response to every 1000 COVID-19 deaths in the preceding week. DISCUSSION: While in general websites shared their users' data with more third parties as COVID-19 progressed in the United States, websites' expected traffic and respect for users' privacy significantly affect such trajectory. CONCLUSIONS: Attention should also be paid to the impact of the pandemic on elevating online privacy threats, and the variation in third-party tracking among different types of websites.

Improving outpatient health care quality: understanding the quality dimensions.

Based on literature review, we derive a set of dimensions that influence patient-perceived health care quality. Utilizing outpatient survey data from 222 different physicians, we identified six underlying quality factors and classified them according to the derived dimensions. These quality factors explain approximately 51 percent of the variation in overall patient-perceived health care quality.