Web and phone-based COVID-19 syndromic surveillance in Canada: A cross-sectional study

Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Comparison of Influenza-Like Illness (ILI) incidence data from the novel LeCellPHIA participatory surveillance system with COVID-19 case count data, Lesotho, July 2020 - July 2021

BACKGROUND

While laboratory testing for infectious diseases such as COVID-19 is the surveillance gold standard, it is not always feasible, particularly in settings where resources are scarce. In the small country of Lesotho, located in sub-Saharan Africa, COVID-19 testing has been limited, thus surveillance data available to local authorities are limited. The goal of this study was to compare a participatory influenza-like illness (ILI) surveillance system in Lesotho with COVID-19 case count data, and ultimately to determine whether the participatory surveillance system adequately estimates the case count data.

METHODS

A nationally-representative sample was called on their mobile phones weekly to create an estimate of incidence of ILI between July 2020 and July 2021. Case counts from the website Our World in Data (OWID) were used as the gold standard to which our participatory surveillance data were compared. We calculated Spearman's and Pearson's correlation coefficients to compare the weekly incidence of ILI reports to COVID-19 case count data.

RESULTS

Over course of the study period, an ILI symptom was reported 1,085 times via participatory surveillance for an average annual cumulative incidence of 45.7 per 100 people (95% Confidence Interval [CI]: 40.7 - 51.4). The cumulative incidence of reports of ILI symptoms was similar among males (46.5, 95% CI: 39.6 - 54.4) and females (45.1, 95% CI: 39.8 - 51.1). There was a slightly higher annual cumulative incidence of ILI among persons living in peri-urban (49.5, 95% CI: 31.7 - 77.3) and urban settings compared to rural areas. The January peak of the participatory surveillance system ILI estimates correlated significantly with the January peak of the COVID-19 case count data (Spearman's correlation coefficient = 0.49; P < 0.001) (Pearson's correlation coefficient = 0.67; P < 0.0001).

CONCLUSIONS

The ILI trends captured by the participatory surveillance system in Lesotho mirrored trends of the COVID-19 case count data from Our World in Data. Public health practitioners in geographies that lack the resources to conduct direct surveillance of infectious diseases may be able to use cell phone-based data collection to monitor trends.

Passive Monitoring of Crowd-Level Cough Counts in Waiting Areas produces Reliable Syndromic Indicator for Total COVID-19 Burden in a Hospital Emergency Clinic

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Our findings highlight the efficacy of aggregated cough count as a valuable syndromic indicator associated with the occurrence of COVID-19 cases. Incorporating this signal into syndromic surveillance systems for such diseases can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

COVID-19 positive cases among asymptomatic individuals during the second wave in Ndola, Zambia

Background

Coronavirus disease 2019 (COVID-19) is a worldwide public health concern for healthcare workers. About 80% of cases appear to be asymptomatic, and about 3% may experience hospitalisation and later die. Less than 20% of studies have looked at the positivity rate of asymptomatic individuals.

Objective

This study investigated the COVID-19 positivity rates among asymptomatic individuals during the second COVID-19 wave at one of Zambia's largest testing centre.

Methods

This was a retrospective cross-sectional study conducted on routine surveillance and laboratory data at the Tropical Diseases Research Centre COVID-19 laboratory in Ndola, Zambia, from 01 December 2020 to 31 March 2021. The study population was made up of persons that had tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection as a requirement for travel. Microsoft Excel was used to come up with an epidemiological curve of daily COVID-19 positive cases; proportions for gender were described using frequencies and percentages.

Results

A total of 11 144 asymptomatic individuals tested for SARS-CoV-2 were sampled for the study and 1781 (16.0%) returned positive results. The median age among those tested was 36 years (interquartile range: 29-46). Testing for COVID-19 peaked in the month of January 2021 (37.4%) and declined in March 2021 (21.0%). The epidemiological curve showed a combination of continuous and propagated point-source transmission.

Conclusion

The positivity rate of 16.0% among asymptomatic individuals was high and could imply continued community transmission, especially during January 2021 and February 2021. We recommend heightened testing for SARS-CoV-2 among asymptomatic individuals.

What this study adds

This study adds critical knowledge to the transmission of COVID-19 among asymptomatic travellers who are usually a key population in driving community infection. This knowledge is critical in instituting evidence-based interventions in the screening and management of travellers, and its control.

Identity and COVID-19 in Canada: Gender, ethnicity, and minority status

BACKGROUND

During the COVID-19 pandemic, growing evidence from the United States, the United Kingdom, and China has demonstrated the unequal social and economic burden of this health crisis. Yet, in Canada, studies assessing the socioeconomic and demographic determinants of COVID-19, and how these determinants vary by gender and ethnic minority status, remain scarce. As new strains of COVID-19 emerge, it is important to understand the disparities to be able to initiate policies and interventions that target and prioritise the most at-risk sub-populations.

AIM

The objective of this study is to assess the socioeconomic and demographic factors associated with COVID-19-related symptoms in Canada, and how these determinants vary by identity factors including gender and visible minority status.

METHODS

We implemented an online survey and collected a nationally representative sample of 2,829 individual responses. The original data collected via the SurveyMonkey platform were analysed using a cross-sectional study. The outcome variables were COVID-19-related symptoms among respondents and their household members. The exposure variables were socioeconomic and demographic factors including gender and ethnicity as well as age, province, minority status, level of education, total annual income in 2019, and number of household members. Descriptive statistics, chi-square tests, and multivariable logistic regression analyses were performed to test the associations. The results were presented as adjusted odds ratios (aORs) at p < 0.05 and a 95% confidence interval.

RESULTS

We found that the odds of having COVID-19-related symptoms were higher among respondents who belong to mixed race [aOR = 2.77; CI = 1.18-6.48] and among those who lived in provinces other than Ontario and Quebec [aOR = 1.88; CI = 1.08-3.28]. There were no significant differences in COVID-19 symptoms between males and females, however, we did find a significant association between the province, ethnicity, and reported COVID-19 symptoms for female respondents but not for males. The likelihood of having COVID-19-related symptoms was also lower among respondents whose total income was $100,000 or more in 2019 [aOR = 0.18; CI = 0.07-0.45], and among those aged 45-64 [aOR = 0.63; CI = 0.41-0.98] and 65-84 [aOR = 0.42; CI = 0.28-0.64]. These latter associations were stronger among non-visible minorities. Among visible minorities, being black or of the mixed race and living in Alberta were associated with higher odds of COVID-19-related symptoms.

CONCLUSION

We conclude that ethnicity, age, total income in 2019, and province were significantly associated with experiencing COVID-19 symptoms in Canada. The significance of these determinants varied by gender and minority status. Considering our findings, it will be prudent to have COVID-19 mitigation strategies including screening, testing, and other prevention policies targeted toward the vulnerable populations. These strategies should also be designed to be specific to each gender category and ethnic group, and to account for minority status.

Reliability of COVID-19 data: An evaluation and reflection

IMPORTANCE

The rapid proliferation of COVID-19 has left governments scrambling, and several data aggregators are now assisting in the reporting of county cases and deaths. The different variables affecting reporting (e.g., time delays in reporting) necessitates a well-documented reliability study examining the data methods and discussion of possible causes of differences between aggregators.

OBJECTIVE

To statistically evaluate the reliability of COVID-19 data across aggregators using case fatality rate (CFR) estimates and reliability statistics.

DESIGN, SETTING, AND PARTICIPANTS

Cases and deaths were collected daily by volunteers via state and local health departments, as primary sources and newspaper reports, as secondary sources. In an effort to begin comparison for reliability statistical analysis, BroadStreet collected data from other COVID-19 aggregator sources, including USAFacts, Johns Hopkins University, New York Times, The COVID Tracking Project.

MAIN OUTCOMES AND MEASURES

COVID-19 cases and death counts at the county and state levels.

RESULTS

Lower levels of inter-rater agreement were observed across aggregators associated with the number of deaths, which manifested itself in state level Bayesian estimates of COVID-19 fatality rates.

CONCLUSIONS AND RELEVANCE

A national, publicly available data set is needed for current and future disease outbreaks and improved reliability in reporting.

Is Methadone Safe for Patients With Opioid Use Disorder and Coronavirus Disease 2019 Infection?

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has led to not only increase in substance misuse, substance use disorder, and risk of overdose but also lack of access to treatment services. Due to lack of knowledge of the course and impact of COVID-19 and outcomes of it's interactions with existing treatments, the Substance Misuse Service Team initiated a safety improvement project to review the safety of opioid substitution treatment, particularly the safety of methadone. This preliminary retrospective cross-sectional audit of safety improvement intiative underscores the importance of providing treatment services to those with opioid use disorders and that methadone is safe among this population with a high burden of comorbidity, most of which leads to negative outcomes from COVID-19. The outcomes show that patients who have COVID-19 should continue with opioid substitution treatment with methadone. Although treatment with methadone is safe, symptomatic patients should be monitored. In addition, patients who take methadone at home should be educated on the risk of overdose due to, and adverse outcomes from, COVID-19 infection. Patients should monitor themselves using pulse oximeter for any signs of hypoxia.

How did Nunavummiut youth cope during the COVID-19 pandemic? A qualitative exploration of the resilience of Inuit youth leaders involved in the I-SPARX project

This study investigated how COVID-19 has affected the wellness of a group of Inuit youth leaders in Nunavut in the context of their involvement with an ongoing mental health research initiative, the Making I-SPARX Fly in Nunavut [I-SPARX] project. The study had three goals: (1) to understand how the pandemic has affected I-SPARX leaders’ perceived involvement in the I-SPARX Project; (2) to build knowledge around how the pandemic has impacted the daily life and wellbeing of youth in Nunavummiut communities; and (3) to acquire a culturally specific understanding of their coping mechanisms and resilience strategies through the lens of Inuit Qaujimajatuqangit (IQ). Nine Inuit youth were interviewed virtually about their participation in I-SPARX, their life during the pandemic, and their coping strategies. Their comments were analysed using inductive thematic analysis. Pandemic challenges, the utility of I-SPARX teachings and participation, and culturally and community-embedded pathways to resilience were discussed.

The implications of COVID-19 on Inuit youth in remote communities are not fully understood. The current study illuminates their experiences of the pandemic to inform future research on ways in which Inuit youth might be supported in situations, such as a global pandemic, that restrict their traditional resilience-enhancing activities and create social isolation.

Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations at risk. Epidemiological modelling is vital to guiding evidence-informed or data-driven decision making. In forced displacement contexts, and in particular refugee and internally displaced people (IDP) settlements, it meets several challenges including data availability and quality, the applicability of existing models to those contexts, the accurate modelling of cultural differences or specificities of those operational settings, the communication of results and uncertainties, as well as the alignment of strategic goals between diverse partners in complex situations. In this paper, we systematically review the limited epidemiological modelling work applied to refugee and IDP settlements so far, and discuss challenges and identify lessons learnt from the process. With the likelihood of disease outbreaks expected to increase in the future as more people are displaced due to conflict and climate change, we call for the development of more approaches and models specifically designed to include the unique features and populations of refugee and IDP settlements. To strengthen collaboration between the modelling and the humanitarian public health communities, we propose a roadmap to encourage the development of systems and frameworks to share needs, build tools and coordinate responses in an efficient and scalable manner, both for this pandemic and for future outbreaks.

Examining the association between reported COVID-19 symptoms and testing for COVID-19 in Canada: a cross-sectional survey

OBJECTIVES

Studies on the management of the COVID-19 pandemic through testing have been conducted in countries that have been hardest hit by this pandemic. Considering the importance of testing in containing the spread of COVID-19, it is useful to have evidence on continuing COVID-19 testing even in countries where the prevalence of COVID-19 is relatively low. We, therefore, examined the association between reported COVID-19 symptoms and testing for COVID-19 in Canada.

DESIGN AND SETTINGS

We conducted an online survey using the SurveyMonkey platform between July and October 2020 across Canada.

PARTICIPANTS

A nationally representative sample size of 2790 adult individuals was used.

RESULTS

Our findings show that respondents who reported that they and/or members of their households had COVID-19 symptoms were more likely to test for COVID-19 (adjusted OR, aOR 1.91; 95% CI 1.32 to 2.76) as compared with those who did not report COVID-19 symptoms. The likelihood of testing for COVID-19 was lower among male respondents compared with females (aOR 0.69; 95% CI 0.49 to 0.96), respondents aged 65-84 compared with those aged 18-44 (aOR 0.62; 95% CI 0.42 to 0.93), and respondents in British Columbia compared with those residing in Quebec. Higher odds of testing for COVID-19 were found among respondents who lived in Alberta compared with those who lived in Quebec (aOR 0.42; 95% CI 0.23 to 0.75) and respondents who had postgraduate education compared with those with high school or less education (aOR 1.84; 95% CI 1.01 to 3.36). The association between reported COVID-19 symptoms and testing for COVID-19 was statistically significant among female respondents (aOR 1.52; 95% CI 1.81 to 3.52) but not among male respondents.

CONCLUSIONS

In conclusion, this study provides evidence in support of the hypothesis that there is significant association between reported COVID-19 symptoms and COVID-19 testing among adult Canadians. The study highlights the need for the Canadian government to prioritise subpopulations (ie, males, those aged 65-85, and those with high school or less education) that have lower likelihood of seeking COVID-19 testing to get tested when they have symptoms.

Comparison of longitudinal trends in self-reported symptoms and COVID-19 case activity in Ontario, Canada

BACKGROUND

Limitations in laboratory diagnostic capacity impact population surveillance of COVID-19. It is currently unknown whether participatory surveillance tools for COVID-19 correspond to government-reported case trends longitudinally and if it can be used as an adjunct to laboratory testing. The primary objective of this study was to determine whether self-reported COVID-19-like illness reflected laboratory-confirmed COVID-19 case trends in Ontario Canada.

METHODS

We retrospectively analyzed longitudinal self-reported symptoms data collected using an online tool-Outbreaks Near Me (ONM)-from April 20th, 2020, to March 7th, 2021 in Ontario, Canada. We measured the correlation between COVID-like illness among respondents and the weekly number of PCR-confirmed COVID-19 cases and provincial test positivity. We explored contemporaneous changes in other respiratory viruses, as well as the demographic characteristics of respondents to provide context for our findings.

RESULTS

Between 3,849-11,185 individuals responded to the symptom survey each week. No correlations were seen been self-reported CLI and either cases or test positivity. Strong positive correlations were seen between CLI and both cases and test positivity before a previously documented rise in rhinovirus/enterovirus in fall 2020. Compared to participatory surveillance respondents, a higher proportion of COVID-19 cases in Ontario consistently came from low-income, racialized and immigrant areas of the province- these groups were less well represented among survey respondents.

INTERPRETATION

Although digital surveillance systems are low-cost tools that have been useful to signal the onset of viral outbreaks, in this longitudinal comparison of self-reported COVID-like illness to Ontario COVID-19 case data we did not find this to be the case. Seasonal respiratory virus transmission and population coverage may explain this discrepancy.

Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base

Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Sex and Gender-Related Differences in COVID-19 Diagnoses and SARS-CoV-2 Testing Practices During the First Wave of the Pandemic: The Dutch Lifelines COVID-19 Cohort Study

Background: Although sex differences are described in Coronavirus Disease 2019 (COVID-19) diagnoses and testing, many studies neglect possible gender-related influences. Additionally, research is often performed in clinical populations, while most COVID-19 patients are not hospitalized. Therefore, we investigated associations between sex and gender-related variables, and COVID-19 diagnoses and testing practices in a large general population cohort during the first wave of the pandemic when testing capacity was limited. Methods: We used data from the Lifelines COVID-19 Cohort (N = 74,722; 60.8% female). We applied bivariate and multiple logistic regression analyses. The outcomes were a COVID-19 diagnosis (confirmed by SARS-CoV-2 PCR testing or physician's clinical diagnosis) and PCR testing. Independent variables included among others participants' sex, age, somatic comorbidities, occupation, and smoking status. Sex-by-comorbidity and sex-by-occupation interaction terms were included to investigate sex differences in associations between the presence of comorbidities or an occupation with COVID-19 diagnoses or testing practices. Results: In bivariate analyses female sex was significantly associated with COVID-19 diagnoses and testing, but significance did not persist in multiple logistic regression analyses. However, a gender-related variable, being a health care worker, was significantly associated with COVID-19 diagnoses (OR = 1.68; 95%CI = 1.30-2.17) and testing (OR = 12.5; 95%CI = 8.55-18.3). Female health care workers were less often diagnosed and tested than male health care workers (ORinteraction = 0.54; 95%CI = 0.32-0.92, ORinteraction = 0.53; 95%CI = 0.29-0.97, respectively). Conclusions: We found no sex differences in COVID-19 diagnoses and testing in the general population. Among health care workers, a male preponderance in COVID-19 diagnoses and testing was observed. This could be explained by more pronounced COVID-19 symptoms in males or by gender inequities.

Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study

Background

The implementation of novel techniques as a complement to traditional disease surveillance systems represents an additional opportunity for rapid analysis.

Objective

The objective of this work is to describe a web-based participatory surveillance strategy among health care workers (HCWs) in two Swiss hospitals during the first wave of COVID-19.

Methods

A prospective cohort of HCWs was recruited in March 2020 at the Cantonal Hospital of St. Gallen and the Eastern Switzerland Children’s Hospital. For data analysis, we used a combination of the following techniques: locally estimated scatterplot smoothing (LOESS) regression, Spearman correlation, anomaly detection, and random forest.

Results

From March 23 to August 23, 2020, a total of 127,684 SMS text messages were sent, generating 90,414 valid reports among 1004 participants, achieving a weekly average of 4.5 (SD 1.9) reports per user. The symptom showing the strongest correlation with a positive polymerase chain reaction test result was loss of taste. Symptoms like red eyes or a runny nose were negatively associated with a positive test. The area under the receiver operating characteristic curve showed favorable performance of the classification tree, with an accuracy of 88% for the training data and 89% for the test data. Nevertheless, while the prediction matrix showed good specificity (80.0%), sensitivity was low (10.6%).

Conclusions

Loss of taste was the symptom that was most aligned with COVID-19 activity at the population level. At the individual level—using machine learning–based random forest classification—reporting loss of taste and limb/muscle pain as well as the absence of runny nose and red eyes were the best predictors of COVID-19.

Estimated Incidence of Coronavirus Disease 2019 (COVID-19) Illness and Hospitalization-United States, February-September 2020

Background

In the United States, laboratory confirmed coronavirus disease 2019 (COVID-19) is nationally notifiable. However, reported case counts are recognized to be less than the true number of cases because detection and reporting are incomplete and can vary by disease severity, geography, and over time.

Methods

To estimate the cumulative incidence SARS-CoV-2 infections, symptomatic illnesses, and hospitalizations, we adapted a simple probabilistic multiplier model. Laboratory-confirmed case counts that were reported nationally were adjusted for sources of under-detection based on testing practices in inpatient and outpatient settings and assay sensitivity.

Results

We estimated that through the end of September, 1 of every 2.5 (95% Uncertainty Interval (UI): 2.0–3.1) hospitalized infections and 1 of every 7.1 (95% UI: 5.8–9.0) non-hospitalized illnesses may have been nationally reported. Applying these multipliers to reported SARS-CoV-2 cases along with data on the prevalence of asymptomatic infection from published systematic reviews, we estimate that 2.4 million hospitalizations, 44.8 million symptomatic illnesses, and 52.9 million total infections may have occurred in the U.S. population from February 27–September 30, 2020.

Conclusions

These preliminary estimates help demonstrate the societal and healthcare burdens of the COVID-19 pandemic and can help inform resource allocation and mitigation planning.

Video consultations during the coronavirus disease 2019 pandemic are associated with high satisfaction for both doctors and patients

Background and Aim

Telehealth has become the standard of care during the COVID-19 outbreak. This study aimed to assess doctor and patient satisfaction of endoscopy-related telehealth clinics with video consultations.

Methods

A prospective observational study of patients consecutively booked to attend two endoscopy-related telehealth clinics at an ambulatory tertiary care setting was conducted from July to October 2020. Data collected from our previously published study using phone consultations (data collected in April-May 2020) were used as a control arm. The primary outcome (satisfaction) was assessed through the six-question score (6Q_score) as per previous research. Secondary outcomes included failure-to-attend (FTA) rate and perceived necessity of physical examination/in-person follow-up appointment.

Results

There were 962 endoscopy clinic appointments between July and October, of which 157 were conducted through video. Data on 127 doctor questionnaires and 94 patient questionnaires were analyzed. The median age (years) of patients reviewed via video [57, interquartile range (IQR) 48-66] was lower than those reviewed via phone (65, IQR 55-74, P < 0.01). Patient average 6Q_score was higher with video compared to phone (85.1% vs 78.4%, P = 0.01), as was doctors' 6Q_score (97.5% vs 91.9%, P = 0.02). FTA rates remained similar between the two assessments (6.4% in April/May and 4.4% between July/October, P = 0.12). The requirement for in-person follow-up/physical examination was identified in two video consultations (1.6%).

Conclusion

Video consultations during the COVID-19 outbreak demonstrated higher patient and doctor satisfaction compared to phone consultations. There was no significant difference in FTA rates and need for in-person follow-up consultations/physical examination between the telehealth two modalities.

Incidence and risk factors of COVID-19-like symptoms in the French general population during the lockdown period: a multi-cohort study

BACKGROUND

Our main objectives were to estimate the incidence of illnesses presumably caused by SARS-CoV-2 infection during the lockdown period and to identify the associated risk factors.

METHODS

Participants from 3 adult cohorts in the general population in France were invited to participate in a survey on COVID-19. The main outcome was COVID-19-Like Symptoms (CLS), defined as a sudden onset of cough, fever, dyspnea, ageusia and/or anosmia, that lasted more than 3 days and occurred during the 17 days before the survey. We used delayed-entry Cox models to identify associated factors.

RESULTS

Between April 2, 2020 and May 12, 2020, 279,478 participants were invited, 116,903 validated the questionnaire and 106,848 were included in the analysis. Three thousand thirty-five cases of CLS were reported during 62,099 person-months of follow-up. The cumulative incidences of CLS were 6.2% (95% Confidence Interval (95%CI): 5.7%; 6.6%) on day 15 and 8.8% (95%CI 8.3%; 9.2%) on day 45 of lockdown. The risk of CLS was lower in older age groups and higher in French regions with a high prevalence of SARS-CoV-2 infection, in participants living in cities > 100,000 inhabitants (vs rural areas), when at least one child or adolescent was living in the same household, in overweight or obese people, and in people with chronic respiratory diseases, anxiety or depression or chronic diseases other than diabetes, cancer, hypertension or cardiovascular diseases.

CONCLUSION

The incidence of CLS in the general population remained high during the first 2 weeks of lockdown, and decreased significantly thereafter. Modifiable and non-modifiable risk factors were identified.

COVID-19 epidemiology in Canada from January to December 2020: the pre-vaccine era

This paper summarizes COVID-19 disease epidemiology in Canada in the pre-vaccine era—from January through to December 2020. Canadian case numbers, risk factors, disease presentations (including severe and critical disease), and outcomes are described. Differences between provinces and territories in geography, population size and density, health demographics, and pandemic impact are highlighted. Key concepts in public health response and mitigation are reviewed, including masking, physical distancing, hand washing, and the promotion of outdoor interactions. Adequate investment in public health infrastructure is stressed, and regional differences in screening and testing strategies are highlighted. The spread of COVID-19 in Canadian workplaces, long-term care homes, and schools is described and lessons learned emphasized. The impact of COVID-19 on vulnerable populations in Canada—including Indigenous Peoples, ethnic minorities and newcomers, people who use drugs, people who are homeless, people who are incarcerated, and people with disabilities—is described. Sex and gender disparities are also highlighted. Author recommendations include strategies to reduce transmission (such as test–trace–isolate), the establishment of nationally standardized definitions and public reporting, the protection of high risk and vulnerable populations, and the development of a national strategy on vaccine allocation.