COVID-19 Self-Reported Symptom Tracking Programs in the United States: Framework Synthesis

Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review

BACKGROUND

The COVID-19 pandemic has prompted the deployment of digital technologies for public health surveillance globally. The rapid development and use of these technologies have curtailed opportunities to fully consider their potential impacts (eg, for human rights, civil liberties, privacy, and marginalization of vulnerable groups).

OBJECTIVE

We conducted a scoping review of peer-reviewed and gray literature to identify the types and applications of digital technologies used for surveillance during the COVID-19 pandemic and the predicted and witnessed consequences of digital surveillance.

METHODS

Our methodology was informed by the 5-stage methodological framework to guide scoping reviews: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the findings. We conducted a search of peer-reviewed and gray literature published between December 1, 2019, and December 31, 2020. We focused on the first year of the pandemic to provide a snapshot of the questions, concerns, findings, and discussions emerging from peer-reviewed and gray literature during this pivotal first year of the pandemic. Our review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines.

RESULTS

We reviewed a total of 147 peer-reviewed and 79 gray literature publications. Based on our analysis of these publications, we identified a total of 90 countries and regions where digital technologies were used for public health surveillance during the COVID-19 pandemic. Some of the most frequently used technologies included mobile phone apps, location-tracking technologies, drones, temperature-scanning technologies, and wearable devices. We also found that the literature raised concerns regarding the implications of digital surveillance in relation to data security and privacy, function creep and mission creep, private sector involvement in surveillance, human rights, civil liberties, and impacts on marginalized groups. Finally, we identified recommendations for ethical digital technology design and use, including proportionality, transparency, purpose limitation, protecting privacy and security, and accountability.

CONCLUSIONS

A wide range of digital technologies was used worldwide to support public health surveillance during the COVID-19 pandemic. The findings of our analysis highlight the importance of considering short- and long-term consequences of digital surveillance not only during the COVID-19 pandemic but also for future public health crises. These findings also demonstrate the ways in which digital surveillance has rendered visible the shifting and blurred boundaries between public health surveillance and other forms of surveillance, particularly given the ubiquitous nature of digital surveillance.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-https://doi.org/10.1136/bmjopen-2021-053962.

Smart Thermometer-Based Participatory Surveillance to Discern the Role of Children in Household Viral Transmission During the COVID-19 Pandemic

Importance

Children's role in spreading virus during the COVID-19 pandemic is yet to be elucidated, and measuring household transmission traditionally requires contact tracing.

Objective

To discern children's role in household viral transmission during the pandemic when enveloped viruses were at historic lows and the predominance of viral illnesses were attributed to COVID-19.

Design, Setting, and Participants

This cohort study of a voluntary US cohort tracked data from participatory surveillance using commercially available thermometers with a companion smartphone app from October 2019 to October 2022. Eligible participants were individuals with temperature measurements in households with multiple members between October 2019 and October 2022 who opted into data sharing.

Main Outcomes and Measures

Proportion of household transmissions with a pediatric index case and changes in transmissions during school breaks were assessed using app and thermometer data.

Results

A total of 862 577 individuals from 320 073 households with multiple participants (462 000 female [53.6%] and 463 368 adults [53.7%]) were included. The number of febrile episodes forecast new COVID-19 cases. Within-household transmission was inferred in 54 506 (15.4%) febrile episodes and increased from the fourth pandemic period, March to July 2021 (3263 of 32 294 [10.1%]) to the Omicron BA.1/BA.2 wave (16 516 of 94 316 [17.5%]; P < .001). Among 38 787 transmissions in 166 170 households with adults and children, a median (IQR) 70.4% (61.4%-77.6%) had a pediatric index case; proportions fluctuated weekly from 36.9% to 84.6%. A pediatric index case was 0.6 to 0.8 times less frequent during typical school breaks. The winter break decrease was from 68.4% (95% CI, 57.1%-77.8%) to 41.7% (95% CI, 34.3%-49.5%) at the end of 2020 (P < .001). At the beginning of 2022, it dropped from 80.3% (95% CI, 75.1%-84.6%) to 54.5% (95% CI, 51.3%-57.7%) (P < .001). During summer breaks, rates dropped from 81.4% (95% CI, 74.0%-87.1%) to 62.5% (95% CI, 56.3%-68.3%) by August 2021 (P = .02) and from 83.8% (95% CI, 79.2%-87.5) to 62.8% (95% CI, 57.1%-68.1%) by July 2022 (P < .001). These patterns persisted over 2 school years.

Conclusions and Relevance

In this cohort study using participatory surveillance to measure within-household transmission at a national scale, we discerned an important role for children in the spread of viral infection within households during the COVID-19 pandemic, heightened when schools were in session, supporting a role for school attendance in COVID-19 spread.

Use of digital technologies for public health surveillance during the COVID-19 pandemic: A scoping review

Throughout the COVID-19 pandemic, a variety of digital technologies have been leveraged for public health surveillance worldwide. However, concerns remain around the rapid development and deployment of digital technologies, how these technologies have been used, and their efficacy in supporting public health goals. Following the five-stage scoping review framework, we conducted a scoping review of the peer-reviewed and grey literature to identify the types and nature of digital technologies used for surveillance during the COVID-19 pandemic and the success of these measures. We conducted a search of the peer-reviewed and grey literature published between 1 December 2019 and 31 December 2020 to provide a snapshot of questions, concerns, discussions, and findings emerging at this pivotal time. A total of 147 peer-reviewed and 79 grey literature publications reporting on digital technology use for surveillance across 90 countries and regions were retained for analysis. The most frequently used technologies included mobile phone devices and applications, location tracking technologies, drones, temperature scanning technologies, and wearable devices. The utility of digital technologies for public health surveillance was impacted by factors including uptake of digital technologies across targeted populations, technological capacity and errors, scope, validity and accuracy of data, guiding legal frameworks, and infrastructure to support technology use. Our findings raise important questions around the value of digital surveillance for public health and how to ensure successful use of technologies while mitigating potential harms not only in the context of the COVID-19 pandemic, but also during other infectious disease outbreaks, epidemics, and pandemics.

Facilitating Real-Time, Multidirectional Learning for Clinicians in a Low-Evidence Pandemic Response

As COVID-19 was declared a health emergency in March 2020, there was immense demand for information about the novel pathogen. This paper examines the clinician-reported impact of Project ECHO COVID-19 Clinical Rounds on clinician learning. Primary sources of study data were Continuing Medical Education (CME) Surveys for each session from the dates of March 24, 2020 to July 30, 2020 and impact surveys conducted in November 2020, which sought to understand participants' overall assessment of sessions. Quantitative analyses included descriptive statistics and Mann-Whitney testing. Qualitative data were analyzed through inductive thematic analysis. Clinicians rated their knowledge after each session as significantly higher than before that session. 75.8% of clinicians reported they would 'definitely' or 'probably' use content gleaned from each attended session and clinicians reported specific clinical and operational changes made as a direct result of sessions. 94.6% of respondents reported that COVID-19 Clinical Rounds helped them provide better care to patients. 89% of respondents indicated they 'strongly agree' that they would join ECHO calls again.COVID-19 Clinical Rounds offers a promising model for the establishment of dynamic peer-to-peer tele-mentoring communities for low or no-notice response where scientifically tested or clinically verified practice evidence is limited.

Sociodemographic Differences in COVID-19 Self-Reported Symptoms by Ethnicity and Older Adults in Bolivia

The effects of COVID-19 revealed the fragility of health systems in the LAC region, with greater risk of death in older people than in younger people, as well as greater vulnerability to infection due to living with people aged 30–59 years, who have a higher prevalence of COVID-19. On the other hand, there is not much information on inequalities in the incidence of COVID-19 in indigenous people, a population with lower immunological resistance. The objectives are: 1) To determine the association between sociodemographic variables with self-reported COVID-19 symptoms. 2) To investigate whether this relationship shows inequalities by ethnicity and age. For that purpose I conducted a cross-sectional analysis using the 2020 Household Survey and investigated the association between sociodemographic variables and self -reported COVID-19 symptoms and explore the contribution of factors such as employment type, household living arrangements, years of education, age, ethnicity, gender, current status of working and residence area. I performed bivariate analysis to establish trends. Subsequently using logistic regressions to establish the risks to self-reported COVID-19 symptoms. A fully interacted model is analysed by ethnicity. I found those who were living alone were less likely than those living in a Couple with/without relatives’ household arrangement to self-reported COVID-19 symptoms (OR = 0.79, 95% CI: 0.66–0.94, p < .01). Odds of the older persons aged 45–59 (OR = 1.44, 95% CI: 1.27–1.62, p < .05) were relatively more likely than younger people (OR = 1.19, 95% CI: 1.05–1.35, p < .01). Indigenous living in a couple with/without children household arrangement were less likely than non-Indigenous (OR = 0.75, 95% CI: 0.62–0.90, p < .01). Odds of Indigenous people of age 30–44 (OR = 1.26, 95% CI: 1.04–1.53, p < .01) were more likely than non-Indigenous. Odds of Indigenous persons of age 45–59 (OR = 1.59, 95% CI: 1.32–1.91, p < .05) were more likely than non-Indigenous (OR = 1.32, 95% CI: 1.12–1.55, p < .01). As conclusions, 45–59 age group shows higher risk factors and those aged 60 + show lower risks. These are increased in people working in managerial, administrative and professional, and technical positions, those living in a household with/without relatives, men, those living in urban areas, and/or non-indigenous people.

Intelligent Pandemic Surveillance via Privacy-Preserving Crowdsensing

Intelligently responding to a pandemic like Covid-19 requires sophisticated models over accurate real-time data, which is typically lacking at the start, e.g., due to deficient population testing. In such times, crowdsensing of spatially tagged disease-related symptoms provides an alternative way of acquiring real-time insights about the pandemic. Existing crowdsensing systems aggregate and release data for pre-fixed regions, e.g., counties. However, the insights obtained from such aggregates do not provide useful information about smaller regions - e.g., neighborhoods where outbreaks typically occur - and the aggregate-and-release method is vulnerable to privacy attacks. Therefore, we propose a novel differentially private method to obtain accurate insights from crowdsensed data for any number of regions specified by the users (e.g., researchers and a policy makers) without compromising privacy of the data contributors. Our approach, which has been implemented and deployed, informs the development of the future privacy-preserving intelligent systems for longitudinal and spatial data analytics.

Performance of the inFLUenza Patient-Reported Outcome Plus (FLU-PRO Plus) Instrument in Patients With Coronavirus Disease 2019

Background

The inFLUenza Patient-Reported Outcome Plus (FLU-PRO Plus) is a patient-reported outcome data collection instrument assessing symptoms of viral respiratory tract infections across 8 body systems. This study evaluated the measurement properties of FLU-PRO Plus in a study enrolling individuals with coronavirus disease 2019 (COVID-19).

Methods

Data from a prospective cohort study (EPICC) in US Military Health System beneficiaries evaluated for COVID-19 was utilized. Adults with symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with FLU-PRO Plus survey information within 1 week of symptom onset were included. Reliability of FLU-PRO Plus was estimated using intraclass correlation coefficient (ICC; 2 days' reproducibility). Known-groups validity was assessed using patient global assessment (PGA) of disease severity. Patient report of return to usual health was used to assess responsiveness (day 1-6/7).

Results

Two hundred twenty-six SARS-CoV-2-positive participants were included in the analysis. Reliability among those who reported no change in their symptoms from one day to the next was high for most domains (ICC range, 0.68-0.94 for day 1 to day 2). Construct validity was demonstrated by moderate to high correlation between the PGA rating of disease severity and domain and total scores (eg, total scores correlation: 0.69 [influenza-like illness severity], 0.69 [interference in daily activities], and -0.58 [physical health]). In addition, FLU-PRO Plus demonstrated good known-groups validity, with increasing domain and total scores observed with increasing severity ratings.

Conclusions

FLU-PRO Plus performs well in measuring signs and symptoms in SARS-CoV-2 infection with excellent construct validity, known-groups validity, and responsiveness to change. Standardized data collection instruments facilitate meta-analyses, vaccine effectiveness studies, and other COVID-19 research activities.

Re-examining COVID-19 Self-Reported Symptom Tracking Programs in the United States: Updated Framework Synthesis

BACKGROUND

Early in the pandemic, in 2020, Koehlmoos et al completed a framework synthesis of currently available self-reported symptom tracking programs for COVID-19. This framework described relevant programs, partners and affiliates, funding, responses, platform, and intended audience, among other considerations.

OBJECTIVE

This study seeks to update the existing framework with the aim of identifying developments in the landscape and highlighting how programs have adapted to changes in pandemic response.

METHODS

Our team developed a framework to collate information on current COVID-19 self-reported symptom tracking programs using the "best-fit" framework synthesis approach. All programs from the previous study were included to document changes. New programs were discovered using a Google search for target keywords. The time frame for the search for programs ranged from March 1, 2021, to May 6, 2021.

RESULTS

We screened 33 programs, of which 8 were included in our final framework synthesis. We identified multiple common data elements, including demographic information such as race, age, gender, and affiliation (all were associated with universities, medical schools, or schools of public health). Dissimilarities included questions regarding vaccination status, vaccine hesitancy, adherence to social distancing, COVID-19 testing, and mental health.

CONCLUSIONS

At this time, the future of self-reported symptom tracking for COVID-19 is unclear. Some sources have speculated that COVID-19 may become a yearly occurrence much like the flu, and if so, the data that these programs generate is still valuable. However, it is unclear whether the public will maintain the same level of interest in reporting their symptoms on a regular basis if the prevalence of COVID-19 becomes more common.