Symptom monitoring after coronavirus disease 2019 (COVID-19) vaccination in a large integrated healthcare system: Separating symptoms from severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection

Abstract

Objective: To describe the incidence of systemic overlap and typical coronavirus disease 2019 (COVID-19) symptoms in healthcare personnel (HCP) following COVID-19 vaccination and association of reported symptoms with diagnosis of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection in the context of public health recommendations regarding work exclusion. Design: This prospective cohort study was conducted between December 16, 2020, and March 14, 2021, with HCP who had received at least 1 dose of either the Pfizer-BioNTech or Moderna COVID-19 vaccine. Setting: Large healthcare system in New England. Interventions: HCP were prompted to complete a symptom survey for 3 days after each vaccination. Reported symptoms generated automated guidance regarding symptom management, SARS-CoV-2 testing requirements, and work restrictions. Overlap symptoms (ie, fever, fatigue, myalgias, arthralgias, or headache) were categorized as either lower or higher severity. Typical COVID-19 symptoms included sore throat, cough, nasal congestion or rhinorrhea, shortness of breath, ageusia and anosmia. Results: Among 64,187 HCP, a postvaccination electronic survey had response rates of 83% after dose 1 and 77% after dose 2. Report of ≥3 lower-severity overlap symptoms, ≥1 higher-severity overlap symptoms, or at least 1 typical COVID-19 symptom after dose 1 was associated with increased likelihood of testing positive. HCP with prior COVID-19 infection were significantly more likely to report severe overlap symptoms after dose 1. Conclusions: Reported overlap symptoms were common; however, only report of ≥3 low-severity overlap symptoms, at least 1 higher-severity overlap symptom, or any typical COVID-19 symptom were associated with infection. Work-related restrictions for overlap symptoms should be reconsidered.

Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach

“…The dataset was created from MGB electronic health record’s COVID-19 Datamart and a REDCap (Research Electronic Data Capture [ 24 ]) survey that collected self-reported symptoms for 1–3 days after each vaccination. Detailed information about the REDCap side effect survey have been previously reported [ 5 ]. For this report, we only used data from the first vaccination.…”

“…Data from individuals were included if they (i) received an mRNA COVID-19 vaccine at a MGB site between December 2020 and April 2021, (ii) responded to at least one day of the REDCap survey, (iii) had documented time-of-day of administration of COVID-19 vaccine and (iv) did not have a COVID-19 diagnosis or positive polymerase chain reaction test within one week after the vaccination (because of overlap of disease and side-effect symptoms [ 5 ]). Individuals who received the Janssen adenovirus-based vaccine were excluded from this analysis for two reasons: only 1486 (<3%) individuals who met our inclusion criteria received a Janssen vaccine, and people with high risk of allergic reaction were recommended for the Janssen vaccine and this could introduce a bias in our dataset [ 25 ].…”

“…At MassGeneralBrigham (MGB), a large integrated healthcare system, surveys were used to obtain information about post COVID-19 vaccination symptoms through email, text message, phone, and smartphone application links as part of employee health monitoring. Several publications using epidemiological analytic techniques have reported important clinical information using this dataset, including the incidence of allergic and non-allergic side effects after mRNA COVID-19 vaccination and their association with variables such as demographic characteristics, allergy history, and prior known infection with SARS-CoV-2 [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. This report explores new potential covariates, including time-of-day for COVID-19 vaccinations (which is an easily modified factor), and utilized a different analysis approach—Machine Learning (ML).…”

Rapid Response for Notification of Monkeypox Exposure, Exposure Risk Assessment and Stratification, and Symptom Monitoring

“…The REDCap technology was in use prior to the current MPX outbreak as part of planned monitoring of HCP caring for patients in our facilities' Biocontainment Unit and the Special Pathogens Unit at the Region 1 Emerging Special Pathogens Treatment Center. Use of this technology was further developed to support MGB during the COVID-19 pandemic, including use for symptom attestation prior to on-site work for HCP, 6 symptom monitoring after COVID-19 vaccination, [7][8][9] and creation of survey tools for COVID-19 exposure notification, risk assessment and stratification. REDCap is a secure, web-based software platform designed to support data capture for research studies.…”

Development and deployment of tools for rapid response notification of Monkeypox exposure, exposure risk assessment and stratification, and symptom monitoring

“…5 At Massachusetts General Hospital (MGH) and within the Mass General Brigham (MGB) system, REDCap technology was in use prior to the current monkeypox outbreak as part of planned monitoring of HCP caring for patients in our Biocontainment Unit and the Special Pathogens Unit at the Region 1 Emerging Special Pathogens Treatment Center. This technology was further developed to support MGB during the COVID-19 pandemic, additions for symptom attestation prior to on-site work for HCP 6 and symptom monitoring after COVID-19 vaccination [7][8][9] and survey tools for COVID-19 exposure notification, risk assessment, and stratification. REDCap is a secure, web-based software platform designed to support data capture for research studies.…”