Tracking the burden, distribution, and impact of Post-COVID conditions in diverse populations for children, adolescents, and adults (Track PCC): passive and active surveillance protocols

Abstract

Background: Track PCC includes five geographic surveillance sites to conduct standardized population-based surveillance to estimate and track Post-COVID Conditions (PCC) by age, sex, race/ethnicity, geographic area, severity of initial infection, and risk factors among persons with evidence of SARS-CoV-2 infection (based on the Council of State and Territorial Epidemiologist [CSTE] case definitions for confirmed cases or laboratory-confirmed evidence of infection).

Methods: The study will estimate the incidence, prevalence, including temporal trends, and duration and severity of PCC symptoms, among children, adolescents, and adults. PCCs include a broad range of symptoms and conditions that continue or develop after acute SARS-CoV-2 infection or COVID-19 illness. Surveillance includes both passive and active components for diverse populations in Arizona, Indiana, and Utah as well as the Bronx Borough, NY, and part of Philadelphia County, PA. Passive surveillance will utilize electronic health records and health information exchanges within each site catchment area to longitudinally follow persons with COVID-19 to estimate PCC occurring at least 30 days after acute COVID-19 illness. Active surveillance will utilize self-report of PCCs from detailed surveys of persons ages 7 years and older with evidence of SARS-CoV-2 infection in the past 3 months. Respondents will complete follow-up surveys at 6-, 12- and 18-months post-infection.

Discussion: These data can help identify which groups are most affected by PCC, and what health differences among demographic groups exist, as well as indicate potential barriers to care. These additional levels of granularity can inform public health action and help direct needed clinical care for patients.

Keywords: Cohort study; Incidence; Post-COVID conditions; Prevalence; Public health; Surveillance.

Fig. 1

Track PCC Sites. Multi-Site Collaborators. Arizona: University of Arizona. Indiana: Indiana University. Bronx Borough, New York City: Boise State University, Bronx Regional Health Information Organization, Comagine Health. Utah: Boise State University, Comagine Health, Utah Health Information Network. Philadelphia County: Temple University

Fig. 2

Timing of symptoms and qualifications of Post-COVID Conditions. a EHR: Electronic Health Record; CPT: Current Procedure Terminology Code; NDC: National Drug Code. *COVID-19 cases will be identified based on a positive laboratory test (i.e., nucleic acid- or antigen-based tests, using LOINC or SNOMED CT codes), encounter-level clinical diagnosis using ICD-10-CM codes, or report of a positive COVID-19 test in clinical records/notes. The date of an individual’s COVID-19 diagnosis is defined as Day 0. PCC is defined as the presence of one or more conditions 1–18 months after COVID-19 diagnosis that was not present prior to COVID-19 diagnosis. **See Additional file 1. b *List of Catchment areas: Arizona (statewide), Indiana (statewide), Philadelphia (i.e., 24 selected zip codes: 19111, 19114—19116, 19120—19126, 19129, 19130, 19132—19136, 19138, 19140, 19141, 19144, 19149, 19152), and the Bronx Borough of New York City. **Questionnaires associated with Resolution of symptoms, Treatments, Quality of life, Mental health, Activities of Daily Living

Fig. 3

Data processing for Track PCC passive surveillance. PCC: Post-COVID Conditions; EHR: Electronic Health Record; COVID: CDC: Centers for Disease Control and Prevention. *Cases are matched across source data by name, date of birth and sex. Visits among cases that are duplicated across clinical and administrative sources are identified by date of visit.  **Vaccination, SARS-COV-2 testing, vital records, and census data