ArticleJMIR public health and surveillance2022
The Importance of Incorporating At-Home Testing Into SARS-CoV-2 Point Prevalence Estimates: Findings From a US National Cohort, February 2022.
Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
- Epidemiological characteristics of paediatric COVID-19 and influenza co-infections in the United States, 2020-2024.Epidemiology and infection · 2026Article
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- Does Home COVID-19 Testing Bias COVID-19 Vaccine Effectiveness Estimates?American journal of preventive medicine · 2026Article
- SARS-CoV-2 testing behavior in symptomatic adults in a U.S. national cohort, 2020-2023: influence of exposure risk, susceptibility and healthcare access.BMC public health · 2025Article
- The potential bias introduced into COVID-19 vaccine effectiveness studies at primary care level due to the availability of SARS-CoV-2 tests in the general population.International journal of epidemiology · 2025Article
- A tight fit of the SIR dynamic epidemic model to daily cases of COVID-19 reported during the 2021-2022 Omicron surge in New York City: A novel approach.Statistical methods in medical research · 2024Article
- The Urgent Need to Implement Point-of-Care RNA Testing for Hepatitis C Virus to Support Elimination.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Review
- SARS-CoV-2 Viral Shedding and Rapid Antigen Test Performance - Respiratory Virus Transmission Network, November 2022-May 2023.MMWR. Morbidity and mortality weekly report · 2024Article
- An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study.Scientific reports · 2024Article
- Self-tests for COVID-19: What is the evidence? A living systematic review and meta-analysis (2020-2023).PLOS global public health · 2024Article
- Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study.JMIR public health and surveillance · 2023Article
- An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: A Covid-19 case study.Research square · 2023Article
- The prevalence of SARS-CoV-2 infection and other public health outcomes during the BA.2/BA.2.12.1 surge, New York City, April-May 2022.Communications medicine · 2023Article
- Comparison of COVID-19 home-testers vs. laboratory-testers in New York State (excluding New York City), November 2021 to April 2022.Frontiers in public health · 2023Article
- Automatic case cluster detection using hospital electronic health record data.Biology methods & protocols · 2023Article
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundPassive, case-based surveillance underestimates the true extent of active infections in the population due to undiagnosed and untested cases, the exclusion of probable cases diagnosed point-of-care rapid antigen tests, and the exclusive use of at-home rapid tests which are not reported as part of case-based surveillance. The extent in which COVID-19 surveillance may be underestimating the burden of infection is likely due to time-varying factors such as decreased test-seeking behaviors and increased access to and availability of at-home testing.
objectiveThe objective of this study is to estimate the prevalence of SARS-CoV-2 based on different definitions of a case to ascertain the extent to which cases of SARS-CoV-2 may be underestimated by case-based surveillance.
methodsA survey on COVID-19 exposure, infection, and testing was administered to calculate point prevalence of SARS-CoV-2 among a diverse sample of cohort adults from February 8, 2022, to February 22, 2022. Three-point prevalence estimates were calculated among the cohort, as follows: (1) proportion positives based on polymerase chain reaction (PCR) and rapid antigen tests; (2) proportion positives based on testing exclusively with rapid at-home tests; and (3) proportion of probable undiagnosed cases. Test positivity and prevalence differences across booster status were also examined.
resultsAmong a cohort of 4328, there were a total of 644 (14.9%) cases. The point prevalence estimate based on PCR or rapid antigen tests was 5.5% (95% CI 4.8%-6.2%), 3.7% (95% CI 3.1%-4.2%) based on at-home rapid tests, and 5.7% (95% CI 5.0%-6.4%) based on the case definition of a probable case. The total point prevalence across all definitions was 14.9% (95% CI 13.8%-16.0%). The percent positivity among PCR or rapid tests was 50.2%. No statistically significant differences were observed in prevalence between participants with a COVID-19 booster compared to fully vaccinated and nonboosted participants except among exclusive at-home rapid testers.
conclusionsOur findings suggest a substantial number of cases were missed by case-based surveillance systems during the Omicron B.1.1.529 surge, when at-home testing was common. Point prevalence surveys may be a rapid tool to be used to understand SARS-CoV-2 prevalence and would be especially important during case surges to measure the scope and spread of active infections in the population.
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