Testing for coronavirus (SARS-CoV-2) infection in populations with low infection prevalence: the largely ignored problem of false positives and the value of repeat testing
Sudlow, C.; Diggle, P.; Warlow, O.; Seymour, D.; Gordon, B.; Walker, R.; Warlow, C.
Show abstract
BackgroundCalls are increasing for widespread SARS-CoV-2 infection testing of people from populations with a very low prevalence of infection. We quantified the impact of less than perfect diagnostic test accuracy on populations, and on individuals, in low prevalence settings, focusing on false positives and the role of confirmatory testing. MethodsWe developed a simple, interactive tool to assess the impact of different combinations of test sensitivity, specificity and infection prevalence in a notional population of 100,000. We derived numbers of true positives, true negatives, false positives and false negatives, positive predictive value (PPV - the percentage of test positives that are true positives) and overall test accuracy for three testing strategies: (1) single test for all; (2) add repeat testing in test positives; (3) add further repeat testing in those with discrepant results. We also assessed the impact on test results for individuals having one, two or three tests under these three strategies. ResultsWith sensitivity of 80%, infection prevalence of 1 in 2,000, and specificity 99.9% on all tests, PPV in the tested population of 100,000 will be only 29% with one test, increasing to > 99.5% (100% when rounded to the nearest %) with repeat testing in strategies 2 or 3. More realistically, if specificity is 95% for the first and 99.9% for subsequent tests, single test PPV will be only 1%, increasing to 86% with repeat testing in strategy 2, or 79% with strategy 3 (albeit with 6 fewer false negatives than strategy 2). In the whole population, or in particular individuals, PPV increases as infection becomes more common in the population but falls to unacceptably low levels with lower test specificity. ConclusionTo avoid multiple unnecessary restrictions on whole populations, and in particular individuals, from widespread population testing for SARS-CoV-2, the crucial roles of extremely high test specificity and of confirmatory testing must be fully appreciated and incorporated into policy decisions.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Correlation of National and Healthcare Workers COVID-19 Infection Data; Implications for Large-scale Viral Testing Programs 92%
- Diagnostic performance and clinical implications of rapid SARS-CoV-2 antigen testing in Mexico using real-world nationwide COVID-19 registry data 92%
- Performance comparison of the Maxim and Sedia Limiting Antigen Avidity assays for incidence surveillance 91%
Similar papers in this journal
- Obtaining prevalence estimates of COVID-19: A model to inform decision-making 93%
- The impact of changes in diagnostic testing practices on estimates of COVID-19 transmission in the United States 92%
- Optimizing the sensitivity of detection of respiratory syncytial virus infections in longitudinal studies using the combination of weekly sample testing and biannual serology 92%
Similar papers in this journal
- Impact of baseline cases of cough and fever on UK COVID-19 diagnostic testing rates: estimates from the Bug Watch community cohort study 90%
- SARS-CoV-2 Antibody Lateral Flow Assay for antibody prevalence studies following vaccine roll out: a Diagnostic Accuracy Study 90%
- Sensitivity of RT-PCR testing of upper respiratory tract samples for SARS-CoV-2 in hospitalised patients: a retrospective cohort study. 89%
Similar papers in this journal
Similar papers in this journal
- Performance of Repeat BinaxNOW SARS-CoV-2 Antigen Testing in a Community Setting, Wisconsin, November-December 2020 91%
- Validity of self-testing at home with rapid SARS-CoV-2 antibody detection by lateral flow immunoassay 91%
- Antigen-based testing but not real-time PCR correlates with SARS-CoV-2 virus culture 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.