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Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA.
Burstyn, Igor; Goldstein, Neal D; Gustafson, Paul.
  • Burstyn I; Department of Environmental and Occupational Health, Drexel University Dornsife School of Public Health, 3215 Market St, Philadelphia, PA, 19104, USA. ib68@drexel.edu.
  • Goldstein ND; Department of Epidemiology & Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA. ib68@drexel.edu.
  • Gustafson P; Department of Epidemiology & Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA.
BMC Med Res Methodol ; 20(1): 146, 2020 06 06.
Article in English | MEDLINE | ID: covidwho-549102
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ABSTRACT

BACKGROUND:

Despite widespread use, the accuracy of the diagnostic test for SARS-CoV-2 infection is poorly understood. The aim of our work was to better quantify misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves using publicly available surveillance data from Alberta, Canada and Philadelphia, USA.

METHODS:

We examined time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, the sensitivity and specificity of the diagnostic test.

RESULTS:

Our analysis revealed that the data were compatible with near-perfect specificity, but it was challenging to gain information about sensitivity. We applied these insights to uncertainty/bias analysis of epidemic curves under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the adjusted epidemic curves likely falls within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among the tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate.

CONCLUSION:

The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Bayes Theorem / Coronavirus Infections / Clinical Laboratory Techniques / Pandemics / Betacoronavirus Type of study: Case report / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Systematic review/Meta Analysis Limits: Humans Country/Region as subject: North America Language: English Journal: BMC Med Res Methodol Journal subject: Medicine Year: 2020 Document Type: Article Affiliation country: S12874-020-01037-4

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Bayes Theorem / Coronavirus Infections / Clinical Laboratory Techniques / Pandemics / Betacoronavirus Type of study: Case report / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Systematic review/Meta Analysis Limits: Humans Country/Region as subject: North America Language: English Journal: BMC Med Res Methodol Journal subject: Medicine Year: 2020 Document Type: Article Affiliation country: S12874-020-01037-4