Bias correction methods for test-negative designs in the presence of misclassification

Combined multiplex panel test results are a poor estimate of disease prevalence without adjustment for test error

Multiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes. When we are dealing with multiplex test panels with many components, test error in the individual components of a panel, even when present at very low levels, can cause significant overall error. Uncertainty in the sensitivity and specificity of the individual tests, and statistical fluctuations in the numbers of false positives and false negatives, will cause large uncertainty in the combined estimates of disease prevalence. In many cases this can be a source of significant bias. In this paper we develop a mathematical framework to characterise this issue, we determine expressions for the sensitivity and specificity of panel tests. In this we identify a counter-intuitive relationship between panel test sensitivity and disease prevalence that means panel tests become more sensitive as prevalence increases. We present novel statistical methods that adjust for bias and quantify uncertainty in prevalence estimates from panel tests, and use simulations to test these methods. As multiplex testing becomes more commonly used for screening in routine clinical practice, accumulation of test error due to the combination of large numbers of test results needs to be identified and corrected for.

Estimating the number of probable new SARS-CoV-2 infections among tested subjects from the number of confirmed cases

OBJECTIVES

In most African countries, confirmed COVID-19 case counts underestimate the number of new SARS-CoV-2 infection cases. We propose a multiplying factor to approximate the number of biologically probable new infections from the number of confirmed cases.

METHODS

Each of the first thousand suspect (or alert) cases recorded in South Kivu (DRC) between 29 March and 29 November 2020 underwent a RT-PCR test and an IgM and IgG serology. A latent class model and a Bayesian inference method were used to estimate (i) the incidence proportion of SARS-CoV-2 infection using RT-PCR and IgM test results, (ii) the prevalence using RT-PCR, IgM and IgG test results; and, (iii) the multiplying factor (ratio of the incidence proportion on the proportion of confirmed -RT-PCR+- cases).

RESULTS

Among 933 alert cases with complete data, 218 (23%) were RT-PCR+; 434 (47%) IgM+; 464 (~ 50%) RT-PCR+, IgM+, or both; and 647 (69%) either IgG + or IgM+. The incidence proportion of SARS-CoV-2 infection was estimated at 58% (95% credibility interval: 51.8-64), its prevalence at 72.83% (65.68-77.89), and the multiplying factor at 2.42 (1.95-3.01).

CONCLUSIONS

In monitoring the pandemic dynamics, the number of biologically probable cases is also useful. The multiplying factor helps approximating it.

Bias assessment of a test-negative design study of COVID-19 vaccine effectiveness used in national policymaking

National test-negative-case-control (TNCC) studies are used to monitor COVID-19 vaccine effectiveness in the UK. A questionnaire was sent to participants from the first published TNCC COVID-19 vaccine effectiveness study conducted by the UK Health Security Agency, to assess for potential biases and changes in behaviour related to vaccination. The original study included symptomatic adults aged ≥70 years testing for COVID-19 between 08/12/2020 and 21/02/2021. A questionnaire was sent to cases and controls tested from 1-21 February 2021. In this study, 8648 individuals responded to the questionnaire (36.5% response). Using information from the questionnaire to produce a combined estimate that accounted for all potential biases decreased the original vaccine effectiveness estimate after two doses of BNT162b2 from 88% (95% CI: 79-94%) to 85% (95% CI: 68-94%). Self-reported behaviour demonstrated minimal evidence of riskier behaviour after vaccination. These findings offer reassurance to policy makers and clinicians making decisions based on COVID-19 vaccine effectiveness TNCC studies.

Characterization of Rotavirus Infection in Hospitalized Children under 5 with Acute Gastroenteritis 5 Years after Introducing the Rotavirus Vaccines in South Korea

We herein characterized rotavirus infection in hospitalized children under 5 years of age with gastroenteritis after introducing rotavirus vaccines in South Korea from 20 February 2012, to 31 March 2013. Enzyme-linked fluorescent immunoassay was performed to detect rotavirus antigens. G and P genotyping was performed using nested multiplex PCR. For the failed PCR samples, sequencing was conducted. We performed a test-negative case-control study to estimate vaccine effectiveness. Vaccine effectiveness was measured using a multivariate logistic regression model. Rotavirus was detected in 16 (13.2%) of the 121 patients, with a seasonal peak in April 2012. The dominant genotypes detected were G3P[8] (33.3%) and G4P[6] (26.7%), and vaccine effectiveness against rotavirus hospitalization was 84.9% [95% CI: 23.2−97.0] in the complete vaccinated group. A higher prevalence of rotavirus infection was observed among children with siblings than those without siblings (p < 0.001). Also, the presence of siblings was significantly associated with a history of nonvaccination (p < 0.001). In conclusion, the prevalence of rotavirus followed a decreasing trend, and there was no evidence of emergences of nonvaccine-type strains. Vaccine effectiveness against rotavirus hospitalization was 84.9%. Although children with siblings were more susceptible to rotavirus infection, they were less likely to receive vaccination against rotavirus.

Understanding Variation in Rotavirus Vaccine Effectiveness Estimates in the United States: The Role of Rotavirus Activity and Diagnostic Misclassification

BACKGROUND

Estimates of rotavirus vaccine effectiveness (VE) in the United States appear higher in years with more rotavirus activity. We hypothesized rotavirus VE is constant over time but appears to vary as a function of temporal variation in local rotavirus cases and/or misclassified diagnoses.

METHODS

We analyzed 6 years of data from eight US surveillance sites on 8- to 59-month olds with acute gastroenteritis symptoms. Children's stool samples were tested via enzyme immunoassay (EIA); rotavirus-positive results were confirmed with molecular testing at the US Centers for Disease Control and Prevention. We defined rotavirus gastroenteritis cases by either positive on-site EIA results alone or positive EIA with Centers for Disease Control and Prevention confirmation. For each case definition, we estimated VE against any rotavirus gastroenteritis, moderate-to-severe disease, and hospitalization using two mixed-effect regression models: the first including year plus a year-vaccination interaction, and the second including the annual percent of rotavirus-positive tests plus a percent positive-vaccination interaction. We used multiple overimputation to bias-adjust for misclassification of cases defined by positive EIA alone.

RESULTS

Estimates of annual rotavirus VE against all outcomes fluctuated temporally, particularly when we defined cases by on-site EIA alone and used a year-vaccination interaction. Use of confirmatory testing to define cases reduced, but did not eliminate, fluctuations. Temporal fluctuations in VE estimates further attenuated when we used a percent positive-vaccination interaction. Fluctuations persisted until bias-adjustment for diagnostic misclassification.

CONCLUSIONS

Both controlling for time-varying rotavirus activity and bias-adjusting for diagnostic misclassification are critical for estimating the most valid annual rotavirus VE.