Covid-19 Vaccine Effectiveness and the Test-Negative Design.
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ABSTRACT:
SUBMITTER: Dean NE
PROVIDER: S-EPMC8451180 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
Covid-19 Vaccine Effectiveness and the Test-Negative Design.
Dean Natalie E NE Hogan Joseph W JW Schnitzer Mireille E ME
The New England journal of medicine 20210908 15
Similar Datasets
Project description:Aim: It is important to assess if clinical trial efficacy translates into real-world effectiveness for COVID-19 vaccines. Materials & methods: We conducted a modified test-negative design (TND) to evaluate the real-world effectiveness of three COVID-19 vaccines. We defined cases in two ways: self-reported COVID-19-positive tests, and self-reported positive tests with ≥1 moderate/severe COVID-19 symptom. Results: Any vaccination was associated with a 95% reduction in subsequently reporting a positive COVID-19 test, and a 71% reduction in reporting a positive test and ≥1 moderate/severe symptom. Conclusion: We observed high effectiveness across all three marketed vaccines, both for self-reported positive COVID-19 tests and moderate/severe COVID-19 symptoms. This innovative TND approach can be implemented in future COVID-19 vaccine and treatment real-world effectiveness studies. Clinicaltrials.gov identifier: NCT04368065.
| S-EPMC9504802 | biostudies-literature
Project description: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.
| S-EPMC10325974 | biostudies-literature
Project description:BackgroundObservational studies are used for estimating vaccine effectiveness under real-world conditions. The practical performance of two common approaches-cohort and test-negative designs-need to be compared for COVID-19 vaccines.MethodsWe compared the cohort and test-negative designs to estimate the effectiveness of the BNT162b2 vaccine against COVID-19 outcomes using nationwide data from the United States Department of Veterans Affairs. Specifically, we (1) explicitly emulated a target trial using follow-up data and evaluated the potential for confounding using negative controls and benchmarking to a randomized trial, (2) performed case-control sampling of the cohort to confirm empirically that the same estimate is obtained, (3) further restricted the sampling to person-days with a test, and (4) implemented additional features of a test-negative design. We also compared their performance in limited datasets.ResultsEstimated BNT162b2 vaccine effectiveness was similar under all four designs. Empirical results suggested limited residual confounding by healthcare-seeking behavior. Analyses in limited datasets showed evidence of residual confounding, with estimates biased downward in the cohort design and upward in the test-negative design.ConclusionVaccine effectiveness estimates under a cohort design with explicit target trial emulation and a test-negative design were similar when using rich information from the VA healthcare system, but diverged in opposite directions when using a limited dataset. In settings like ours with sufficient information on confounders and other key variables, the cohort design with explicit target trial emulation may be preferable as a principled approach that allows estimation of absolute risks and facilitates interpretation of effect estimates.
| S-EPMC11022682 | biostudies-literature
Project description:Test-negative-design COVID-19 vaccine effectiveness (VE) studies use symptomatic SARS-CoV-2-positive individuals as cases and symptomatic SARS-CoV-2-negative individuals as controls to evaluate COVID-19 VE. To evaluate the potential bias introduced by the correlation of COVID-19 and influenza vaccination behaviors, we assessed changes in estimates of VE of bivalent vaccines against COVID-19-associated hospitalizations and emergency department/urgent care (ED/UC) encounters when considering influenza vaccination status or including or excluding influenza-positive controls using data from the multi-state VISION vaccine effectiveness network. Analyses included encounters during October 2022 - February 2023, a period of SARS-CoV-2 and influenza cocirculation. When considering influenza vaccination status or including or excluding influenza-positive controls, COVID-19 VE estimates were robust, with most VE estimates against COVID-19-associated hospitalization and ED/UC encounters changing less than 5 percentage points. Higher proportions of influenza-positive patients among controls, influenza vaccination coverage, or VE could impact these findings; the potential bias should continue to be assessed.
| S-EPMC11823735 | biostudies-literature
Project description:The test-negative design (TND) is a popular method for evaluating vaccine effectiveness (VE). A "classical" TND study includes symptomatic individuals tested for the disease targeted by the vaccine to estimate VE against symptomatic infection. However, recent applications of the TND have attempted to estimate VE against infection by including all tested individuals, regardless of their symptoms. In this article, we use directed acyclic graphs and simulations to investigate potential biases in TND studies of COVID-19 VE arising from the use of this "alternative" approach, particularly when applied during periods of widespread testing. We show that the inclusion of asymptomatic individuals can potentially lead to collider stratification bias, uncontrolled confounding by health and healthcare-seeking behaviors (HSBs), and differential outcome misclassification. While our focus is on the COVID-19 setting, the issues discussed here may also be relevant in the context of other infectious diseases. This may be particularly true in scenarios where there is either a high baseline prevalence of infection, a strong correlation between HSBs and vaccination, different testing practices for vaccinated and unvaccinated individuals, or settings where both the vaccine under study attenuates symptoms of infection and diagnostic accuracy is modified by the presence of symptoms.
| S-EPMC11879563 | biostudies-literature
Project description:Test-negative designs (TNDs) are used to assess vaccine effectiveness (VE). Protection from infection-induced immunity may confound the association between case and vaccination status, but collecting reliable infection history can be challenging. If vaccinated individuals have less infection-induced protection than unvaccinated individuals, failure to account for infection history could underestimate VE, though the bias is not well understood. We simulated individual-level SARS-CoV-2 infection and COVID-19 vaccination histories and a TND. VE against symptomatic infection and VE against severe disease estimates unadjusted for infection history underestimated VE compared to estimates adjusted for infection history, and unadjusted estimates were more likely to be below 0%, which could lead to an incorrect interpretation that COVID-19 vaccines are harmful. TNDs assessing VE immediately following vaccine rollout introduced the largest bias and potential for negative VE against symptomatic infection. Despite the potential for bias, VE estimates from TNDs without prior infection information are useful because underestimation is rarely more than 8 percentage points.
| S-EPMC11579392 | biostudies-literature
Project description:Influenza vaccination is the most practical means available for preventing influenza virus infection and is widely used in many countries. Because vaccine components and circulating strains frequently change, it is important to continually monitor vaccine effectiveness (VE). The test-negative design is frequently used to estimate VE. In this design, patients meeting the same clinical case definition are recruited and tested for influenza; those who test positive are the cases and those who test negative form the comparison group. When determining VE in these studies, the typical approach has been to use logistic regression, adjusting for potential confounders. Because vaccine coverage and influenza incidence change throughout the season, time is included among these confounders. While most studies use unconditional logistic regression, adjusting for time, an alternative approach is to use conditional logistic regression, matching on time. Here, we used simulation data to examine the potential for both regression approaches to permit accurate and robust estimates of VE. In situations where vaccine coverage changed during the influenza season, the conditional model and unconditional models adjusting for categorical week and using a spline function for week provided more accurate estimates. We illustrated the two approaches on data from a test-negative study of influenza VE against hospitalization in children in Hong Kong which resulted in the conditional logistic regression model providing the best fit to the data.
| S-EPMC5545127 | biostudies-other
Project description:BackgroundReal-world COVID-19 vaccine effectiveness (VE) studies are investigating exposures of increasing complexity accounting for time since vaccination. These studies require methods that adjust for the confounding that arises when morbidities and demographics are associated with vaccination and the risk of outcome events. Methods based on propensity scores (PS) are well-suited to this when the exposure is dichotomous, but present challenges when the exposure is multinomial.ObjectiveThis simulation study aimed to investigate alternative methods to adjust for confounding in VE studies that have a test-negative design.MethodsAdjustment for a disease risk score (DRS) is compared with multivariable logistic regression. Both stratification on the DRS and direct covariate adjustment of the DRS are examined. Multivariable logistic regression with all the covariates and with a limited subset of key covariates is considered. The performance of VE estimators is evaluated across a multinomial vaccination exposure in simulated datasets.ResultsBias in VE estimates from multivariable models ranged from -5.3% to 6.1% across 4 levels of vaccination. Standard errors of VE estimates were unbiased, and 95% coverage probabilities were attained in most scenarios. The lowest coverage in the multivariable scenarios was 93.7% (95% CI 92.2%-95.2%) and occurred in the multivariable model with key covariates, while the highest coverage in the multivariable scenarios was 95.3% (95% CI 94.0%-96.6%) and occurred in the multivariable model with all covariates. Bias in VE estimates from DRS-adjusted models was low, ranging from -2.2% to 4.2%. However, the DRS-adjusted models underestimated the standard errors of VE estimates, with coverage sometimes below the 95% level. The lowest coverage in the DRS scenarios was 87.8% (95% CI 85.8%-89.8%) and occurred in the direct adjustment for the DRS model. The highest coverage in the DRS scenarios was 94.8% (95% CI 93.4%-96.2%) and occurred in the model that stratified on DRS. Although variation in the performance of VE estimates occurred across modeling strategies, variation in performance was also present across exposure groups.ConclusionsOverall, models using a DRS to adjust for confounding performed adequately but not as well as the multivariable models that adjusted for covariates individually.
| S-EPMC11811671 | biostudies-literature
Project description:ObjectivesTo estimate vaccine effectiveness after the first and second dose of ChAdOx1 nCoV-19 against symptomatic COVID-19 and infection in a socially vulnerable community in Brazil when Gamma and Delta were the predominant variants circulating.MethodsWe conducted a test-negative study in the community Complexo da Maré, the largest group of slums (n = 16) in Rio de Janeiro, Brazil, from January 17, 2021 to November 27, 2021. We selected RT-qPCR positive and negative tests from a broad community testing program. The primary outcome was symptomatic COVID-19 (positive RT-qPCR test with at least one symptom) and the secondary outcome was infection (any positive RT-qPCR test). Vaccine effectiveness was estimated as 1 - OR, which was obtained from adjusted logistic regression models.ResultsWe included 10 077 RT-qPCR tests (6,394, 64% from symptomatic and 3,683, 36% from asymptomatic individuals). The mean age was 40 (SD: 14) years, and the median time between vaccination and RT-qPCR testing among vaccinated was 41 (25-75 percentile: 21-62) days for the first dose and 36 (25-75 percentile: 17-59) days for the second dose. Adjusted vaccine effectiveness against symptomatic COVID-19 was 31.6% (95% CI, 12.0-46.8) 21 days after the first dose and 65.1% (95% CI, 40.9-79.4) 14 days after the second dose. Adjusted vaccine effectiveness against COVID-19 infection was 31.0% (95% CI, 12.7-45.5) 21 days after the first dose and 59.0% (95% CI, 33.1-74.8) 14 days after the second dose.DiscussionChAdOx1 nCoV-19 was effective in reducing symptomatic COVID-19 in a socially vulnerable community in Brazil when Gamma and Delta were the predominant variants circulating.
| S-EPMC8828302 | biostudies-literature
Project description:It is important to understand real-world BNT162b2 COVID-19 vaccine effectiveness (VE), especially among racial and ethnic minority groups. We performed a test-negative case-control study to measure BNT162b2 COVID-19 VE in the prevention of COVID-19-associated acute respiratory illness (ARI) hospitalizations at two Atlanta hospitals from May 2021-January 2023 and adjusted for potential confounders by multivariate analysis. Among 5139 eligible adults with ARI, 2763 (53.8%) were enrolled, and 1571 (64.5%) were included in the BNT162b2 analysis. The median age was 58 years (IQR, 44-68), 889 (56.6%) were female, 1034 (65.8%) were African American, 359 (22.9%) were White, 56 (3.6%) were Hispanic ethnicity, 645 (41.1%) were SARS-CoV-2-positive, 412 (26.2%) were vaccinated with a primary series, and 273 (17.4%) had received ≥1 booster of BNT162b2. The overall adjusted VE of the BNT162b2 primary series was 58.5% (95% CI 46.0, 68.1), while the adjusted VE of ≥1 booster was 78.9% (95% CI 70.0, 85.1). The adjusted overall VE of primary series for African American/Black individuals was 64.0% (95% CI 49.9, 74.1) and 82.7% (95% CI 71.9, 89.4) in those who received ≥1 booster. When analysis was limited to the period of Omicron predominance, overall VE of the primary series decreased with widened confidence intervals (24.5%, 95% CI -4.5, 45.4%), while VE of ≥1 booster was maintained at 60.9% (95% CI 42.0, 73.6). BNT162b2 primary series and booster vaccination provided protection against COVID-19-associated ARI hospitalization among a predominantly African American population.
| S-EPMC11209557 | biostudies-literature