Investigating confounding in network-based test-negative design influenza vaccine effectiveness studies-Experience from the DRIVE project

Abstract

Background: Establishing a large study network to conduct influenza vaccine effectiveness (IVE) studies while collecting appropriate variables to account for potential bias is important; the most relevant variables should be prioritized. We explored the impact of potential confounders on IVE in the DRIVE multi-country network of sites conducting test-negative design (TND) studies.

Methods: We constructed a directed acyclic graph (DAG) to map the relationship between influenza vaccination, medically attended influenza infection, confounders, and other variables. Additionally, we used the Development of Robust and Innovative Vaccines Effectiveness (DRIVE) data from the 2018/2019 and 2019/2020 seasons to explore the effect of covariate adjustment on IVE estimates. The reference model was adjusted for age, sex, calendar time, and season. The covariates studied were presence of at least one, two, or three chronic diseases; presence of six specific chronic diseases; and prior healthcare use. Analyses were conducted by site and subsequently pooled.

Results: The following variables were included in the DAG: age, sex, time within influenza season and year, health status and comorbidities, study site, health-care-seeking behavior, contact patterns and social precautionary behavior, socioeconomic status, and pre-existing immunity. Across all age groups and settings, only adjustment for lung disease in older adults in the primary care setting resulted in a relative change of the IVE point estimate >10%.

Conclusion: Our study supports a parsimonious approach to confounder adjustment in TND studies, limited to adjusting for age, sex, and calendar time. Practical implications are that necessitating fewer variables lowers the threshold for enrollment of sites in IVE studies and simplifies the pooling of data from different IVE studies or study networks.

Keywords: adjustment; confounders; covariate; influenza vaccine effectiveness; test-negative design.

FIGURE 1

Directed acyclic graph showing the relationship between current influenza vaccination and medically attended influenza infection. Exposure and arrows pointing toward the exposure are shown in green, outcome and arrows pointing toward the outcome in orange, other variables and arrows in blue. Adapted from Lane et al. * indicates the variable was not included in the DAG by Lane et al. † Healthcare seeking behavior: In addition to whether or not an individual seeks care, the timing of care seeking is important and applying time‐related restriction factors (regarding too long interval since symptom onset, too short interval since vaccination, and presentation when influenza is not circulating) is recommended. DAG, directed acyclic graph

FIGURE 2

Predictors of vaccination among test‐negative controls and predictors of testing positive for influenza (case) and testing negative for influenza (control), by vaccination status, based on odds ratios adjusted for age, sex, calendar time, and season that do not include one in the 95%CI. Variables that are predictors of both vaccination status and the outcome are marked in bold. The covariates included in the analyses were presence of at least one, at least two, and at least three chronic diseases (≥1, ≥2, ≥3 chronic); presence of cancer, cardiovascular disease, diabetes, lung disease, renal disease, and obesity; and number of hospitalizations and primary care visits in the last year.’‐’ signifies no significant predictors.