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

Anke L Stuurman; Miriam Levi; Philippe Beutels; Hélène Bricout; Alexandre Descamps; Gaël Dos Santos; Ian McGovern; Ainara Mira-Iglesias; Jos Nauta; Laurence Torcel-Pagnon; Jorne Biccler; DRIVE consortium

doi:10.1111/irv.13087

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

Influenza Other Respir Viruses. 2023 Jan;17(1):e13087. doi: 10.1111/irv.13087. Epub 2022 Dec 22.

Authors

Affiliations

¹ P95 Epidemiology and Pharmacovigilance, Leuven, Belgium.
² Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.
³ Epidemiology Unit, Department of Prevention, Tuscany Centre Health Authority, Florence, Italy.
⁴ Medical Department, Sanofi, Lyon, France.
⁵ Inserm CIC 1417, Assistance Publique Hôpitaux de Paris, Hôpital Cochin, Université de Paris, Paris, France.
⁶ Epidemiology, Value Evidence and Outcomes, GSK, Wavre, Belgium.
⁷ Center or Outcomes Research and Epidemiology, Medical Affairs, Seqirus Inc., Cambridge, Massachusetts, USA.
⁸ Vaccine Research Department, Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO - Public Health), Valencia, Spain.
⁹ Department of Innovation & Development, Established Pharmaceuticals Division, Abbott Healthcare Products B.V., Weesp, The Netherlands.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Case-Control Studies
Humans
Influenza A Virus, H3N2 Subtype
Influenza Vaccines*
Influenza, Human* / epidemiology
Influenza, Human* / prevention & control
Seasons
Treatment Outcome
Vaccination
Vaccine Efficacy

Substances

Influenza Vaccines