Seasonal influenza vaccine effectiveness estimates: Development of a parsimonious case test negative model using a...

Influenza vaccine effectiveness (VE) is increasingly estimated using the case-test negative study design. Cases have a symptom complex consistent with influenza and test positive for influenza, while non-cases have the same symptom complex but test negative. We aimed to determine a parsimonious logistic regression model for this study design when applied to patients in the community.

To determine the minimum covariate set required, we used a previously published systematic review to find covariates and restriction criteria commonly included in case-test negative logistic regression models. Covariates were assessed for inclusion using a directed acyclic graph. We used data from the Victorian Influenza Sentinel Practice Network from 2007 to 2013, excluding the pandemic year of 2009, to test the model. VE was estimated as (1-adjusted OR) * 100%. Changes in model fit from addition of specified covariates were examined. Restriction criteria were examined using change in VE estimate. VE was estimated for each year, all years aggregated, and for influenza type and sub-type.

Using publicly available software, the directed acyclic graph indicated that covariates specifying age, time within the influenza season, immunocompromising comorbid conditions and year or study site, where applicable, were required for closure. The inclusion of sex was not required. Inclusions and exclusions were validated when testing the variables (when collected) with our data. Restriction by time between onset and swab was supported by the data. VE for all years aggregated was estimated as 53% (95%CI 38, 64). VE was estimated as 42% (95%CI 19, 59) for H3N2, 75% (95%CI 51, 88) for H1N1pdm09 and 63% (95%CI 38, 79) for influenza B.

Theoretical covariates specified by the directed acyclic graph were validated when tested against surveillance data. A parsimonious model using the case test negative design allows regular estimates of VE and aggregated estimates by year.