Promoting Influenza Vaccination In General Practice Waiting Rooms

Overview

Most of family physicians (FPs) use advertising in their waiting rooms in order to educate patients. Our objective was to assess an advertising campaign for influenza vaccination using posters and pamphlets in FPs' waiting rooms. Registry based 2/1 cluster randomized controlled trial. Clusters gathered the listed patients over the age of 16 of 75 randomized FPs. The trial was conducted during the 2014-2015 influenza vaccination campaign. Intervention group, 25 FPs received and exposed in their waiting rooms pamphlets and one poster promoting the influenza vaccination campaign (added to the usual mandatory information). Control group (50 FPs), usual waiting room. The main outcome was the number of vaccination units delivered in pharmacies. Data were first extracted for 2013-2015 from the SIAM-ERASME claim database of the Health Insurance Fund of Lille-Douai (Northern France). The association between the intervention and the main outcome was assessed trough a generalized estimating equation.

Every autumn the French National Health Insurance conducts a seasonal influenza immunization campaign. Since 2009, in France like in many other countries, the seasonal influenza immunization uptake has been decreasing in people over 65 years of age and in people with diverse target chronic diseases or pregnancy. It has remained far under the national and European objective of 75%. For this reason, public advertising has been intensified (TV, newspapers and magazines) and the involvement of health professionals was stimulated, particularly by means of encounters with Health Insurance delegates, and posters and pamphlets to be exposed in FPs' waiting rooms. We conducted a preliminary systematic review (in press) to seek for publications assessing the effect of audio-visual aids in primary health waiting rooms. It appears that most articles about this topic have a low level of evidence and that the effect on patient health behaviour has only been assessed on surrogate outcomes. The existence of an effect on behaviour change is controversial, and if any, it is small sized, needing more than 10,000 subjects to be demonstrated. Our aim is to evaluate the effect of the advertising campaign using posters and pamphlets in FPs' waiting rooms on patients behaviour measuring the number of influenza vaccination units delivered in community pharmacies. We designed a single blinded 2/1 registry based cluster randomized controlled trial. The outcome is the number of seasonal influenza vaccination sets released in community pharmacies. The target population are patients over the age of 65 or having a chronic disease requiring seasonal influenza immunization (like chronic obstructive pulmonary disease (COPD) or diabetes). Patients are informed about the anonymous use of their data and can refuse to participate at any time. A cluster is defined as the patients over the age of 16, who are registered by the Health Insurance on the participating FPs' patients' lists. A computerized random draw is used to allocate FPs in each group. In the intervention group, FPs will receive and expose in their waiting rooms 135 pamphlets and one poster (added to the usual mandatory information) withdrawing all the other posters. In the control group, waiting rooms are kept in their usual state. To evaluate the number of FPs needed for the trial, we set an interclass correlation coefficient of 0.02, for α = 5% and β = 20%. With a predicted rate of influenza vaccination delivery of 0.65 in the intervention group and 0.60 in the control group, and a target size by FP of 400 patients, 75 FPs have to be enrolled (50 in the control group and 25 in the intervention group). Baseline is defined as the 2013-2014 vaccination campaign. Data are extracted between October 15, 2014 and February 28, 2015 from the SIAM-ERASME claim database of the Lille-Douai district Health Insurance Fund on patient level. To assess the association between the vaccination status (dependent variable) and the group intervention/control, because of the hierarchical structure of the data and the high incidence rate of the main outcome, we will use a generalized estimating equation (GEE) Poisson regression clustering by GP; an exchangeable working correlation matrix will be used. The analysis will be carried out using R software (version 3.3.1) and the package Geepack.