A study comparing the effectiveness of three warning labels on the package of driving-impairing medicines.

BACKGROUND: Several medicines are known to potentially impair patients' driving fitness. Appropriate communication towards patients about this risk can be supported by the use of package warning labels. OBJECTIVE: To compare the effectiveness of a standing practice yellow/black label-with written warning-with a newly developed rating model in communicating risk on driving-impairing medicines (DIMs). Furthermore, the added value of a side-text in the rating model was determined. SETTING: Community pharmacies in the Netherlands. METHOD: In a cross-sectional study, patients with a first dispensing of a DIM were asked by their community pharmacists (n = 38) to fill out a written questionnaire to compare each of the three warning labels. A 2 [yellow/black label vs. rating model (pair 1) and rating model with side-text vs. rating model without side-text (pair 2)] × 3 [category of driving-impairment: I = minor risk, II = moderate risk, III = severe risk] design was used. The category of driving-impairment varied per respondent, depending on the DIM the patient collected. MAIN OUTCOME MEASURE: (1) estimated level of driving risk valued by patients (2) intention to change driving behaviour after seeing the warning label. RESULTS: An estimated number of 992 patients were approached. As 298 questionnaires were analysed, the net response rate was 30%. With the yellow/black label, respondents considered DIMs of all three categories of driving-impairment to equally impair driving fitness, while with the rating model the estimated risk was higher when the category referred to a higher level of driving-impairment. Addition of a side-text to the rating model resulted in a significantly higher estimated level of driving risk and a significant increase in intention to change driving behaviour. Only 8.0% of the patients using a category III DIM estimated the level of driving risk correctly when seeing the yellow/black label, while this was 26.7% for the rating model and 43.0% for the rating model with side-text. CONCLUSION: The yellow/black label, which is standing practice in the Netherlands, is less effective in terms of estimated risk and intention to change driving behaviour, compared to a newly developed rating model. This model is even more effective when a side-text is added. Implementation of the rating model in clinical practice should be considered.

How effective are pictograms in communicating risk about driving-impairing medicines?

OBJECTIVES: To evaluate and compare the effectiveness of 2 pictograms in communicating risk in terms of respondents' level of understanding, estimated level of driving risk, and intention to change driving behavior. The added value of a side-text was also investigated. METHODS: Two experiments were conducted among 270 drivers visiting a pharmacy. Experiment one used a 2 (rating model vs. triangle model pictogram, same side-text) × 3 (minor vs. moderate vs. severe driving risk) between-subjects design. Respondents (n = 30 per condition) were exposed to one of the 6 conditions. To verify the added value of the side-text, a 2 (rating model with side-text vs. rating model without side-text) × 3 (same categories as before) between-subjects design was used. RESULTS: Although the majority of the respondents understood that the pictograms were related to driving behavior, less than 10 percent and about 36 percent of the respondents looking at the triangle model and at the rating model, respectively, understood it fully. For all categories of risk, respondents who saw the rating model pictogram associated the pictogram significantly more often with risk of medication intake for driving than those who saw the triangle model pictograms. Those exposed to the triangle model overestimated the driving risk of the lowest category and underestimated the risk of the highest category; 78.8 percent of the respondents stated they were (very) likely to change their driving behavior if they were confronted with the pictogram. The added value of the side-text was not confirmed. CONCLUSIONS: Despite not being fully self-explanatory in conveying warnings and safety-related information, the pictograms evaluated in this research provided good insight into the different levels of driving risks, especially the rating model pictogram, because respondents' intentions to change their driving behaviors increased with higher categories of risk. The added value of the side-text in the rating model pictogram was not confirmed in this research. Pictograms can be seen as a valuable means to reinforce both written and spoken information given to patients by health care providers at the time of consultation.

A European approach to categorizing medicines for fitness to drive: outcomes of the DRUID project.

AIMS: To illustrate (i) the criteria and the development of the DRUID categorization system, (ii) the number of medicines that have currently been categorized, (iii) the added value of the DRUID categorization system and (iv) the next steps in the implementation of the DRUID system. METHODS: The development of the DRUID categorization system was based on several criteria. The following steps were considered: (i) conditions of use of the medicine, (ii) pharmacodynamic and pharmacokinetic data, (iii) pharmacovigilance data, including prevalence of undesirable effects, (iv) experimental and epidemiological data, (v) additional data derived from the patient information leaflet, existing categorization systems and (vi) final categorization. DRUID proposed four tiered categories for medicines and driving. RESULTS: In total, 3054 medicines were reviewed and over 1541 medicines were categorized (the rest were no longer on the EU market). Nearly half of the 1541 medicines were categorized 0 (no or negligible influence on fitness to drive), about 26% were placed in category I (minor influence on fitness to drive) and 17% were categorized as II or III (moderate or severe influence on fitness to drive). CONCLUSIONS: The current DRUID categorization system established and defined standardized and harmonized criteria to categorize commonly used medications, based on their influence on fitness to drive. Further efforts are needed to implement the DRUID categorization system at a European level and further activities should be undertaken in order to reinforce the awareness of health care professionals and patients on the effects of medicines on fitness to drive.

Predictors for patient knowledge and reported behaviour regarding driving under the influence of medicines: a multi-country survey.

BACKGROUND: Reports on the state of knowledge about medicines and driving showed an increased concern about the role that the use of medicines might play in car crashes. Much of patient knowledge regarding medicines comes from communications with healthcare professionals. This study, part of the DRUID (Driving Under the Influence of Drugs, alcohol and medicines) project, was carried out in four European countries and attempts to define predictors for knowledge of patients who use driving-impairing medicines. The influence of socio-demographic variables on patient knowledge was investigated as well as the influence of socio-demographic factors, knowledge and attitudes on patients' reported behaviour regarding driving under the influence of medicines. METHODS: Pharmacists handed out questionnaires to patients who met the inclusion criteria: 1) prevalent user of benzodiazepines, antidepressants or first generation antihistamines for systemic use; 2) age between 18 and 75 years old and 3) actual driver of a motorised vehicle. Factors affecting knowledge and reported behaviour towards driving-impairing medicines were analysed by means of multiple linear regression analysis and multiple logistic regression analysis, respectively. RESULTS: A total of 633 questionnaires (out of 3.607 that were distributed to patients) were analysed. Patient knowledge regarding driving under the influence of medicines is better in younger and higher educated patients. Information provided to or accessed by patients does not influence knowledge. Patients who experienced side effects and who have a negative attitude towards driving under the influence of impairing medicines are more prone to change their driving frequency behaviour than those who use their motorised vehicles on a daily basis or those who use anti-allergic medicines. CONCLUSIONS: Changes in driving behaviour can be predicted by negative attitudes towards driving under the influence of medicines but not by patients' knowledge regarding driving under the influence of medicines. Future research should not only focus on information campaigns for patients but also for healthcare providers as this might contribute to improve communications with patients regarding the risks of driving under the influence of medicines.