WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Some commonly prescribed medications can be a hazardous to traffic safety.• Fifteen categorization systems are currently available in Europe. However, none of these systems clearly reports the methodology that was followed in order to categorize medications that impair driving.• None of the existing categorization systems are currently implemented at European level. WHAT THIS STUDY ADDS• This study describes standardized and harmonized criteria to categorize medications according to their potential to impair fitness to drive. • This study proposes a European categorization system of medications that impair driving that covers all the most frequently prescribed medications.• The proposed categorization system can be seen as a tool to improve prescribing and dispensing procedures of medications that impair driving as well as an instrument to make patients aware of the role medications play in traffic safety. AIMSTo 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. METHODSThe 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. RESULTSIn 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). CONCLUSIONSThe 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.

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.

BackgroundReports 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.MethodsPharmacists 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.ResultsA 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.ConclusionsChanges 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.

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.

Purpose: Pictograms can increase public awareness about driving-impairing effects of medicines. However, pictograms that are not clear will negatively affect the comprehension of the message. Older and low educated adults are particularly vulnerable to misunderstandings. Comprehension is expected to be influenced by preference for the type of pictograph, but little is known about the preference of pictograms among drivers of different age groups and education levels. This study aims to investigate older and lower educated adults' preference for a pictogram (triangle model pictogram versus rating model pictogram) related to the influence of taking driving-impairing medicines on driving fitness. Methods:Interviews among 270 drivers visiting a pharmacy were conducted. Participants were asked about their preference for the best pictogram expressing a warning message and expressing levels of impairment. A comparison between a pictogram with a more complex design (rating model) and an already implemented one (triangle model) was made.Results: 74.4% of the participants preferred the rating model to express warning messages and 82.6% preferred this model to express levels of impairment. However, older and low educated participants were more likely to prefer the triangle model over the more complex rating model. Age was the strongest predictor influencing participants' preference for pictograms to express a warning message and levels of impairment. Young participants (18-39 years old) with high education level had the highest preference for the rating model, whereas older participants (> 60 years old) with low education level showed the lowest preference for this pictogram system. Conclusion:Age and education level are sensitive factors to be considered when designing a pictogram. In order to be equally well understood by older and low educated adults, pictograms should have a simple design and make use of familiar objects.