Martín-Cantera et al. BMC Public Health 2010, 10:136 http://www.biomedcentral.com/1471-2458/10/136 Page 5 of 9 Figure 1 Flow Chart: Algorithm of the study. Data collection In the inclusion visit, the health professional will gather the relevant medical data and history (illnesses, medica- tion, alcohol consumption and psychoactive substances) from the patient’s clinical record, after checking and completing it together with the patient. Then, relevant data on behaviour and risk-perception of suffering from an RTI, and data on previous traffic accidents will be collected. A yearly follow up via telephone is expected to be performed. Study variables We dispose of two data collection instruments in order to carry out this study -A survey, which will be carried out by health profes- sionals in a face to face interview. Data will be obtained from the clinical register, and double-checked by the researcher and patient during the visit. In this survey, the following will be noted: 1. Demo- graphic data: age, gender, civil status, place of origin, level of studies, social class (according to the categories proposed by the Spanish Epidemiology Society). 2. Med- ication: long-term medication, acute medication (taken in the last 30 days). 3. Chronic health conditions related to higher risk of RTI (Table 3). 4. Alcohol consumption (measured using the AUDIT-C test). 5. Consumption of psychoactive substances (cocaine, ecstasy and others) during the last year, as self-reported by the patient and self-declared dependency level of psychoactive sub- stances in the last month. 6. Estimated level of percep- tion risk, as assessed by the health professional, and rated using a scale from 0 (no risk) to 10 (maximum risk). 7. Driving data: type of driver’s license, length of time driver’s license held; type of vehicle most usually driven (moped, motorbike, car, others); professional dri- ver (y/n); time spent driving in hours per week; type of road usually used (urban, motorway or others); safety behaviours (use of seatbelts, use of helmet, use of car seats for children and adherence to speed limits). 8. Col- lision background in the last year (with or without injury); History of medical attention for traffic road inju- ries in the last year. 9. Self-declared self medicating. 10. Self evaluation of state of health (using the SF-12 test). 11. Self-perception of risk. Individuals will be asked to score their own risk for RTIs, when compared to sub- jects with their same gender and age, rated in a scale from 0 to 10. -Telephone survey: In the telephone survey carried out annually by a trained interviewer the following data will be collected and reviewed: 1. Collisions in the last year: date, time of day and day of the week when it occurred; type of road where the collision occurred; vehicle being driven and other vehicles implicated in the crash; medical attention needed (Yes/No/type); need for a sick leave due to traffic injuries (Yes/No/duration); after-effects (Yes/ No/type); approximate material costs/vehicle repairs needed (Yes/No/Costs). 2. Newly diagnosed long-term conditions (in the last year). 3. Chronic and acute medi- cation recently taken (time taken and coded according to therapeutic risk group). 4. Self-reported consumption of psychoactive substances. 5. Changes in driving condi- tions: type of vehicle usually driven, time spent driving in hours per week; type of road usually used (urban, motor- way or others); safety behaviour (use of seatbelts, use of helmet, use of car seats for children and adherence to speed limits). 6. Changes in level of self-perception of risk (as reported by the subject of study). Statistical analysis The characteristics of the population will be outlined using a descriptive univariant analysis, calculating the average, standard deviation, median, minimum and max- imum values for continuous variables, and frequency and percentages for categorical variables. The results of the descriptive part will be expressed with their confidence interval (CI 95%). In order to investigate the distribution of the risk factors associated with suffering an RTI according to age and gender, bivariant comparisons will be carried out using the Chi- square test between categorical variables and the