Risk Level of Suffering From Traffic Injury in Primary Health Care: LESIONAT Project (LESIONAT)
Recruitment status was: Enrolling by invitation
- To know the distribution of risk elements of traffic injuries
- To study the distribution of risk elements related with the possibility of suffering from a TI in relation to medication, chronic health problems, and consumption of psychoactive substances (alcohol and others).
- To know the relation between levels of perception of risk of suffering from a TI and the presence of chronic problems, medications, or consumption of psychoactive substances.
|Traffic Accident Chronic Disease Prevalence of Psychoactive Substances in Road Traffic Relative Risk Estimation of Accident for Impaired Drivers|
|Study Design:||Observational Model: Ecologic or Community
Time Perspective: Cross-Sectional
|Official Title:||Risk Level of Suffering From Traffic Injury in Primary Health Care: LESIONAT Project|
|Study Start Date:||January 2009|
|Estimated Study Completion Date:||September 2009|
|Estimated Primary Completion Date:||June 2009 (Final data collection date for primary outcome measure)|
Design: Observational, transversal, and multicentric research
Field of study: 20 PHC users from urban areas
Population of study: random consecutive sampling of ≥ 16 years old drivers with open medical history.
Measure's tools: Two means for data collection: survey among health professionals with medical history and structured telephonic survey about behaviours and risk perceptions in drivers.
Variables: Sociodemographic data, chronic pathologies related to TI, consumption of medications ,Alcohol consumption (AUDIT-C test), Psychoactive substances consumption (self declared), Level of perception of risk according to professionals.
Telephone survey: Class and age of driving license, Type of roads, Weekly driving time, Safety behaviours, Record of collisions/injuries in last year,Self perception of health level(SF-12) andSelf perception of risk level.
An descriptive analysis of population will be performed, a distribution of risk elements associated to TI will be described through bivariant analyses, and for describing the factors associated to perceived risk levels a linear regression multiple model will be built.
Please refer to this study by its ClinicalTrials.gov identifier: NCT00778440
|Unitat Suport Recerca Barcelona. IDIAP Jordi Go|
|Barcelona, Spain, 08006|
|Principal Investigator:||Carlos Martin, MD, PhD||IDIAP Jordi Gol|