thèse doctorat de l'université bordeaux 2 - ISPED-Enseignement à ...

to Dec 08 were retrieved and photocopied from regional NHMP offices with permission ofthe officer in charge.Variables coded from accident registers included date, time, number and type of involvedvehicles, number of persons injured or who died in a reported crash, and whether crashoccurred during maintenance works. Variables coded from crash reports included date, time,location, direction of lane (North-bound or South-bound), light, weather, horizontal andvertical road profile, road surface and shoulder condition, ongoing maintenance, and crashcause [106].Type of crash was defined as single vehicle, same direction, opposite direction,sidewise, pedestrian. When more than one type was identified, crashes were coded as crash ofmost vulnerable involved road user; the vulnerability decreasing order was: pedestrian;opposite direction; sidewise or at intersection; single vehicle; same direction [106]. Details onnumber, injury severity, and type of road user involved (pedestrian, riders of two-wheelers, oroccupants of cars/jeeps, minibuses, buses, or trucks) were coded separately. Severity wasdefined as ‘severe’ when involved person was transported to hospital and ‘fatal’ wheninvolved road user died at crash scene or at hospital in first 24 hours of the event [85].AnalysisInformation on crashes from registers and reports were linked to make a single file based oncrash location (km) and crash date, available for all crashes. Crash, fatality, and severe injuryper 10 9 vehicle-km travelled for vehicle type and direction were computed using traffic countssurvey [83]. Proportion of crashes, fatalities, and injuries on HWZ were computed andcompared to other zones. Information on HWZ dates was only available for the Hyderabad-Hala sub-section. Due to this limitation, crash, fatality and injury rates for work and normaltraffic zone were computed only for the 50-km-long sub-section. Crash, fatality, and severeinjury risks according to road directions, vehicle types and traffic conditions were comparedusing rate ratios with 95% confidence intervals, rate differences, and attributable riskproportions where appropriate [104, 107]. Associations of factors with HWZs crashes wereestimated from a multiple logistic regression model, including all variables weakly associated(P