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Journal Article

Citation

Chalabe AM, Zumbay BE, Leinecker LE. Inj. Prev. 2012; 18(Suppl 1): A226.

Copyright

(Copyright © 2012, BMJ Publishing Group)

DOI

10.1136/injuryprev-2012-040590w.6

PMID

unavailable

Abstract

Introduction Injuries are a problem of transport municipal health. Since the creation of Sentinel Injury Unit working in schools and community training, evaluating population-based survey results with knowledge of the Traffic Act.

Objectives To validate the survey on Traffic Law. Classify groups of people and points that require treatment.

Methods A multiple correspondence analysis on surveys conducted by using the Softawre SPADN 4.0. Variables: gender, age, condition, knowledge of minimum age, safety features, requirements, prohibitions and limits established by the National Traffic Law.

Results Of a total of 795 surveys conducted in August 2011, there was a cluster analysis with hierarchical classification, and according to the dendrogram were chosen in April the following classes: CLASS 1: 27.04%, pedestrians, sex women (72%), with ignorance of the overtaking vehicle rules, circulation, obligations in case of accidents, road safety, bans, among others. CLASS 2:10.57% cyclists, with cat's eye as a safety, they know minimum age requirements and limits for the road and ignore rules (60, 71%), CLASS 3: 18.36%, vehicle motorcycle age 12-21 years, with 89.8% use case, 34% do not know minimum age, CLASS 4: 44.03% male, 32-52 years in motor vehicle, drivers who know rules and prohibitions for driving (53.2%).

Importance/Contribution in the Field This type of analysis allows us to classify the groups to specifically target, allowing suggest specific actions in prevention. This analysis allows to suggest hypotheses to explain and propose policy interventions.

This is an abstract of a presentation at Safety 2012, the 11th World Conference on Injury Prevention and Safety Promotion, 1-4 October 2012, Michael Fowler Center, Wellington, New Zealand. Full text does not seem to be available for this abstract.

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