PW 1768 Assessing motorcycle helmet use in developing countries – advantages of naturalistic observation over hospital based and road side questionnaire surveys. (November 2018)

• Record Type: Journal Article
• Authors: Siebert, Felix Wilhelm; Perego, Paolo; Albers, Deike; Lwin, Aye Moe; Santikarn, Chamaiparn
• Is Part Of: Injury prevention. Volume 24(2018)Supplement 2
• Page Start: A181
• Page End: A181
• Publication Date: 2018-11
• Subjects: Children's accidents -- Prevention -- Periodicals; Accidents -- Prevention -- Periodicals; 617.1
• DOI: 10.1136/injuryprevention-2018-safety.500
• Languages: English
• ISSNs: 1353-8047

Abstract

Abstract : Although motorcycle helmets are vital to prevent heavy injuries and fatalities in motorcycle crashes, only one third of low- and middle-income countries (LMIC) regularly collects helmet use data. When data is available, it is often undetailed or based on small sample sizes. Hence, methods for regular and detailed monitoring of motorcycle helmet use in LMIC are needed. In the light of the application in LMIC, resource-efficiency of these methods has to be considered. Common methods to estimate motorcycle helmet use (naturalistic observation, self-reports in questionnaire surveys, hospital based surveys) were compared for their accuracy, practicality, and efficiency. Original empirical data on motorcycle helmet use was collected in Myanmar and Tanzania from video-based naturalistic observation, hospital based surveys, and road side questionnaires. This data was further compared to existing data sources for helmet use in those countries. In Myanmar, the helmet use rate registered in hospitals was significantly lower than the helmet use rate observed in the surrounding area. In Tanzania, self-reported helmet use in questionnaire surveys was significantly higher than observed helmet use. Questionnaire surveys and hospital registration systems were more time consuming and labor-intensive to set up than naturalistic video-based observation. Video-based naturalistic observation is an efficient method to assess helmet use and yields more accurate helmet use estimates than other methods. Participants' responses in questionnaire surveys on helmet use were found to be biased toward higher helmet use numbers, most likely due to social desirability in participants' responses. Hospital based registration underestimated helmet use in Myanmar, as riders were more likely to end up in the hospital without a helmet. A regular helmet use assessment through video based naturalistic observation will allow road safety actors in LMIC to efficiently collect accurate and detailed data of motorcycle helmet use.