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Using street imagery and crowdsourcing internet marketplaces to measure motorcycle helmet use in Bangkok, Thailand
  1. Hasan S. Merali1,
  2. Li-Yi Lin2,
  3. Qingfeng Li3,
  4. Kavi Bhalla4
  1. 1 Division of Pediatric Emergency Medicine, McMaster Children’s Hospital, Hamilton, Ontario, Canada
  2. 2 Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
  3. 3 Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
  4. 4 Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
  1. Correspondence to Dr Hasan S. Merali, Division of Pediatric Emergency Medicine, McMaster Children’s Hospital, Hamilton, ON L8S 4K1, Canada; meralih{at}mcmaster.ca

Abstract

Introduction The majority of Thailand’s road traffic deaths occur on motorised two-wheeled or three-wheeled vehicles. Accurately measuring helmet use is important for the evaluation of new legislation and enforcement. Current methods for estimating helmet use involve roadside observation or surveillance of police and hospital records, both of which are time-consuming and costly. Our objective was to develop a novel method of estimating motorcycle helmet use.

Methods Using Google Maps, 3000 intersections in Bangkok were selected at random. At each intersection, hyperlinks of four images 90° apart were extracted. These 12 000 images were processed in Amazon Mechanical Turk using crowdsourcing to identify images containing motorcycles. The remaining images were sorted manually to determine helmet use.

Results After processing, 462 unique motorcycle drivers were analysed. The overall helmet wearing rate was 66.7 % (95% CI 62.6 % to 71.0 %). Taxi drivers had higher helmet use, 88.4% (95% CI 78.4% to 94.9%), compared with non-taxi drivers, 62.8% (95% CI 57.9% to 67.6%). Helmet use on non-residential roads, 85.2% (95% CI 78.1 % to 90.7%), was higher compared with residential roads, 58.5% (95% CI 52.8% to 64.1%). Using logistic regression, the odds of a taxi driver wearing a helmet compared with a non-taxi driver was significantly increased 1.490 (p<0.01). The odds of helmet use on non-residential roads as compared with residential roads was also increased at 1.389 (p<0.01).

Conclusion This novel method of estimating helmet use has produced results similar to traditional methods. Applying this technology can reduce time and monetary costs and could be used anywhere street imagery is used. Future directions include automating this process through machine learning.

  • traumatic brain injury
  • helmet
  • public health
  • motorcycle
  • surveillance
  • low-middle income country
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Footnotes

  • Funding Funding for this study was provided by the Alpha Chapter of the Delta Omega Public Health Honors Society at Johns Hopkins University.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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