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Assessing the reliability and validity of direct observation and traffic camera streams to measure helmet and motorcycle use
  1. Heather N Zaccaro1,
  2. Emily C Carbone2,
  3. Nishita Dsouza2,
  4. Michelle R Xu3,
  5. Mary C Byrne2,
  6. John D Kraemer3
  1. 1Department of International Health, Georgetown University, Washington, DC, USA
  2. 2Department of Human Science, Georgetown University, Washington, DC, USA
  3. 3Department of Health Systems Administration, Georgetown University, Washington, DC, USA
  1. Correspondence to Professor John D Kraemer, Department of Health Systems Administration, Georgetown University, 231 St Mary's Hall, 3700 Reservoir Road, Washington, DC 20057, USA; jdk32{at}


There is a need to develop motorcycle helmet surveillance approaches that are less labour intensive than direct observation (DO), which is the commonly recommended but never formally validated approach, particularly in developing settings. This study sought to assess public traffic camera feeds as an alternative to DO, in addition to the reliability of DO under field conditions. DO had high inter-rater reliability, κ=0.88 and 0.84, respectively, for cycle type and helmet type, which reinforces its use as a gold standard. However, traffic camera-based data collection was found to be unreliable, with κ=0.46 and 0.53 for cycle type and helmet type. When bicycles, motorcycles and scooters were classified based on traffic camera streams, only 68.4% of classifications concurred with those made via DO. Given the current technology, helmet surveillance via traffic camera streams is infeasible, and there remains a need for innovative traffic safety surveillance approaches in low-income urban settings.

  • direct observation
  • traffic camera
  • motorcycles
  • helmets

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