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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.

This study aims to quantify and describe the risk of non-fatal pedestrian injury among persons who use wheelchairs in the US. Cases of pedestrian injury between 2002 and 2010 among persons using wheelchairs were identified in the National Electronic Injury Surveillance System to generate national injury estimates. Between 2002 and 2010, an estimated 9348 (95% CI 4912 to 13,784) people were treated in emergency departments for non-fatal pedestrian injuries sustained while using wheelchairs. Using wheelchair-use denominators calculated from the Survey of Income and Program Participation, this equates to an incidence rate of 31.3 (95% CI 16.4 to 46.1) per 100,000 person-years. Injury risk was 3.5 times higher for men than women (p

Objectives Bicycling and helmet surveillance, research, and programme evaluation depend on accurate measurement by direct observation, but it is unclear whether weather and other exogenous factors introduce bias into observed counts of cyclists and helmet use. Methods To address this issue , a time series was created of cyclists observed at two observation points in Washington, DC, at peak commuting times and locations between September 2012 and February 2013. Using multiple linear regression with Newey-West SEs to account for possible serial correlation, the association between various factors and cyclist counts and helmet use was investigated. Results The number of cyclists observed per 1 h session was significantly associated with predicted daily high temperature, chance of rain, and actual rain. Additionally, fewer cyclists were observed on Fridays. Helmet use was significantly lower during evening commutes than morning and also lower on Fridays. Helmet use was not associated with weather variables. Controlling for observable cyclists characteristics weakened the association between helmet use and the time of day and day of the week, but it did not eliminate that association. Conclusions Direct observation to measure commuter cycling trends or evaluate interventions should control for weather and day of week. Measurement of helmet use is unlikely to be meaningfully biased by weather factors, but time of day and day of week should be taken into account. Failing to control for these factors could lead to significant bias in assessments of the level of, and trends in, commuter cycling and helmet use.