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Optimising the cost-effectiveness of speed limit enforcement cameras
  1. Shukai Li,
  2. Boshen Jiao,
  3. Zafar Zafari,
  4. Peter Muennig
  1. Mailman School of Public Health, Global Research Analytics for Population Health, Columbia University Mailman School of Public Health, New York City, New York, USA
  1. Correspondence to Shukai Li, Global Research Analytics for Population Health, Columbia University Mailman School of Public Health, New York, NY 10040, USA; lishukai{at}


Background Using the 140 speed cameras in New York City (NYC) as a case study, we explore how to optimise the number of cameras such that the most lives can be saved at the lowest cost.

Methods A Markov model was built to explore the economic and health impacts of speed camera installations in NYC as well as the optimal number and placement. Both direct and indirect medical savings associated with speed cameras are weighed against their cost. Health outcomes are measured in terms of quality-adjusted life years (QALYs).

Results Over the lifetime of an average NYC resident, the existing 140 speed cameras increase QALYs by 0.00044 units (95% credible interval (CrI) 0.00027 to 0.00073) and reduce costs by US$70 (95% CrI US$21 to US$131) compared with no speed cameras. The return on investment would be maximised where the number of cameras more than doubled to 300. This would further increase QALY gains per resident by 0.00083 units (95% CrI 0.00072 to 0.00096) while reducing medical costs by US$147 (95% CrI US$70 to US$221) compared with existing speed cameras. Overall, this increase in cameras would save 7000 QALYs and US$1.2 billion over the lifetime of the current cohort of New Yorkers.

Conclusion Speed cameras rank among the most cost-effective social policies, saving both money and lives.

  • speed limit enforcement cameras
  • motor vehicle collisions
  • injury prevention
  • cost-effectiveness analysis

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  • Contributors All the authors initiated and designed the study and participated in revising or reviewing the manuscript. All the authors have approved the final manuscript for submission. SL, BJ and ZZ designed the the model. SL and BJ supplied required simulation parameters. SL designed the HR analysis, ran the model simulations and conducted required data processing and analysis and also conducted a critical review of the manuscript for important intellectual content. ZZ and PM advised on technical implementation of simulation design.

  • Funding This study was funded by Global Research Analytics for Population Health at the Mailman School of Health, Columbia University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

  • Competing interests None declared.

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