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The impact of urban speed reduction programmes on health system cost and utilities
  1. Babak Mohit1,
  2. Zohn Rosen2,
  3. Peter A Muennig2
  1. 1 Center for Evaluation of Value and Risk in Health (CEVR), Tufts Medical Center, Boston, Massachusetts, USA
  2. 2 Mailman School of Public Health, Columbia University, New York City, New York, USA
  1. Correspondence to Dr Babak Mohit, Center for Evaluation of Value and Risk in Health (CEVR), Tufts Medical Center, 35 Kneeland Street, Boston, MA 02111, USA; bmohit1{at}


Background Vehicle speed changes impact the probability of injuring a pedestrian in ways that differ from the way that it impacts the probability of a collision or of death. Therefore, return on investment in speed reduction programmes has complex and unpredictable manifests. The objective of this study is to analyse the impact of motor vehicle speed reduction on the collision-related morbidity and mortality rates of urban pedestrians.

Methods and Findings We created a simple way to estimate the public health impacts of traffic speed changes using a Markov model. Our outcome measures include the cost of injury, quality-adjusted life years (QALYs) gained and probability of death and injury due to a road traffic collision. Our two-way sensitivity analysis of speed, both before the implementation of a speed reduction programme and after, shows that, due to key differences in the probability of injury compared with the probability of death, speed reduction programmes may decrease the probability of death while leaving the probability of injury unchanged. The net result of this difference may lead to an increase in injury costs due to the implementation of a speed reduction programme. We find that even small investments in speed reductions have the potential to produce gains in QALYs.

Conclusions Our reported costs, effects and incremental cost-effectiveness ratios may assist urban governments and stakeholders to rethink the value of local traffic calming programmes and to implement speed limits that would shift the trade-off to become between minor injuries and no injuries, rather than severe injuries and fatalities.

  • Speed Reduction
  • Economic Analysis
  • Urban Development
  • Public Health
  • Burden of Disease

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  • Contributors BM did the literature search, developed the main underlying probability relations, developed the Markov model, ran the Markov model and generated results, wrote the original paper and managed the revisions. ZR provided key intellectual insights in generating the Markov model and edited the first draft and later revisions. PAM developed the idea and the initiative for the research, provided guiding insights towards model development and provided major edits towards the final manuscript.

  • Funding This research was supported by Grant 1 R49 CE002096 from the National Center for Injury Prevention and Control of the CDC to the Center for Injury Epidemiology and Prevention at Columbia University Medical Center. The contents of the manuscript are the sole responsibility of the authors and do not necessarily reflect the official views of the funding agency.

  • Competing interests None declared.

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