LAUSR

Background England has a highly detailed and prescriptive transport appraisal process for active mode (walking, cycling) projects, guided by the Department for Transport Guidance. Current guidance is based on the relative risks adopted from the previous version of the World Health Organization (WHO) Health economic assessment tools (HEAT). The benefits are estimated for age group 15-64 by using average mortality for this age, and benefits are converted into economic outcomes. In this study we developed a new method to inform the future update of the guidance. Methods We developed an approach in which we used data on current pedestrians and cyclists to predict the demographic characteristics of new cyclists, and the distance and speed of their trips. Specifically the age and gender distribution of the new active travelers were assumed to be same with the existing cyclists and pedestrians, calculated from the National Travel Survey. Rather than using whole population averages we used age and gender specific travel distance, speed and background mortality data to estimate behavioural change and mortality for four different age groups (20-49, 50-64, 65-80 and 80+) and sex. The changes in premature mortality were estimated using four different dose-response function (DRF) methods, including the one used in current appraisal. The numbers of deaths were converted to Years of Life Lost (YLLs) and to economic benefits using costs per life year method. The impact of parameter and model uncertainties to the results was tested using Monte Carlo based sensitivity analyses methods. Results The developed method provided slightly smaller health benefits if we used the original age and gender assumptions that exclude older adults. However, when we based our calculation on empirical evidence on the likelihood of cycling by age, benefits were higher. The results for four different DRF methods were similar with 24% difference between smallest and highest benefits. The model was more sensitive to assumptions related to potential decrease or increase of the usage over time and assumed number of walking and cycling days per year. Conclusions The new method provides good background for further development of the Transport Analysis Guidance. It highlights the importance of using empirical data on propensity to cycle among different groups rather than assuming that some groups never cycle. Sensitivity analysis indicates that main uncertainties are related to usage of the new infrastructure rather than the DRFs indicating opportunities for future research.