Road traffic casualties in Great Britain at daylight savings time transitions: a causal regression discontinuity design analysis

OBJECTIVE: To determine whether daylight savings time (DST) transitions have an effect on road traffic casualties in Great Britain using causal regression discontinuity design (RDD) analysis. We undertake aggregate and disaggregate spatial and temporal analyses to test the commonly referenced sleep and light hypotheses. DESIGN: The study takes the form of a natural experiment in which the DST transitions are interventions to be evaluated. Two outcomes are tested: (1) the total number of casualties of all severities and (2) the number of fatalities. DATA: Data were obtained from the UK Department for Transport STATS19 database. Over a period of 14 years between 2005 and 2018, 311 766 total casualties and 5429 fatalities occurred 3 weeks on either side of the Spring DST transition and 367 291 total casualties and 6650 fatalities occurred 3 weeks on either side of the Autumn DST transition. PRIMARY OUTCOME MEASURE: An RDD method was applied. The presence of a causal effect was determined via the degree of statistical significance and the magnitude of the average treatment effect. RESULTS: All significant average treatment effects are negative (54 significant models out of 287 estimated), indicating that there are fewer casualties following the transitions. Overall, bootstrapped summary statistics indicate a reduction of 0.75 in the number of fatalities (95% CI −1.61 to –0.04) and a reduction of 4.73 in the number of total casualties (95% CI −6.08 to –3.27) on average per year at both the Spring and Autumn DST transitions combined. CONCLUSIONS: The results indicate minor reductions in the number of fatalities following the DST transitions, and thus, our analysis does not support the most recent UK parliamentary estimate that there would be 30 fewer fatalities in Great Britain if DST was to be abolished. Furthermore, the results do not provide conclusive support for either the sleep or light hypotheses.