Road traffic casualties in Great Britain at daylight savings time transitions: a causal regression discontinuity design analysis
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Author(s)
Singh, Ramandeep
Sood, Rohan
Graham, Daniel
Type
Journal Article
Abstract
Objectives: To determine whether daylight savings time (DST) transitions have an effect on road traffic casualties in Great Britain using causal regression discontinuity design 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: (i) the total number of casualties of all severities (ii) the number of fatalities.
Data: Data are obtained from the UK Department for Transport STATS19 database. Over a period of 14 years between 2005 and 2018, 311,766 total casualties and 5,429 fatalities occurred 3 weeks either side of the Spring DST transition and 367,291 total casualties and 6,650 fatalities occurred 3 weeks either side of the Autumn DST transition.
Primary outcome measure: A regression discontinuity design method (RDD) is applied. The presence of a causal effect is determined via the degree of statistical significance and 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, -0.04) and a reduction of 4.73 in the number of total casualties (95% CI: -6.08, -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 were to be abolished. Furthermore, the results do not provide conclusive support for either the sleep or 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: (i) the total number of casualties of all severities (ii) the number of fatalities.
Data: Data are obtained from the UK Department for Transport STATS19 database. Over a period of 14 years between 2005 and 2018, 311,766 total casualties and 5,429 fatalities occurred 3 weeks either side of the Spring DST transition and 367,291 total casualties and 6,650 fatalities occurred 3 weeks either side of the Autumn DST transition.
Primary outcome measure: A regression discontinuity design method (RDD) is applied. The presence of a causal effect is determined via the degree of statistical significance and 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, -0.04) and a reduction of 4.73 in the number of total casualties (95% CI: -6.08, -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 were to be abolished. Furthermore, the results do not provide conclusive support for either the sleep or light hypotheses.
Date Issued
2022-04-25
Date Acceptance
2022-03-28
Citation
BMJ Open, 2022, 12 (4)
ISSN
2044-6055
Publisher
BMJ Journals
Journal / Book Title
BMJ Open
Volume
12
Issue
4
Copyright Statement
© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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Subjects
public health
sleep medicine
statistics & research methods
Accidents, Traffic
Causality
Humans
Regression Analysis
Seasons
United Kingdom
Humans
Regression Analysis
Seasons
Accidents, Traffic
Causality
United Kingdom
1103 Clinical Sciences
1117 Public Health and Health Services
1199 Other Medical and Health Sciences
Publication Status
Published
Article Number
ARTN e054678