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Would Stronger Seat Belt Laws Reduce Motor Vehicle Crash Deaths?

A Semi-Bayesian Analysis

Harper, Sam

doi: 10.1097/EDE.0000000000000990
Social Epidemiology
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SDC

Background: For policy questions where substantial empirical background information exists, conventional frequentist policy analysis is hard to justify. Bayesian analysis quantitatively incorporates prior knowledge, but is not often used in applied policy analysis.

Methods: We combined 2000–2016 data from the Fatal Analysis Reporting System with priors based on past empirical studies and policy documents to study the impact of mandatory seat belt laws on traffic fatalities. We used a Bayesian data augmentation approach to combine information from prior studies with difference-in-differences analyses of recent law changes to provide updated evidence on the impact that upgrading to primary enforcement of seat belt laws has on fatalities.

Results: After incorporating the evidence from past studies, we find limited evidence to support the hypothesis that recent policy upgrades affect fatality rates. We estimate that upgrading to primary enforcement reduced fatality rates by 0.37 deaths per billion vehicle miles traveled (95% posterior interval -0.90, 0.16), or a rate ratio of 0.96 (95% posterior interval 0.91, 1.02), and increased the proportion of decedents reported as wearing seat belts by 7 percentage points (95% posterior interval 5, 8), or a risk ratio of 1.18 (95% posterior interval 1.13, 1.24).

Conclusions: Bayesian methods can provide credible estimates of future policy impacts, especially for policy questions that occur in dynamic environments, such as traffic safety.

From the Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.

Submitted March 30, 2018; accepted January 29, 2019.

S.H. was supported by a Chercheur Boursier Junior 2 from the Fonds de recherche du Québec—Santé.

The authors report no conflicts of interest.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).

Description of the process by which someone else could obtain the data and computing code required to replicate the results reported in your submission: A replication data set, including the raw data and statistical code to reproduce the article results, is publicly available and posted on Dr. Harper’s Open Science Foundation Page: https://osf.io/em2y7/.

Correspondence: Sam Harper, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Room 36B, Montreal, QC H3A 1A2, Canada. E-mail: sam.harper@mcgill.ca.

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