CommentaryLethal misconceptions: interpretation and bias in studies of traffic deaths
Introduction
A complacent attitude toward traffic deaths might be reasonable if crashes were difficult to predict, hard to prevent, or costly to avoid. In a series of articles, however, our group has shown that seemingly minor differences in behavior can lead to measurable differences in traffic deaths. For example, U.S. presidential elections lead to a 19% increase in traffic deaths during the hours of polling and the average Super Bowl leads to a 41% increase in traffic deaths during the hours following the broadcast [1], [2]. These and other studies [3], [4], [5], [6], [7], [8], [9], [10], [11] call into question community attitudes that characterize accidents as random acts, divine planning, or unavoidable fates. Instead, scientific studies prove how easily the risks of a traffic death can be increased or decreased throughout society.
Studies of traffic deaths, however, often fail to change clinical practice for at least three reasons. First, the traditional practice of medicine focuses more on relieving suffering in the aftermath of an event rather than primary prevention in otherwise asymptomatic patients [12]. Second, the failures to prevent traffic deaths can stay invisible to clinicians if reports are not relayed back to health care providers [13], [14]. Third, effective methods for preventing traffic deaths rarely take the form of prescribing a medication or a surgical procedure; instead, most interventions involve added inconvenience to the patient and clinician [15]. As a consequence, formal medical training and ongoing clinical practice can accentuate rather than mitigate complacent attitudes toward traffic deaths.
We wondered whether the inbuilt structure of traffic death studies also thwarts progress for more thoughtful attention and better patient outcomes. In accord with scientific standards, effect sizes in traffic death studies are usually quantified with numerical units such as an odds ratio or relative risk reduction [16]. In turn, the relative risk reductions almost never reach 100% and sometimes range to single-digit integers (Table 1). Well-intentioned readers, therefore, may have difficulty distinguishing trivial from substantial levels of effectiveness. The purpose of this article is to explain why small differences in observed risks may indicate important opportunities for physicians to save lives from traffic deaths.
Section snippets
Baseline frequency
Traffic deaths are a remarkably frequent problem currently ranked as the ninth most common cause of death worldwide and projected to rise to the fifth place by the year 2030 [24]. Collectively, this baseline risk amounts to about 3,000 deaths per day on a global basis [25], [26]. This burden extends throughout high- and low-income countries [27]; for example, the United States has about 3–5% of the total global population and about 3–5% of the total global traffic deaths [25], [28], [29]. A
Summary
This article has summarized 10 reasons why small differences in observed risks may sometimes represent important findings in traffic death studies. Much of the material will be familiar to investigators skilled in statistical design; however, the framework may help guide those who are either inexperienced or confronted by an unenthused audience. Each of the 10 concepts can explain why a seemingly minor odds ratio can have a major implication to human health related to traffic risks. However,
Acknowledgments
This project was supported by the Canada Research Chair in Medical Decision Sciences and the Comprehensive Research Experience for Medical Students program of the University of Toronto Faculty of Medicine. The views expressed in this article are those of the authors and do not necessarily reflect the Ontario Ministry of Health. We wish to thank the following for helpful comments: William Chan and Neil Dattani.
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Conflicts of interest: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of data; and preparation, review, or approval of the manuscript. All authors have no financial or personal relationships or affiliations that could influence the decisions and work on this manuscript. The lead author (D.A.R.) had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the analysis.