1 Clinical Trials Research Unit, University of Auckland, New Zealand.
2 Institute for International Health, University of Sydney, Australia.
3 Centre for Statistics in Medicine, University of Oxford, UK.
4 Department of Community Health, University of Auckland, New Zealand.
Correspondence: Dr Gary Whitlock, Clinical Trial Service Unit and Epidemiology Studies Unit, Harkness Building, Radcliffe Infirmary, Oxford OX2 6HE, UK. E-mail: gary.whitlock{at}ctsu.ox.ac.uk
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Abstract |
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Methods In a cohort study of 10 525 New Zealand men and women, BMI was assessed in 19921993 (baseline), and data on deaths and hospitalizations for motor vehicle driver injury were obtained by record linkage to national health databases for the period 19881998. Hazard ratios (HR) and CI were estimated by Cox regression.
Results During a mean 10.3 years of follow-up, 139 fatal and non-fatal driver injury cases occurred (85 before baseline and 54 after). A U-shaped association was observed between driver injury risk and BMI, both crudely and after adjustment for covariates, which included age, sex, driving exposure, and alcohol intake (P-values for quadratic trend 0.02). Participants in the highest (
28.7 kg/m2; HR = 2.00, 95% CI: 1.183.39) and lowest (<23.5 kg/m2; HR = 2.17, 95% CI: 1.273.73) quartiles of BMI were twice as likely to have experienced a driver injury during the follow-up period as participants in the reference quartile (25.928.6 kg/m2; HR = 1.00).
Conclusion Further research is needed to corroborate or refute the hypothesis that BMI is a risk factor for serious motor vehicle driver injury.
Accepted 3 October 2002
This study investigated the hypothesis that risk of motor vehicle driver injury is associated with body mass index (BMI). The hypothesis was based on evidence that low BMI is associated with higher risks of certain types of bone fracture,1,2 and that high BMI is associated with an increased prevalence of sleep apnoea,3,4 a condition that has been linked to motor vehicle crashes.5 Limited evidence about this hypothesis is available from a previous small cross-sectional study6 and at least two case series,7,8 but to date there does not appear to be any relevant evidence from cohort studies.
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Materials and Methods |
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Exposure
Body mass index (weight [kg]/height [m]2) was calculated from weight and height as measured at baseline by study nurses, with participants shoes and heavy outer garments removed. For this analysis, BMI was categorized by quartile (<23.5, 23.525.8, 25.928.6 and 28.7 kg/m2).
Confounders
Driving exposure was estimated from two sources: age- and sex-specific data on driving exposure in the 19891990 New Zealand Household Travel Survey,10 and self-reported occupation (which was classified by the investigators as likely to involve much, some, little or no driving). Age, sex, alcohol intake (self-reported maximum daily intake during the preceding 3 months), area of residence (population 200 000, 20 000200 000, or <20 000), and marital status (married/living with a partner, divorced/separated/widowed, or never married/lived with partner) were based on data reported by participants in a questionnaire administered at baseline. Self-reported occupation at baseline was classified by Ganzebooms International Socioeconomic Index.11
Outcome
Participants were classified as cases if they had been injured between 1988 and 1998 while driving a motor vehicle, and the injury led to hospitalization and/or death. Data on deaths and hospitalizations were obtained by record linkage to national databases held by the New Zealand Ministry of Health. All cases had an International Classification of Diseases Ninth Revision (ICD-9) N-code in the range 800999, an E-code in the range 810829, and evidence that they had been driving at the time of the crash (either a free text narrative description indicating this, or an E-code fourth digit of 0 or 2).
Statistical methods
Hazard ratios (HR) and CI were calculated using Cox proportional hazards models (PHREG in SAS Release 8.00). In the main analyses, the time origin was specified as 1 January 1988, and follow-up was terminated at the date of driver injury, date of death (from any cause) or 31 December 1998, whichever occurred first. Adjustments were made for covariates by inserting continuous (age) or categorical (all other covariates) terms into the Cox models (see also Table 1 footnote). Unless stated otherwise, the results were adjusted for age, sex, and cohort only. P-values for trend were estimated by inserting polynomial contrasts into the Cox models.12
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Results |
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Driver injury and body mass index
A U-shaped association was observed between driver injury risk and BMI, both crudely and after adjustment for covariates (P-values for quadratic trend 0.02; Table 1
second highest quartile of BMI (25.928.6 kg/m2; henceforth the reference group) appeared to have the lowest driver injury risk. Relative to this group, participants in the highest (
28.7 kg/m2; HR = 2.00, 95% CI: 1.183.39) and lowest (<23.5 kg/m2; HR = 2.17, 95% CI: 1.273.73) quartiles were twice as likely to have experienced a driver injury during the follow-up period. The risk for participants in the second lowest quartile (23.525.8 kg/m2; HR = 1.73, 95% CI: 1.012.96) also appeared to be higher than that for the reference group.
After further adjustment for covariates, the three non-reference HR increased, most notably the HR for the lowest quartile. For participants in this group, additional adjustment for alcohol intake, driving exposure, area of residence, marital status, and occupational status increased the HR from 2.17 (95% CI: 1.273.73) to 2.79 (95% CI: 1.505.19).
Broadly consistent U-shaped associations were observed in both the prospective and retrospective periods (though the power to detect associations within these periods would have been limited by the smaller number of cases). For example, in both periods the data were reasonably compatible with higher risks of driver injury for participants with BMI <25.9 kg/m2 (prospective: HR = 1.62, 95% CI: 0.763.48; retrospective: HR = 2.14, 95% CI: 1.134.05) or BMI 28.7 kg/m2 (prospective: HR = 2.18, 95% CI: 0.994.83; retrospective: HR = 1.93, 95% CI: 0.963.90).
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Discussion |
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However, it does seem possible that the findings could have been materially influenced by chance (the study had a moderately small number of cases), driver injury leading to changes in BMI (some outcomes were retrospective), or residual confounding by driving exposure (which was measured crudely). Residual confounding by driving exposure could have biased the HR in either direction, but to have fully accounted for the observed U-shaped association, people in the highest and lowest quartiles would have needed to drive about twice as much as people in the second highest quartile, even after adjustment for age- and sex-specific levels of driving. Although truck drivers, for example, might tend to have high BMI values,13 we cannot think of plausible mechanisms whereby such a strong U-shaped pattern of driving exposure would exist at a population level (just 2% of participants in this study were truck drivers).
Further studies, which could potentially include cohort or case-control studies, are needed to corroborate or refute the hypothesis that BMI is a risk factor for serious driver injury. Studies with more cases than the present study could potentially examine whether the association differs according to type of injury or type of vehicle.
If there really is a U-shaped relationship between driver injury risk and BMI, possible causal mechanisms could include increased risks of falling asleep at the wheel for drivers with a high BMI,35 and increased risks of bone fracture for drivers with a low BMI.1,2 Additionally, there might be aspects of vehicle designsuch as seatbelt effectiveness or safetythat are not optimal for under- or overweight people. Driver injury and high BMI are both common in western (and other) populations, so if the association is causal, a large number of driver injuries might be attributable to BMI.
KEY MESSAGES
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Acknowledgments |
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Referefences |
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