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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 57:M186-M191 (2002)
© 2002 The Gerontological Society of America

Risk Factors for Motor Vehicle Crashes in Older Women

Karen L. Margolisa, Roxanne Pieper Keranib, Paul McGovernc, Thomas Songerd, Jane A. Cauleyd and Kristine E. Ensrud for the Study of Osteoporotic Fractures Research Groupc,e

a Division of Clinical Epidemiology, Hennepin County Medical Center, Minneapolis, Minnesota
b Division of Allergy and Infectious Diseases, School of Medicine, University of Washington, Seattle
c Division of Epidemiology, School of Public Health, University of Minnesota, Minneapolis
d Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pennsylvania
e Center for Chronic Disease Outcomes Research, Minneapolis VA Medical Center, Minnesota

Karen L. Margolis, Division of Clinical Epidemiology, Hennepin County Medical Center (865B), 701 Park Avenue South, Minneapolis, MN 55415 E-mail: margo006{at}tc.umn.edu.


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Background. Motor vehicle crash and fatality rates are higher per mile driven for elderly drivers, with an exponential increase above age 75. Identifying elderly drivers who are at risk for automobile crashes may help direct interventions to reduce their high rate of injuries and deaths.

Methods. Subjects were 1416 women aged 65 to 84 enrolled in the Portland, Ore. site of the Study of Osteoporotic Fractures. Motor vehicle crash information for the years 1986–1995 for each participant was obtained from the Oregon State Department of Transportation. Items from questionnaires, interviews, and physical examinations were tested prospectively for associations with the occurrence of motor vehicle crashes.

Results. About one third of participants (415 of 1416) had a motor vehicle crash during a mean follow-up time of 5.7 years. After adjustment for age and weekly driving mileage, risk factors significantly associated with motor vehicle crashes were a fall in the previous year [hazard ratio (HR) 1.53, 95% confidence interval (CI) 1.26–1.86], a greater orthostatic systolic blood pressure drop (HR 1.11 per 12.5 mm Hg, 95% CI 1.01–1.22), and increased foot reaction time (HR 1.10 per 0.06 second, 95% CI 1.00–1.22). Other neuromuscular tests, functional status, medical diagnoses, vision tests, and cognitive tests did not predict motor vehicle crashes in this study population.

Conclusions. This prospective study with extended follow-up of a large cohort of elderly women has identified crash risk factors that can be measured in the clinical setting. Further study is needed to determine if interventions aimed at these risk factors can decrease the risk of motor vehicle crashes.

BY the year 2020, it is estimated that there will be 50 million elderly persons eligible to drive in the United States (1). Motor vehicle crash rates adjusted for miles driven are higher for elderly drivers, with an exponential increase above age 75 (1). A similar pattern is observed with the driver fatality rate (1). Identifying elderly drivers who are at high risk for automobile crashes may help direct interventions to reduce the high rate of injuries and deaths.

The health status of older drivers has been the central focus of previous studies on motor vehicle crashes among the elderly. Age-related changes in processing of sensory input, cognitive capacity, and neuromuscular function may all affect an elderly driver's ability to operate a motor vehicle safely. Reduced visual acuity, poor contrast sensitivity, glaucoma, cataracts, and hearing impairment have been related inconsistently to motor vehicle crashes (2)(3)(4)(5)(6)(7)(8). The Useful Field of View, a test of visual processing speed, has a stronger and more consistent relationship with motor vehicle crashes (6)(8)(9). Some studies have shown associations between cognitive impairment (6)(10) or depression (10)(11), and the risk of automobile crashes, but results have been inconsistent (6)(8)(9)(10)(11). Associations between the use of medications with central nervous system effects, medical conditions, and functional impairments and motor vehicle crashes are also controversial (6)(10)(12)(13)(14).

Most previous studies have been limited by cross-sectional or retrospective study designs, small sample sizes, and limited information on potential risk factors. The Study of Osteoporotic Fractures (SOF) overcomes these limitations with its prospective design, detailed clinical examinations, extended follow-up, and large sample of elderly community-dwelling women. The purpose of this study was to describe the motor vehicle crash risk over a 10-year period and to identify risk factors for motor vehicle crashes in elderly women.


   Methods
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 Abstract
 Methods
Results
 Discussion
 References
 
Study Population
The Study of Osteoporotic Fractures is a prospective study designed to identify risk factors for fractures in elderly women (15). Nonblack women aged 65 and older were recruited (N = 9704) for the baseline examination using population-based lists from 1986–1988 in Baltimore County, Md.; Portland, Ore.; Minneapolis, Minn.; and the Monongahela Valley near Pittsburgh. Seven subsequent exams have been conducted at approximately 2-year intervals. Motor vehicle accident data were only available from the state of Oregon; therefore, only participants from the Portland site are included in the present study (n = 2421). We further excluded 152 women who did not attend Visit 2, 250 women with drivers' licenses issued by states other than Oregon, 554 women who reported at Visit 2 that they had not driven in the past 12 months, 43 women who declined to provide their drivers' license numbers, and 6 women who were aged 85 years or older at baseline. Thus, our analyses were conducted on 1416 women.

Data Collection
Data from the baseline exam (1986–1988), Visit 2 (1988– 1989), and Visit 4 (1992–1994) are included in this analysis. Unless noted otherwise, all data used in this analysis were collected at the baseline visit. Driving exposure was measured at Visit 2 and Visit 4 by asking participants if they had driven a car in the past 12 months, and if so, on average, how many miles per week they had driven.

At baseline, the participants were asked the number of falls they had experienced in the previous year. Physical activity was measured using a modified Paffenbarger questionnaire, whereby an estimate of total kilocalories expended per week in recreational activities was calculated (16)(17). Participants were also asked, "Do you walk for exercise?" and "At least once per week, do you engage in any regular activity long enough to work up a sweat?"

Functional status was assessed by self-report of any difficulty performing each of six activities of daily living: walking two to three blocks on level ground, climbing up 10 steps without stopping, walking down 10 steps, preparing meals, doing heavy housework, and shopping for groceries or clothes (18)(19). At Visit 2, participants completed the Geriatric Depression Scale (20). The presence of depression was defined as a positive response to six or more symptoms (21). Participants were asked whether they had ever been diagnosed by a physician with diabetes, stroke, osteoarthritis, or cataracts. Use of medications to aid sleep in the past 12 months was assessed, and the names of the medications were recorded. Participants were asked to report the average number of alcoholic drinks they consumed per week during the past year.

Blood pressure was measured both in the supine position after a 5-minute rest period and after standing for 1 minute. Neuromuscular function tests included ability to rise from a chair five times without using the arms, number of step-ups completed in 10 seconds, average of right and left grip strength using a hand-held dynamometer (Preston Grip Dynamometer, Takei Kikikogyo, Tokyo, Japan), walking speed over a 6-meter course, time to complete a walk in a tandem position, and the number of seconds to complete 10 taps with the dominant foot. At Visit 2, dominant hand and foot reaction times were measured by depressing a pad in response to a light cue. The results of 10 trials were averaged.

Corrected visual acuity was measured by the letter charts using the Bailey and Lovie method (22), and acuity was examined as both a dichotomous "impaired acuity" variable (visual acuity of 20/40 or worse) and as a continuous variable [log minimum angle of resolution (logMAR) (23)]. Contrast sensitivity was measured using the Vistech Vision Contrast Test System 6500 (24) at 10 feet for spatial frequencies ranging from 1.5 to 18 cycles per degree. The scores for low and high spatial frequencies were averaged separately (25). Near depth perception was measured with the Randot Stereotest Contoured Circles test (26). Distance depth perception was measured using the Howard Dolman apparatus and was scored as the standard deviation of four trials (27). The Mini-Mental Status Examination (28) was administered at Visit 1, and the Trail Making Part B (29) and Digit Symbol Substitution (30) tests were administered at Visit 2 to assess cognitive function.

All participants were asked to provide their drivers' license numbers during Visit 2. Motor vehicle crash information for the years 1986–1995 for each participant was obtained from the Driver and Motor Vehicle Services, division of the Oregon State Department of Transportation. The date of any crash that resulted in a police report was recorded. Participants who had any motor vehicle accident from enrollment in SOF until the end of July 1995 were considered to have the outcome of interest.

Data Analysis
Bivariable relationships were examined with chi-square and t tests. Alcohol consumption was highly skewed, with a large proportion of nondrinkers. Therefore, drinks per week were categorized into five levels, with the first level representing those who reported never drinking, the second level representing former drinkers, and the next three levels representing the remaining participants divided into tertiles based on reported drinks per week. Cox proportional hazards regression was utilized for multivariable analyses. The hazard ratio was determined before and after adjustment both for age in 5-year increments and for weekly miles driven in 50-mile increments. For all other continuous variables, it was expressed per one standard deviation. Independent variables that had a relationship at a significance level of p < .10 with the outcome variable after adjustment for age and miles driven per week were included in a multivariable model. We tested for interactions between variables that entered the final multivariable model by adding an interaction term to the model.

There were 772 participants who reported that they had continued driving through Visit 4 and had no record of a motor vehicle accident before July 31, 1995 (the latest date for which motor vehicle information was available). These individuals were considered to be at risk from their date of enrollment until July 31, 1995. Persons who had a motor vehicle accident (n = 415) were considered to be at risk from their time of enrollment until the date of their first accident. Participants who reported that they had stopped driving between visits 2 and 4 (n = 89) were censored halfway between their Visit 2 and Visit 4 dates, as the specific date on which they stopped driving was not available. Nonattenders at Visit 4 (n = 51) were censored halfway between their Visit 2 dates and the midway point of Visit 4 (July 31, 1993), as information regarding whether they were still driving was not available. Participants who died at any time after Visit 2 (n = 89) were censored on their date of death.


   Results
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 Abstract
 Methods
 Results
Discussion
 References
 
The demographic and driving characteristics of the study population are shown in Table 1 . At baseline, the average age of participants was 71 years. The mean number of reported weekly miles driven was 55, or just under 3000 annual miles driven. About one third of participants (415 of 1416) had at least one motor vehicle crash during an average follow-up time of 5.7 years (Table 1 ). The rate of crashes was 0.07 per driver per year.


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  		Table 1. Characteristics of the Study Population*

 
Self-reported risk factors and their associations with crashes are shown in Table 2 , before and after adjustment for age and miles driven per week. Each 5 years of age increased the relative risk of a motor vehicle crash by 16%. Each additional 50 miles driven per week increased the risk of a motor vehicle crash by 14%. A history of one or more falls increased the risk of a motor vehicle crash by approximately 50%. Women who reported they walked for exercise had a borderline lower risk. Compared with women who did not drink alcohol, we found an increased risk of motor vehicle crashes in women who drank but consumed one drink or less per week, but not in women who drank somewhat more heavily.


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  		Table 2. Association of Self-Reported Risk Factors With Crashes

 
Only three physical examination findings showed an association with motor vehicle crashes (Table 3 ). For each standard deviation increase in the orthostatic systolic blood pressure drop and foot reaction time, there was an approximately 10% higher risk of motor vehicle crashes. There was also a small increase in the motor vehicle crash risk with worsening visual acuity modeled continuously as logMAR.


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  		Table 3. Association of Physical Examination Findings With Crashes

 
The results of the multivariable model are shown in Table 4 . Age and miles driven per week remained associated with motor vehicle crashes. We found a statistically significant interaction between walking for exercise and falling in the past 12 months. An increased risk of motor vehicle crashes among women who fell was primarily restricted to the women who did not walk for exercise, with an adjusted risk ratio of approximately 2. The association between minimal alcohol consumption and motor vehicle crashes also was present in the multivariable model. Both systolic blood pressure drop and foot reaction time were associated with motor vehicle crashes in the multivariate model.


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  		Table 4. Multivariable Model (n = 1226)

 

   Discussion
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 Abstract
 Methods
 Results
 Discussion
References
 
This prospective study provides important new information on motor vehicle crash risk in elderly women drivers. One in three women in our study had a motor vehicle crash serious enough to generate a police report during approximately 6 years of follow-up. Motor vehicle crashes could be predicted by several self-reported variables: increased age, increased number of miles driven weekly, and a history of falling in those who did not walk for exercise. Compared to nondrinkers, very light drinkers had an increased risk of motor vehicle crashes, whereas moderate drinkers did not. Only two physical examination findings were weakly associated with the risk of motor vehicle crashes: a drop in systolic blood pressure upon standing and foot reaction time. None of the SOF measurements of functional status, medical diagnoses, vision, cognitive function, depression, or use of sleep medications were predictors of motor vehicle crashes in this study population.

Three studies have examined the association between a history of falls and motor vehicle crashes, but results were inconsistent (6)(11)(31). In the case-control study by Sims and colleagues (6), older drivers with at least one at-fault crash in the previous 6 years and a history of a fall in the previous 2 years had more than twice the odds of a motor vehicle crash. In a later prospective study, the same authors (11) also found a doubled motor vehicle crash risk in people who reported frequent falling or tripping. Marottoli and coworkers (32) found that people who reported not walking had a two-fold increased risk of motor vehicle crashes, but did not report on whether any association with falls was found. Our data support the limited evidence that falls are associated with motor vehicle crashes and further suggest that the motor vehicle crash risk in women who fall is confined to those who are sedentary.

The mechanism of the association of falls and motor vehicle crashes may be mediated by the association of falls with other risk factors. In the SOF study, falls are associated with increasing age, poorer health status, the presence of one or more medical conditions, not walking for exercise, functional impairment, trouble with dizziness, depression, weight loss, slow gait speed, inability to stand from a chair, poor visual acuity, benzodiazepine use, anticonvulsant use, and antidepressant use (33). Although most of these variables were not individually associated with motor vehicle crashes in our study sample, we speculate that falling may be a marker for elderly women in whom these risk factors combine to produce impairments that also impede driving ability.

Our study also found a weak association between orthostatic systolic blood pressure change and motor vehicle crashes. No association was reported in two other studies, in which blood pressure drop upon standing was modeled as a categorical variable (6)(11). We did not find a significant association between systolic blood pressure drop of 20 mm Hg or more and motor vehicle crashes, a characteristic found in 22% of the participants, nor was self-reported dizziness upon standing associated with motor vehicle crashes (data not shown). This study is the first to examine the association of reaction time with motor vehicle crashes. Slower foot reaction time could be predicted to interfere with removing the foot from the accelerator or applying it to the brake pedal, but these results need confirmation.

We did not find an association between other neuromuscular tests or functional status and motor vehicle crashes. This is in general agreement with previous studies (6)(11)(14)(32). Most previous studies have not found an association of cognitive impairment with motor vehicle crashes when standardized screening instruments have been used (9)(11)(32)(34). However, the women in SOF were fairly healthy and cognitively intact when they entered the study; therefore, the lack of association of these variables with motor vehicle crashes is not unexpected.

Our data are consistent with the majority of other studies that have found no association of diabetes (6)(11)(31)(34) and osteoarthritis (6)(11)(31)(34)(35) with motor vehicle crashes. The evidence for stroke as a risk factor for motor vehicle crashes is less consistent, with two positive studies (11)(34) and two negative studies (6)(31). Women in SOF were not asked if they had heart disease until Visit 4, so we were unable to investigate heart disease as a risk factor for motor vehicle crashes. However, one cross-sectional study (35) and two case-control studies (31)(34) have found an age-independent association between heart disease and motor vehicle accidents. Our findings are in accord with other studies that have found no association of visual acuity, contrast sensitivity, or ocular disease with motor vehicle crashes (2)(7)(9)(11)(32).

Previous studies have had inconsistent findings with regard to depression as a motor vehicle crash risk factor, with two studies reporting an association (11)(14) and three studies reporting no association (6)(32)(34). Only 3.5% of the women in SOF scored high on the Geriatric Depression Scale; therefore, our study's power to find an association of depression with motor vehicle crashes is limited.

Although three other prospective studies found that the use of benzodiazepines was not associated with motor vehicle crashes (6)(11)(34), these findings are contradicted by several case-control studies with much greater power due to the large number of subjects studied (36)(37). Two other studies of older drivers found no association of alcohol consumption with motor vehicle crashes. If the association between very light drinking and motor vehicle crashes is due to physically frail individuals giving up drinking, former drinkers should have an elevated motor vehicle crash risk compared to life-long abstainers, which we did not find. It is possible that the finding is due to chance, given the number of associations examined.

A major methodological issue in research on motor vehicle crashes in the elderly is the measurement of driving exposure. This is particularly important given that older drivers voluntarily curtail their driving as they develop progressive sensory, cognitive, and physical impairment (12). Studies that do not adequately account for driving exposure will thus underestimate the impact that these impairments have on motor vehicle crash risk. In our study, weekly driving mileage was self-reported using a single question, and we lacked specific information about when women stopped driving. Data from the National Personal Transportation Survey shows that women over age 65 drove 3308 miles annually in 1983 and 4750 miles annually in 1990 (38). This suggests that there may have been a modest degree of underreporting of miles driven by the women in our sample. Furthermore, driving exposure was not measured until 2 years after the start of motor vehicle crash ascertainment. Changes in driving behavior between Visit 1 and Visit 2, including a reduction in weekly mileage driven in response to a motor vehicle crash, are not captured by our data. This may have biased some associations.

Several of our potential motor vehicle crash risk factors (depression, hand and foot reaction times, Trail Making Part B, and Digit Symbol Substitution) were also measured at Visit 2 in 1988–1989. Because motor vehicle crashes from 1986–1995 were included, about one third preceded the measurement of these five variables. However, limiting follow-up from Visit 2 onward would have unacceptably lowered our study power. The lack of true prospective follow-up with respect to these variables may have obscured or weakened any association with motor vehicle crashes.

Our results should be interpreted with caution for a number of other reasons. The study sample consisted of predominantly white women from Oregon. Our outcome data were limited to motor vehicle accidents that resulted in a police report; therefore, near misses, moving violations, and less serious crashes were not ascertained, nor do we know which of the motor vehicle crashes resulted in injuries. We may have underestimated the number of crashes because we did not have the ability to ascertain crashes that occurred outside of Oregon. No information was available on other diseases, in particular cardiovascular disease.

In conclusion, this prospective study identified motor vehicle crash risk factors that can be measured in the clinical setting: increased age, increased weekly mileage driven, a history of falls in sedentary women, increased systolic blood pressure drop, and foot reaction time. Other neuromuscular, vision, or cognitive tests and knowledge of functional status and medical comorbidity appeared to contribute little to predicting motor vehicle crash risk in this relatively healthy, unimpaired group of women. Future research is needed to examine whether change in physical and cognitive functions predicts motor vehicle crashes and whether the risk of motor vehicle crashes can be lowered by interventions aimed at ameliorating modifiable risk factors, such as a history of falls or slow foot reaction time.


   Acknowledgments
 
This research was supported by Public Health Service grants AG05407, AR35582, AG05394, AR35584, and AR35583. Dr. Margolis was supported by K23 award HL03996.

Received June 18, 2001

Accepted September 25, 2001


   References
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 Abstract
 Methods
 Results
 Discussion
 References
 

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