FREEMAN, ELLEN E. MS; MUÑOZ, BEATRIZ MS; TURANO, KATHLEEN A. PhD; WEST, SHEILA K. PhD
Vision loss occurs in older adults as a result of such age-related diseases as cataract, glaucoma, and age-related macular degeneration. It is estimated that 16% of older adults ages 80 to 85 years old are visually impaired.1 There are many decisions for an older adult with vision loss to make about which activities can be safely done and which cannot. One of the hardest decisions to be made is when to no longer drive a car, because a decision to stop driving often has life-changing implications. Although car crashes are relatively rare events, making a bad decision about driving fitness can have tragic results. Older adults have a fourfold higher mileage-adjusted risk of a fatal crash than middle-aged adults,2 and poor visual function is one factor in this increased risk.3–5
Most state regulations require visual acuity of 20/40 to drive a car in an unrestricted fashion, but other types of visual function, like visual fields, contrast sensitivity, and glare sensitivity, are typically not tested.6 Although visual acuity is easy to measure, it is probably not the most relevant measure of visual function with respect to driving. Driving is a complex task requiring an ability to see objects at varying levels of contrast and light, to continuously scan the road for obstacles, to judge oncoming traffic, to read signs, and so on. Many measures of visual function are thought to be important for driving performance.4,5,7–13
Aside from licensing renewal regulations that may require an older adult to stop driving, research shows that older adults also tend to regulate their own driving. One study found that although 35 of 140 (25%) older adults reported vision as the main reason for driving cessation, only four of these 35 cited licensing problems resulting from vision, indicating that the majority of people had self-regulated their driving in the face of visual impairment.14 This analysis examines whether older adults are more likely to stop driving as a result of poorer visual function and/or recent 2-year visual function loss in acuity, contrast sensitivity, central visual fields, lower peripheral visual fields, and glare sensitivity. These vision measures were chosen because of previous literature that described an increased crash risk or worse driving performance on closed circuit tracks or simulators.3–5,9–13 Other studies have examined vision impairment and driving cessation, but have been constrained by a cross-sectional design, which cannot address temporality or by a lack of multiple, objectively measured tests of visual function.15,16 This study also examines what factors may modify the relationship between vision loss and driving cessation such as cognitive impairment or the availability of other drivers in the house.
The Salisbury Eye Evaluation (SEE) project is a population-based study of a random sample of 2520 older adults living in Salisbury, Maryland, in 1993.1 The sample was selected from the Health Care Financing Administration Medicare database. Eligibility criteria were the following: age between 65 and 84 years, residence near Salisbury, Maryland, not in a nursing home, ability to communicate, and a score >17 on the Mini-Mental State Exam. Of the 3906 eligible persons, 2520 completed both the home questionnaire and the medical examination (65% response rate). There were four rounds of the SEE study, which were completed over an 8-year period. The study was approved by the Joint Committee on Clinical Investigation at Johns Hopkins University. Informed consent was obtained for all participants.
The population of interest for this analysis was the 2030 individuals at baseline who were still driving; of those, 1824 returned to round two 2 years later (90%). Reasons for not returning were death (51%) and relocation or refusal (49%). Because vision change from round one to round two is key to understanding the role of worsening vision in driving cessation, these 1824 individuals are the relevant cohort for this analysis.
At each of the four rounds, interviewer-administered questionnaires were given to participants. Information was obtained on demographic information, medical history, and driving history. The questions on driving history inquired about having ever driven and the miles driven in the last year. Participants were asked to give personal contact information and contact information for a friend or family member not living with them. Cognition was assessed using the Mini-Mental State Exam17 and depressive symptoms using the General Health Questionnaire Part D.18
In addition, another questionnaire on driving history was asked in the summer of 2003 by telephone. We were able to obtain completed interviews from 1309 of 1824 (72%) of the cohort. Of the 1309, 76% were participant interviews and 24% were proxy interviews, done when the participant had died or was unable to communicate. Participants or proxies were asked to answer questions about whether and when the participant had stopped driving and whether there had been other drivers in the house at the times of the four SEE rounds. In a subsample of individuals (n = 39), good subject-proxy agreement was found for date of driving cessation (Pearson’s correlation = 0.9) and number of other drivers in the house (kappa = 0.8).
Measurement of Visual Function.
Visual function was evaluated both at baseline and follow up during an examination performed at a SEE clinic as described previously.1 Presenting visual acuity was measured binocularly under normal luminance using Early Treatment of Diabetic Retinopathy Study charts and protocols.19 The luminance of the chart was 130 cd/m2, backilluminated. Visual acuity was scored as the numbers of letters read correctly and then, using the technique of Bailey and colleagues, converted to logarithm of the minimum angle of resolution (log minimum angle resolution) units.20 Contrast sensitivity was measured monocularly using a Pelli-Robson chart that was illuminated at approximately 100 cd/m2 from an overhead light. Measures for the better eye were used in subsequent analyses. Visual field was measured monocularly using a Humphrey Visual Field Analyzer with the 81-point single threshold (24 dB) full-field (60°) screen test as has been described previously.21 Binocular visual fields were estimated “from a composite of the more sensitive of the two visual field locations for each eye.”22 A total of 96 visual field locations composed the binocular visual field. The binocular central visual field (20° radius) was measured using 56 centrally located points, whereas the binocular lower peripheral visual field (>20°and 60°) was measured using 22 points located in the lower peripheral field. The remaining 18 points represent the binocular upper peripheral visual field, which is not presented, because preliminary analyses and the existing literature led us to believe it would not be important. Finally, glare sensitivity was assessed using a Brightness Acuity Tester, an illuminated (medium setting, 350 cd/m2) white hemisphere with an aperture for the participant to look through to read a test chart, as described previously.23 Participants first read a Pelli-Robson chart with no glare source and then they read a different Pelli-Robson chart with the Brightness Acuity Tester. The score is the number of letters read correctly without glare minus the number of letters read correctly with the glare source.
Both baseline visual function and 2-year visual function loss were evaluated for their potential association with driving cessation. Baseline visual function for acuity, contrast sensitivity, central and lower peripheral visual fields, and glare sensitivity was examined as three-level categorical variables. The cutoffs for baseline acuity were chosen so that the most severe category was 20/40 or worse, which is the legal binocular acuity requirement for unrestricted driving in Maryland.24 The cutoffs for the other four baseline vision measures were based approximately on the 50th and 90th percentiles of the distributions.
The 2-year change in visual function in acuity, contrast sensitivity, central and lower peripheral visual fields, and glare sensitivity was examined by taking the difference between scores from rounds one and two so that positive scores were consistent with worse loss, whereas negative scores indicated a gain in function. Then, three-level categorical variables were created to represent no change or gain, mild loss, and modest loss. The reference group included those who had little vision change or who gained vision. Cutoffs were chosen to balance functionally significant losses and adequate numbers in each category for analysis. The worst category for presenting visual acuity loss was defined as having lost >2 lines of visual acuity between rounds one and two, because visual acuity can be ascertained within ± 1 line with 95% confidence.25 The worst category for contrast sensitivity loss was defined as 6 letters of loss between rounds one and two in the better eye, because a significant change is considered to be ± 0.18 log units (± 3.6 letters).26 Reliability estimates are not known for visual field or glare sensitivity so cutoffs for these were chosen to be the most extreme values that assured adequate numbers for analysis. The worst category for central visual field loss was defined as an increase of 8 in the number of points missed from round one to round two, whereas lower peripheral visual field loss was defined as an increase of 8 in the number of points missed. Finally, the worst category for glare sensitivity loss was defined as 5 letters of loss in glare sensitivity scores in the better eye between rounds one and two.
If information on driving status was obtained from the summer 2003 survey, that information was used for date of driving cessation. Individuals were asked if they had driven in the last 6 months. If they answered “yes,” they were considered to be current drivers. If they responded “no,” then they were asked the last time they remembered having driven and this date was used as the date of driving cessation.
If we were unable to contact the participant or a proxy for the summer 2003 survey, then information on driving status was used from the SEE round last attended. For example, if we were unable to contact a person in the summer 2003 survey, yet we knew driving cessation was reported at round two, then the date of the round two examination was used as the date of driving cessation.
Cognitive impairment was defined as a Mini-Mental State Exam score <24. Depressive symptoms were defined as a report of one or more affirmative responses out of seven questions using binary scoring on Part D of the General Health Questionnaire.
To evaluate potential bias, those who did not return to round two were compared on key characteristics with those who did return.
Of those who did return to round two, those who stopped driving were compared with those who did not to examine differences between the two groups.
The time from the baseline examination until the date of driving cessation or censoring was calculated for each person. If driving was not stopped, individuals were censored according to the last point in time we had information on driving status. For example, if an individual answered the 2003 survey, and driving was not stopped, the person was censored at the end of the study, if still alive, or else at the date of death. However, if an individual was not able to be contacted for the 2003 survey, and driving was not stopped according to the last round of SEE data we had obtained on that participant, then the person was censored at the date of that last round. Kaplan-Meier methods were used to estimate the probability of driving cessation by visual function category over time and the log rank test was used to test for statistically significant differences.
Time until driving cessation was modeled using Cox proportional hazards to adjust for health (diabetes, stroke, depression, self-report of general health status, cognitive impairment) and demographic factors (age, sex, race); these factors were chosen because it was determined beforehand that they would likely be associated with both vision and driving cessation and thus could act as confounders. Effect modifiers such as gender, cognitive impairment, and having no other drivers in the house were examined through stratification and the inclusion of interaction terms in the model.
A separate model was run for each of the five measures of visual function that included both the baseline measure and the 2-year loss measure. For example, a model was run that included both baseline acuity and 2-year acuity loss for their relationship to driving cessation. Next, a model with all of the vision variables was examined to determine which vision variables were associated with driving cessation while adjusting for all other vision variables.
The proportionality assumption was checked graphically through examination of the log–log survival curves and also by checking the scaled Schoenfeld residuals against log-time for each independent variable.
Of the 1824 drivers at baseline, 386 stopped driving during follow up (21%). Those who stopped driving were more likely to be older, female, black, to have had a greater number of comorbidities, a worse self-report of general health, cognitive impairment, diabetes, stroke, and depression than those who did not stop driving (Table 1). These differences persisted after age adjustment. The time to driving cessation by age category is shown in Figure 1 using Kaplan-Meier procedures. A majority of older adults in this cohort continued to drive 8 years after baseline even in the oldest category of adults ages 80 to 84 years at baseline.
Some of the measures of visual function are moderately correlated with one another (Table 2). Central visual fields and lower peripheral visual fields have a correlation of 0.55 and acuity and contrast sensitivity have a correlation of –0.44.
The proportion of drivers who stopped during follow up is presented in Table 3 by visual function measure. The baseline measures of visual function that were associated with driving cessation during follow up included acuity, contrast sensitivity, central and lower peripheral visual fields (age-adjusted linear trend p value < 0.05). In addition, several 2-year losses in visual function were also associated with driving cessation including acuity, central visual fields, and lower peripheral visual fields (age-adjusted linear trend p value < 0.05). Neither baseline glare sensitivity nor 2-year glare sensitivity loss was associated with driving cessation.
To determine if the time to driving cessation differed by visual function, Kaplan-Meier survival curves were plotted. The results confirmed the associations described in Table 3 (data not shown).
The results of regression analyses, which adjusted for demographic and health variables, revealed several measures of visual function that were independent predictors of driving cessation (Table 4). The baseline measures of visual function associated with time to driving cessation included acuity, contrast sensitivity, central and lower peripheral visual fields (linear trend p value < 0.05). Also, 2-year losses in acuity, contrast sensitivity, and lower peripheral visual fields were associated with time to driving cessation (linear trend p value < 0.05). Central visual field loss was not associated with time to driving cessation. Also, after adjustment, neither baseline glare sensitivity nor glare sensitivity loss was an independent predictor of driving cessation.
With all of the vision variables in the same model, the visual function measures still associated with driving cessation included baseline contrast sensitivity, central and lower peripheral visual fields (linear trend p value < 0.05), 2-year contrast sensitivity loss, and 2-year lower peripheral visual field loss (linear trend p value < 0.05) (Table 5). Neither baseline acuity nor 2-year acuity loss was associated with driving cessation with other vision variables in the model.
Other baseline independent predictors of driving cessation included older age (hazard ratio [HR] = 1.10, 95% confidence interval [CI] 1.07, 1.12), fair or poor self-report of general health status (HR = 1.80, 95% CI 1.42, 2.27), MMSE score <24 (HR = 1.94, 95% CI 1.36, 2.77), diabetes (HR = 1.55, 95% CI 1.17, 2.04), and stroke (HR = 1.90, 95% CI 1.36, 2.67). Also, men were less likely to stop driving (HR = 0.70, 95% CI 0.56, 0.88). Having no other drivers in the house was not associated with driving cessation, although the hazard ratio indicated that those with no other drivers in the house were somewhat less likely to stop driving (HR = 0.87, 95% CI 0.68, 1.13).
No statistically significant interactions between gender, cognitive impairment, or no other drivers in the house with visual function were detected (data not shown). For example, those who had a 2-year loss in contrast sensitivity were more likely to stop driving regardless of whether they were cognitively impaired (interaction term p value = 0.44).
Those characteristics of the 206 drivers who did not return to round two were compared with those who did return to round two. They were more likely to be older, male, to have reported more comorbidities or a worse general health status, and to have had a history of diabetes and stroke than those who did return (Table 6). These differences persisted after age adjustment.
In sensitivity analyses, to determine if including those who stopped driving before round two (when vision loss was measured between rounds one and two) affected our inferences, these individuals were excluded. We found that the inferences were unchanged, although the hazard ratios were all slightly attenuated (data not shown).
Several measures of visual function and changes in visual function appear to be predictors of driving cessation. These included visual acuity, contrast sensitivity, central and lower peripheral visual fields, and 2-year losses in acuity, contrast sensitivity, and lower peripheral visual fields. In addition, many of the measures of visual function were related to the likelihood of driving cessation in a stepwise fashion such that as vision became worse, older adults were more likely to stop driving. Interestingly, glare sensitivity was not associated with driving cessation. Our findings may suggest that older adults may be able to compensate for this type of compromised vision, perhaps by driving at a time of the day when glare is less of a problem. Alternatively, the measure of glare sensitivity we used may not measure a form of visual function that is associated with driving cessation. The lack of an association with glare is not likely the result of the choice of the cutoff, because the null associations were confirmed using continuous measures of baseline glare sensitivity and 2-year glare sensitivity change.
With the vision variables together in the same model, three visual function measures stood out as most important in their relationship to driving cessation. Baseline and 2-year change scores in contrast sensitivity and lower peripheral visual fields and baseline central visual fields were associated with driving cessation while adjusting for other types of visual function and demographic and health variables. Neither baseline acuity nor 2-year acuity loss was still statistically significantly associated with driving cessation. Because acuity and contrast sensitivity are moderately correlated (r = 0.44), having them in the model together reduced their respective hazard ratios, although the hazard ratios for contrast sensitivity remained statistically significant, indicating its greater association with driving cessation. This finding suggests that the loss of contrast sensitivity may be more of a stimulus to driving cessation than loss of acuity.
It is interesting that visual field measures were associated with driving cessation because at least one study has found that many older adults are unaware of their visual field loss.5 Perhaps it is not necessary to realize one’s vision has deteriorated per se, because someone may simply realize a general feeling of being less safe behind the wheel and thus may stop driving. The associations with central and lower peripheral visual fields are unlikely to be the result of their correlation with other visual function variables because they persist after adjusting for other visual function variables.
It was somewhat surprising that 2-year central visual field loss was not associated with driving cessation, especially given that worse baseline central visual field scores were associated with cessation. It is unlikely that our categorical definition of central visual field loss was not severe enough because our results were confirmed using a continuous measure. Perhaps it takes people a longer time to realize they have lost vision in their central visual field (perhaps as a result of compensation strategies like increased head scanning) and thus longer to stop driving. Although we know of no studies that have evaluated the time it takes to realize various visual function declines and how compensation strategies may affect this, there is some indirect evidence to support this possible explanation. A study by Szlyk et al. found that a group of patients with peripheral visual field loss reported more accidents than a similar group of patients with central visual field loss and control subjects with normal sight, despite central and peripheral visual field loss leading to similar degradations in driving simulator performance. The authors speculated that this could be the result of the differential effectiveness of compensation strategies for those with central versus peripheral visual field loss.9
Cognitive impairment was independently associated with driving cessation. Other studies have found this as well, but have also found that up to one-fourth of patients with dementia continue to drive.27 Aside from the increased crash risk in individuals with dementia,28,29 one might also worry that these individuals may fail to recognize other signs of physical decline that could further increase their crash risk. However, there were no signs to indicate that the associations between visual function and driving cessation were different in those individuals with cognitive impairment.
Our prospective results are consistent with other cross-sectional studies that have found that older adults with visual acuity impairment,16 visual fields impairment,16 and self-report of poor vision15,30 are more likely to have stopped driving. We are not aware of studies investigating recent losses in vision and how older adults changed their driving. In addition, our results are consistent with other literature that shows that multiple types of visual function are important for driving performance.4,5,8,9,11–13 Finally, the suggested association of those with no other drivers in the house being less likely to stop driving is similar to another study that found that those with other drivers in the house were more likely to stop driving (odds ratio = 1.84, p < 0.01).31
One potential source of bias is loss to follow up, especially if there was differential loss according to vision characteristics and driving cessation. However, those who did not return to round two were only 10% of participants and are unlikely to have greatly impacted the associations. Those who did not return to round two had worse baseline vision (full data not shown). In addition, they were probably more likely to have had 2-year vision loss because they were older, had more diabetes and stroke, reported worse general health, and had a greater number of comorbidities. Furthermore, in the summer 2003 survey, we were able to contact 36% of those who did not return to round two to ask about their driving status, and they were more likely to have stopped driving compared with those who did return to round two (29% vs. 24%). Therefore, it is unlikely that excluding those lost to follow up before round two would have greatly changed our results.
We recognize the limitation of relying on the self-report of driving cessation. Some may have had trouble remembering the precise date of cessation, which would result in noise in the measurement. This would likely bias the findings toward the null, making our findings conservative. In addition, for some individuals, if we did not have a precise date of cessation, we used the date of the first SEE examination in which they reported having stopped driving, which is a conservative estimate (rather than assume driving cessation midway in the interval between examinations, thus giving shorter time to driving cessation). This resulted in dates of driving cessation that were later than they should have been for a subgroup of people, because at the date of the examination, the participants had already stopped. However, these 500 people were no more or less likely to have had vision loss than those who we did contact to get a more precise date. Therefore, this should not have greatly biased our results.
One may be concerned that at the SEE rounds, those with poor vision may have inaccurately reported driving cessation as a result of feeling that it was the more socially desirable answer. However, this problem was likely minimized because individuals answered questions about their driving before they had their vision examined and also because the SEE staff developed a relationship with the participants and they would have known if the participant had driven in.
Finally, because the SEE population is an older cohort and we were asking them to recall information, cognitive impairment was a concern. Therefore, at the end of the summer 2003 survey, we asked the Short Portable Mental Status examination.32 There were 46 individuals who completed the interview and then scored >3 on the mental examination, which could indicate dementia.33 When they were excluded, our overall results were not changed. Moreover, there was good correlation between proxy response and participant response in the cases in which both were asked independently, suggesting good agreement.
We have found in a prospective study that several objective measures of visual function and changes in visual function were predictors of driving cessation. We did not find subgroups of people (like those with cognitive impairment) with poor visual function who were not likely to stop driving. Given that, on average, older adults with poorer visual function scores were more likely to stop driving, one strategy to attempt to reduce the fatal crash risk of older adults could be to continue to search for subgroups of people who are less likely to self-regulate their driving after experiencing visual function or other declines so that interventions to promote self-regulation can be developed to target these individuals.
Dr. Sheila West is supported by a grant from the National Institute on Aging AG16294. She is also supported through funds as a senior scientific investigator by Research to Prevent Blindness.
Ellen E. Freeman, MS
Room 129, Wilmer Eye Institute
Johns Hopkins University
600 North Wolfe St.
Baltimore, MD 21287
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