April 2009
Volume 50, Issue 4
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Glaucoma  |   April 2009
Evaluating Clinical Change and Visual Function Concerns in Drivers and Nondrivers with Glaucoma
Author Affiliations
  • Nancy K. Janz
    From the Departments of Health Behavior and Health Education,
  • David C. Musch
    Epidemiology, and
    Department of Ophthalmology and Visual Sciences, School of Medicine, University of Michigan, Ann Arbor, Michigan; and the
  • Brenda W. Gillespie
    Biostatistics, School of Public Health, and the
  • Patricia A. Wren
    Department of Wellness, Health Promotion, and Injury Prevention, School of Health Sciences, Oakland University, Rochester, Michigan.
  • Leslie M. Niziol
    From the Departments of Health Behavior and Health Education,
    Department of Ophthalmology and Visual Sciences, School of Medicine, University of Michigan, Ann Arbor, Michigan; and the
Investigative Ophthalmology & Visual Science April 2009, Vol.50, 1718-1725. doi:10.1167/iovs.08-2575
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      Nancy K. Janz, David C. Musch, Brenda W. Gillespie, Patricia A. Wren, Leslie M. Niziol; Evaluating Clinical Change and Visual Function Concerns in Drivers and Nondrivers with Glaucoma. Invest. Ophthalmol. Vis. Sci. 2009;50(4):1718-1725. doi: 10.1167/iovs.08-2575.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

purpose. To compare drivers and nondrivers, and to describe the specific concerns of drivers, among individuals with glaucoma.

methods. Six hundred seven patients with newly diagnosed glaucoma treated at 14 clinical centers of the Collaborative Initial Glaucoma Treatment Study were randomly assigned to initial medicine or surgery and were followed up every 6 months. Driving status (drivers versus nondrivers) and patient-reported visual function were determined by the Visual Activities Questionnaire and the National Eye Institute Visual Function Questionnaire. Clinical evaluation included visual field mean deviation (MD) and visual acuity. Statistical comparisons were made using t, χ2, and exact tests and regression and Rasch analyses.

results. Drivers were more likely than nondrivers to be male, white, married, employed, and more educated and to have higher incomes and fewer comorbidities. More than 50% of drivers reported at least “some” difficulty performing tasks involving glare, whereas 22% reported at least “some” difficulty with tasks requiring peripheral vision. At 54 months, drivers with moderate/severe bilateral visual field loss (VFL) reported greater difficulty with night driving and tasks involving visual search and visual processing speed than drivers with less bilateral VFL (all P < 0.05). Although those who remained drivers over follow-up had better MD in both eyes than those who became nondrivers because of eyesight, a number of drivers had marked VFL.

conclusions. Inquiring about specific difficulties with tasks related to glare, visual processing speed, visual search, and peripheral vision in driving, especially among patients with substantial bilateral visual field damage, will enable physicians to more effectively counsel patients regarding driving.

Patients with glaucoma rate driving as very important in preserving independence. The two most important concerns identified by glaucoma patients in a recent study were the risk for visual field loss (VFL) leading to an inability to drive and the fear of long-term blindness. 1 Ang and Eke 2 found that though most glaucoma patients in their study retained useful vision, almost half (47%) eventually lost vision, resulting in driving ineligibility. 
Several previous studies found that glaucoma is an important risk factor for motor vehicle accidents, 3 4 5 6 particularly among the elderly. 3 4 5 Although visual acuity is only weakly related to crashes, peripheral vision may play a more critical role. 7 The combination of impaired visual processing and glaucoma may play a role in the cause of automobile crashes among older driver that result in injuries. 5 Szlyk et al. 8 found a higher incidence of real-world and simulator accidents among patients with glaucoma than in controls. 
Less well studied among glaucoma patients is the difficulty performing specific tasks related to safe driving. Bowers et al., 9 in a small sample of 28 drivers, found that those with worse visual fields (VFs) had significantly poorer scores for changing lanes, driving around curves, and anticipatory skills. In a study of on-road driving performance, those with glaucoma were six times more likely to have difficulty detecting peripheral hazards and reacting to unexpected events than those with normal vision. 6 Glaucoma patients also report more difficulty driving at night than during the day 10 and more difficulty driving at any time than a normally sighted control group. 11  
Current research has considered the impact of bilateral VF status on quality of life (QOL), including driving concerns. 12 13 14 15 McKean-Cowdin et al. 14 found that bilateral VFL had the greatest impact on QOL, with driving difficulties among the subscales that showed the greatest differences, depending on the degree of unilateral and bilateral VFL. In a separate paper that used a binocular VF summation score, the same authors reported that persons with glaucomatous VFL had greater difficulty with driving activities; most of these persons were unaware of their glaucoma diagnosis. 15 Finally, Freeman et al. 13 found that persons with bilateral glaucoma were more likely to report difficulties with night driving than those with unilateral or no glaucoma. 
This study investigated four questions using data from the Collaborative Initial Glaucoma Treatment Study (CIGTS): (1) How do drivers differ from nondrivers? (2) How much difficulty do drivers report for specific visual tasks related to driving? (3) How does reported difficulty with driving tasks differ depending on bilateral clinical visual status? (4) Are drivers who become nondrivers over time different from those who remain drivers? 
Methods
Sample and Procedures
The study protocol, eligibility criteria, and recruitment procedures have been described. 16 In brief, 607 subjects were enrolled in the CIGTS between October 1993 and April 1997. Participants were patients with newly diagnosed open-angle glaucoma from 14 clinical centers in the United States. Eligible participants were between 25 and 75 years of age and agreed to be followed up for a minimum of 5 years; consent had to be obtained for additional follow-up. The CIGTS protocol and informed consent were approved by institutional review boards at the University of Michigan and at all clinical sites, and adhered to the tenets of the Declaration of Helsinki. After giving written informed consent, patients were randomly assigned to initial trabeculectomy (n = 300) or initial medical therapy (n = 307). For more specifics about the treatment course, see Lichter et al. 17  
Enrollment, Evaluation, and Follow-up
Before randomization, subjects underwent comprehensive ophthalmologic examination and baseline telephone QOL interviews. The age, race, and sex of each patient were recorded at the clinic site, whereas information on education, income, marital status, employment status, and number of nonocular comorbidities (0–13) was collected during the QOL interview. Standardized follow-up ophthalmologic examinations and QOL interviews were conducted every 6 months after the initial treatment. CIGTS QOL interviews were administered by trained telephone interviewers from a centralized interviewing facility. 
QOL Measures
The QOL protocol included generic and condition-specific measures and has been described in detail. 18 The CIGTS used the Visual Activities Questionnaire (VAQ) 19 and the National Eye Institute Visual Function Questionnaire (NEI-VFQ) 20 to assess patients’ perceptions of their visual functioning and the impact of visual problems on their daily activities, including driving. Both measures have established reliability and validity. 19 20 When the trial began, the 33-item VAQ was deemed to be the most relevant condition-specific measure of functional status available. Five years later, the NEI developed and released a measure of functional status, the 51-item VFQ 21 and, later, a 25-item version. 20 The latter was added to the CIGTS QOL protocol in 1998. 
The VAQ asks about problems patients may have performing everyday tasks that involve visual function. Each item describes a vision problem and asks how often this problem occurs on a five-point scale from 1 (never) to 5 (always) or “not applicable.” The VAQ includes eight subscales: color discrimination, glare disability, light-dark adaptation, acuity/spatial vision, depth perception, peripheral vision, visual search, and visual processing speed. The VAQ total score and subscale scores were calculated as a mean of all items or the items in that subscale, respectively. Higher scores indicate worse visual functioning. Five items across four different subscales (glare disability, peripheral vision, visual search, and visual processing speed) specifically refer to problems when driving. Three of the items relate to driving in general, and two are particular to night driving. The VAQ was administered at every interview time point. 
The NEI-VFQ was first administered at the 54-month follow-up and was then administered annually thereafter. The measure includes three questions focused on current driving status, degree of difficulty driving during the day and at night, and reasons nondrivers stopped driving. 
Defining Drivers and Nondrivers
Current driving status was assessed every 6 months over the course of the CIGTS study. From 6 months to 48 months, drivers were defined as persons who endorsed any of the five driving-related items of the VAQ. Beginning at 54 months, with the availability of the NEI-VFQ, we were able to define drivers by their response to a specific item about current driving status and to determine, among those who became nondrivers, whether they gave up driving because of their eyesight or for another reason. 
Clinical Measures
Every 6 months patients underwent a comprehensive ophthalmologic examination including automated perimetry with the Humphrey 24–2 full-threshold VF testing protocol. The VF test mean deviation (MD) provides an overall measure of VFL in each eye. Best-corrected visual acuity (VA) was measured bilaterally using a modification of the Early Treatment Diabetic Retinopathy Study protocol, with scores at baseline ranging between 70 and 100 (i.e., 20/40 or better). 16  
Data Analyses
In brief, we examined patient-reported visual function and clinical characteristics in a large group of patients with newly diagnosed glaucoma. Cross-sectional analyses were conducted 6 months after diagnosis, and longitudinal analysis was conducted over 54 months in relation to driving status. 
The first QOL questionnaire was completed just after the glaucoma diagnosis was made. To avoid the influence of recent diagnosis or anticipation of treatment, we used 6-month clinical and QOL findings as a reference for comparison with subsequent values over follow-up. By 6 months, almost all CIGTS patients had completed their initial treatment course. 
Demographic comparisons of drivers with nondrivers were made using χ2 tests. Comparisons of clinical variables and patient-reported visual function (VAQ measure) at 6 and 54 months between drivers and nondrivers were made with two sample t-tests. Linear regression was used to compare drivers and nondrivers on these same outcomes while adjusting for sex, race, education, employment, income, and number of comorbidities. Rasch analyses were performed on the five VAQ driving items using commercial software (WINSTEPS, version 3.66.0; Winsteps, Chicago, IL). The Rasch model is an ordinal (adjacent category) logit regression model on the item responses with main effects for each person and item. The person/item measures (logits) are the beta coefficients from this model. 
Comparisons between 6-month and follow-up measures of MD in better and worse eyes were made using paired t-tests among subjects who were drivers at 6 months. VF change from 6 to 54 months was compared using two-sample t-tests in patients who remained drivers compared with those who became nondrivers during this time. 
The MD at the 54-month follow-up was used to characterize patients in three categories, similar to those used by McKean-Cowdin et al. 14 :
  1.  
    No bilateral VF damage and/or mild unilateral damage (No Bi) = (a) both eyes MD > −2 dB or (b) better eye MD > −2 dB and [−6 dB ≤ worse eye MD ≤ −2 dB].
  2.  
    Mild bilateral VF damage and/or moderate/severe unilateral damage (Mild Bi) = (a) worse eye MD < −6 dB and better eye MD > −2 dB, or (b) both eyes with −6 dB ≤ MD ≤ −2 dB, or (c) worse eye MD < −6 dB and [−6 dB ≤ better eye MD ≤ −2 dB].
  3.  
    Moderate/severe bilateral damage (ModSev Bi) = both eyes MD < −6 dB.
Comparisons among these categories of VF damage were made using χ2 tests and extended Fisher exact tests for categorical variables. All analyses were performed using statistical software (SAS version 9.1; SAS Institute, Carey, NC). 
Results
Table 1displays sociodemographic differences between 471 drivers and 84 nondrivers who completed the 6-month follow-up. Significant differences (P < 0.001) between drivers and nondrivers were found for sex, race, education, employment, income, and marital status. Specifically, 60% of drivers were male compared with 27% of nondrivers. Most drivers were white (65%), whereas most nondrivers were black (62%). Education past high school was reported by 57% of drivers but by only 26% of nondrivers. More drivers than nondrivers were employed full time (53% vs. 23%) or had incomes in excess of $40,000 (44% vs. 6%). Most drivers (66%) but only 30% of nondrivers were married. Drivers reported significantly fewer comorbidities (P < 0.001). No significant association was found between driving status and age (P = 0.087) or living alone (P = 0.130). At 6 months, the distribution of initial treatment (medicine or surgery) did not differ significantly between drivers and nondrivers (P = 0.409). At 54 months, those who dropped out of the study compared with those who remained did not differ by age, sex, race, education, employment status, marital status, number of comorbidities, or baseline VF MD, but dropouts were significantly more likely to have lower incomes and worse visual acuity. No significant differences were observed between study participants and dropouts based on driving status at baseline. 
Differences in clinical outcomes and self-reported difficulties with visual function tasks between drivers and nondrivers are displayed in Table 2 . On average, drivers had better MD and VA than nondrivers in both the better eye and the worse eye at 6 months and 54 months. These differences were significant (all P < 0.014) in every case except MD in the worse eye at 6 months (P = 0.613). Nondrivers reported significantly more difficulties with general visual function tasks at 6-month and 54-month follow-up in their total VAQ score and all subscale scores (all P ≤ 0.05) except glare disability and light/dark adaptation. When the analyses comparing drivers and nondrivers were repeated adjusting for factors found significant in Table 1(sex, race, education, employment, income, marital status, and number of nonocular comorbidities), most clinical differences remained significant at 54 months, whereas the observed differences between drivers and nondrivers regarding general visual function tasks were no longer significant. 
Figure 1displays responses to the five VAQ items specifically related to driving at 6 months. Drivers more often reported difficulties with glare than with visual search, peripheral vision, or visual processing speed. For glare-related tasks, more than 50% of drivers reported at least “some” difficulty. More than 20% of drivers reported “often” or “always” having difficulty seeing the road at night in the rain because of headlights. Approximately 22% reported at least “some” difficulty with driving tasks requiring peripheral vision. 
We then used Rasch analysis to score the five driving items of the VAQ. 22 23 Figure 2shows the resultant person and item frequencies. Referring to person measures for the 429 drivers at 54 months, the most negative logit score (lowest bar) reflected the 95 (22%) who answered “never” to all five driving items. In contrast, five drivers (1%) answered “always” to all driving items (highest person bar). The items were well targeted (similar means) for people who indicated some degree of difficulty with visual function tasks related to safe driving. The item measure distribution showed that the two glare items were endorsed more readily than the other three VAQ driving items. Of the remaining three items, visual processing speed was the least readily endorsed. The in-fit mean squares (not shown) were all within the recommended range (0.7–1.3), 23 indicating all five items contributed to variability in the driving score. 
The association between bilateral VFL and the frequency of difficulty with specific driving tasks at the 54-month follow-up (Fig. 3)was next considered. We averaged the responses for the two glare questions, as justified in Figure 2by their similar response patterns. Drivers with ModSev Bi VFL were more likely to report at least “some” difficulty with all five driving tasks compared with those with Mild Bi or No Bi VFL. In terms of visual search, 38% of drivers with ModSev Bi VFL reported at least “sometimes” having difficulty with objects from the side unexpectedly appearing in their field of view while driving at night compared with 31% of those with Mild Bi VFL and 25% of those with No Bi VFL. Regarding peripheral vision, 35% of drivers with ModSev Bi VFL reported at least “sometimes” having difficulty changing lanes because of trouble seeing other cars compared with 17% of those with No Bi VFL. Approximately 41% of subjects with ModSev Bi VFL (compared with 29% with Mild Bi VFL and 23% with No Bi VFL) reported at least “sometimes” having difficulty driving using visual processing speed. For tasks involving visual search and visual processing speed, the differences noted among the three categories of bilateral VFL were significant (P = 0.009 and P = 0.001, respectively). The association with peripheral vision was marginally significant (P = 0.060). 
The same bilateral VFL categories were used to evaluate two National Eye Institute Visual Function Questionnaire items at 54 months (difficulty with daytime and nighttime driving). In both cases, drivers with ModSev Bi VFL reported more difficulty than those with No Bi VFL (P = 0.018 and P = 0.009, respectively). Among drivers who did not drive at night (n = 24), 16 responded it was (at least in part) because of their eyesight, and eight responded it was “for other reasons.” Overall, fewer persons reported difficulty with daytime driving, though those with ModSev Bi VFL were more likely to report “a little” difficulty. For those who drove at night, 76% with ModSev Bi VFL (compared with 56% with Mod Bi VFL and 45% with No Bi VFL) rated night driving at least “a little” difficult. 
Clinical change over time between those who remained drivers and those who became nondrivers by 54 months of follow-up is shown in Table 3 . Subjects whose driving and nondriving status shifted more than three times in the 4-year period (n = 100) were excluded from this analysis. There were no significant differences in the average MD change in the worse or better eye over time among those who remained drivers or those who became nondrivers. However, mean VA in both the worse and the better eye significantly worsened over time among those who remained drivers and those who became nondrivers. The mean VA of the better eye showed decreases of 1.0 and 4.2 letters from 6 to 54 months for drivers and nondrivers (P < 0.001 and P = 0.006, respectively). The difference between these two mean decreases was significant (P = 0.033). When the analyses included adjustment for significant sociodemographic factors and number of comorbidities (see Table 1 ), the 6- to 54-month clinical change (MD and VA) in drivers compared with those who became nondrivers was significant and became even stronger. 
Figure 4displays the average MD scores in the worse eye over the first 6.5 years of follow-up by driving status at each QOL interview. Drivers had consistently better MD values than nondrivers. At 4.5 years, when responses from the NEI-VFQ became available, the plot shows average MD scores among three groups (drivers, nondrivers because of eyesight, and nondrivers for other reasons). Drivers and nondrivers who stopped for reasons other than eyesight showed similar MD scores, roughly between −5 and −6 dB. At 54 months, those who became nondrivers because of eyesight compared with those who became nondrivers for other causes were more likely to have significant worsening of their MD and VA over time. In addition, drivers at baseline who became nondrivers by 54 months compared with those who remained drivers were more likely to be black, to be less educated, and to have more comorbidities. 
To further illustrate the clinical differences between these three groups, we present box plots comparing the MD of the worse and the better eye between drivers, nondrivers for nonocular reasons, and nondrivers because of eyesight at 54 months (Fig. 5) . The general trend is for drivers to have better MD values at 54 months in the worse and the better eye than nondrivers who have stopped driving because of eyesight. Nevertheless, the lower whiskers and outlier points on Figure 5indicate that there are a number of drivers who have marked VF damage in both the worse and the better eye. 
Discussion
After adjustment for demographic and health variables, this study found that drivers and nondrivers differed on clinical measures of vision (VF and VA) but did not differ on patient-reported difficulty with general tasks of daily living requiring vision. Difficulty with visual tasks specific to driving (five questions from several VAQ subscales) increased with the degree of VFL among drivers. Drivers with moderate to severe bilateral VF damage consistently reported more difficulty with safe-driving visual tasks than those with mild or no bilateral damage. This finding is consistent with those of other recent studies suggesting that bilateral VF damage may place patients at higher driving risk. 13 14 15 24  
Although we found that difficulties with tasks involving glare were reported by the highest percentage of drivers, reported difficulty with tasks involving visual search and visual processing speed varied more, depending on the degree of bilateral VFL. Haymes et al. 6 found an increase in accidents among patients with glaucoma, with the strongest risk factor impaired UFOV, a test of visual information processing speed. When drivers were asked a general question about “difficulty with day driving in familiar places,” we found only a small percentage reported even “a little” difficulty. When the questions addressed more specific visual tasks, such as “difficulty changing lanes” or “cars surprising them from the side,” substantially more drivers reported at least some difficulty, particularly those with bilateral VF damage. 
Not surprisingly, night driving was found to be more difficult than daytime driving. Three quarters of drivers reported at least a little difficulty with night driving, consistent with previous studies. 11 12 13 In a 2-year prospective study, Freeman et al. 25 found that drivers with more limited peripheral VFs were more likely to stop night driving, even when controlling for demographics, health status, and comorbidities. They also found a strong association between bilateral VFL and difficulty with night driving. 
Ophthalmologists are often asked to counsel patients on when or whether they should drive. Combining patients’ visual function as assessed by objective tests with more specific questions about difficulties when driving may help providers identify those patients at higher risk. Findings from Rasch analysis 23 suggest that asking glaucoma patients about glare may identify patients who are at risk for vision problems while driving. We observed that glaucoma patients notice glare as the first symptom of problems with driving and vision. Given the importance of driving and the impact that visual disorders have on driving ability, future research should build on the items considered here to develop a validated driving questionnaire that would assist physicians in discussions with their patients. 
It is also important to assess the amount of driving patients do and to determine whether visual abilities can meet the demand. If not, the need to reduce or stop driving should be discussed with the patient and documented in the medical record. Including family members in discussions about driving may be beneficial; research has shown that, compared with persons without glaucoma, glaucoma patients are more likely to report family concerns about their driving. 26 In states with limited drivers licenses, physicians can make appropriate referrals for patients so that they do not drive in circumstances that place themselves and others at higher risk. 14  
In on-road driving performance, drivers with glaucoma have been found to execute many driving maneuvers safely but are far more likely than those with normal vision to have difficulty detecting peripheral obstacles and reacting to unexpected events. 24 Along with developing a validated questionnaire, reliable functional tests are needed to assess driving deficits in the clinical area. Owsley et al. 7 suggest using tests of visual attention and processing speed that can identify drivers at high risk. To preserve a patient’s independence, it is necessary to understand which VF defects lead to visual disability and which can be compensated for by the remaining vision or cognitive factors. 12 Drivers should also incorporate new technologies when available (e.g., designing new window positioning and mirror areas to assist drivers with particular visual concerns 12 and using new autonavigation systems). Other possibilities include encouraging the use of public transportation when available. 
Over the 5-year follow-up, few CIGTS participants reported that they stopped driving because of vision. Consistent with our findings regarding comorbidities, previous studies have reported the presence of chronic conditions (e.g., cardiovascular disease, stroke, and diabetes), and poorer self-reported health can contribute to a change in driving status. 27 28 29 30 Although other investigators 27 28 29 31 32 33 have found that older women are more likely to discontinue driving than their male counterparts, no gender differences were observed in those who stopped driving over the course of this study. Additional studies are needed to discern the association between cognitive processing and decisions about driving. 27 32 33  
The nondrivers in this study who stopped driving because of vision did have substantially lower MD values than those who remained drivers. A change in vision has been found by previous investigators to be a significant predictor of driving cessation. 27 28 29 30 31 32 However, our results also indicate that some drivers with substantial VFL continue to drive. Focus groups with drivers and former drivers may identify the factors most relevant when deciding whether to stop driving. 
This study’s strengths include a large sample of well-characterized glaucoma patients. The longitudinal design allowed comparisons of changes in driving status and assessment of QOL and bilateral clinical outcomes over time. The VAQ assessed difficulties with driving tasks across a number of important visual function areas. Limitations include a small number of drivers who became nondrivers over the study’s course, thereby restricting our ability to identify factors associated with driving cessation. Second, we cannot distinguish the impact of glaucoma on driving from that of other conditions or aging in the absence of a comparison group of persons without glaucoma. Third, the study sample represented persons who were willing to enroll in a randomized treatment trial, but these subjects may not be representative of glaucoma patients seeking initial treatment. Fourth, the clinical measures were eye specific, and any combination of these measures may not reflect true binocular vision. Finally, duration and type of driving (i.e., driving exposure) were not factored into the associations we report. Previous studies have found that drivers with glaucoma are more likely than drivers without glaucoma to self-regulate their driving, thereby avoiding potentially difficult situations such as driving at night, in rush hour, on expressways, in unfamiliar areas, and during inclement weather. 26 34  
Conclusions
Patients with glaucoma indicate greater difficulty in performing safe driving tasks with worsening VFs, particularly when bilateral damage is present. More targeted discussions about driving should be included as part of regular clinical care. When clinical examination reveals glaucomatous visual deficits, physician inquiries about driving exposure and difficulties with specific driving tasks can be useful when counseling patients regarding safe driving. 
 
Table 1.
 
Characteristics of Drivers and Nondrivers at 6-Month Follow-up
Table 1.
 
Characteristics of Drivers and Nondrivers at 6-Month Follow-up
Categorical Variables* Driver Frequency (%) (n = 471) Nondriver Frequency (%) (n = 84) P , †
Age range (y) 0.087
 25–49 119 (25.3) 12 (14.3)
 50–64 198 (42.0) 42 (50.0)
 65–74 149 (31.6) 28 (33.3)
Sex <0.001
 Male 283 (60.1) 23 (27.4)
 Female 188 (39.9) 61 (72.6)
Race <0.001
 White 307 (65.2) 21 (25.0)
 Black 144 (30.6) 52 (61.9)
 Asian and other 20 (4.3) 11 (13.1)
Education <0.001
 <High School 71 (15.1) 44 (52.4)
 High School 134 (28.5) 18 (21.4)
 >High School 266 (56.5) 22 (26.2)
Employment <0.001
 Employed (fulltime/parttime) 246 (52.9) 19 (22.9)
 Unemployed 74 (15.9) 34 (41.0)
 Retired 145 (31.2) 30 (36.1)
Income <0.001
 <$10,000 49 (11.1) 29 (39.7)
 $10,000–40,000 199 (45.2) 40 (54.8)
 >$40,000 192 (43.6) 4 (5.5)
Marital status <0.001
 Never married 47 (10.0) 18 (21.4)
 Married 309 (65.6) 25 (29.8)
 Separated/widowed/divorced 115 (24.4) 41 (48.8)
Live alone 0.130
 No 400 (84.9) 65 (78.3)
 Yes 71 (15.1) 18 (21.7)
Continuous Variable Driver Mean (SD) (n = 471) Nondriver Mean (SD) (n = 84) P , ‡
Nonocular comorbidities <0.001
 Number (range, 0–13) 1.2 (1.4) 2.2 (1.7)
Table 2.
 
Comparison of Drivers and Nondrivers by Clinical and Self-Reported Visual Function Variables at Baseline (6 months) and 54-Month Follow-up
Table 2.
 
Comparison of Drivers and Nondrivers by Clinical and Self-Reported Visual Function Variables at Baseline (6 months) and 54-Month Follow-up
Continuous Variables 6 Months 54 Months
Driver Mean (SD) (n = 471) Nondriver Mean (SD) (n = 84) Unadjusted P * Adjusted P , † Driver Mean (SD) (n = 429) Nondriver Mean (SD) (n = 81) Unadjusted P * Adjusted P , †
MD
 Better Eye −2.1 (2.7) −2.9 (3.0) 0.014 0.966 −1.9 (3.1) −3.5 (3.7) <0.001 0.007
 Worse Eye −5.7 (4.9) −5.9 (4.0) 0.613 0.429 −5.4 (5.2) −7.0 (4.9) 0.012 0.080
VA
 Better Eye 87.7 (4.9) 85.1 (5.4) <0.001 0.012 86.9 (5.7) 83.2 (6.9) <0.001 0.003
 Worse Eye 83.2 (7.5) 79.7 (11.0) 0.007 0.095 81.5 (10.6) 75.3 (14.4) 0.001 0.003
VAQ
 Total score 1.9 (0.7) 2.2 (0.8) 0.003 0.647 1.9 (0.7) 2.1 (0.9) 0.025 0.458
 Color discrimination 1.5 (0.8) 1.7 (0.9) 0.047 0.368 1.5 (0.8) 1.8 (1.0) 0.008 0.134
 Glare disability 2.4 (1.0) 2.4 (1.1) 0.660 0.228 2.3 (1.1) 2.2 (1.2) 0.186 0.073
 Light/dark adaptation 2.3 (1.0) 2.5 (1.1) 0.074 0.940 2.3 (1.1) 2.5 (1.2) 0.254 0.640
 Acuity/spatial vision 2.4 (1.0) 2.8 (1.2) 0.008 0.374 2.2 (1.0) 2.6 (1.1) 0.008 0.458
 Depth perception 1.5 (0.7) 1.8 (0.9) 0.001 0.124 1.5 (0.7) 1.8 (0.9) 0.005 0.170
 Peripheral vision 1.7 (0.8) 1.9 (0.9) 0.014 0.902 1.7 (0.8) 1.9 (0.9) 0.019 0.471
 Visual search 2.0 (0.9) 2.2 (0.9) 0.040 0.903 1.9 (0.9) 2.2 (1.1) 0.052 0.467
 Visual processing speed 1.8 (0.7) 2.2 (0.9) 0.001 0.164 1.7 (0.8) 2.1 (1.0) 0.008 0.521
Figure 1.
 
VAQ Driving Items by percentage reporting specific visual function concerns among drivers at 6 months.
Figure 1.
 
VAQ Driving Items by percentage reporting specific visual function concerns among drivers at 6 months.
Figure 2.
 
Item difficulty and patient driving ability map (back-to-back histograms of Rasch scores).
Figure 2.
 
Item difficulty and patient driving ability map (back-to-back histograms of Rasch scores).
Figure 3.
 
VAQ and NEI-VFQ Driving Items by level of bilateral VFL at 54 months. Findings reported for glare (VAQ) are the average of drivers’ responses to the two VAQ items on glare.
Figure 3.
 
VAQ and NEI-VFQ Driving Items by level of bilateral VFL at 54 months. Findings reported for glare (VAQ) are the average of drivers’ responses to the two VAQ items on glare.
Table 3.
 
Clinical Comparison of Drivers at 6 Months Who Remained Drivers versus Those Who Became Nondrivers through 54-Month Follow-up
Table 3.
 
Clinical Comparison of Drivers at 6 Months Who Remained Drivers versus Those Who Became Nondrivers through 54-Month Follow-up
VF Measure n * 6 Months Mean (SD) 54 Months, † Mean (SD) Difference Mean (SD) Adjusted Difference Least Squares Mean (SE) P , ‡
MD
 Better eye
  Remain driver 337 −2.0 (2.7) −1.8 (3.1) 0.2 (2.1) 0.3 (0.3) 0.143
  Became nondriver 29 −2.5 (2.1) −3.3 (2.8) −0.7 (2.7) −0.9 (0.5) 0.158
  P 0.091, § 0.008, ∥
 Worse eye
  Remain driver 337 −5.5 (4.8) −5.4 (5.3) 0.1 (3.0) 0.3 (0.4) 0.606
  Became nondriver 29 −6.8 (5.4) −8.3 (5.1) −1.5 (4.8) −1.3 (0.7) 0.106
  P 0.093, § 0.013, ∥
VA
 Better eye
  Remain driver 340 87.9 (4.6) 86.9 (5.3) −1.0 (4.6) −0.4 (0.6) <0.001
  Became nondriver 30 87.1 (6.4) 82.9 (9.4) −4.2 (7.7) −3.9 (1.0) 0.006
  P 0.033, § 0.001, ∥
 Worse eye
  Remain driver 340 83.5 (7.3) 81.6 (10.4) −1.9 (9.8) −1.4 (1.3) <0.001
  Became nondriver 30 82.0 (9.0) 75.8 (14.9) −6.2 (13.7) −5.5 (2.1) 0.020
  P 0.105, § 0.054, ∥
Figure 4.
 
Mean deviation in worse eye over time by driving status based on the VAQ (0–4.5 years) and NEI-VFQ (4.5–6.5 years).
Figure 4.
 
Mean deviation in worse eye over time by driving status based on the VAQ (0–4.5 years) and NEI-VFQ (4.5–6.5 years).
Figure 5.
 
Box plot showing the distribution of MD values at 54 months by National Eye Institute-Visual Function Questionnaire driving status. Boxes extend from the 25th to 75th percentiles. The crossbar is at the median, and the plus sign indicates the mean. Whiskers extend to the nearest point within 1.5 times the interquartile range. Points beyond whiskers are indicated as outliers.
Figure 5.
 
Box plot showing the distribution of MD values at 54 months by National Eye Institute-Visual Function Questionnaire driving status. Boxes extend from the 25th to 75th percentiles. The crossbar is at the median, and the plus sign indicates the mean. Whiskers extend to the nearest point within 1.5 times the interquartile range. Points beyond whiskers are indicated as outliers.
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Figure 1.
 
VAQ Driving Items by percentage reporting specific visual function concerns among drivers at 6 months.
Figure 1.
 
VAQ Driving Items by percentage reporting specific visual function concerns among drivers at 6 months.
Figure 2.
 
Item difficulty and patient driving ability map (back-to-back histograms of Rasch scores).
Figure 2.
 
Item difficulty and patient driving ability map (back-to-back histograms of Rasch scores).
Figure 3.
 
VAQ and NEI-VFQ Driving Items by level of bilateral VFL at 54 months. Findings reported for glare (VAQ) are the average of drivers’ responses to the two VAQ items on glare.
Figure 3.
 
VAQ and NEI-VFQ Driving Items by level of bilateral VFL at 54 months. Findings reported for glare (VAQ) are the average of drivers’ responses to the two VAQ items on glare.
Figure 4.
 
Mean deviation in worse eye over time by driving status based on the VAQ (0–4.5 years) and NEI-VFQ (4.5–6.5 years).
Figure 4.
 
Mean deviation in worse eye over time by driving status based on the VAQ (0–4.5 years) and NEI-VFQ (4.5–6.5 years).
Figure 5.
 
Box plot showing the distribution of MD values at 54 months by National Eye Institute-Visual Function Questionnaire driving status. Boxes extend from the 25th to 75th percentiles. The crossbar is at the median, and the plus sign indicates the mean. Whiskers extend to the nearest point within 1.5 times the interquartile range. Points beyond whiskers are indicated as outliers.
Figure 5.
 
Box plot showing the distribution of MD values at 54 months by National Eye Institute-Visual Function Questionnaire driving status. Boxes extend from the 25th to 75th percentiles. The crossbar is at the median, and the plus sign indicates the mean. Whiskers extend to the nearest point within 1.5 times the interquartile range. Points beyond whiskers are indicated as outliers.
Table 1.
 
Characteristics of Drivers and Nondrivers at 6-Month Follow-up
Table 1.
 
Characteristics of Drivers and Nondrivers at 6-Month Follow-up
Categorical Variables* Driver Frequency (%) (n = 471) Nondriver Frequency (%) (n = 84) P , †
Age range (y) 0.087
 25–49 119 (25.3) 12 (14.3)
 50–64 198 (42.0) 42 (50.0)
 65–74 149 (31.6) 28 (33.3)
Sex <0.001
 Male 283 (60.1) 23 (27.4)
 Female 188 (39.9) 61 (72.6)
Race <0.001
 White 307 (65.2) 21 (25.0)
 Black 144 (30.6) 52 (61.9)
 Asian and other 20 (4.3) 11 (13.1)
Education <0.001
 <High School 71 (15.1) 44 (52.4)
 High School 134 (28.5) 18 (21.4)
 >High School 266 (56.5) 22 (26.2)
Employment <0.001
 Employed (fulltime/parttime) 246 (52.9) 19 (22.9)
 Unemployed 74 (15.9) 34 (41.0)
 Retired 145 (31.2) 30 (36.1)
Income <0.001
 <$10,000 49 (11.1) 29 (39.7)
 $10,000–40,000 199 (45.2) 40 (54.8)
 >$40,000 192 (43.6) 4 (5.5)
Marital status <0.001
 Never married 47 (10.0) 18 (21.4)
 Married 309 (65.6) 25 (29.8)
 Separated/widowed/divorced 115 (24.4) 41 (48.8)
Live alone 0.130
 No 400 (84.9) 65 (78.3)
 Yes 71 (15.1) 18 (21.7)
Continuous Variable Driver Mean (SD) (n = 471) Nondriver Mean (SD) (n = 84) P , ‡
Nonocular comorbidities <0.001
 Number (range, 0–13) 1.2 (1.4) 2.2 (1.7)
Table 2.
 
Comparison of Drivers and Nondrivers by Clinical and Self-Reported Visual Function Variables at Baseline (6 months) and 54-Month Follow-up
Table 2.
 
Comparison of Drivers and Nondrivers by Clinical and Self-Reported Visual Function Variables at Baseline (6 months) and 54-Month Follow-up
Continuous Variables 6 Months 54 Months
Driver Mean (SD) (n = 471) Nondriver Mean (SD) (n = 84) Unadjusted P * Adjusted P , † Driver Mean (SD) (n = 429) Nondriver Mean (SD) (n = 81) Unadjusted P * Adjusted P , †
MD
 Better Eye −2.1 (2.7) −2.9 (3.0) 0.014 0.966 −1.9 (3.1) −3.5 (3.7) <0.001 0.007
 Worse Eye −5.7 (4.9) −5.9 (4.0) 0.613 0.429 −5.4 (5.2) −7.0 (4.9) 0.012 0.080
VA
 Better Eye 87.7 (4.9) 85.1 (5.4) <0.001 0.012 86.9 (5.7) 83.2 (6.9) <0.001 0.003
 Worse Eye 83.2 (7.5) 79.7 (11.0) 0.007 0.095 81.5 (10.6) 75.3 (14.4) 0.001 0.003
VAQ
 Total score 1.9 (0.7) 2.2 (0.8) 0.003 0.647 1.9 (0.7) 2.1 (0.9) 0.025 0.458
 Color discrimination 1.5 (0.8) 1.7 (0.9) 0.047 0.368 1.5 (0.8) 1.8 (1.0) 0.008 0.134
 Glare disability 2.4 (1.0) 2.4 (1.1) 0.660 0.228 2.3 (1.1) 2.2 (1.2) 0.186 0.073
 Light/dark adaptation 2.3 (1.0) 2.5 (1.1) 0.074 0.940 2.3 (1.1) 2.5 (1.2) 0.254 0.640
 Acuity/spatial vision 2.4 (1.0) 2.8 (1.2) 0.008 0.374 2.2 (1.0) 2.6 (1.1) 0.008 0.458
 Depth perception 1.5 (0.7) 1.8 (0.9) 0.001 0.124 1.5 (0.7) 1.8 (0.9) 0.005 0.170
 Peripheral vision 1.7 (0.8) 1.9 (0.9) 0.014 0.902 1.7 (0.8) 1.9 (0.9) 0.019 0.471
 Visual search 2.0 (0.9) 2.2 (0.9) 0.040 0.903 1.9 (0.9) 2.2 (1.1) 0.052 0.467
 Visual processing speed 1.8 (0.7) 2.2 (0.9) 0.001 0.164 1.7 (0.8) 2.1 (1.0) 0.008 0.521
Table 3.
 
Clinical Comparison of Drivers at 6 Months Who Remained Drivers versus Those Who Became Nondrivers through 54-Month Follow-up
Table 3.
 
Clinical Comparison of Drivers at 6 Months Who Remained Drivers versus Those Who Became Nondrivers through 54-Month Follow-up
VF Measure n * 6 Months Mean (SD) 54 Months, † Mean (SD) Difference Mean (SD) Adjusted Difference Least Squares Mean (SE) P , ‡
MD
 Better eye
  Remain driver 337 −2.0 (2.7) −1.8 (3.1) 0.2 (2.1) 0.3 (0.3) 0.143
  Became nondriver 29 −2.5 (2.1) −3.3 (2.8) −0.7 (2.7) −0.9 (0.5) 0.158
  P 0.091, § 0.008, ∥
 Worse eye
  Remain driver 337 −5.5 (4.8) −5.4 (5.3) 0.1 (3.0) 0.3 (0.4) 0.606
  Became nondriver 29 −6.8 (5.4) −8.3 (5.1) −1.5 (4.8) −1.3 (0.7) 0.106
  P 0.093, § 0.013, ∥
VA
 Better eye
  Remain driver 340 87.9 (4.6) 86.9 (5.3) −1.0 (4.6) −0.4 (0.6) <0.001
  Became nondriver 30 87.1 (6.4) 82.9 (9.4) −4.2 (7.7) −3.9 (1.0) 0.006
  P 0.033, § 0.001, ∥
 Worse eye
  Remain driver 340 83.5 (7.3) 81.6 (10.4) −1.9 (9.8) −1.4 (1.3) <0.001
  Became nondriver 30 82.0 (9.0) 75.8 (14.9) −6.2 (13.7) −5.5 (2.1) 0.020
  P 0.105, § 0.054, ∥
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