METHODS by linzhengnd

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Appendix e-1: Methods

Subjects

         All subjects were community dwelling, independently living, licensed active

drivers. Drivers with PD were recruited from the Movement Disorders Clinics at the

Department of Neurology, University of Iowa and Veterans Affairs Medical Center, both

in Iowa City.

         Inclusion criteria: Active drivers with idiopathic PD and elderly drivers without

neurological disease (control group) were enrolled. All had a valid State driver’s license

and driving experience of greater than 10 years.

         Exclusion criteria: These were cessation of driving prior to encounter; acute

illness or active, confounding medical conditions such as vestibular disease, alcoholism

or other forms of drug addiction within the past 2 years; other neurologic disease leading

to dementia and motor dysfunction (excluded by review of medical records, available

imaging studies, cognitive testing, clinical interview and physical examination);

secondary parkinsonism; Parkinson-plus syndromes; treatment with centrally acting

dopaminergic blockers within 180 days prior to baseline or with any investigational drug

within 60 days prior to baseline; major psychiatric disease not in remission; diseases of

the optic nerve, retina, or ocular media with corrected visual acuity less than 20/50.

         Standard Protocol Approvals, Registrations, and Patient Consents. The study

was approved by the Institutional Review Boards and Human Subjects Office of the

University of Iowa. A written informed consent was obtained from all participants in the

study.

   Driving Outcomes
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   Driving Cessation. We determined the driving status and date of driving cessation by

reviewing data collected from multiple sources including follow-up telephone calls

conducted three to seven years after baseline assessment, clinic records, Driving Habits

Questionnaire (DHQ)e1 during annual study visits, state driving records, and death dates

from the Social Security Death Index if no other information was available. We reviewed

Iowa Department of Transportation (DOT) driving records, which were requested once

per year for a minimum of four years following baseline for indication of license

suspension, revocation, or rescission. Based on the above, the earliest evidence of driving

cessation was used to calculate elapsed time since baseline. For cases where no evidence

of driving cessation was noted, we used the last date of known driving as the censoring

time for this outcome.

   Moving Violations were tracked from annually requested Iowa DOT driving records.

Only citations resulting from driver actions while the vehicle was in motion were

included in analyses. Parking and other paperwork violations were excluded.

   Motor Vehicle Crashes were tracked from the DHQ,e1 phone calls, and from Iowa

DOT driving records. The first evidence of a crash from any of these sources was used to

calculate the time elapsed since baseline.

   Detailed police reports were requested for each crash listed on a participant’s driving

record that occurred during the study period. Police crash reports were reviewed by two

research assistants to determine if the crash should be categorized as “at-fault”, “not at-

fault”, or “not enough information”. Crashes that did not have a police report on file

were categorized as “no report on file”. A third rater was used for discrepancies between
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the first and second raters. Crashes in which the participant was not the driver were

excluded from analyses.

Potential risk factors of real-life outcomes:

Off-Road Testing Battery:

       The battery methodology has been described our previous work.e2 For all tests,

raw scores were used for analysis. The Useful Field of View (UFOV) task (Visual

Attention Analyzer Model 3000, Visual Resources Inc) measures speed (in msec) of

visual processing, divided attention, and selective attention and has been applied to test

at-risk elderly drivers and patients with PD.e2 We used the total of 4 subtests of the

UFOV task in our analyses.e2 Contrast sensitivity (CS) was measured using the Pelli-

Robson chart.e2 The best corrected visual acuity was assessed using the ETDRS chart for

far visual acuity (FVA) and reduced Snellen chart for near visual acuity (NVA), both

expressed as LogMAR (logarithm of the minimum angle of resolution).e2

       The Rey-Osterreith Complex Figure Test Copy version (CFT-COPY) and Block

Design subtest (BLOCKS) from the WAIS-R indexed visuoconstructional ability. The

CFT-Recall version assessed visual anterograde memory. The Benton Visual Retention

Test (BVRT) assessed visual working memory, the Trail-Making Test subtest A (TMT-

A) visual search and visual motor speed, and subtest B (TMT-B) executive functions,

working memory and attentional set shifting. Rey Auditory Verbal Learning Test

(AVLT) measured anterograde verbal memory. Judgment of Line Orientation (JLO)

assessed visuospatial perception. Controlled Oral Word Association (COWA) assessed

language and executive function abilities.e2 We also calculated a composite measure of

cognition (COGSTAT) as described before.e2
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       The Unified Parkinson’s Disease Rating Scale (UPDRS) and timed motor tests

such as tapping and walking speed were administered to all subjects with PD (Table 1).e3

Total daily levodopa-equivalent amount (mg) of antiparkinsonian medications was

calculated using an established formula.e2 The Schwab-England Activities of Daily

Living (SE-ADL) scale measured overall disability in PD.e2

       We performed all off road and on road tests (outlined below) at times when the

subject would normally feel ready to drive, i.e., during the “on” times, and allowed

subjects to rest as needed.

Self-reported driver habits and history:

       We assessed driving habits and history using the Driver Habits Questionnaire

(DHQ).e1 The DHQ is interviewer-administered and includes information on current

driving status and self assessed quality of driving, driving exposure (e.g., miles/week,

days/week), dependence on other drivers, driving difficulty under specific situations (e.g.,

night, rush hour, etc.), driving space, and self-reported crashes and citations. A risk

lowering score was calculated by adding up number of driving situations which the driver

avoided over the last 2 months before baseline (e.g., not driving at night, maximum=8).



The Road Test

       The experimental drive was conducted aboard ARGOS, a mid-sized instrumented

vehicle with an automatic transmission and hidden instrumentation and sensors.e4-e9

Experimental performance data such as steering wheel position and vehicle speed were

digitized at 10 Hz and reduced to means, SDs, or counts. Driver’s lane tracking and

driving behavior were recorded by videotape at 10 frames per second using miniature
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lipstick-size cameras mounted unobtrusively within the vehicle. Digital driving data were

superimposed on multiplex views using four channels of video. Control of speed27 and

lane position28 are critical aspects of driving, and unplanned lane deviations occur with

degradation of driving performance.29

       The road test in ARGOS was usually administered within a few weeks of

cognitive and visual testing, sometimes on the same day. The subjects were seated

comfortably in the driver’s seat. The experimenter sat in the front passenger seat to give

standard instructions, oversee the audiovideo equipment, and operate the dual controls, if

needed. The experimental drive lasted approximately 45 minutes and started after the

driver acclimated to ARGOS on a short test drive. The subjects drove across residential

city streets, suburban commercial strips, rural two-lane highways, and a four-lane 65 mph

speed limit freeway. All subjects took the same drive route. Secondary tasks such as route

following were interspersed across approximately 7 miles of the drive (“on-task”

segments). Road testing was carried out only during the day (usually between 9:00 AM

and 4:00 PM) on specific roads surrounding Iowa City and during the optimal motor

response to antiparkinsonian medications (“on” time). Drivers were not tested in

inclement weather that might cause poor visibility or road conditions. The assessment

incorporated important elements, such as turns, stopping at a stop sign, and maintaining

vehicle control.

       Driver familiarity with the region where the drive was administered was assessed

(as “yes” or “no” obtained by asking the driver about prior driving experience in and

around Iowa City)7 and incorporated as a factor into analyses.
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Safety Errors

       A professional driving instructor, different from the person who administered the

drive, reviewed the video data.e9,e10 As shown in our previous worke6, the driving

instructor reviewed tapes with a multiplex view using four channels of video (including

forward roadway the driver should see and position of the car relative to the lane) with

superimposed digital driving data, which included speed, enabling comparison of the

actual speed to the speed limit at any moment of the drive and detection lane deviation

errors. This approach allowed a standard review of all drives, including multiple views of

the driver, car, road and traffic. The reviewer assessed the number and type of safety

errors committed by the subjects, using a list of 76 error types (e.g., “unsafe passing”)

organized into 15 categories (e.g., “stop signs”, “lane observance”, etc.).e9,e10 This list

was based on the Iowa Department of Transportation’ Drive Test Scoring Standards

(September 7, 2005 version). The subjects were told to drive as they would in their usual

life and there was no overall pass-fail judgment.e9 The primary outcome measure was the

total number of safety errors. All other comparisons (error categories, “serious” errors)

were of exploratory nature. Of the 76 error types, 30 were classified as “serious”, which

were seen across different error categories.e9,e10 The “serious” errors were those that were

classified as “failure” errors by the Iowa DOT. However, as the subjects did not take this

road test as an official licensing test and we did not use a pass/fail system, we classified

these errors as “serious” errors. For each subject, we tabulated the total number of safety

errors, the number of safety errors within each category, and the total number of

“serious” safety errors.
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       Using randomly chosen 30 drive video tapes (10 with PD, 10 with Alzheimer’s

disease, and 10 controls) across the studies in our laboratory for repeated analysis, the

intra-rater correlation for total safety error counts was 95%, while the inter-rater

correlation (review by a second professional driving instructor with similar qualifications

and experience) was 73%, as we previously reported.e9

Statistical analysis

       We calculated descriptive statistics for baseline variables (e.g., demographic

features, vision, cognition, driving history and habits) in the control and PD (also indices

of parkinsonism) groups. The groups were compared using the Wilcoxon Rank Sum test

and Fisher’s exact test, depending on the scale of the variable. Relationships between

driving habits and cognition, vision, and parkinsonism were explored using Spearman

rank correlations within PD.

       We first compared the occurrence of our three outcomes between groups using

Fisher’s exact test. To accommodate the varying amounts of follow-up from driver to

driver, we used survival analysis methods. Our defined real-life driving outcome

variables were the time from the baseline evaluation to driving cessation, to the

occurrence of first crash, and to the first citation. Follow-up times were censored at the

last available evaluation for subjects who did not experience any of these three outcomes.

These time-to-event outcomes were analyzed using Kaplan-Meier curves to estimate the

probability of avoiding these outcomes over time, with the complements of these

probabilities termed as cumulative incidences. These estimates and their standard errors

were used to obtain 95% confidence intervals for cumulative incidences at selected times.

Logrank tests were used to make unadjusted between-group comparisons based on the
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KM curves. Cox proportional hazards regression models were used to compare the risk of

these events between groups with adjustment for key covariates, namely, age, gender,

education, and miles driven per week at baseline. Additional adjustments were done as

indicated.

       Cox proportional hazards regression models examined associations between

potential risk factors and the time to real life driving outcomes within PD drivers. For

each outcome, hazard ratios (HR) for individual risk factor variables (e.g., cognitive,

visual, parkinsonism, self-report on driving characteristics) were adjusted for

demographic factors (age, education, gender) and driving exposure (miles/week). To

facilitate comparisons across risk factors, the hazard ratios were expressed in terms of 1

SD change in the risk factor unless otherwise specified. All p-values throughout this

report are for two-sided alternative hypotheses.

                                       e-References



e1. Owsley, C, Stalvey, B, Wells, J, et al. Older drivers and cataract: driving habits and

     crash risk. J Gerontol A Biol Sci Med Sci. 1999; 54:M203-M211.


e2. Uc, EY, Rizzo, M, Anderson, SW, et al. Visual dysfunction in Parkinson disease

     without dementia. Neurology. 2005; 65:1907-1913.


e3. Defer, GL, Widner, H, Marie, RM, et al. Core assessment program for surgical

     interventional therapies in Parkinson's disease (CAPSIT-PD). Mov Disord. 1999;

     14:572-584.
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e4. Uc, EY, Rizzo, M, Anderson, SW, et al. Driver landmark and traffic sign

     identification in early Alzheimer's disease. J Neurol Neurosurg Psychiatry. 2005;

     76:764-768.


e5. Uc, EY, Rizzo, M, Anderson, SW, et al. Driver route-following and safety errors in

     early Alzheimer disease. Neurology. 2004; 63:832-837.


e6. Uc, EY, Rizzo, M, Anderson, SW, et al. Driving with distraction in Parkinson

     disease. Neurology. 2006; 67:1774-1780.


e7. Uc, EY, Rizzo, M, Anderson, SW, et al. Impaired visual search in drivers with

     Parkinson's disease. Ann Neurol. 2006; 60:407-413.


e8. Uc, EY, Rizzo, M, Anderson, SW, et al. Impaired navigation in drivers with

     Parkinson's disease. Brain. 2007; 130:2433-2440.


e9. Dawson, JD, Anderson, SW, Uc, EY, et al. Predictors of driving safety in early

     Alzheimer disease. Neurology. 2009; 72:521-527.


e10. Dawson, JD, Uc, EY, Anderson, SW, et al. Ascertainment of on-road safety errors

     based on video review. In: Boyle LN, Lee JD, McGehee DV et al., eds. Proceedings

     of Driving Assessment 2009: The Fifth International Driving Symposium on

     Human Factors in Driver Assessment, Training and Vehicle Design. Iowa City,

     Iowa: University of Iowa, 2009:419-426.

								
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