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Table 1.  Demographic and Health Characteristics of the Sample
Demographic and Health Characteristics of the Sample
Table 2.  Visual Function at Baseline in the Sample
Visual Function at Baseline in the Sample
Table 3.  Age-Adjusted Associations Between Baseline Visual Function and At-Fault Crash and Near-Crash Involvement During 6 Months After Baseline Based on Naturalistic Driving Data (N = 154)
Age-Adjusted Associations Between Baseline Visual Function and At-Fault Crash and Near-Crash Involvement During 6 Months After Baseline Based on Naturalistic Driving Data (N = 154)
1.
Congdon  N, O’Colmain  B, Klaver  CC,  et al; Eye Diseases Prevalence Research Group.  Causes and prevalence of visual impairment among adults in the United States.   Arch Ophthalmol. 2004;122(4):477-485. doi:10.1001/archopht.122.4.477 PubMedGoogle ScholarCrossref
2.
McCloskey  LW, Koepsell  TD, Wolf  ME, Buchner  DM.  Motor vehicle collision injuries and sensory impairments of older drivers.   Age Ageing. 1994;23(4):267-273. doi:10.1093/ageing/23.4.267 PubMedGoogle ScholarCrossref
3.
Owsley  C, Ball  K, McGwin  G  Jr,  et al.  Visual processing impairment and risk of motor vehicle crash among older adults.   JAMA. 1998;279(14):1083-1088. doi:10.1001/jama.279.14.1083 PubMedGoogle ScholarCrossref
4.
Rubin  GS, Ng  ES, Bandeen-Roche  K, Keyl  PM, Freeman  EE, West  SK.  A prospective, population-based study of the role of visual impairment in motor vehicle crashes among older drivers: the SEE Study.   Invest Ophthalmol Vis Sci. 2007;48(4):1483-1491. doi:10.1167/iovs.06-0474 PubMedGoogle ScholarCrossref
5.
Huisingh  C, McGwin  G  Jr, Wood  J, Owsley  C.  The driving visual field and a history of motor vehicle collision involvement in older drivers: a population-based examination.   Invest Ophthalmol Vis Sci. 2014;56(1):132-138. doi:10.1167/iovs.14-15194 PubMedGoogle ScholarCrossref
6.
Friedman  C, McGwin  G  Jr, Ball  KK, Owsley  C.  Association between higher order visual processing abilities and a history of motor vehicle collision involvement by drivers ages 70 and over.   Invest Ophthalmol Vis Sci. 2013;54(1):778-782. doi:10.1167/iovs.12-11249 PubMedGoogle ScholarCrossref
7.
Shinar  D, Treat  JR, McDonald  ST.  The validity of police reported accident data.   Accid Anal Prev. 1983;15(3):175-191. doi:10.1016/0001-4575(83)90018-0 Google ScholarCrossref
8.
McGuire  FL.  The nature of bias in official accident and violation records.   J Appl Psychol. 1973;57:300-305. doi:10.1037/h0034728 Google ScholarCrossref
9.
Dingus  TA, Klauer  SG, Neale  VL,  et al.  The 100-Car Naturalistic Driving Study, Phase II—Results of the 100-Car Field Experiment. National Highway Traffic Safety Administration, US Dept of Transportation; 2006. doi:10.1037/e624282011-001
10.
Dingus  TA, Guo  F, Lee  S,  et al.  Driver crash risk factors and prevalence evaluation using naturalistic driving data.   Proc Natl Acad Sci U S A. 2016;113(10):2636-2641. doi:10.1073/pnas.1513271113 PubMedGoogle ScholarCrossref
11.
Klauer  SG, Guo  F, Simons-Morton  BG, Ouimet  MC, Lee  SE, Dingus  TA.  Distracted driving and risk of road crashes among novice and experienced drivers.   N Engl J Med. 2014;370(1):54-59. doi:10.1056/NEJMsa1204142 PubMedGoogle ScholarCrossref
12.
Friedrich  TE, Duerksen  KN, Elias  LJ.  Overestimation of self-reported driving exposure: results from the SHRP2 Naturalistic Driving Study.   Traffic Inj Prev. 2019;20(2):128-133. doi:10.1080/15389588.2018.1549731 PubMedGoogle ScholarCrossref
13.
Agramunt  S, Meuleners  L, Chow  KC, Ng  JQ, Morlet  N.  A validation study comparing self-reported travel diaries and objective data obtained from in-vehicle monitoring devices in older drivers with bilateral cataract.   Accid Anal Prev. 2017;106:492-497. doi:10.1016/j.aap.2016.10.021 PubMedGoogle ScholarCrossref
14.
Seacrist  T, Douglas  EC, Hannan  C, Rogers  R, Belwadi  A, Loeb  H.  Near crash characteristics among risky drivers using the SHRP2 naturalistic driving study.   J Safety Res. 2020;73:263-269. doi:10.1016/j.jsr.2020.03.012 PubMedGoogle ScholarCrossref
15.
Guo  R, Klauer  SG, Hankey  JM, Dingus  TA.  Near crashes as crash surrogate for naturalistic driving studies.   Transp Res Rec. 2010;2147(1):66-74. doi:10.3141/2147-09 Google Scholar
16.
Owsley  C, McGwin  G  Jr, Antin  JF, Wood  JM, Elgin  J.  The Alabama VIP Older Driver Study rationale and design: examining the relationship between vision impairment and driving using naturalistic driving techniques.   BMC Ophthalmol. 2018;18(1):32. doi:10.1186/s12886-018-0686-5 PubMedGoogle ScholarCrossref
17.
Owsley  C, Stalvey  BT, Wells  J, Sloane  ME, McGwin  G  Jr.  Visual risk factors for crash involvement in older drivers with cataract.   Arch Ophthalmol. 2001;119(6):881-887. doi:10.1001/archopht.119.6.881 PubMedGoogle ScholarCrossref
18.
Ball  KK, Roenker  DL, Wadley  VG,  et al.  Can high-risk older drivers be identified through performance-based measures in a Department of Motor Vehicles setting?   J Am Geriatr Soc. 2006;54(1):77-84. doi:10.1111/j.1532-5415.2005.00568.x PubMedGoogle ScholarCrossref
19.
Swain  TA, McGwin  G  Jr, Wood  JM, Owsley  C.  Motion perception as a risk factor for motor vehicle collision involvement in drivers ≥70 years.   Accid Anal Prev. 2021;151:105956. doi:10.1016/j.aap.2020.105956 PubMedGoogle Scholar
20.
Beck  RW, Moke  PS, Turpin  AH,  et al.  A computerized method of visual acuity testing: adaptation of the Early Treatment of Diabetic Retinopathy Study testing protocol.   Am J Ophthalmol. 2003;135(2):194-205. doi:10.1016/S0002-9394(02)01825-1 PubMedGoogle ScholarCrossref
21.
Pelli  DG, Robson  JG, Wilkins  AJ.  The design of a new letter chart for measuring contrast sensitivity.   Clin Vis Sci. 1988;2(3):187-199.Google Scholar
22.
Elliott  DB, Bullimore  MA, Bailey  IL.  Improving the reliability of the Pelli-Robson contrast sensitivity test.   Clin Vis Sci. 1991;6:471-475.Google Scholar
23.
Leat  SJ, Legge  GE, Bullimore  MA.  What is low vision? a re-evaluation of definitions.   Optom Vis Sci. 1999;76(4):198-211. doi:10.1097/00006324-199904000-00023 PubMedGoogle ScholarCrossref
24.
Edwards  JD, Ross  LA, Wadley  VG,  et al.  The useful field of view test: normative data for older adults.   Arch Clin Neuropsychol. 2006;21(4):275-286. doi:10.1016/j.acn.2006.03.001 PubMedGoogle ScholarCrossref
25.
Vargas-Martín  F, García-Pérez  MA.  Visual fields at the wheel.   Optom Vis Sci. 2005;82(8):675-681. doi:10.1097/01.opx.0000175624.34252.73 PubMedGoogle ScholarCrossref
26.
Nelson-Quigg  JM, Cello  K, Johnson  CA.  Predicting binocular visual field sensitivity from monocular visual field results.   Invest Ophthalmol Vis Sci. 2000;41(8):2212-2221.PubMedGoogle Scholar
27.
Kwon  M, Huisingh  C, Rhodes  LA, McGwin  G  Jr, Wood  JM, Owsley  C.  Association between glaucoma and at-fault motor vehicle collision involvement among older drivers: a population-based study.   Ophthalmology. 2016;123(1):109-116. doi:10.1016/j.ophtha.2015.08.043 PubMedGoogle ScholarCrossref
28.
Lacherez  P, Turner  L, Lester  R, Burns  Z, Wood  JM.  Age-related changes in perception of movement in driving scenes.   Ophthalmic Physiol Opt. 2014;34(4):445-451. doi:10.1111/opo.12140 PubMedGoogle ScholarCrossref
29.
Folstein  MF, Folstein  SE, McHugh  PR.  “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician.   J Psychiatr Res. 1975;12(3):189-198. doi:10.1016/0022-3956(75)90026-6 PubMedGoogle ScholarCrossref
30.
Radloff  LS.  The CES-D scale: a self-report depression scale for research in the general population.   Appl Psychol Meas. 1977;1:385-401. doi:10.1177/014662167700100306 Google ScholarCrossref
31.
Owsley  C, McGwin  G  Jr, Sloane  M, Wells  J, Stalvey  BT, Gauthreaux  S.  Impact of cataract surgery on motor vehicle crash involvement by older adults.   JAMA. 2002;288(7):841-849. doi:10.1001/jama.288.7.841 PubMedGoogle ScholarCrossref
32.
Hankey  JM, Perez  MA, McClafferty  J.  Description of the SHRP2 Naturalistic Database and the Crash, Near-Crash and Baseline Datasets. Virginia Tech Transportation Institute, 2016.
33.
McClafferty  J, Perez  MA, Hankey  JM.  Identification of Consented Driver Trips in the SHRP2 Naturalistic Driving Study Data Set. Virginia Tech Transportation Institute; 2015.
34.
Cross  JM, McGwin  G  Jr, Rubin  GS,  et al.  Visual and medical risk factors for motor vehicle collision involvement among older drivers.   Br J Ophthalmol. 2009;93(3):400-404. doi:10.1136/bjo.2008.144584 PubMedGoogle ScholarCrossref
35.
Owsley  C, McGwin  G  Jr, Searcey  K.  A population-based examination of the visual and ophthalmological characteristics of licensed drivers aged 70 and older.   J Gerontol A Biol Sci Med Sci. 2013;68(5):567-573. doi:10.1093/gerona/gls185 PubMedGoogle ScholarCrossref
36.
Ball  K, Owsley  C, Sloane  ME, Roenker  DL, Bruni  JR.  Visual attention problems as a predictor of vehicle crashes in older drivers.   Invest Ophthalmol Vis Sci. 1993;34(11):3110-3123.PubMedGoogle Scholar
37.
Wadley  VG, Bull  TP, Zhang  Y,  et al.  Cognitive processing speed is strongly related to driving skills, financial abilities, and other instrumental activities of daily living in persons with MCI and mild dementia.   J Gerontol A Biol Sci Med Sci. 2020;glaa312. Published online December 12, 2020. doi:10.1093/gerona/glaa312PubMedGoogle Scholar
38.
Roenker  DL, Cissell  GM, Ball  KK, Wadley  VG, Edwards  JD.  Speed-of-processing and driving simulator training result in improved driving performance.   Hum Factors. 2003;45(2):218-233. doi:10.1518/hfes.45.2.218.27241 PubMedGoogle ScholarCrossref
39.
Wood  JM.  Age and visual impairment decrease driving performance as measured on a closed-road circuit.   Hum Factors. 2002;44(3):482-494. doi:10.1518/0018720024497664 PubMedGoogle ScholarCrossref
40.
Green  KA, McGwin  G  Jr, Owsley  C.  Associations between visual, hearing, and dual sensory impairments and history of motor vehicle collision involvement of older drivers.   J Am Geriatr Soc. 2013;61(2):252-257. doi:10.1111/jgs.12091 PubMedGoogle ScholarCrossref
41.
Merickel  J, High  R, Dawson  J, Rizzo  M.  Real-world risk exposure in older drivers with cognitive and visual dysfunction.   Traffic Inj Prev. 2019;20(suppl 2):S110-S115. doi:10.1080/15389588.2019.1688794 PubMedGoogle ScholarCrossref
42.
Wood  JM, Anstey  KJ, Kerr  GK, Lacherez  PF, Lord  S.  A multidomain approach for predicting older driver safety under in-traffic road conditions.   J Am Geriatr Soc. 2008;56(6):986-993. doi:10.1111/j.1532-5415.2008.01709.x PubMedGoogle ScholarCrossref
43.
Wood  JM, Black  AA, Mallon  K, Kwan  AS, Owsley  C.  Effects of age-related macular degeneration on driving performance.   Invest Ophthalmol Vis Sci. 2018;59(1):273-279. doi:10.1167/iovs.17-22751 PubMedGoogle ScholarCrossref
44.
Keay  L, Munoz  B, Duncan  DD,  et al.  Older drivers and rapid deceleration events: Salisbury Eye Evaluation Driving Study.   Accid Anal Prev. 2013;58:279-285. doi:10.1016/j.aap.2012.06.002 PubMedGoogle ScholarCrossref
45.
Chevalier  A, Coxon  K, Chevalier  AJ,  et al.  Predictors of older drivers’ involvement in rapid deceleration events.   Accid Anal Prev. 2017;98:312-319. doi:10.1016/j.aap.2016.10.010 PubMedGoogle ScholarCrossref
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    1 Comment for this article
    Visua Impairment and MVAs.
    Charles brill, MD | Thomas Jefferson University
    Did any of these drivers also have cognitive impairment?
    CONFLICT OF INTEREST: None Reported
    Original Investigation
    April 29, 2021

    Naturalistic Driving Techniques and Association of Visual Risk Factors With At-Fault Crashes and Near Crashes by Older Drivers With Vision Impairment

    Author Affiliations
    • 1School of Medicine, Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, Birmingham
    • 2School of Public Health, Department of Epidemiology, University of Alabama at Birmingham, Birmingham
    • 3Center for Vision and Eye Research, School of Optometry and Vision Science, Queensland University of Technology, Brisbane, Queensland, Australia
    • 4Vulnerable Road User Safety, Virginia Tech Transportation Institute, Blacksburg
    JAMA Ophthalmol. 2021;139(6):639-645. doi:10.1001/jamaophthalmol.2021.0862
    Key Points

    Question  What are visual risk factors associated with at-fault crashes and near crashes by older drivers with age-related eye conditions objectively assessed by naturalistic driving methods using video-confirmed events?

    Findings  This cohort study of 154 drivers 70 years or older found that vision impairment in contrast sensitivity, processing speed, and motion perception at baseline was moderately associated with the rate of at-fault crashes and near crashes during the subsequent 6 months of driving.

    Meaning  Motor vehicle crash reports used in the study of driver safety are frequently biased and incomplete; this study avoids these shortcomings by documenting vision impairment characteristics associated with collision risk among older drivers with age-related eye conditions by using naturalistic driving methods.

    Abstract

    Importance  Government motor vehicle crash reports used in the study of driver safety can be biased and incomplete. Naturalistic driving methods using in-vehicle instrumentation have been developed in recent years to objectively measure crashes and near crashes as they occur on the road using video and vehicle kinematic data.

    Objective  To examine visual risk factors associated with at-fault crashes and near crashes among older drivers, most of whom have age-related eye conditions associated with vision impairment.

    Design, Setting, and Participants  This prospective cohort study was conducted at an academic ophthalmology clinic from January 1, 2015, to November 10, 2018, among 154 adults 70 years of age or older who were legally licensed in Alabama and who reported currently driving at least 4 days per week; 137 of 151 participants (90.7%) had an age-related eye condition in at least 1 eye. Drivers participated in a baseline visual function assessment followed by installation of a data acquisition system recording multichannel synchronized video and vehicle kinematics in their personal vehicle. Drivers were instructed to drive for 6 months as they normally would during everyday life. Statistical analysis was performed from June 15 to September 15, 2020.

    Main Outcomes and Measures  The rate of combined incident at-fault crashes and near crashes, defined by the number of events and the number of miles driven.

    Results  The sample consisted of 154 drivers (85 men [55.2%]; mean [SD] age, 79.3 [5.1] years). Visual functions associated with crash and near-crash involvement included impaired contrast sensitivity (rate ratio [RR], 2.7; 95% CI, 1.3-5.5), moderate (RR, 2.3; 95% CI, 1.1-4.9) and severe (RR, 5.0; 95% CI, 2.2-11.7) slowing in visual processing speed, and elevated motion perception thresholds for a drifting grating (RR, 1.9; 95% CI, 1.1-3.5). Those with impaired peripheral visual field sensitivity had increased rates of crashes and near crashes (RR, 1.8; 95% CI, 1.0-3.3); however, this finding was not statistically significant (P = .07).

    Conclusions and Relevance  With the use of naturalistic driving methods in which crashes and near crashes involving older drivers are objectively measured as they occur on the road, associations have been identified between impaired contrast sensitivity, slowed visual processing speed, and impaired motion perception and an increased rate of a combined total of at-fault crashes and near crashes.

    Introduction

    Older adults have the highest prevalence of vision impairment in the US.1 Epidemiologic studies on older drivers have examined the visual requirements for driver safety, focusing on the types of vision impairment associated with increased collision risk.2-6 The primary outcome in many studies is crash involvement, defined by motor vehicle crash reports providing details on the collision (eg, driver demographic characteristics, time, and place). These reports are completed by a police officer who attends the scene after the crash has been reported. The police officer does not actually see the crash take place but collects information about it from witnesses and by observing the vehicle(s) and roadway after the crash. Although a motor vehicle crash report is an objective government document, it is prone to bias from the police officer and witnesses.7 Another limitation is that some crashes are not reported to the police and go uncounted in assessing risk factors associated with crash involvement.8

    Quiz Ref IDDuring the past 15 to 20 years, a novel method for studying driver safety has been developed—naturalistic driving using instrumented vehicles.9-11 Multichannel video cameras and sensors are installed in participants’ vehicles recording several data streams. Participant drivers go about their everyday driving for an extended time period (months or years), creating an objective driving record that can be used to identify crashes after the study. In computing the crash rate (crashes per mile driven), the actual mileage of the car is used, rather than the driver’s self-reported mileage, which is unreliable.12-14 Near crashes can also be identified, occurring more commonly than crashes with similar associated factors, which allows them to be used as crash surrogates.9,15 In our novel prospective cohort study, we studied the association between visual function at baseline in drivers 70 years of age or older and at-fault crash and near-crash involvement during the subsequent 6 months. We specifically focused on older adults, most with age-related eye conditions that engender vision impairment, with the goal of identifying the visual risk factors associated with objectively defined at-fault crashes and near crashes.

    Methods

    Quiz Ref IDThis study, conducted from January 1, 2015, to November 10, 2018, is based on the Alabama VIP Older Driver Study using naturalistic driving to study driver safety and performance among older adults.16 Institutional review board approval was obtained from the University of Alabama at Birmingham (UAB) and Virginia Tech. Written informed consent was obtained from the participants. The target population was older adults who recently had an appointment at the Callahan Eye Hospital Clinics at UAB. Our goal was to recruit approximately 90% of our sample having age-related eye conditions, including age-related macular degeneration, cataract, diabetic retinopathy or macular edema, glaucoma, and other conditions that have the potential for impairing vision. Our goal was to recruit the other 10% from persons who did not have age-related eye conditions. Eye diagnoses were obtained from the electronic health record. Inclusion criteria at baseline were aged 70 years or older, legally licensed in Alabama, reported driving 4 days or more per week, owned a motor vehicle, and were willing to have a data acquisition system (DAS) installed in their vehicle.

    Study visits took place at the Clinical Research Unit at Callahan Eye Hospital. Potential participants were contacted by letter and received a telephone call to schedule an in-person screening visit. This visit was to examine the person’s vehicle to assess its suitability for DAS installation and to discuss installation details and the study process. If the vehicle was suitable for installation, a second visit was scheduled within 1 to 2 weeks for installation and completion of the baseline protocol. Enrollees were compensated for their participation.

    At baseline, demographic variables were obtained through interview. Several visual and visuocognitive tests were conducted under binocular conditions unless otherwise noted. Tests were those associated with crash involvement using motor vehicle crash reports.3,5,17-19 Visual acuity testing was included because it is a screening method for driving licensure in all states. Distance visual acuity with habitual distance correction while driving (if any) was measured using the electronic visual acuity tester20 and expressed as logMAR. Impaired acuity was defined as worse than 20/40. Contrast sensitivity was assessed using the Pelli-Robson chart21 and scored as log sensitivity using the letter-by-letter method.22 Impaired contrast sensitivity was defined as worse than 1.5 log sensitivity.23 Visual processing speed was assessed by the Useful Field of View subtest 2,24 measuring the amount of time needed (in milliseconds) to discriminate 2 targets in central vision and simultaneously localize a peripheral target at 10° eccentricity in any of 8 radial directions. Processing speeds from 150 to 350 milliseconds were defined as moderately impaired, and those greater than 350 milliseconds were defined as severely impaired.6 Visual field sensitivity was assessed for each eye separately using a custom test for the Humphrey Field Analyzer Model II-I developed for a previous study.5 Visual field sensitivity was assessed monocularly so that the fixation tracking system could be used. Light sensitivity was measured using the full-threshold procedure and white stimulus-size III targets presented at 20 visual field locations selected to be those that fall within the visual field area when a driver gazes through a vehicle’s windshield or to the vehicle’s dashboard.5,25 Because driving is performed using both eyes together, the monocular fields from each participant were combined to form a binocular field of 21 points spanning 60° to the right and left, 15° to the superior field, and 30° to the inferior field.26 The sensitivity at each test location was defined by the more sensitive point (higher value in decibel units) of the 2 eyes (target grid published previously).5,27 The visual field was divided into specific regions, and those in the lowest quartile of sensitivity were defined as having impaired vision.5 Motion perception was assessed using a drifting Gabor test.28 This test presented a 3-cycle per degree vertical sinusoidal grating filtered with a gaussian envelope. Participants were asked to identify the grating’s drifting direction (right vs left), with the drift rate (in units of hertz) varying during a 2-down and 1-up staircase with 8 reversals. Thresholds were defined as the mean of the last 6 reversals. Impaired motion perception was defined as thresholds worse than the median value.19

    General cognitive status, depressive symptoms, and general health were evaluated. The Mini-Mental State Examination (MMSE)29 was used to assess general cognitive status, and the Center for Epidemiological Studies–Depression (CES-D) scale30 was used to assess depressive symptoms. A general health condition checklist asked about medical problems in 17 areas (eg, heart disease and diabetes).31

    During the baseline visit, the DAS was installed in the participant’s vehicle by trained personnel at an automotive garage. The DAS was developed by the Virginia Tech Transportation Institute (VTTI) and is identical to that used in the Strategic Highway Research Program 2 study.10 The following components of the system were mounted into the vehicle: 5-channel video cameras of the roadway environment and driver (views of 83° forward, 55° left side, 99° rear, 99° front seat passenger and cabin snapshot, and 55° of steering wheel and driver controls), accelerometers, a global positioning system, and a hard drive installed in the trunk or within the vehicle. The vehicle network provided accelerator position, brake actuation, and speed. After installation was complete, the driver was informed that the DAS would not impact the operation of the vehicle; their experience in driving and controlling the vehicle would be identical to when the DAS was absent. The DAS started 30 seconds after the ignition was turned on and powered down when the ignition was off. Participants were asked to drive their instrumented vehicle as they would normally for the 6-month duration of the study. During this period, the “health” of the DAS was checked remotely by VTTI through cell phone networking; if there was a malfunction, VTTI notified UAB staff, who then scheduled a return visit for maintenance. When the DAS data drive approached being full, the drive was retrieved by UAB staff who transmitted the recorded data to VTTI for processing; a new data drive was then installed in the vehicle. The DAS was deinstalled at the end of the 6-month follow-up period, restoring the vehicle to its prestudy condition.

    The outcome of interest was based on the total number of at-fault crashes and at-fault near crashes combined. Given that at-fault crashes and near crashes are significantly associated with each other,32 combining the events into a single outcome was appropriate. Previous work has shown that the roadway circumstances underlying crashes and near crashes are similar.15 Trained VTTI analysts identified at-fault crashes and near crashes from the data streams; they were masked to the participant’s status on variables collected at UAB. Crashes were defined as events in which the participant’s vehicle had contact with any object (other vehicles, pedestrians, cyclists, or animals), at any speed, including nonpremeditated departures from the roadway where at least 1 tire leaves the paved or intended travel surface of the road.32 Near crashes were defined as any circumstance that required a rapid evasive maneuver by the participant’s vehicle or any other vehicle, pedestrian, cyclist, or animal to avoid a crash.32 Methods for crash and near-crash identification were identical to those used in the Strategic Highway Research Program 2 study.10,32 Analysts first identified trip files in which the driver was the consenting participant, destroying files from nonconsenting drivers.33 Software with defined triggers was used to detect sudden and extreme changes recorded by physical sensors and the vehicle network (eg, longitudinal acceleration and deceleration, lateral acceleration, yaw rate, and advanced safety system activation) to identify potential safety-critical events. Analysts then reviewed a temporal window of video around the event to confirm that it was indeed a crash or near crash. Crashes and near crashes could also be detected by analysts while viewing individual video files of trips. Participants were also asked to telephone the UAB coordinator when a crash occurred, and then the crash was communicated to VTTI for confirmation by the DAS data streams. Motor vehicle crash reports from governmental jurisdictions were not used to identify crashes.

    Only crashes and near crashes deemed at fault by raters were considered. These events were coded as “at fault” when there was demonstrable video evidence that the driver committed an error leading to the event. A system for establishing intrarater and interrater coding agreement was established previously at VTTI.32 The mean intrarater agreement was 91% (range, 73%-94%); agreement between each rater against an expert rater was 88% for crash and near-crash events.

    Statistical Analysis

    Statistical analysis was performed from June 15 to September 15, 2020. Crash and near-crash data identified by VTTI analysts were linked to data collected at UAB using participant numbers. The primary outcome was the rate of combined incidents, defined by the total number of at-fault crashes and near crashes during the 6-month follow-up period and number of miles driven for each participant. We did not focus on all events regardless of fault because at-fault events are more closely associated with drivers’ visual characteristics than all events.34 Associations between visual function and at-fault crashes and near crashes were assessed using rate ratios (RRs) and 95% CIs from Poisson regression models scaled for deviance. An offset for the natural log of miles driven was included. All models were adjusted for age. Visual function measures were categorized because the assumption of a linear association was not met. The level of significance was set at P < .05 (2-tailed). All analyses were completed in SAS, version 9.4 (SAS Institute Inc).

    Results

    In all, 321 drivers met eligibility criteria after completing a telephone screening; 280 participated in an in-person screening visit. Of these 280 persons, 162 met inclusion criteria and passed the vehicle screening. A total of 159 drivers enrolled in the study, representing a 49.5% participation rate. Shortly after installation of the DAS, garage personnel determined that the vehicles for 5 drivers prevented proper DAS or vehicle operation or presented driver inconvenience (radio static); therefore, the analysis sample was reduced to 154 drivers. Of these drivers, 146 (94.8%) participated for the 6-month follow-up period, for a mean (SD) 177 (6) days of follow-up. Eight drivers (5.1%) left the study before 6 months for the following reasons: vehicle needed repairs with implementation delayed (n = 4), a serious medical issue prevented driving (n = 2), death (n = 1), and newly developed DAS problems (n = 1). These 8 drivers participated for a mean (SD) of 105 (40) days. Participants drove a mean (SD) of 3846 (4375) miles (6189.5 [7040.9] km). There were 26 at-fault crashes and 55 at-fault near crashes, totaling 81 events. Of the 81 events, most crashes and near crashes (71 [87.7%]) occurred during the day. In terms of the number of vehicles involved, 55 crashes or near crashes (67.9%) involved other vehicles.

    Approximately half the sample of drivers were in their 70s (85 [55.2%]), with the rest in their 80s (66 [42.9%]) and 90s (3 [1.9%]) (Table 1). More than half of the participants were men (85 [55.2%]). In terms of race/ethnicity, 28 participants (18.2%) were Black, and 126 (81.8%) were White, representative of the racial/ethnic makeup of north central Alabama. Cognitive status (MMSE scores) was normal in 152 participants (98.7%), and 151 participants (98.1%) did not exhibit depression. There was a range of chronic medical conditions. The common age-related eye conditions (age-related macular degeneration, cataract, diabetic retinopathy or macular edema, and primary open-angle glaucoma) and other ocular conditions were represented in the sample; 137 of 151 participants (90.7%) had an ocular condition in at least 1 eye, with the balance of the sample having normal eye health.

    Table 2 summarizes the visual function status in the sample. Three drivers (1.9%) had visual acuity worse than 20/40, and 17 (11%) had contrast sensitivity worse than 1.5 log sensitivity. Almost half of participants had slowed visual processing speed, with 47 of 125 (37.6%) having moderate slowing and 15 of 125 (12.0%) having severe slowing.35 The visual field sensitivity for the overall field and by region illustrates the lowest decibel quartiles; the median value (0.14 Hz) is provided for the drifting grating motion test.

    Quiz Ref IDAge-adjusted associations between visual function and incident at-fault crash and near crash involvement are provided in Table 3. Only 3 participants had impaired visual acuity (worse than 20/40); as a result, the association between visual acuity and crash and near-crash involvement was not evaluated. Impaired contrast sensitivity was associated with crash and near-crash involvement (RR, 2.7; 95% CI, 1.3-5.5). Slowed visual processing speed (Useful Field of View subtest 2) was associated with increased crash and near-crash risk, both for moderate slowing (150-350 milliseconds [RR, 2.3; 95% CI, 1.1-4.9]) and more strongly for severe slowing(>350 milliseconds [RR, 5.0; 95% CI, 2.2-11.7]). Elevation in drifting grating thresholds was associated with crash and near-crash involvement (RR, 1.9; 95% CI, 1.1-3.5). Visual field sensitivity, overall and by region, was not significantly associated with crash and near-crash involvement; those with impaired sensitivity in the peripheral visual field had increased rates of crash and near-crash involvement (RR, 1.8; 95% CI, 1.0-3.3); this finding was not statistically significant (P = .07). Confounding was assessed but not present for sex, race/ethnicity, MMSE score, CES-D score, educational level, number of medical conditions, lens status, glaucoma, age-related macular degeneration, and diabetic retinopathy or diabetic macular edema.

    Discussion

    Using naturalistic driving data, this study has gone beyond the limitations of motor vehicle crash report data7,8 to identify visual risk factors associated with at-fault crashes among older drivers with age-related eye conditions. This is the first study in this population, to our knowledge, that includes near crashes, expanding the number of events in the analysis.

    Three visual risk factors associated with motor vehicle crash reports in previous studies were confirmed with naturalistic driving. Both moderate and severe slowing in visual processing speed elevated collision risk for older drivers, agreeing with accident report research.3,4,18,36 This finding is also reflective of studies on driving performance problems among older adults for whom slowed processing speed is associated with performance errors.37-39 Contrast sensitivity impairment was associated with crash and near-crash involvement, again agreeing with analyses based on motor vehicle crash reports,17,19,40 and with a naturalistic driving study assessing the association between impaired contrast sensitivity and vehicle control on interstate highways at night.41 Elevated thresholds in motion perception of a drifting grating also increased crash and near-crash involvement, similar to a recent collision risk analysis using motor vehicle crash reports,19 and is consistent with previous research identifying motion perception impairment as associated with driving performance problems among older adults.39,42,43

    A novel aspect of this naturalistic driving study of drivers with vision impairment is our ability to use near crashes as a crash surrogate measure9,15 because they are impossible to examine in motor vehicle crash report studies. As defined, a near crash requires a “last-second” evasive maneuver by the driver to avoid the crash. Near crashes and crashes have similar causal mechanisms, yet they have different safety outcomes because of the rapid evasive maneuver of a near crash.9,15 In the present study, near crashes occurred at approximately twice the rate of crashes (RR, 1.9; 95% CI, 1.4-2.6).32

    Because there were only 3 drivers with visual acuity worse than 20/40, we were unable to evaluate the association between impaired visual acuity and at-fault crash and near-crash involvement. The prevalence of visual acuity impairment worse than 20/40 in a study of older drivers will be lower than in a population-based study of older adults, in general, because vision standards prevent drivers with significant acuity impairment from receiving a license. Such studies may be feasible in some states whose acuity standard extends to 20/60 or 20/100. Despite the government’s intent to provide a visual acuity standard that enhances safety, epidemiologic studies have largely failed to identify such an association with crash involvement. This is an interesting paradox; the primary vision screening test used by the government for licensure does not have a demonstrated association with driver safety.

    Two previous studies have also used naturalistic driving to study rapid deceleration events among older drivers; the authors defined these events as near crashes.44,45 However, these authors did not use video to confirm that the events were actually near crashes, unlike our study. Although rapid deceleration events can be near crashes, not all these events are near crashes, and some near crashes do not involve rapid decelerations.

    Strengths and Limitations

    Quiz Ref IDThis study has some strengths, including our use of naturalistic driving data to examine crashes by older drivers with age-related eye conditions to avoid the biases of motor vehicle crash reports. We used an outcome combining crashes and near crashes as an end point to improve our understanding of visual risk profiles. Earlier studies on near crashes were defined solely by rapid deceleration events,44,45 whereas we validated near crashes by human observers viewing a video of the event. This study also has some limitations. With a small sample, we could not confirm a previously identified association between visual field impairment and crash involvement.5 In addition, our study was not designed to understand crash risk per diagnostic category.

    Conclusions

    Naturalistic driving by older drivers can identify and clarify the types of vision impairment most closely aligned with at-fault crash and near-crash events as they occur in everyday life. This information could inform licensure guidelines for persons with these functional problems and facilitate developing rehabilitation options.

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    Article Information

    Accepted for Publication: March 5, 2021.

    Published Online: April 29, 2021. doi:10.1001/jamaophthalmol.2021.0862

    Corresponding Author: Cynthia Owsley, PhD, School of Medicine, Department of Ophthalmology and Visual Sciences, University of Alabama at Birmingham, 1720 University Blvd, Ste 609, Birmingham, AL 35233 (cynthiaowsley@uabmc.edu).

    Author Contributions: Dr Owsley had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: McGwin, Wood, Antin, Owsley.

    Acquisition, analysis, or interpretation of data: Swain, McGwin, Antin, Owsley.

    Drafting of the manuscript: Swain, McGwin, Owsley.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Swain, McGwin.

    Obtained funding: Owsley.

    Administrative, technical, or material support: Swain, Antin, Owsley.

    Supervision: Antin, Owsley.

    Conflict of Interest Disclosures: Dr Owsley reported receiving personal fees from Johnson & Johnson outside the submitted work. No other disclosures were reported.

    Funding/Support: Funding was obtained from National Institutes of Health grants R01EY18966, P30AG22838, and P30EY03039, the EyeSight Foundation of Alabama, and Research to Prevent Blindness.

    Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    Congdon  N, O’Colmain  B, Klaver  CC,  et al; Eye Diseases Prevalence Research Group.  Causes and prevalence of visual impairment among adults in the United States.   Arch Ophthalmol. 2004;122(4):477-485. doi:10.1001/archopht.122.4.477 PubMedGoogle ScholarCrossref
    2.
    McCloskey  LW, Koepsell  TD, Wolf  ME, Buchner  DM.  Motor vehicle collision injuries and sensory impairments of older drivers.   Age Ageing. 1994;23(4):267-273. doi:10.1093/ageing/23.4.267 PubMedGoogle ScholarCrossref
    3.
    Owsley  C, Ball  K, McGwin  G  Jr,  et al.  Visual processing impairment and risk of motor vehicle crash among older adults.   JAMA. 1998;279(14):1083-1088. doi:10.1001/jama.279.14.1083 PubMedGoogle ScholarCrossref
    4.
    Rubin  GS, Ng  ES, Bandeen-Roche  K, Keyl  PM, Freeman  EE, West  SK.  A prospective, population-based study of the role of visual impairment in motor vehicle crashes among older drivers: the SEE Study.   Invest Ophthalmol Vis Sci. 2007;48(4):1483-1491. doi:10.1167/iovs.06-0474 PubMedGoogle ScholarCrossref
    5.
    Huisingh  C, McGwin  G  Jr, Wood  J, Owsley  C.  The driving visual field and a history of motor vehicle collision involvement in older drivers: a population-based examination.   Invest Ophthalmol Vis Sci. 2014;56(1):132-138. doi:10.1167/iovs.14-15194 PubMedGoogle ScholarCrossref
    6.
    Friedman  C, McGwin  G  Jr, Ball  KK, Owsley  C.  Association between higher order visual processing abilities and a history of motor vehicle collision involvement by drivers ages 70 and over.   Invest Ophthalmol Vis Sci. 2013;54(1):778-782. doi:10.1167/iovs.12-11249 PubMedGoogle ScholarCrossref
    7.
    Shinar  D, Treat  JR, McDonald  ST.  The validity of police reported accident data.   Accid Anal Prev. 1983;15(3):175-191. doi:10.1016/0001-4575(83)90018-0 Google ScholarCrossref
    8.
    McGuire  FL.  The nature of bias in official accident and violation records.   J Appl Psychol. 1973;57:300-305. doi:10.1037/h0034728 Google ScholarCrossref
    9.
    Dingus  TA, Klauer  SG, Neale  VL,  et al.  The 100-Car Naturalistic Driving Study, Phase II—Results of the 100-Car Field Experiment. National Highway Traffic Safety Administration, US Dept of Transportation; 2006. doi:10.1037/e624282011-001
    10.
    Dingus  TA, Guo  F, Lee  S,  et al.  Driver crash risk factors and prevalence evaluation using naturalistic driving data.   Proc Natl Acad Sci U S A. 2016;113(10):2636-2641. doi:10.1073/pnas.1513271113 PubMedGoogle ScholarCrossref
    11.
    Klauer  SG, Guo  F, Simons-Morton  BG, Ouimet  MC, Lee  SE, Dingus  TA.  Distracted driving and risk of road crashes among novice and experienced drivers.   N Engl J Med. 2014;370(1):54-59. doi:10.1056/NEJMsa1204142 PubMedGoogle ScholarCrossref
    12.
    Friedrich  TE, Duerksen  KN, Elias  LJ.  Overestimation of self-reported driving exposure: results from the SHRP2 Naturalistic Driving Study.   Traffic Inj Prev. 2019;20(2):128-133. doi:10.1080/15389588.2018.1549731 PubMedGoogle ScholarCrossref
    13.
    Agramunt  S, Meuleners  L, Chow  KC, Ng  JQ, Morlet  N.  A validation study comparing self-reported travel diaries and objective data obtained from in-vehicle monitoring devices in older drivers with bilateral cataract.   Accid Anal Prev. 2017;106:492-497. doi:10.1016/j.aap.2016.10.021 PubMedGoogle ScholarCrossref
    14.
    Seacrist  T, Douglas  EC, Hannan  C, Rogers  R, Belwadi  A, Loeb  H.  Near crash characteristics among risky drivers using the SHRP2 naturalistic driving study.   J Safety Res. 2020;73:263-269. doi:10.1016/j.jsr.2020.03.012 PubMedGoogle ScholarCrossref
    15.
    Guo  R, Klauer  SG, Hankey  JM, Dingus  TA.  Near crashes as crash surrogate for naturalistic driving studies.   Transp Res Rec. 2010;2147(1):66-74. doi:10.3141/2147-09 Google Scholar
    16.
    Owsley  C, McGwin  G  Jr, Antin  JF, Wood  JM, Elgin  J.  The Alabama VIP Older Driver Study rationale and design: examining the relationship between vision impairment and driving using naturalistic driving techniques.   BMC Ophthalmol. 2018;18(1):32. doi:10.1186/s12886-018-0686-5 PubMedGoogle ScholarCrossref
    17.
    Owsley  C, Stalvey  BT, Wells  J, Sloane  ME, McGwin  G  Jr.  Visual risk factors for crash involvement in older drivers with cataract.   Arch Ophthalmol. 2001;119(6):881-887. doi:10.1001/archopht.119.6.881 PubMedGoogle ScholarCrossref
    18.
    Ball  KK, Roenker  DL, Wadley  VG,  et al.  Can high-risk older drivers be identified through performance-based measures in a Department of Motor Vehicles setting?   J Am Geriatr Soc. 2006;54(1):77-84. doi:10.1111/j.1532-5415.2005.00568.x PubMedGoogle ScholarCrossref
    19.
    Swain  TA, McGwin  G  Jr, Wood  JM, Owsley  C.  Motion perception as a risk factor for motor vehicle collision involvement in drivers ≥70 years.   Accid Anal Prev. 2021;151:105956. doi:10.1016/j.aap.2020.105956 PubMedGoogle Scholar
    20.
    Beck  RW, Moke  PS, Turpin  AH,  et al.  A computerized method of visual acuity testing: adaptation of the Early Treatment of Diabetic Retinopathy Study testing protocol.   Am J Ophthalmol. 2003;135(2):194-205. doi:10.1016/S0002-9394(02)01825-1 PubMedGoogle ScholarCrossref
    21.
    Pelli  DG, Robson  JG, Wilkins  AJ.  The design of a new letter chart for measuring contrast sensitivity.   Clin Vis Sci. 1988;2(3):187-199.Google Scholar
    22.
    Elliott  DB, Bullimore  MA, Bailey  IL.  Improving the reliability of the Pelli-Robson contrast sensitivity test.   Clin Vis Sci. 1991;6:471-475.Google Scholar
    23.
    Leat  SJ, Legge  GE, Bullimore  MA.  What is low vision? a re-evaluation of definitions.   Optom Vis Sci. 1999;76(4):198-211. doi:10.1097/00006324-199904000-00023 PubMedGoogle ScholarCrossref
    24.
    Edwards  JD, Ross  LA, Wadley  VG,  et al.  The useful field of view test: normative data for older adults.   Arch Clin Neuropsychol. 2006;21(4):275-286. doi:10.1016/j.acn.2006.03.001 PubMedGoogle ScholarCrossref
    25.
    Vargas-Martín  F, García-Pérez  MA.  Visual fields at the wheel.   Optom Vis Sci. 2005;82(8):675-681. doi:10.1097/01.opx.0000175624.34252.73 PubMedGoogle ScholarCrossref
    26.
    Nelson-Quigg  JM, Cello  K, Johnson  CA.  Predicting binocular visual field sensitivity from monocular visual field results.   Invest Ophthalmol Vis Sci. 2000;41(8):2212-2221.PubMedGoogle Scholar
    27.
    Kwon  M, Huisingh  C, Rhodes  LA, McGwin  G  Jr, Wood  JM, Owsley  C.  Association between glaucoma and at-fault motor vehicle collision involvement among older drivers: a population-based study.   Ophthalmology. 2016;123(1):109-116. doi:10.1016/j.ophtha.2015.08.043 PubMedGoogle ScholarCrossref
    28.
    Lacherez  P, Turner  L, Lester  R, Burns  Z, Wood  JM.  Age-related changes in perception of movement in driving scenes.   Ophthalmic Physiol Opt. 2014;34(4):445-451. doi:10.1111/opo.12140 PubMedGoogle ScholarCrossref
    29.
    Folstein  MF, Folstein  SE, McHugh  PR.  “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician.   J Psychiatr Res. 1975;12(3):189-198. doi:10.1016/0022-3956(75)90026-6 PubMedGoogle ScholarCrossref
    30.
    Radloff  LS.  The CES-D scale: a self-report depression scale for research in the general population.   Appl Psychol Meas. 1977;1:385-401. doi:10.1177/014662167700100306 Google ScholarCrossref
    31.
    Owsley  C, McGwin  G  Jr, Sloane  M, Wells  J, Stalvey  BT, Gauthreaux  S.  Impact of cataract surgery on motor vehicle crash involvement by older adults.   JAMA. 2002;288(7):841-849. doi:10.1001/jama.288.7.841 PubMedGoogle ScholarCrossref
    32.
    Hankey  JM, Perez  MA, McClafferty  J.  Description of the SHRP2 Naturalistic Database and the Crash, Near-Crash and Baseline Datasets. Virginia Tech Transportation Institute, 2016.
    33.
    McClafferty  J, Perez  MA, Hankey  JM.  Identification of Consented Driver Trips in the SHRP2 Naturalistic Driving Study Data Set. Virginia Tech Transportation Institute; 2015.
    34.
    Cross  JM, McGwin  G  Jr, Rubin  GS,  et al.  Visual and medical risk factors for motor vehicle collision involvement among older drivers.   Br J Ophthalmol. 2009;93(3):400-404. doi:10.1136/bjo.2008.144584 PubMedGoogle ScholarCrossref
    35.
    Owsley  C, McGwin  G  Jr, Searcey  K.  A population-based examination of the visual and ophthalmological characteristics of licensed drivers aged 70 and older.   J Gerontol A Biol Sci Med Sci. 2013;68(5):567-573. doi:10.1093/gerona/gls185 PubMedGoogle ScholarCrossref
    36.
    Ball  K, Owsley  C, Sloane  ME, Roenker  DL, Bruni  JR.  Visual attention problems as a predictor of vehicle crashes in older drivers.   Invest Ophthalmol Vis Sci. 1993;34(11):3110-3123.PubMedGoogle Scholar
    37.
    Wadley  VG, Bull  TP, Zhang  Y,  et al.  Cognitive processing speed is strongly related to driving skills, financial abilities, and other instrumental activities of daily living in persons with MCI and mild dementia.   J Gerontol A Biol Sci Med Sci. 2020;glaa312. Published online December 12, 2020. doi:10.1093/gerona/glaa312PubMedGoogle Scholar
    38.
    Roenker  DL, Cissell  GM, Ball  KK, Wadley  VG, Edwards  JD.  Speed-of-processing and driving simulator training result in improved driving performance.   Hum Factors. 2003;45(2):218-233. doi:10.1518/hfes.45.2.218.27241 PubMedGoogle ScholarCrossref
    39.
    Wood  JM.  Age and visual impairment decrease driving performance as measured on a closed-road circuit.   Hum Factors. 2002;44(3):482-494. doi:10.1518/0018720024497664 PubMedGoogle ScholarCrossref
    40.
    Green  KA, McGwin  G  Jr, Owsley  C.  Associations between visual, hearing, and dual sensory impairments and history of motor vehicle collision involvement of older drivers.   J Am Geriatr Soc. 2013;61(2):252-257. doi:10.1111/jgs.12091 PubMedGoogle ScholarCrossref
    41.
    Merickel  J, High  R, Dawson  J, Rizzo  M.  Real-world risk exposure in older drivers with cognitive and visual dysfunction.   Traffic Inj Prev. 2019;20(suppl 2):S110-S115. doi:10.1080/15389588.2019.1688794 PubMedGoogle ScholarCrossref
    42.
    Wood  JM, Anstey  KJ, Kerr  GK, Lacherez  PF, Lord  S.  A multidomain approach for predicting older driver safety under in-traffic road conditions.   J Am Geriatr Soc. 2008;56(6):986-993. doi:10.1111/j.1532-5415.2008.01709.x PubMedGoogle ScholarCrossref
    43.
    Wood  JM, Black  AA, Mallon  K, Kwan  AS, Owsley  C.  Effects of age-related macular degeneration on driving performance.   Invest Ophthalmol Vis Sci. 2018;59(1):273-279. doi:10.1167/iovs.17-22751 PubMedGoogle ScholarCrossref
    44.
    Keay  L, Munoz  B, Duncan  DD,  et al.  Older drivers and rapid deceleration events: Salisbury Eye Evaluation Driving Study.   Accid Anal Prev. 2013;58:279-285. doi:10.1016/j.aap.2012.06.002 PubMedGoogle ScholarCrossref
    45.
    Chevalier  A, Coxon  K, Chevalier  AJ,  et al.  Predictors of older drivers’ involvement in rapid deceleration events.   Accid Anal Prev. 2017;98:312-319. doi:10.1016/j.aap.2016.10.010 PubMedGoogle ScholarCrossref
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