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. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Alzheimer Dis Assoc Disord. 2021 Jul-Sep;35(3):250–257. doi: 10.1097/WAD.0000000000000443

Driving Habits of Older Adults- A Population Based Study

Tejal Bhojak 1, Yichen Jia 2, Erin Jacobsen 1, Beth E Snitz 3, Chung-Chou H Chang 2,4, Mary Ganguli 1,3,5
PMCID: PMC8387314  NIHMSID: NIHMS1672457  PMID: 33769988

Abstract

Objectives:

To describe factors associated with driving history, habits, and self-reported driving difficulties of 1982 older adults in a population-based survey.

Setting:

Community

Participants:

Age-stratified random population sample drawn from publicly available voter registration list.

Design:

Participants underwent assessments including cognitive testing and self-reported current and past driving status, instrumental activities of daily living, self-rated health, social supports, physical limitations, and depressive symptoms. We built multivariable logistic regression models to identify factors associated with never having driven, having ceased driving, and reporting difficulties while driving.

Results:

In the multivariable model, “never drivers” were more likely than “ever drivers” to be older, female, less educated and to leave home less frequently. Former drivers were significantly older, more likely to be women, have lower test performance in the cognitive domain of attention, have more Instrumental Activities of Daily Living (IADL) difficulties, leave home less frequently and have visual field deficits in the right eye than current drivers. Current drivers with reported driving difficulties were more likely than those without difficulties to have lower test performance in attention but higher in memory, were more likely to report depressive symptoms and to have both vision and hearing loss.

Conclusion:

Age, female sex, marital status, and education appear to be associated with driving cessation. Cognitive and functional impairments, mood symptoms and physical health also seem to influence driving cessation and reduction. Our findings may have implications for clinicians in assessing and educating their patients and families on driving safety.

Keywords: aging, community, motor vehicles, epidemiology

Introduction:

Driving motor vehicles is a rite of passage into American adulthood, a key instrumental activity of daily living (IADL), and a complex process that requires integration of multiple cognitive, motor, and sensory domains1,2,3, that can be impaired in normal aging as well as in several disorders associated with aging. Older drivers over 65 are 16% more likely to cause accidents than younger drivers 4 despite being more likely to follow speed limits, wear seat belts, avoid alcohol while driving, and avoid rush hour driving, driving at night, and driving in unfamiliar areas 5. Approximately 200,000 of the 30 million drivers over age 65 are injured in motor vehicle crashes annually. 6 A higher proportion of drivers over 75 have fatal accidents than drivers aged 30–74.7 Approximately 4% of drivers over 75 have dementia diagnoses.8 On the other hand, driving cessation is associated with increased social isolation, depression, morbidity and mortality, and increased need for long term care.9

Multiple previous studies have reported on driving habits and difficulties in samples of older patients in clinical settings, 10,11 but few reports are from population-based samples rather than samples of patients seeking care in specialty clinics 12,13. Here, we describe a wide array of health and cognition related factors associated with driving history, social habits, and self-reported driving difficulties of older adults participating in a large population-based survey.

METHODS

Study site and population

Our study cohort named the Monongahela-Youghiogheny Healthy Aging Team (MYHAT) is an age-stratified random population sample drawn from the publicly available voter registration lists for a small-town region of Pennsylvania (USA).14

Standard Protocol Approvals, Registrations, and Consents.

As previously reported, community outreach, recruitment, and assessment protocols were approved by the University of Pittsburgh Institutional Review Board for protection of human subjects. Inclusion criteria were (a) age ≥65 years, (b) living within the selected small towns, (c) not already in long-term care institutions. Individuals were ineligible if they (d) were too ill to participate, (e) had severe vision or hearing impairments, (f) had decisional incapacity. A total of 2036 participants provided written informed consent during the baseline recruitment period between 2006 and 2008. As the project was designed to study mild cognitive impairment, we screened out those who, at study entry, exhibited substantial impairment by scoring <21/30 on the age-education-corrected Mini-Mental State Evaluation (MMSE).15,16 The remaining 1,982 individuals underwent detailed evaluations.

Assessments.

At study entry, the assessment included but was not limited to demographics, living arrangements, independence in Instrumental Activities of Daily Living (IADL) measured by the OARS (Older American Resources and Services) Scale17, depressive symptoms using a modified Center for Epidemiologic Studies-Depression scale (mCES-D)18,19, self-reported health history, social support and engagement such as mobility within the community, number of confidantes and belonging to an organization, hearing and vision difficulties, and a neuropsychological evaluation. The neuropsychological test battery tapped the cognitive domains of attention, executive function, memory, language, and visuospatial function; we created composite scores for each domain, as described earlier 19 (See Supplement 1). Following the assessment, all participants were rated on the Clinical Dementia Rating (CDR®) Staging Instrument, as previously detailed 20. On the CDR, ratings of 0, 0.5, and 1 indicate normal cognition, mild cognitive impairment or very mild dementia, and mild dementia.

The primary questions regarding driving included (1) whether they had ever driven motor vehicles; if so, (2) whether they were currently driving; and if so, (3) whether they were experiencing any difficulties while driving.

Statistical Methods:

We examined frequencies and percentages or means and standard deviations of the variables of interest. We used chi-square and t-tests to compare these variables between (1) “never drivers” and “ever drivers,” (2) current drivers and past drivers, (3) current drivers with and without reported difficulties.

In the regression models (Tables 1,2,3), “ever drivers,” current drivers, and current drivers without difficulties were treated as the reference groups, respectively, for each driving outcome. Unadjusted simple logistic regression models were fit to examine the association of each covariate (listed under Assessments) with each of the driving outcomes. Then, multiple logistic regression models were fit to assess these associations first adjusting for demographics only and then adjusting for all covariates i.e., fully adjusted models.

Table 1:

Never vs Ever Drivers

Ever Drivers N = 1832 Never Drivers N = 147 Unadjusted Multivariable model adjusted for age, sex and education Multivariable model fully adjusted for all covariates
n (%) or mean (SD) n (%) or mean (SD) OR (95%CI) P OR (95%CI) P OR (95%CI) P
Age 77.26 (7.35) 82.22 (6.87) 1.10 (1.07, 1.13) <0.001 N/A 1.05 (1.01, 1.09) 0.027
Female 1065 (58.1) 142 (96.6) 20.45 (8.34, 50.14) <0.001 17.57 (5.91, 52.30) <0.001
Education (ref: ≤ high school) high school 818 (44.7) 76 (51.7) 0.39 (0.27, 0.57) <0.001 0.52 (0.31, 0.88) 0.015
≥ high school 796 (43.4) 19 (12.9) 0.10 (0.06, 0.17) <0.001 0.17 (0.08, 0.34) <0.001
Married (ref: =0) 940 (51.3) 39 (26.5) 0.34 (0.23, 0.50) <0.001 1.01 (0.65, 1.55) 0.974 0.56 (0.30, 1.05) 0.069
CDR (ref: CDR=0) CDR = 0.5 491 (26.8) 54 (36.7) 1.64 (1.15, 2.33) 0.006 1.37 (0.92, 2.02) 0.118 0.82 (0.48, 1.40) 0.478
CDR ≥ 1 17 (0.9) 4 (2.7) 3.50 (1.15, 10.62) 0.027 3.29 (0.78, 13.86) 0.105 1.11 (0.06, 19.31) 0.945
Domain score: Attention 0.03 (0.77) −0.43 (0.76) 0.45 (0.36, 0.57) <0.001 0.64 (0.49, 0.84) 0.001 0.77 (0.55, 1.10) 0.15
Domain score: Executive 0.01 (0.76) −0.51 (0.90) 0.47 (0.39, 0.58) <0.001 0.63 (0.49, 0.81) <0.001 1.00 (0.64, 1.57) 0.985
Domain score: Language 0.04 (0.77) −0.53 (1.03) 0.51 (0.43, 0.60) <0.001 0.70 (0.55, 0.88) 0.002 0.86 (0.56, 1.31) 0.481
Domain score: Memory 0.02 (0.79) −0.45 (0.91) 0.52 (0.43, 0.63) <0.001 0.68 (0.53, 0.88) 0.003 1.03 (0.68, 1.56) 0.873
Domain score: Visuospatial 0.04 (0.99) −0.54 (0.95) 0.53 (0.44, 0.65) <0.001 0.77 (0.61, 0.97) 0.028 0.87 (0.64, 1.19) 0.388
IADL (ref: = 0) 273 (14.9) 67 (45.6) 4.78 (3.37, 6.78) <0.001 2.63 (1.78, 3.90) <0.001 1.73 (0.99, 3.02) 0.056
Subjective Health (ref: Poor and Fair) Good 835 (45.7) 67 (45.6) 0.54 (0.36, 0.81) 0.003 0.52 (0.34, 0.81) 0.004 0.58 (0.33, 1.02) 0.057
Very good and excellent 694 (38.0) 35 (23.8) 0.34 (0.21, 0.54) <0.001 0.41 (0.25, 0.68) 0.001 0.53 (0.27, 1.01) 0.054
mCESD score 0.90 (2.04) 1.41 (2.47) 1.09 (1.03, 1.17) 0.005 1.03 (0.96, 1.11) 0.381 1.00 (0.89, 1.13) 0.974
# of prescription meds 4.33 (3.15) 5.09 (3.30) 1.07 (1.02, 1.13) 0.006 1.05 (1.00, 1.11) 0.067 0.97 (0.89, 1.04) 0.388
Current Smoking (ref: = 0) 135 (7.4) 10 (6.8) 0.92 (0.47, 1.79) 0.809 1.12 (0.55, 2.28) 0.763 0.98 (0.39, 2.48) 0.963
Current Drinking (ref: = 0) 1232 (67.4) 66 (44.9) 0.39 (0.28, 0.55) <0.001 0.66 (0.46, 0.95) 0.027 0.84 (0.54, 1.32) 0.453
Live alone (ref: = 0) 705 (38.5) 71 (48.3) 1.49 (1.07, 2.09) 0.020 0.59 (0.40, 0.87) 0.007 0.45 (0.25, 0.79) 0.006
Feel lonely (ref: = 0) 143 (7.8) 20 (13.6) 1.85 (1.12, 3.06) 0.016 1.04 (0.60, 1.80) 0.887 0.64 (0.27, 1.50) 0.302
Leave home (ref: Daily) 2–6x/week 625 (34.2) 88 (59.9) 4.70 (3.11, 7.09) <0.001 2.51 (1.62, 3.88) <0.001 1.75 (1.05, 2.93) 0.033
<2–4x/month 101 (5.5) 26 (17.7) 8.59 (4.94, 14.93) <0.001 3.47 (1.89, 6.36) <0.001 2.77 (1.23, 6.23) 0.014
# of confidantes 4.38 (3.22) 4.27 (2.48) 0.99 (0.93, 1.05) 0.703 0.99 (0.93, 1.06) 0.839 1.02 (0.95, 1.09) 0.552
Belong to organization (ref: =0) 1566 (85.7) 124 (84.4) 0.90 (0.57, 1.43) 0.663 0.84 (0.50, 1.39) 0.493 1.46 (0.72, 2.98) 0.297
Arthritis (ref: = 0) 1224 (66.9) 114 (77.6) 1.71 (1.15, 2.55) 0.009 1.20 (0.78, 1.85) 0.398 1.19 (0.70, 2.04) 0.517
Vision acuity denominator: Right 64.02 (88.21) 94.02 (150.81) 1.02 (1.01, 1.03) 0.001 1.01 (1.00, 1.03) 0.085 1.02 (1.00, 1.04) 0.102
Vision acuity denominator: Left 65.08 (96.62) 87.48 (125.31) 1.02 (1.00, 1.03) 0.019 1.00 (0.99, 1.02) 0.75 0.99 (0.97, 1.01) 0.462
Extraocular movement problem: Right (ref: =0) 18 (1.0) 2 (1.4) 1.39 (0.32, 6.07) 0.658 0.79 (0.14, 4.38) 0.789 2.52 (0.32, 19.62) 0.378
Extraocular movement problem: Left (ref: =0) 17 (0.9) 1 (0.7) 0.74 (0.10, 5.59) 0.769 0.42 (0.04, 4.12) 0.46 0.42 (0.03, 5.79) 0.519
Visual field deficits: Right (ref: =0) 73 (4.0) 6 (4.1) 1.02 (0.44, 2.40) 0.956 0.70 (0.28, 1.77) 0.455 0.39 (0.10, 1.52) 0.176
Visual field deficits: Left (ref: =0) 74 (4.1) 12 (8.4) 2.14 (1.13, 4.04) 0.019 1.69 (0.82, 3.51) 0.158 1.60 (0.54, 4.71) 0.396
Hearing loss (ref: None) Right 141 (7.7) 11 (7.5) 1.13 (0.59, 2.16) 0.712 0.68 (0.33, 1.37) 0.278 0.87 (0.39, 1.95) 0.731
Left 125 (6.8) 13 (8.8) 1.51 (0.82, 2.76) 0.185 1.11 (0.57, 2.16) 0.758 1.25 (0.56, 2.78) 0.589
Bilateral 116 (6.3) 23 (15.6) 2.87 (1.76, 4.69) <0.001 1.81 (1.05, 3.14) 0.034 1.57(0.76, 3.24) 0.219
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Table 2:

Current Drivers vs Past Drivers

Current Drivers N = 1554 Past Drivers N = 278 Unadjusted Multivariable model adjusted for age, sex and education Multivariable model adjusted for all covariates
n (%) or mean (SD) n (%) or mean (SD) OR (95%CI) P OR (95%CI) P OR (95%CI) P
Age 76.37 (7.11) 82.27 (6.64) 1.13 (1.11, 1.15) <0.001 N/A 1.07 (1.03, 1.10) <0.001
Female 856 (55.1) 209 (75.2) 2.47 (1.85, 3.30) <0.001 4.48 (2.64, 7.61) <0.001
Education (ref: ≤ high school) high school 690 (44.4) 128 (46.0) 0.46 (0.32, 0.65) <0.001 0.89 (0.52, 1.54) 0.687
≥ high school 709 (45.6) 87 (31.3) 0.30 (0.21, 0.44) <0.001 0.70 (0.39, 1.25) 0.228
Married (ref: =0) 845 (54.4) 95 (34.2) 0.44 (0.33, 0.57) <0.001 0.93 (0.68, 1.27) 0.639 0.71 (0.40, 1.27) 0.251
CDR (ref: CDR=0) CDR = 0.5 374 (24.1) 117 (42.1) 2.47 (1.89, 3.23) <0.001 2.16 (1.60, 2.90) <0.001 1.05 (0.68, 1.65) 0.816
CDR ≥ 1 5 (0.3) 12 (4.3) 18.93 (6.58, 54.47) <0.001 29.07 (8.64, 97.81) <0.001 1.37 (0.20, 9.27) 0.75
Domain score: Attention 0.11 (0.75) −0.44 (0.72) 0.35 (0.29, 0.42) <0.001 0.47 (0.38, 0.58) <0.001 0.67 (0.49, 0.91) 0.011
Domain score: Executive 0.12 (0.69) −0.57 (0.87) 0.30 (0.24, 0.36) <0.001 0.38 (0.31, 0.47) <0.001 0.62 (0.42, 0.92) 0.017
Domain score: Language 0.12 (0.71) −0.46 (0.92) 0.42 (0.36, 0.49) <0.001 0.59 (0.49, 0.70) <0.001 1.18 (0.81, 1.72) 0.379
Domain score: Memory 0.10 (0.74) −0.45 (0.88) 0.42 (0.35, 0.49) <0.001 0.55 (0.45, 0.67) <0.001 0.91 (0.64, 1.31) 0.625
Domain score: Visuospatial 0.13 (0.99) −0.54 (0.82) 0.47 (0.39, 0.55) <0.001 0.58 (0.48, 0.70) <0.001 0.88 (0.67, 1.15) 0.337
IADL (ref: = 0) 118 ( 7.6) 155 (55.8) 15.34 (11.34, 20.74) <0.001 10.90 (7.90, 15.04) <0.001 3.59 (2.25, 5.73) <0.001
Subjective Health (ref: Poor and Fair) Good 702 (45.2) 133 (48.2) 0.55 (0.40, 0.75) <0.001 0.42 (0.30, 0.60) <0.001 0.72 (0.43, 1.22) 0.223
Very good and excellent 627 (40.4) 67 (24.2) 0.31 (0.21, 0.44) <0.001 0.28 (0.19, 0.41) <0.001 0.89 (0.49, 1.61) 0.697
mCESD score 0.81 (1.99) 1.38 (2.26) 1.11 (1.06, 1.17) <0.001 1.08 (1.02, 1.15) 0.007 0.97 (0.87, 1.08) 0.576
# of prescription meds 4.11 (3.05) 5.59 (3.39) 1.15 (1.10, 1.19) <0.001 1.15 (1.11, 1.20) <0.001 1.05 (0.99, 1.12) 0.135
Current smoking (ref: = 0) 114 (7.3) 21 (7.6) 1.03 (0.64, 1.68) 0.897 1.68 (0.99, 2.85) 0.054 1.22 (0.57, 2.57) 0.609
Current drinking (ref: = 0) 1093 (70.3) 139 (50.0) 0.42 (0.33, 0.55) <0.001 0.60 (0.45, 0.79) <0.001 0.84 (0.57, 1.24) 0.38
Live alone (ref: = 0) 569 (36.6) 136 (48.9) 1.66 (1.28, 2.14) <0.001 0.79 (0.59, 1.07) 0.123 0.64 (0.37, 1.12) 0.118
Feel lonely (ref: = 0) 105 (6.8) 38 (13.7) 2.19 (1.47, 3.25) <0.001 1.27 (0.82, 1.96) 0.282 1.08 (0.51, 2.26) 0.847
Leave home (ref: Daily) 2–6x/week 488 (31.5) 137 (49.6) 4.87 (3.53, 6.72) <0.001 3.35 (2.39, 4.70) <0.001 2.12 (1.39, 3.24) <0.001
<2–4x/month 22 (1.4) 79 (28.6) 62.30 (36.33, 106.85) <0.001 39.52 (22.33, 69.95) <0.001 10.86 (4.95, 23.83) <0.001
# of confidantes 4.47 (3.31) 3.88 (2.59) 0.93 (0.88, 0.98) 0.005 0.94 (0.89, 0.99) 0.021 1.00 (0.94, 1.07) 0.962
Belong to organization (ref: =0) 1354 (87.3) 212 (76.5) 0.47 (0.35, 0.65) <0.001 0.35 (0.25, 0.50) <0.001 0.42 (0.25, 0.70) 0.001
Arthritis (ref: = 0) 1025 (66.0) 199 (72.1) 1.33 (1.00, 1.77) 0.048 1.05 (0.77, 1.42) 0.772 0.83 (0.54, 1.28) 0.402
Vision acuity denominator: Right 56.38 (76.25) 108.63 (130.41) 1.04 (1.03, 1.06) <0.001 1.04 (1.03, 1.05) <0.001 1.02 (1.00, 1.05) 0.015
Vision acuity denominator: Left 56.57 (82.44) 115.08 (146.32) 1.04 (1.03, 1.05) <0.001 1.03 (1.02, 1.04) <0.001 1.02 (1.00, 1.04) 0.081
Extraocular movement problem: Right (ref: =0) 12 (0.8) 6 (2.2) 2.87 (1.07, 7.72) 0.036 2.23 (0.73, 6.78) 0.157 2.14 (0.38, 12.19) 0.39
Extraocular movement problem: Left (ref: =0) 11 (0.7) 6 (2.2) 3.15 (1.15, 8.58) 0.025 2.41 (0.74, 7.81) 0.142 2.46 (0.38, 16.07) 0.348
Visual field deficits: Right (ref: =0) 44 (2.9) 29 (10.7) 4.06 (2.49, 6.61) <0.001 3.71 (2.16, 6.39) <0.001 3.72 (1.55, 8.97) 0.003
Visual field deficits: Left (ref: =0) 48 (3.1) 26 (9.7) 3.31 (2.01, 5.43) <0.001 2.99 (1.71, 5.23) <0.001 1.24 (0.51, 3.01) 0.629
Hearing loss (ref: None) Right 114 (7.3) 27 (9.7) 1.53 (0.98, 2.39) 0.061 0.91 (0.56, 1.50) 0.718 1.07 (0.56, 2.04) 0.841
Left 96 (6.2) 29 (10.5) 1.95 (1.26, 3.04) 0.003 1.31 (0.81, 2.12) 0.277 1.61 (0.82, 3.18) 0.168
Bilateral 89 (5.7) 27 (9.7) 1.96 (1.24, 3.10) 0.004 1.11 (0.68, 1.81) 0.688 0.65 (0.31, 1.35) 0.245
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Table 3:

Drivers without and with difficulty

w/o difficulty N = 1234 w/ difficulty N = 317 Unadjusted Multivariable model adjusted for age, sex and education Multivariable model adjusted for all covariates
n (%) or mean (SD) n (%) or mean (SD) OR (95%CI) P OR (95%CI) P OR (95%CI) P
Age 76.19 (7.06) 77.04 (7.30) 1.02 (1.00, 1.03) 0.058 N/A 1.02 (1.00, 1.05) 0.095
Female 672 (54.5) 182 (57.4) 1.13 (0.88, 1.45) 0.345 1.16 (0.83, 1.61) 0.383
Education (ref: ≤ high school) high school 552 (44.7) 137 (43.2) 0.78 (0.52, 1.19) 0.252 0.74 (0.46, 1.21) 0.236
≥ high school 565 (45.8) 143 (45.1) 0.80 (0.53, 1.21) 0.29 0.77 (0.46, 1.26) 0.294
Married (ref: =0) 684 (55.4) 160 (50.5) 0.82 (0.64, 1.05) 0.114 0.89 (0.68, 1.17) 0.412 1.10 (0.65, 1.84) 0.726
CDR (ref: CDR=0) CDR = 0.5 269 (21.8) 105 (33.1) 1.80 (1.37, 2.36) <0.001 1.81 (1.37, 2.39) <0.001 1.98 (1.41, 2.80) <0.001
CDR ≥ 1 2 (0.2) 3 (0.9) 6.91 (1.15, 41.62) 0.035 7.56 (1.24, 46.06) 0.028 7.37 (0.53, 101.85) 0.136
Domain score: Attention 0.15 (0.75) −0.01 (0.73) 0.75 (0.63, 0.89) 0.001 0.76 (0.64, 0.91) 0.003 0.74 (0.59, 0.93) 0.01
Domain score: Executive 0.13 (0.68) 0.06 (0.72) 0.86 (0.72, 1.03) 0.093 0.90 (0.74, 1.11) 0.324 0.92 (0.68, 1.24) 0.573
Domain score: Language 0.12 (0.70) 0.13 (0.73) 1.01 (0.85, 1.20) 0.939 1.12 (0.92, 1.37) 0.274 1.20 (0.89, 1.62) 0.225
Domain score: Memory 0.09 (0.72) 0.17 (0.82) 1.16 (0.98, 1.37) 0.082 1.35 (1.11, 1.64) 0.003 1.85 (1.39, 2.47) <0.001
Domain score: Visuospatial 0.12 (0.97) 0.13 (1.06) 1.01 (0.89, 1.16) 0.843 1.06 (0.92, 1.22) 0.427 1.03 (0.86, 1.24) 0.714
IADL (ref: = 0) 85 ( 6.9) 33 (10.4) 1.57 (1.03, 2.40) 0.036 1.45 (0.94, 2.23) 0.089 0.85 (0.48, 1.49) 0.567
Subjective Health (ref: Poor and Fair) Good 551 (44.7) 150 (47.3) 0.72 (0.51, 0.00) 0.056 0.70 (0.49, 0.98) 0.04 0.90 (0.59, 1.36) 0.613
Very good and excellent 520 (42.2) 105 (33.1) 0.53 (0.37, 0.00) 0.001 0.52 (0.36, 0.75) <0.001 0.65 (0.40, 1.03) 0.066
mCESD score 0.71 (1.91) 1.20 (2.27) 1.11 (1.05, 1.17) <0.001 1.10 (1.05, 1.17) <0.001 1.10 (1.02, 1.20) 0.017
# of prescription meds 4.02 (3.05) 4.44 (3.08) 1.04 (1.00, 1.09) 0.03 1.04 (1.00, 1.09) 0.03 1.01 (0.96, 1.06) 0.676
Current smoking (ref: = 0) 87 (7.1) 27 (8.5) 1.23 (0.78, 1.93) 0.373 1.30 (0.82, 2.06) 0.259 1.19 (0.69, 2.04) 0.532
Current drinking (ref: = 0) 882 (71.5) 209 (65.9) 0.77 (0.59, 1.00) 0.049 0.81 (0.62, 1.05) 0.116 0.86 (0.63, 1.18) 0.359
Live alone (ref: = 0) 438 (35.5) 130 (41.1) 1.27 (0.99, 1.64) 0.063 1.17 (0.89, 1.54) 0.258 1.35 (0.80, 2.27) 0.256
Feel lonely (ref: = 0) 79 (6.4) 26 (8.2) 1.30 (0.82, 2.07) 0.263 1.22 (0.76, 1.94) 0.41 0.69 (0.35, 1.38) 0.296
Leave home (ref: Daily) 2–6x/week 366 (29.7) 120 (37.9) 1.46 (1.12, 1.89) 0.004 1.39 (1.06, 1.82) 0.016 1.24 (0.90, 1.69) 0.185
<2–4x/month 16 (1.3) 6 (1.9) 1.67 (0.64, 4.32) 0.292 1.59 (0.61, 4.13) 0.341 1.07 (0.30, 3.85) 0.912
# of confide 4.57 (3.46) 4.06 (2.67) 0.94 (0.90, 0.99) 0.015 0.94 (0.90, 0.99) 0.015 0.95 (0.90, 1.00) 0.036
Belong to organization (ref: =0) 1069 (86.8) 283 (89.3) 1.26 (0.85, 1.87) 0.246 1.23 (0.83, 1.83) 0.298 1.23 (0.77, 1.96) 0.397
Arthritis (ref: = 0) 814 (66.0) 209 (65.9) 1.00 (0.77, 1.29) 0.977 0.97 (0.74, 1.26) 0.8 0.85 (0.63, 1.16) 0.311
Vision acuity denominator: Right 52.47 (66.09) 71.88 (106.25) 1.03 (1.01, 1.04) <0.001 1.03 (1.01, 1.04) <0.001 1.03 (1.01, 1.05) 0.001
Vision acuity denominator: Left 54.25 (74.87) 65.98 (107.48) 1.01 (1.00, 1.03) 0.064 1.01 (1.00, 1.02) 0.12 1.01 (0.99, 1.02) 0.457
Extraocular movement problem: Right (ref: =0) 6 (0.5) 6 (1.9) 4.00 (1.28, 12.50) 0.017 3.85 (1.23, 12.08) 0.021 0.81
(0.08, 8.48)		 0.859
Extraocular movement problem: Left (ref: =0) 4 (0.3) 7 (2.3) 7.06 (
2.05, 24.28)		 0.002 6.74 (1.95, 23.32) 0.003 12.99
(0.91, 185.68)		 0.059
Visual field deficits: Right (ref: =0) 33 (2.7) 11 (3.6) 1.34 (0.67, 2.67) 0.414 1.29 (0.64, 2.59) 0.474 0.66 (0.25, 1.75) 0.405
Visual field deficits: Left (ref: =0) 35 (2.9) 13 (4.2) 1.50 (0.78, 2.87) 0.221 1.46 (0.76, 2.80) 0.255 1.57 (0.64, 3.86) 0.324
Hearing loss (ref: None) Right 80 (6.5) 34 (10.8) 1.75 (1.14, 2.67) 0.01 1.65 (1.07, 2.54) 0.023 1.74 (1.07, 2.84) 0.027
Left 79 (6.4) 17 (5.4) 0.88 (0.51, 1.52) 0.655 0.85 (0.49, 1.46) 0.548 0.81 (0.43, 1.51) 0.504
Bilateral 69 (5.6) 20 (6.3) 1.19 (0.71, 2.00) 0.509 1.10 (0.65, 1.87) 0.716 0.84 (0.45, 1.58) 0.593
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Results:

The study sample had a mean age of 77 (7.6) years, was 61.0% women, 94.7% European American and 86.2% had a high school or greater education.

“Never Drivers” vs. “Ever Drivers”

Among MYHAT participants, 147 (7.4%) reported never having driven a motor vehicle. Comparing “never drivers” to “ever drivers” ( Table 1), in the unadjusted analysis, those who had never driven were significantly older, more likely to be women, had lower levels of education, were currently single, had lower test performance across all cognitive domains, had difficulty with more IADLs, reported worse self-rated health, more depressive symptoms, took more prescription medications, were more likely to live alone, to feel lonely, leave home less frequently; they were more likely to report arthritis, and to have both vision and hearing loss. They were less likely to report any current alcohol consumption.

After adjusting for age, sex, and education, never drivers remained more likely to have lower performance across all cognitive domains, have difficulty with more IADLs, report worse self-rated health, to leave home less frequently, be likely to live alone, have a greater frequency of bilateral hearing loss, and be less likely to consume alcohol.

In the fully adjusted model (adjusting for all covariates), never drivers were more likely to be older, female, less educated, to not live alone, and to leave home less frequently.

Former Drivers vs. Current Drivers

In MYHAT, 278 (15.2%) participants had previously driven motor vehicles but were no longer driving. In the unadjusted analysis, (Table 2), former drivers were significantly older, more likely to be women, have lower levels of education, be currently single, have lower test performance across all cognitive domains, have greater difficulty with IADLs, have worse self-rated health, report more depressive symptoms, report taking a larger number of prescription medications, be more likely to live alone, be more likely to feel lonely, leave home less frequently, have fewer confidantes, be less likely to belong to an organization, be more likely to report arthritis, be more likely to have both vision problems and hearing loss, and less likely to currently consume alcohol.

After adjusting for age, sex and education, former drivers remained significantly more likely to have lower performance across all cognitive domains, have greater difficulty with IADLs, to report depressive symptoms, report taking a larger number of prescription medications, leave home less frequently, have fewer confidantes, be less likely to belong to an organization, and be more likely to have vision problems. In addition, in this group, former drivers were less likely to be current smokers and alcohol drinkers than current drivers.

In the fully adjusted multivariable model, former drivers were significantly older, more likely to be women, have lower test performance in the cognitive domain of attention and executive function, have more difficulty with IADLs, leave home less frequently, and have visual field deficits in the right eye.

Current Drivers with and without reported difficulties while driving

In MYHAT, 317 (20.4%) participants were currently driving but reported having one or more types of difficulty while driving. Of these, 255 ( 80.44%) had difficulty seeing at night, 134 ( 42.27 %) had difficulty seeing in bad weather or bright sunlight, 8 ( 2.52.%) got lost in familiar places, 1 ( 0.32 %) forgot where they parked, 1 ( 0.32 %) had difficulty controlling the car, 8 ( 2.52 %) had difficulty with concentration, 33 ( 10.41%) had accidents and 8 ( 2.52 %) got confused while driving. Comparing drivers who had difficulty driving vs those without difficulty (Table 3), in the unadjusted analysis, drivers with difficulty were significantly older, had lower test performance in the cognitive domain of attention, had greater difficulty with IADLs, had worse self-rated health, had more depressive symptoms, reported taking more prescription medications, left home less frequently, and were more likely to have both vision and hearing loss.

After adjusting for demographics, drivers who reported driving difficulties were more likely to have lower test performance in the cognitive domain of attention but higher scores for memory, report worse self-rated health, report depressive symptoms, report taking a larger number of prescription medications, leave home less frequently, have fewer confidants, and were more likely to report both vision and hearing loss.

In the fully adjusted multivariable model, drivers with difficulty were more likely to have lower cognitive test performance in attention but higher in memory, were more likely to report depressive symptoms, and more likely to have both vision and hearing loss.

In unadjusted post-hoc analyses, we compared current alcohol users (current drinkers) with former alcohol users (past drinkers). Past drinkers were significantly more likely than current drinkers to have never driven (Chi-square=9.977, df=1, P=0.002), to have ceased driving (Chi-square=43.023, df=1, P<0.001), and, if currently driving, to report difficulties with driving (Chi-square=8.823, df=1, P=0.003)

Discussion:

In this population-based study of older adults in a small-town, post-industrial, economically disadvantaged region of southwestern Pennsylvania, we identified factors associated with never having driven a motor vehicle, with having given up driving, and with current drivers’ self-reported difficulties while driving. There were some similarities and some differences among the three groups of factors. We report the comparison between never drivers and ever drivers to provide context for the remaining comparisons, rather than to shed light on aging- or health-related changes in driving. In the fully adjusted models, like never-drivers, former drivers were significantly more likely to be female, older, and to leave home less frequently. Like former drivers, current drivers with difficulties were more likely to have lower cognitive test performance, with former drivers testing worse on attention, and current drivers with difficulties testing worse on attention but better on memory, compared to their respective reference groups. In addition, never-drivers were less educated, former drivers were more likely to report difficulty with IADLs, and current drivers with difficulties were more likely to report depressive symptoms.

In the MYHAT cohort, women were more likely to be never-drivers, former drivers, and current drivers with difficulties, consistent with a meta-analysis 21 in which women, both with and without dementia, were six times more likely to stop driving than men. Women tend to self-regulate driving more than men 22possibly leading them to earlier driving cessation. There might also be a cohort effect since earlier generations of women were often not the primary household drivers, had less driving experience, and were less confident in their driving abilities 12. Clinical experience suggests older men may tend more than women to view driving ability as an integral part of their identity and social status and be more reluctant to cease driving. Future studies could explore whether this gender effect continues as the population and especially the work force becomes more diverse, and women increasingly earn their own incomes and are lifelong drivers.

In MYHAT, older age was associated with both driving cessation and driving difficulty, consistent with previous studies 11. Older adults are more likely to experience health and cognitive decline, vision difficulties, take more prescription medications, and have functional limitations which may interfere with the ability to drive. Physical changes such as reduced grip and muscle strength, reduced flexibility, and motor speed can impair driving ability. Reduced neck rotation due to cervical joint disease may impair drivers’ ability to turn their heads to check their blind spots and see relevant stimuli in the periphery2. A systematic review showed that drivers with more chronic medical conditions tended to avoid or cease driving. 23. As in our unadjusted models, a previous study found that older adults with poor self-rated health were significantly less likely to drive12. We found former drivers and current drivers with difficulties were taking a higher number of prescription medications, possibly reflecting higher medical burden including physical limitations that directly impact their ability to drive.

Driving requires integration of multiple domains including cognitive, motor and sensory functions. Important functions essential to driving include visual information selection, visual perception, and executive function, episodic, semantic and procedural memory, as well as complex attention and processing speed.1,2,3,Although dementia and mild cognitive impairment clearly affect driving ability, normal aging can also contribute to subtle cognitive changes that influence driving ability.3 Among our study participants, all three groups were noted to have lower performances on cognitive testing across most domains compared to their reference groups, although this difference failed to reach statistical significance between the never vs. ever driver groups after controlling for all other covariates.

In the fully adjusted models, significantly worse performance on attention and executive tasks was observed in former drivers compared to current drivers, while current drivers with difficulties had significantly worse performance than those without difficulties in attention but better performance on memory tasks. A recent literature review showed that low scores on various measures of attention were associated with increased crash risk and on-road driving performance.23 Visual attention is a process that selects visual stimuli based on their spatial location and is crucial in driving, for example when selecting roadside targets. Impaired visual attention is an early feature of dementia but is also seen in normal aging. Selective attention is the ability to focus on specific information in the environment while ignoring irrelevant information, essential when driving and affected by the aging process. A key component of attention is speed of information processing, i.e., the speed with which cognitive activities and motor responses occur. This slowing can impact complex IADLs such as driving. In MYHAT, former drivers reported more difficulty than current drivers with performing other IADLs independently.

We found significantly worse performance on executive function tasks among both never-drivers and former drivers, compared to their respective reference groups, although only in the unadjusted models for never-drivers. Executive function includes the ability to adapt to novel situations, volition, planning, anticipation and effective performance as well as working memory. Older adults perform worse than younger adults in tasks involving working memory, the ability to momentarily hold information in memory while simultaneously manipulating that information. 24 Impaired executive function can also manifest as personality changes, increased impulsivity, decreased cognitive flexibility, and impaired insight, which could affect the ability to self-regulate. In one study, drivers who had accidents made more errors on executive functioning tasks that reflect mental rigidity and had a poorer ability to plan and solve problems.25

Unexpectedly, the fully adjusted models revealed significantly better performance on memory tasks in among current drivers with difficulties than in those without difficulties. This finding might reflect better self-awareness; individuals with memory deficits might fail to recognize or forget to report problems they encountered while driving. 26 Episodic memory, impaired early in Alzheimer’s dementia, includes registration, acquisition and encoding of information, such as knowing where to find the car keys. Impaired later in the disease course are semantic memory, including knowledge of the world, such as knowing what the colors of the traffic lights mean, and also procedural memory which allows a learned skill to be used automatically, such as physically handling the controls of the car.

With regard to sensory impairments, in the fully adjusted models, both former drivers and current drivers with difficulty were significantly more likely to have vision problems, and current drivers with difficulty were also more likely to experience hearing loss. Vision, the most important sensory input while driving, typically declines with age along with speed of visual processing. Useful Field of View (UFOV) tests visual awareness in the peripheral field view, and impaired UFOV is associated with poor driving and increased crash rates.1, 2,27 Driving fitness determination typically screens for static visual acuity, which some but not all studies have shown to be associated with car crashes. 28 Dynamic visual acuity is the ability to resolve the fine detail of a moving target and therefore is a key sensory function for driving. Dynamic visual acuity declines with age much earlier than static visual acuity and does not necessarily correspond to decline in static visual acuity.29 Studies comparing dynamic and static visual acuity have shown a stronger association between dynamic visual acuity and car crashes.28,29 While many studies show associations between impaired vision and driving cessation 30,31, few have examined associations between hearing impairment and driving performance. In one study, self-reported hearing loss was associated with increased crash risk 32. Another study found no association between hearing impairment and driving mobility 33 but did observe that older adults with moderate to severe hearing impairment performed significantly worse on UFOV.

Our former drivers were more likely to report symptoms of depression and increased difficulty with IADLs than current drivers. Drivers reporting difficulties had an elevated likelihood of reporting depressive symptoms. Possibly, limited mobility and social isolation due to driving difficulties contributed to their depression. Alternatively, participants with depression might experience functional impairment or medical comorbidities that affect their ability to drive. In several studies, loss of independence related to driving cessation was associated with an increased risk of depression, increase in functional limitation, and increased mortality. In addition, chronic medical conditions that contributed to driving cessation in the first place continued to progress and were associated with increased mortality and functional decline. 34,35,36 Loss of mobility could lead to reduced activity outside the home, increased social isolation, feelings of loss of independence and loss of personal identity. Potentially, lack of transport options following driving cessation could contribute to decreased access to health care providers and therefore worsening health.

Both never-drivers and former drivers were less likely to currently consume alcohol and former drivers were also less likely to smoke, although these associations lost significance in the fully adjusted models. Unadjusted post hoc analyses showed that past drinkers were more likely to be never drivers, to have ceased driving, and have difficulties with driving. Reduced mobility arising from driving cessation or driving difficulty could have led to decreased access to alcohol and cigarettes. Moderate and heavy alcohol use in late life is associated with increased medical comorbidity, increased ADL dysfunction, increased depression and cognitive impairment. 37 Further, older adults may have medical comorbidities that required them to stop drinking or smoking, potentially explaining the observed associations between driving habits and reduction or cessation of alcohol intake and smoking. Two studies 38,39 examined the role of alcohol in crashes involving older versus younger adults. The associations between driving with light to moderate alcohol should be explored further in future studies, especially as the age of the driving population increases.

Since this cohort was recruited from an economically disadvantaged small-town area which is under-served by public transportation, it is possible that more older adults were still driving despite impairments and difficulties than might have been the case in a larger metropolitan area. We did not directly examine participants’ individual economic status, but our fully adjusted models did find never driving to be associated with lower educational level, possibly reflecting lower socioeconomic status and likelihood of vehicle ownership.

Strengths and limitations of this study both derive from it being a survey of a large population-based cohort of adults. Participants were recruited randomly from the community, rather than, e.g., referrals to a geriatric or memory clinic, thus minimizing selection biases related to factors such as underlying cognitive impairment, medical issues, or differential access to specialty health care. As is typical of population studies, all information reported here was obtained by self-report, except the neuropsychological test data and vision and hearing assessments, without access to medical records or accident data or, in most cases, to knowledgeable informants. Some current drivers may have under-reported their difficulties with driving. Since this article only reports cross-sectional data, we cannot confidently attribute causality or even identify the directions of the observed associations. This being an underprivileged and under-studied population from a small-town area adds value to our findings; however, it is of largely European descent and our findings should be replicated in more diverse populations. To our knowledge, the association of alcohol use with driving status in older adults has not been previously reported and warrants further investigation. Our findings do not generalize to people with MMSE <21 which was the cut off score for inclusion in our cohort.

Our findings can help health care providers identify their patients whose driving histories and abilities warrant more careful attention. Further follow up of the MYHAT cohort will allow us to identify risk factors for future driving difficulties and cessation among those who are currently driving. Clinical researchers may use our findings to target patient populations for prevention research and studies involving performance-based driving assessment data. The snapshot we have provided of driving-related issues among older adults in a low-SES community may be useful to planners and policy makers with regard to transportation safety needs. The increased availability of newer automobile safety features, ride-sharing services, and even autonomous vehicles will likely change the landscape for older drivers. Changes in older adults’ driving habits with the aging of more recently born cohorts (e.g., baby boomers) should be investigated in future research.

Supplementary Material

Supplemental Data File

ACKNOWLEDGMENTS

The authors thank all participants in the MYHAT cohort, and all MYHAT project staff.

The work reported here was supported in part by grant # R01AG023651 from the National Institute on Aging, NIH, US DHHS.

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