Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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assessments of health status) does not optimally exploit the elicited information and is not legitimate. For example, assigning the numerical values of 1, 2, 3, and 4 to perceived individual health conditions of “poor, fair, good, and excellent” assumes that the difference between “2” and “3”is equal to the difference between “3” and “4.” There is no direct metric relationship between an individual’s ranking of his/her perceived health status and his/her decision to drive or not to drive. Nor is there a direct metric relationship between perceived health status and the decision of how much to drive. As the numbers of older drivers increase, we must consider how perceptual (visual), cognitive, and/or psychomotor impairments of the elderly affect their ability to navigate a vehicle. In 1998, Eby et al. published an extensive review of the literature on older drivers’ capabilities. Age-related changes on visual perception (i.e., anatomical changes, eye movements, sensitivity to light, dark adaptation, visual acuity, spatial contrast sensitivity, visual field, space perception, motion perception, color perception), cognitive factors (i.e., attention, perceptual style – field dependence/independence, short-term and long-term memory, problem solving, spatial cognition), and psychomotor skills (i.e., reaction time, flexibility, and coordination) and their effects on driving abilities are well-documented. Owsley, McGwin, and Ball (1998) studied visual risk factors among elderly drivers involved in crashes that resulted in injury. In this study, elderly drivers with a useful field of view reduction of more than 40% were at least 20 times more likely to be involved in a crash involving injury than were those with minor visual limitations. The study suggested “that visual processing impairment, a major cause of disability in older adults, and glaucoma, projected to affect almost 1.6 million older adults in the U.S. by the year 2000, increase older drivers’ risk for involvement in an injurious crash” (Owsley, McGwin, and Ball, 1998, p. 112). In a study to compare predictors of driving fitness, Duchek et al. (1998) examined the results of several vision tests, psychometric tests, and diagnoses of mild Alzheimer’s Disease. This study indicated that a diagnosis of mild dementia alone may not be the best predictor of impaired driving and that results of the visual search task may be better GM Project G.6 3 - 12 October 2000
predictors. It should be noted that the visual search task is not the same as visual acuity tests administered at drivers licensing sites. Driver licensing tests are not considered adequate to identify older drivers that might be involved in crashes (Ball, 1997, p. 46). Several studies have shown that older drivers, in general, recognize their limitations and self-regulate their driving to times and places that they consider safest. Self-regulation may reduce crash risk for older drivers, in that it keeps them from being involved in situations in which they have difficulty maneuvering (Ball et al., 1998, p. 321). In a literature review on the mobility needs of older women, Wallace and Franc (1999) examined the disability-mortality paradox between men and women and its implications for future gender differences among elderly drivers. According to Wallace and Franc’s research, women over age 70 were about twice as likely as men to report health limitations. Although women have a longer life expectancy than men, women report more disabling chronic diseases, such as degenerative arthritis, than men. While studies have documented more chronic heart disease among men than among women, improvements are being made in treatments of heart disease, and it is possible that the health of elderly men will improve. Health conditions such as degenerative arthritis make driving difficult or impossible, but there is no evidence that the rate of degenerative arthritis is declining. “Although the literature has converged on the finding that visual and cognitive impairments contribute to unsafe driving, there is little agreement across studies about which medical conditions and functional impairments elevate crash risk” (Sims et al., 1998, p. 556). GM Project G.6 3 - 13 October 2000
Page 1: ORNL-6963 Projecting Fatalities in
Page 4 and 5: 4. ANALYSIS OF DATA SETS ..........
Page 6 and 7: GM Project G.6 vi October 2000
Page 8 and 9: LIST OF TABLES Table ES-1 Elderly D
Page 10 and 11: Table A.5.3 Projected Total Fatalit
Page 12 and 13: NASS National Automotive Sampling S
Page 14 and 15: Over the past century, the numbers
Page 16 and 17: Figure ES-1. Projected Active Drive
Page 18 and 19: Table ES-1. Elderly Driver Fatality
Page 20 and 21: 1. INTRODUCTION America is graying.
Page 22 and 23: of existing data bases, including h
Page 24 and 25: various data sources and their abil
Page 26 and 27: Finally, data sets should contain,
Page 28 and 29: • Examine data sources to determi
Page 30 and 31: health care, resulting in longer li
Page 32 and 33: Age group Table 3.2. Projected Rati
Page 34 and 35: that individuals will not retire at
Page 36 and 37: analysis. Distribution of the elder
Page 38 and 39: More elderly people live in rural a
Page 42 and 43: 3.2.2 Impacts of Increasing Frailty
Page 44 and 45: current vision screening tests at d
Page 46 and 47: Number of Licenses (thousands) 100
Page 48 and 49: 3.3.2 Changes in Driving Habits In
Page 50 and 51: Transportation Survey (NPTS) data,
Page 52 and 53: tourist services, and “smart” s
Page 54 and 55: Specific highway enhancements to re
Page 56 and 57: 3.4.2 Other GM Project Results (Pro
Page 58 and 59: • As age increased, being struck
Page 60 and 61: men using mid-1990s data, this gend
Page 62 and 63: major contributing factor as was dr
Page 64 and 65: Total (percent) 100 80 60 40 20 0 A
Page 66 and 67: cohort behavior and conditions for
Page 68 and 69: 4.2.3 Drivers and Driving Distance
Page 70 and 71: The HRS and the AHEAD studies are c
Page 72 and 73: these data and weights. Because of
Page 74 and 75: U.S. Census Bureau (http://www.cens
Page 76 and 77: 5. OVERVIEW OF THE MODELING SYSTEM
Page 78 and 79: or acquisition costs, of the goods
Page 80 and 81: institutionalized settings, our pro
Page 82 and 83: elationships between health and the
Page 84 and 85: 6. THE PROPORTION OF THE ELDERLY PO
Page 86 and 87: Employment status represents a deri
Page 88 and 89: statistical relationship exists bet
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more likely to be drivers if there
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Statistics (BLS) projections beyond
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considerably greater proportional c
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Table 6.3 (continued) Western Men W
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Daily Person Trips/Person Figure 7.
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Second, VMT as a consumption item i
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century on which we are unable to o
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The health status effects are posit
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significant one in the final set of
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empirically derived regression coef
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GM Project G.6 7 - 14 October 2000
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Table 7.3. Determinants of Projecte
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8. FATAL CRASH RATES 8.1 MODELING F
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The other fatality risk concept we
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dropped. Since these measures have
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have a regular pattern over the age
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Table 8.2. Total Risk Crash Rate Re
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projection the asymptotic behavior
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percentages of people driving, and
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9.1.1 Computations The projection o
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Figure 9.2, the combination of thes
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Table 9.1. Sensitivity of Total Dri
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material, dampening effect on drive
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Table 9.2. Sensitivity of 2025 Proj
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Age group Table 9.6. Sensitivity of
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9.3.1 The Effect of Time on VMT Pro
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Figure 9.4. Projected VMT, Women (U
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Figure 9.6. Component Contributions
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Step 1: Introduction Step 2: Decisi
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Step 3a: Modifying Income at differ
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Clicking on one of the sheet tabs w
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10. SUMMARY, CONCLUSIONS, AND RECOM
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similarly relied on a regression mo
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trend of fatal crashes in the futur
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The base of exposure to crash risk
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population, and a slightly smaller
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Second, improvements in health stat
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projections of VMT may be high. Our
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of entry/exit, safety, security, et
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Blincoe, Lawrence. 1994. Estimating
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College of Commerce and Business Ad
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National Association of Development
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Suen, S. Ling, C. G. B. Mitchell, a
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GLOSSARY Baby boomers Persons born
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Table A.1.2. Projections of Vehicle
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Table A.1.4. Projections of Vehicle
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Table A.2.2. Projections of Drivers
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Table A.2.4. Projections of Drivers
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Table A.3.2. Elder Driver Fatality
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Table A.3.4. Elder Driver Fatality
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Table A.4.2. Projected Elderly Driv
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Table A.4.4. Projected Elderly Driv
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Table A.5.2. Projected Total Fatali
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Table B.1. Projections of Non-Insti
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Table B.3. Projections of Non-Insti
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B.2. INDEPENDENT VARIABLES PROJECTI
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Table B.5. Male DRI Income Projecti
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B.2.1.2. Projection Adjustments to
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B.2.5 Seatbelt Use Our projections
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C.3. DECREASING GROWTH RATES OF DRI
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GM Project G.6 C - 4 October 2000 T
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Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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