Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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• As age increased, being struck as a pedestrian became more likely than striking a pedestrian as a motor vehicle driver. • Poisson regression models developed to predict expected numbers of crashes per driver for specific types of crashes estimated the following: 21.0 crashes/year for every 1,000 male drivers aged 65-74 residing in “urban” counties with populations of 100,000-300,000, of which 1.2 of these would result in a moderate to fatal injury in the other vehicle. 3.4.3 Gender Effects Studies have examined gender differences in fatal crashes. For example, in 1998, male drivers (all ages) were involved in almost three times as many fatal crashes as female drivers (National Highway Traffic Safety Administration, 1999A). Evans, Gerrish, and Taheri (1998) compared gender-based crash rates for elderly drivers and found that the curves of these gender-based crash rates, when plotted, were remarkably similar. In addition, when the rates of traffic deaths to all deaths was plotted, there was very little gender difference between the ages of 20 and 70. In 1995, Massie, Campbell, and Williams studied crash data and travel data to produce crash involvement rates per VMT by age and gender. They observed that “men had a higher risk than women of experiencing a fatal crash, while women had higher rates of involvement in injury crashes and all police-reported crashes” (p. 73). Continuing this line of study in 1997, Massie, Green, and Campbell studied the effects of driver age, driver gender, time of day, and average annual mileage on severe and fatal crashes. Men consistently have a higher risk of crash involvement per mile driven than women. It had been thought that this greater involvement was because they drove more (greater exposure). After adjusting for the greater number of average annual miles driven by men than women, this study suggested that, if women drove similar amounts, women’s involvement rates would be lower than men’s regardless of crash severity (Massie, Green, and Campbell, 1997). In addition, after adjustment to make average GM Project G.6 3 - 30 October 2000
annual miles equal for both genders, men’s crash-involvement rates for injury and property-damage-only were higher than women’s rates. Although Massie, Green, and Campbell have no definitive explanation for why women’s crash rates are so much lower than men’s crash rates (after adjustment for equal average annual mileage), they offer some suggestions concerning why the injury and property damage rates (crashes/VMT) are higher for women than men. It is possible that women are involved in a greater number of injury and property-damage-only crashes per VMT because of driving inexperience, which is due to their lower average annual mileage; therefore, when the rates are adjusted for this lower mileage, the differences in fatal crash rates become even greater and the women’s crash rates for non-fatal crashes drop below those of men. Another possible explanation involves the types of streets on which women drive. Persons with low average annual mileage (in this case, women) drive more on urban and local streets and roads than they drive on rural interstates. Urban and local streets have higher crash rates than do rural interstates. One study identified gender-specific health factors related to crash risks. Factors that place elderly female drivers at risk include living alone and experiencing back pain. Risk factors for men include being employed, scoring low on word-recall tests, having a history of glaucoma, or using antidepressants. The number of miles driven and use of antidepressants were the most significant risk factors for men (Hu et al., 1998, p. 569). Stamatiadis (1999) examined gender distinctions in crashes involving elderly drivers. He used two-vehicle crash records for Kentucky between 1994 and 1996, eliminating crashes for which he could not determine which one of the two drivers was at fault. He then calculated a crash rate for age-gender groups based on the percent of crashes for which the driver was at fault divided by the percent of crashes for which the driver (same age-gender group) was not at fault. Although there were no significant differences in the crash rates of males and females for ages up to 55, statistically significant (5% level) gender differences were noted for age groups 55-64, 65-74, and over 75. Females were more often at fault than males for these age groups. Stamatiadis concluded that, while elderly women have higher accident and at-fault rates than elderly GM Project G.6 3 - 31 October 2000
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ORNL-6963 Projecting Fatalities in
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4. ANALYSIS OF DATA SETS ..........
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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 40 and 41: assessments of health status) does
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 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
Page 90 and 91: more likely to be drivers if there
Page 92 and 93: Statistics (BLS) projections beyond
Page 94 and 95: considerably greater proportional c
Page 96 and 97: Table 6.3 (continued) Western Men W
Page 98 and 99: Daily Person Trips/Person Figure 7.
Page 100 and 101: Second, VMT as a consumption item i
Page 102 and 103: century on which we are unable to o
Page 104 and 105: The health status effects are posit
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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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