Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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Table 8.2. Total Risk Crash Rate Regression ß Prob ? 2 Intercept -14.6373 0.0001 Age65 -2.8306 0.0001 Age70 -2.1929 0.0001 Age75 -2.0658 0.0001 Age80 -1.1724 0.0001 Midwest -0.1186 0.0002 Northeast -0.1213 0.0008 South 0.2505 0.0001 Sb*age65 -0.7053 0.0001 Sb*age70 -1.2005 0.0001 Sb*age75 -0.6562 0.0001 Sb*age80 -0.7641 0.0001 Sb*age85 -1.5809 0.0001 NE 80 M -0.2824 0.0013 S 80 85 F -0.5416 0.0001 W 85M*Time -0.0329 0.0018 W 65 F -0.102 0.1840 W 75 80 F -0.2289 0.0018 W 85 F -0.7147 0.0001 The great majority of the variables had an extremely highly significant effect on the total fatal crash rate. The age dummy variables indicate higher fatality rates for this measure in older age groups. As in the elder driver crash risk model, this measured rate was somewhat higher in the southern Census region, with a coefficient on the southern region dummy variable of 0.25 to be added to the general constant term of -14.64. Also as in the case of the driver crash risk, no clear age pattern emerged in the effect of seatbelt use, but the greatest effectiveness of seatbelt use also emerged for the 85+ and 70-74 age groups, as in the driver crash risk regression. And as in that other crash rate, the time trend on 85+ men in the western Census region was negative. We acknowledge that the use of simple crash per mile measures to characterize the risk facing drivers has been criticized on the grounds, among others, that the crash rate per mile does not appear to be constant for drivers who average substantially different annual mileages (Janke 1991). Janke notes that drivers with low annual VMT tend to have higher crash rates per mile than do drivers with high annual mileage, to a considerable extent because GM Project G.6 8 - 9 October 2000
the low-mileage drivers are driving disproportionately on city streets as opposed to expressways. For example, crash rates per mile from California in the 1980s were 2.75 times as high on open-access streets as on expressways. One of the important implications of this empirical finding is that mileage by itself may not be a satisfactory measure of the exposure to crashes. For example, Janke notes that part of the linearly measured crash risk of, say, elder drivers should be attributed to where they drive, and only part of it to their age. Stratification of driver populations according to various criteria is one recommended strategy for reducing this nonlinearity in the mileage-accident relationship. Our analysis of crash risk begins with a stratification of the elderly as opposed to all age groups and continues the stratification with the interacted age-seatbelt use variable, which permits crash risk to vary within elderly age groups. Data on the predominant use of one type of roadway were not available in observational units compatible with the state-wide FARS data used in the crash rate regressions. Even if such roadway data had been available, projecting the values of those variables (possibly as the percent of driving on one or the other type of roadway) to 2025 would have been a major challenge. Overall, we believe the combined effect of driver age and roadways driven in our measure of age-specific crash risk to be satisfactory: if older drivers tend to drive proportionally more on city streets than do mid-career drivers (ages 35-55) and consequently have higher crash rates per mile, that is an acceptable indicator of the crash rates expected for older drivers, even if some of the differential between their rates and those of younger drivers is attributable to driving location. 8.2 FATAL CRASH RATE PROJECTIONS Two variants of older driver fatality rates , the older driver fatality rate and the total fatality rate from crashes involving an older driver, are projected with the originally estimated regression equation (3). Since the values both of these dependent variables can take are constrained (neither fatality rate can go below zero), it was necessary to retain in the GM Project G.6 8 - 10 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
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LIST OF TABLES Table ES-1 Elderly D
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Table A.5.3 Projected Total Fatalit
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NASS National Automotive Sampling S
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Over the past century, the numbers
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Figure ES-1. Projected Active Drive
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Table ES-1. Elderly Driver Fatality
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1. INTRODUCTION America is graying.
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of existing data bases, including h
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various data sources and their abil
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Finally, data sets should contain,
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• Examine data sources to determi
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health care, resulting in longer li
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Age group Table 3.2. Projected Rati
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that individuals will not retire at
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analysis. Distribution of the elder
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More elderly people live in rural a
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assessments of health status) does
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3.2.2 Impacts of Increasing Frailty
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current vision screening tests at d
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Number of Licenses (thousands) 100
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3.3.2 Changes in Driving Habits In
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Transportation Survey (NPTS) data,
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tourist services, and “smart” s
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Specific highway enhancements to re
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3.4.2 Other GM Project Results (Pro
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• As age increased, being struck
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men using mid-1990s data, this gend
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major contributing factor as was dr
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Total (percent) 100 80 60 40 20 0 A
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cohort behavior and conditions for
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4.2.3 Drivers and Driving Distance
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The HRS and the AHEAD studies are c
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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
Page 106 and 107: significant one in the final set of
Page 108 and 109: empirically derived regression coef
Page 110 and 111: GM Project G.6 7 - 14 October 2000
Page 112 and 113: Table 7.3. Determinants of Projecte
Page 114 and 115: 8. FATAL CRASH RATES 8.1 MODELING F
Page 116 and 117: The other fatality risk concept we
Page 118 and 119: dropped. Since these measures have
Page 120 and 121: have a regular pattern over the age
Page 124 and 125: projection the asymptotic behavior
Page 130 and 131: percentages of people driving, and
Page 132 and 133: 9.1.1 Computations The projection o
Page 134 and 135: Figure 9.2, the combination of thes
Page 136 and 137: Table 9.1. Sensitivity of Total Dri
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Page 140 and 141: Table 9.2. Sensitivity of 2025 Proj
Page 142 and 143: Age group Table 9.6. Sensitivity of
Page 144 and 145: 9.3.1 The Effect of Time on VMT Pro
Page 146 and 147: Figure 9.4. Projected VMT, Women (U
Page 148 and 149: Figure 9.6. Component Contributions
Page 150 and 151: Step 1: Introduction Step 2: Decisi
Page 152 and 153: Step 3a: Modifying Income at differ
Page 154 and 155: Clicking on one of the sheet tabs w
Page 156 and 157: 10. SUMMARY, CONCLUSIONS, AND RECOM
Page 158 and 159: similarly relied on a regression mo
Page 160 and 161: trend of fatal crashes in the futur
Page 162 and 163: The base of exposure to crash risk
Page 164 and 165: population, and a slightly smaller
Page 166 and 167: Second, improvements in health stat
Page 168 and 169: projections of VMT may be high. Our
Page 170 and 171: 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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