Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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projection the asymptotic behavior of the dependent variable imposed by the form of the equation. For projections of future years’ values of the dependent variables, the projected levels of the independent variables were substituted into the respective logistic equations and the new values of the dependent variables calculated. We did not use the time trend in the projection of older driver fatality rates. The projection of household income to support projection of both fatal crash rates was identical to its projection for the previous components. The age and region variables were dummy variables, and they simply took values of 1 in each projection year, to be multiplied by the estimated regression coefficients. This is also the case with the interacted dummy variables age/gender/region and West/men. The doubly interacted dummy variable West/male/year was projected as the time trend variable was projected, but only for the group of males in the western Census region. The seatbelt use variable was projected using predicted values of a regression on time of 1991-1996 NHTSA rates and the U.S. DOT’s 2025 expected rate of use at 85%, on time. Data were available from 1983-1996, but inconsistent data due to a change in methodology in 1991 required us to use only the 1991- 1996 data and 2025 projection in the regression (Appendix B.2.5). The interaction of these projections with age used the same percent use with each age group but multiplied that usage by a separate regression coefficient in the projection. Finally, the projection of the numbers of fatal crashes required the use of population projections, which were furnished by U.S. Census Bureau projections. The modification of total projected population by the fraction projected to be institutionalized (AHCA, taken from Statistical Abstract of the United States) yielded the projected non-institutionalized population. Projections of all other independent variables were embodied in the projections of the other components. Figure 8.3 illustrates the projected decline in fatal crash rates among elderly drivers. One can observe that the oldest age group (85+) has the highest historical crash rates per 100 GM Project G.6 8 - 11 October 2000
million miles driven, with the 1995 national averages being 16.83 deaths per 100 million miles driven for men and 16.23 per 100 million miles driven for women. Additionally, the positive relationship between age and fatal crash risk holds for both genders and all regions. The oldest age groups have also had much sharper rates of historical decline in these rates than some of the younger elderly groups. Our projections reflect this information, with the oldest age groups declining at a much sharper rate (to 66% of their observed 1995 rates in 2025) than the younger groups, which decline to around 85% of their observed 1995 rates in the 2025 projections. These projected national trends are virtually identical between the two genders at the youngest (65-69) and oldest (85+) age groups, but vary to some degree among the middle groups, with men generally declining at a slower rate. Fatal crash rates among drivers were fairly consistent among regions, except for those drivers in the South, who had higher fatality rates across virtually all age groups and both genders. Tables A.3.1-A.3.4 in Appendix A show these age and regional differences in greater detail. Our total fatality rate from crashes involving an older driver follows similar trends, with the oldest age groups having rates that start higher and drop relatively more dramatically than the youngest elderly age groups. We also see the same positive relationship between age and risk as we do in the fatal crash rate of drivers, as well as the similarity between genders. As with the driver fatal crash rate, southern men have higher absolute rates per 100 million miles than men in other regions. Tables A.5.1-A.5.4 in the Appendix show these differences in greater detail. Income growth and projected growth in seatbelt use contribute roughly equally to these projected declines in driver risk ranging from around 60%-40% to 50%-50%, depending on age/gender group. Tables 8.3 and 8.4 show these contributions at the national and regional levels. Differences are more substantial across age groups than between genders within any particular age group, primarily because the regression coefficient on the GM Project G.6 8 - 12 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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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
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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 122 and 123: Table 8.2. Total Risk Crash Rate Re
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
Page 138 and 139: material, dampening effect on drive
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
Page 172 and 173: 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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