Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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The other fatality risk concept we project is the “total fatality crash rate,” which is the total number of fatalities, regardless of age or occupant status, associated with a crash in which at least one older driver is involved. The rate of total fatality risk is measured in a more complex fashion than the driver crash rate. Each driver in a fatal crash which involves an older driver is assigned equal responsibility for each fatality. Then, the sum of the fatalities attributed to each driver is found for a driver’s age-gender-region group. For example, if a crash involving three fatalities occurs between a man aged 65 and a woman aged 72, 1.5 fatalities would be attributed to the 65-69 age group for men, and 1.5 fatalities would be attributed to the 70-74 age group for women, both in the respective region of the accident. If a two-vehicle crash involving a 67 year old woman and a 45 year old man resulted in four deaths, two of them would be attributed to the 65-69 female age group, and two would be excluded from the measure as attributable to an age group outside the study. In assigning responsibility in general, an individual older driver’s contribution to age/gender/region total equals the number of fatalities divided by the number of drivers involved. The total number of fatalities attributed to each age-gender-region group is then divided by the annual number of aggregate miles driven in that group, in similar fashion to the driver fatality risk measure. Total fatality risk is also presented in terms of number of annual fatalities per 100 million miles driven. Historical trends in our measure of “total risk” mirrored trends in the “driver risk” measure. The casualty data from the 1983, 1990, and 1995 FARS were used. FARS reports information on fatal crashes by state, disaggregated by age in one-year increments and by gender. We aggregated the individuals to 5-year age groups and the states to Census regions. Corresponding variables on income, health status, VMT, etc., were created as age/gender/region averages from NPTS and NHIS data. The availability of automobile seat belts, beginning in the 1960s and growing relatively slowly until well into the 1970s, represents one independent variable used in this equation. Seat belt use can be viewed as an indicator of technology that shifts the degree of safety GM Project G.6 8 - 3 October 2000
available, given income and an array of related prices. This effect on safety, as proxied by the seatbelt usage variable, was the same for all people at any given time. The income effect on safety choices had a differential effect across individuals both at any point in time, as individuals (actually groups in the aggregation required for this regression) with higher incomes purchased vehicles that provide greater safety at a given time, and over time, as the general level of incomes for all people rose, leading them to choose greater levels of safety across the board. Empirical evidence indicated that regional effects were important as well, so presence in one of the four Census regions was also used as a set of binary variables. Seat belt use and time were highly correlated, as is to be expected with the market penetration of this innovation, along with the passage of legislation mandating seatbelt use. We experimented with combinations of one of the two variables and the residual of the regression of the other variable on the variable entered in untransformed version. The final version of the model interacted seatbelt use with age, 1 but the residual of the regression of time on seat belt use as a variable was never significant. Consequently, we excluded time from both estimated models. Interacting seatbelt use with age permits the identification of differential effectiveness of seatbelt use in preventing fatalities across age groups. Thus the full array of independent variables for this equation included income, health status, seat belt use, age, gender, and time. As we explain below, we did not use all of these variables in the final estimation used in the projection model. The smaller sample size available for these models forced several choices between variables that, in a more perfect world, would have been retained in the regression model. We estimated this regression for all age groups and both genders, but we accounted for individuals’ 5-year age groups and gender with binary variables instead of estimating separate regressions. The effect of seat belt use was virtually identical between men and women, so the gender-seat belt interaction was 1 That is, a separate variable was created for seatbelt use for each of our 5-year age groups: e.g., a variable for seatbelt use among 65-69 year olds, another for 70-74 year olds, etc. GM Project G.6 8 - 4 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
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
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 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 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
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
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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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