Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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these data and weights. Because of the national scope and scientific sampling, the NASS GES data seemed especially appropriate for making regional and national accident projections. The major limitations with GES data were that data were not available on a state/regional level and did not contain injury information prior to 1988. Because of these limitations of the GES data, this data source was not used in development of our model. The Crash Outcome Data Evaluation System (CODES) is also maintained by NHTSA’s NCSA. CODES uses probabilistic data linkage to merge data from eclectic sources such as motor vehicle crash reports, emergency medical services, hospitals, insurance claims, and death certificates. In probabilistic data linkage, data records are merged on the basis of estimates of the probability that they are from the same target (e.g., accident or driver), rather than conventional merging by ID variables that are definite links (and which do not necessarily exist). The probability estimates are computed from data variables such as time, location, and type of vehicle. Although probabilistic data linkage is automated by computers, some case-by-case tuning of the results is still necessary, and the process is laborious. CODES data are from seven states. The primary objective of CODES was to investigate effectiveness of seat belts and motorcycle helmets (and to prepare a report to Congress). Because CODES was limited in scope to only seven states, this data source was not used in development of our model. The State Crash Data system, available from NCSA, includes a census of all crashes from 17 states. Data, which cover the time period of 1989-1994, are not uniform from state to state in either reporting criteria or coding schemes. Tabular reports are available on (1) trends, (2) crashes, (3) vehicles, and (4) people. Because the State Crash Data was limited in scope to 17 states, this data source was not used in the development of the model. Index and the Property Insurance Loss Register (PILR) are maintained by the American Insurance Services Group (AISG). Index is a national clearinghouse for bodily- injury and automobile insurance claims data with a database of over 50 million claims. PILR GM Project G.6 4 - 8 October 2000
collects first-party property claims data. PILR was merged with Index; together they contain over one billion records and receive 18 million claims each year. Index was used by CODES. Index and PILR are used primarily as tools for preventing insurance fraud. These data sources were not used in our model because of institutional barriers to obtaining the data. Seat belt use has been shown to reduce the impact and casualty rate of a crash. The projected use of seat belts among the elderly was obtained from NHTSA and incorporated in the model (Section B.2.5 and Table B.10). 4.2.6 Other Data Categories Considered In addition to the obvious impact of population, income, driver status and driving distance, health, and crash risk on projecting numbers of casualties of the elderly for the next 25 years, there were other potential factors that we examined. These included education level, transportation/highway budgets, and the impact of safer infrastructure/vehicles/drivers in the future [including possible impacts of the Intelligent Transportation System (ITS)]. Although these factors were not incorporated within our model, further research is needed to determine how they may impact future casualties (see also Chapters 3 and 10). 4.3 CONCLUSIONS Table 4.1 lists the data sources examined and provides a cross-sectional reference to the data categories. GM Project G.6 4 - 9 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.
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 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 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
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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
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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
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Table 8.2. Total Risk Crash Rate Re
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projection the asymptotic behavior
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GM Project G.6 8 - 13 October 2000
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GM Project G.6 8 - 15 October 2000
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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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