Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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More elderly people live in rural areas than in metropolitan areas. The marked growth of the elderly in rural areas is a result of aging-in-place, out-migration of young people to more urban areas with better paying jobs, and in-migration of retirees from urban areas (Rogers, 1999).The number of older persons varies among the regions. The greatest concentration of the elderly (for both rural and urban areas) is in the South (see Figures 3.1-3.4). Because non-metropolitan America is less economically well off and has fewer public transportation options, the elderly, especially those who do not drive or own an automobile, find it difficult to reach health care facilities, to attend social events, or to meet basic shopping needs (National Association of Development Organizations Research Foundation, 1999). 3.2 HEALTH ISSUES The U.S. Census Bureau has projected the future population of the elderly. These projections, however, do not forecast the health of this population. Physical and cognitive health is directly related to driving abilities as well as to survival in crashes. As seniors age, the occurrences of physical and cognitive impairments and overall frailty increase. Among all elderly, in 1996, 10.5% were unable to carry on a major activity; this percentage can be compared with 3.5% of those under 65 years of age. In addition, over 27% of seniors listed their health as fair or poor, with very little difference between the sexes (Administration on Aging, 1999). With obvious implications for both safety and mobility, it is especially important to attempt to quantify (1) the numbers of elderly with disabilities that could limit their driving skills and (2) the correlation between health impairments and the decisions of whether to drive and how much to drive. The elderly are experiencing better health than in the past. Manton, Corder, and Stallard (1997) studied chronic disease trends among the elderly in the United States using GM Project G.6 3 - 10 October 2000
the National Long Term Care Surveys (NLTCS) and saw much lower chronic disability rates. Freedman and Martin (1998), using data from the Survey of Income and Program Participation (SIPP) also found large health improvements in the elderly. In addition, Freedman and Martin found that these improvements have been greatest among those 80 and older. Manton, Stallard, and Singer (1994, p. 61) explain this trend as due to decreased smoking, increased education, and better life styles (more exercise and better nutrition) among seniors. It should be noted, however, that Wallace and Franc do not concur with these studies and contend that the conclusions need further verification (Wallace and Franc, 1999, p. 18). If, indeed, disability rates are declining, the ability of the elderly to continue to drive to later ages would be extended. Because of increases in the number of elderly and a decline in mortality, the number of nursing home residents is predicted to approximately double (from 1.67 million residents of nursing facilities in 1990) between 1990 and 2025 (American Health Care Association, 1997, p. 7). Nursing home residents average 3.67 activities of daily living (ADL) limitations each. The typical nursing home resident is an 82-year-old woman who needs assistance with some personal activities (American Health Care Association, 1998). 3.2.1 Impacts of Failing Health on Driving Abilities Older persons are more likely to suffer from health problems (Eby et al., 1998, p. 39). Specific medication taken by older persons to alleviate poor health conditions may affect driving ability. In addition, certain medical conditions may affect driving ability. Although certain drugs (e.g., benzodiazepines) and certain severe health conditions (e.g., Alzheimer’s Disease) obviously affect driving ability, research is not clear cut on other drugs and health conditions associated with advancing age. What is true, however, is that older persons experience increasing medical problems. The problem is how to tie health measures (e.g., “excellent,” “poor”) to driving measures (e.g., whether to drive, how much to drive). Wolinsky (1990) contends that metric coding of perceived health status (and, by implication, more objective categorical GM Project G.6 3 - 11 October 2000
Page 1: ORNL-6963 Projecting Fatalities in
Page 4 and 5: 4. ANALYSIS OF DATA SETS ..........
Page 6 and 7: 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 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 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
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statistical relationship exists bet
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more likely to be drivers if there
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Statistics (BLS) projections beyond
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considerably greater proportional c
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Table 6.3 (continued) Western Men W
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Daily Person Trips/Person Figure 7.
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Second, VMT as a consumption item i
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century on which we are unable to o
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The health status effects are posit
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significant one in the final set of
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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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