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Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

Projecting Fatalities in Crashes Involving Older Drivers, 2000-2025

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seatbelt use variable was <strong>in</strong>variant across gender. There is no smooth pattern of <strong>in</strong>crease or<br />

decrease <strong>in</strong> the contribution of either variable as we move up the age groups. Among men,<br />

seatbelt use contributes the most among the 70-74 and 85+ groups, 60% to 63%, and the<br />

least to decreases <strong>in</strong> driver crash risk among 65-69, 75-79, and 80-84 year olds, from 40%<br />

to 42%. Growth <strong>in</strong> seatbelt use contributes the most to decreases <strong>in</strong> women’s driver crash<br />

risk among the 65-69 year olds, around 37%, the least among the 80-84 group, and at an<br />

<strong>in</strong>termediate level among the 70-74 and 75-79 year olds, at 55% and 49%.<br />

Table 8.3. The Determ<strong>in</strong>ants of Projected Changes <strong>in</strong> Driver Risk, National Level<br />

Men Women<br />

Age Income Seatbelt Income Seatbelt<br />

65-69 58.09% 41.94% 62.46% 37.44%<br />

70-74 39.92% 59.90% 44.13% 55.78%<br />

75-79 57.71% 42.19% 50.54% 49.37%<br />

80-84 59.19% 40.68% 62.40% 37.40%<br />

85+ 37.17% 62.93% 38.69% 61.42%<br />

While seatbelt use is a traditional focus of concern, the substantial contribution of<br />

<strong>in</strong>come growth to decreas<strong>in</strong>g crash risk <strong>in</strong> these projections is important as 40% to 60% of<br />

the decrease <strong>in</strong> this risk <strong>in</strong>dicator is attributable to <strong>in</strong>creas<strong>in</strong>g <strong>in</strong>come. Our model<strong>in</strong>g has not<br />

specified the routes of effect of higher real, elderly <strong>in</strong>come, but we have po<strong>in</strong>ted to the most<br />

likely possibilities as ability to afford safer equipment and generally higher valuation of safety<br />

which may spill over <strong>in</strong>to driv<strong>in</strong>g practices as well as equipment purchases. Income and<br />

health are generally positively correlated, although <strong>in</strong> the regressions underly<strong>in</strong>g these<br />

projections, better health, as measured by our <strong>in</strong>dicator, would have elevated crash rates. The<br />

health-<strong>in</strong>come-crash rate relationship needs further research.<br />

Regional variations <strong>in</strong> the different variables’ contributions to the crash rate decl<strong>in</strong>e<br />

are not as strik<strong>in</strong>g as they have been <strong>in</strong> some of the other projections, as Table 8.4 clearly<br />

shows. The seatbelt contribution to crash rate decl<strong>in</strong>e is largest among western women 85+,<br />

at 83% (followed by western men <strong>in</strong> the same age group, at 74%), and the lowest is among<br />

GM Project G.6 8 - 14<br />

October <strong>2000</strong>

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