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The role of exposure in comparisons of crash risk among different drivers and driving environments∗
THE ROLE OF EXPOSURE IN COMPARISONS OF CRASH RISK AMONG DIFFERENT DRIVERS AND DRIVING ENVIRONMENTS*
‘Department of Preventive Medicine and Biostatistics, University ofToronto, Toronto, Ontario, Canada; ‘Department of Industrial Engineering, University of Toronto, Toronto. Ontario, Canada: jHuman Factors North. Toronto. Ontario, Canada: Wniversit& Laval, Quebec City. Quebec. Canada
Abslraet-Crash
rates based on drivers. driver-kilometres, and driver-days in the denominator were compared, using survey estimates of time and distance driven and the annual frequency of traffic srashes in Ontario. Rates by age, sex, and region were computed for all crashes and for crashes resulting in injury or fatality. Young male drivers remained at high risk for al1 types ~fdenomjnator~ older women hacl high rates when distance was inAded it%the denomjnator. When time spent driving was substituted. men and women drivers over 60 had very similar rates, For ~om~arjsons ofrural residents with urban and northern residents. time and d~stan~egive equivaIent results. These endings suggest that apparent difF&ences in crash risk per kiIometre, whether for older women or for urban drivers. is explained by differences in typical driving speed and environment. Exposure time is better than distance to explain crash risk among drivers and regions with very different driving patterns and environments. When studying the etiology of disease, it is common for epidemiologists to use measures of duration of exposure, such as person-years at risk, as a natural modifier of risk: the value ofsuth measures for the risk of traffic crashes is widety acknowledged: so too, unfortunately, are the probfems of good and retiable estimates. In previous work (fhipman et af. 1992) we exami~~d daily estimates af time spent driving and distance driven to measure exposure to risk. The different patterns among age groups, sex, and region of residence indicated not only appreciable variation in the amount of driving done by these groups in the driving population. but also differences in the average speeds of travel: i.e. some drivers had taken more or less time to travel the same distance. Speed is a function of e~v~r~nmentas weII as the choice ofthe driver_ and is typica!Iy reduced in conditions perceived to be more hazardous. Limited access highways are usuafJy considered “safer” than streets with more opportunities for traffic comlict. yet many cautious drivers avoid this environment. Many deficiencies have been identified in using distance to quantify exposure to crash risk. The rela-
tionship between distance and other aspects ofexposure, which often vary substantially among drivers. is not a simple one. These other aspects include time of day, type of road and speed ~~h~~rnan 1982), the density of traffic conflicts and drivers’ reaction to them {Risk and Shaoul 1982). Other measures or variants ofdistan~e have been considered to measure exposure: Risk and Shaouf f 1982) describe their efforts at identifying and counting hazards on road segments and the close correlation they observed with the frequency of traffic crashes. Janke ( 199 1) has addressed similar issues in examining attempts to modify the estimates of distance (by adding a constant, by treating it as a muttiplicative factor, etc.) to iflustrate the lack of proportionality in the re~at~o~sh~~ between simple distance driven and collision risk. She considered the use of “induced exposure”. based on the characteristics of drivers involved in traffic crashes but not at fault, might be more promising. As the driving populations of Europe and North America shift to include a larger proportion of older people, any increase in crash risk-however measured-in these age groups is viewed with alarm by public health and driver licensing ofhciais ahke (Jan ke I99 i ). The concern is analogous to the more famihar concerns for the increased risks of younger
208
M. L. CHIPMAN et al.
drivers. Whether the units are population, driver-distance, or driver-time, the exposure of drivers at both extremes of age is typically less than the exposure of established, middle-aged drivers, so that crash rates based on exposure can change dramatically (Mayhew et al. 198 1). Thus rates based on the population of drivers typically decrease with increasing age, and are higher for men than women. For rates based on driver-kilometres, there may be a U-shaped curve, highest at the extremes of age? where exposure is lowest, and lowest for the middle-aged driver, as noted by Janke ( 199 1). Thus, when exposure to risk is included in the calculations, the changes in hazard with age would be enlarged for young people, and might be reversed for older drivers. There is, in addition, a growing concern for drivers who are newly licensed and not necessarily also young (Mayhew and Simpson 1990). Examples include women who acquire a driver’s license at a later age than men, and immigrants, a growing proportion of whom arrive in Canada from countries with very different opportunities to drive and very different road environments. Exposure patterns in these new drivers are likely to be different both from those of younger drivers who are also recently licensed and from those of established drivers of comparable age and sex. Comparisons between men and women drivers are plagued by great differences in the type as well as the amount of driving (Chipman 1982) which make interpretation of well-known differences in collision risk difficult. If women cover shorter distances, in different environments, are they at greater, less, or the same risk of trafic crash per kilometre or per hour as men? fn data collected in 1988 from a survey of Ontario drivers, it has been possible to obtain measures of exposure in terms of both time and distance. Previous work has shown that the differences in exposure between men and women drivers, among age groups or among drivers living in urban, rural, or northern areas of Ontario are not the same for time spent driving as they are for distance (Chipman et al. 1992). In this paper we will compare crash rates among drivers by age, sex, and place of residence to see whether time. distance, or some combination is more appropriate to describe drivers’ exposure to the risk of traffic crash. METHODS The survey of Ontario drivers on which these analyses are based was carried out in October 1988 and has been reported elsewhere (Smiley et al. 199 1). Briefly, a random sample, stratified by age group, sex,
and region of the province, was drawn from the file of licensed drivers: the strata included six age groups and the regions of Northern Ontario, rural southern Ontario, and urban southern Ontario. Thirty-six strata were required to include all combinations of age group, sex, and region. The 330-335 drivers selected in each stratum were sent a questionnaire which included (i) questions about how and when they learned to drive and other demographic details and (ii) a trip log to record the driving they did in one or three specihc days of the week. Drivers who provided both time and distance, but whose responses indicated travel at exceptionally low or high speeds, were excluded as providers of dubious data. This criterion is identical to that used in an earlier paper (Chipman et al. 1992). Drivers reporting time or distance but not both were also excluded as providers of potentially dubious data. Drivers reporting zero for both time and distance were included in these analyses, as valid nondrivers in the survey interval. The means of the times spent driving and the distances driven per day were calculated for each stratum. The mean daily distance and time spent driving were used to estimate the annual driver-kilometres and driver days for all drivers in the population of each stratum. Accident data, in the form of annual figures for each stratum, were obtained from provincial records for 1988. Estimates of population size for the denominator came from two sources. For the first source, the population size was calculated from the sampling fraction for each stratum used in the original sampling plan. These data were sufficient to compare drivers from different regions, but were not reliable for other comparisons, especially among age groups. It was apparent, from the pattern of response rates to the survey, that the driver file from which the sample was drawn had not been purged for some time of drivers who had died, moved, or not renewed their license. This problem left regional comparisons relatively unaffected, but resulted in the underestimation of rates for older and younger drivers, since older drivers were more likely to have died and younger drivers were more likely to have moved. For these comparisons other estimates of population size were required. The second source was a previous study that had used the provincial driver records (Hauer et al. i 99 1); crash and population data were available by age and sex (but not region) for drivers who had renewed their license in a specific year. These data, together with crash data and weighted averages of the exposure data to reflect diff‘erences in population size in the three regions, were used for age- and sex-specific crash rates.
Role of exposure in comparisons of crash risk
Annual crash rates by stratum were calculated per 100 drivers, per million driver-kilometres, and per 1,000 driver-days, where a “day” is taken as 24 hours. All crashes, whether resulting in fatality, personal injury, or property damage were considered; the analyses were repeated considering only fatal and inju~-producing events. Age- and sex-specific crash rates were calculated dim&y, with a standard error and 95% confidence interval derived for the ratio estimate of crashes per driver-exposure as described by Armitage and Berry f 1987). Adjusted crash rates based on the linear regression model were computed for each region by type of crash; regions were compared controlling for age and sex of drivers in each stratum. The comparisons were tested using standard F or t-tests as appropriate. RESULTS There was a total of 3, t 58 (85.7%) survey respondents with ‘“non-dubious” data, from the 3,586 respondents to this survey. The numbers and the means for daily time and distance of these drivers are shown in Table 1. The numbers of drivers with “non-dubious” data were highest for middle-aged drivers and dropped most markedly for drivers 80 years old or more. Since the samples selected were of equal size in each stratum, this is largely a reflection of differences in response rate, which was 36% overall. but varied from 13% to 54% in individual strata (Smiley et al. 199 I >. As noted in ear-her work (Chipman et aI_ 19X?), both time and distance vary dramaticalfy between age groups and between men and women drivers. Between regions, drivers in the north and urban areas ofthe southern part ofthe province appear similar, with drivers in rural parts of southern Ontario
209
spending both more time on the road and covering greater distances. ‘The rates by age group and sex for each denominator are shown in Table 2 for all crashes. Traditional crash rates, per 100 licensed drivers, exhibit the familiar pattern of high values for young men, decreasing or remaining stable with increasing age. Rates for drivers over 80 (a highly select group in Ontario, due to stringent licensing requirements for drivers ofthis age) are the only ones to reach or exceed the rates for young drivers. Rates are substantially higher at each age for men than for women. When distance is included in the denominator, the pattern remains the same for young drivers but changes for older drivers. Most noteworthy is that rates are now consistently (and substantially) higher for older women than for older men. The confidence intervals are wider for these rates than for the simple basic rates: the addition of variable exposure data to the denominator has affected the precision of each estimate, The rates per thousand driver-days exhibit a similar pattern among age groups for each sex, with retatively high rates for the very young and the very old. The difference between men and women drivers of the same age, however, is much reduced for older drivers and more marked for younger drivers. These rates are for all crashes, regardless of severity. The increased frailty of elderly people and the associated increase in the risk of injury even in relatively minor colhsions, together with the association of speed with more severe outcomes, led us to consider that these patterns might be different if comparisons were restricted to crashes with severe conseTable 2. Annual total crash rates per driver, driver-km, and driverday (with 95% confidence limits)
Table I, Summary ofdaily time and distanceestimates or region -Drivers Sex and age groups Men 16-19 20-74 25-B 60-69 70-79 go+ Women 16 19 20-24 25-59 60-69 70-79 HI+ Regions of Ontario Northern Southern rural Southern urban
by age, sex. Denominator
Time (min)
Distance (km)
256 234 338 349 260 76 301 275 318 355 245 91
43.9 61.3 74.9 64.0 54.0 29.9 31.8 42.0 42.2 38.3 29.8 24.8
35.9 52.7 50.2 44.3 38.1 27.5 22.6 31.3 29.9 19.2 14.2 14.1
1024 f I64 970
45.8 54.8 46.6
32.8 44.0 31.2
Age
Men
Women
Per 100 drivers
16-19 20-24 25-59 60-69 K-79 xl)+
11.6 9.5 7.1 4.2 5.3 7.2
+ 0.4 f 0.2 ?!I0.t + 0.1 -t 0.2 i 0.5
4.8 4.1 3.2 2.1 3.1 6.0
Per Id driver-kifometres
16-19 20-24 25-59 60-69 IO-79 so+
8.8 4.9 3.9 3.1 3.1 5.9
ri: 2.9 I I.5 5 0.5 f 0.6 -t 1.7 I 4.8
5.8 3.6 2.3 4.1 5.4 IO.1
i- I.8 t 1.1 4 0.8 IL I.0 + I.9 I 6.9
Per IO’ driver-days
16-19 20-24 25-59 60-69 X-79 8Oi
IO.4 * 3.2 6.1 + I.7 3.6 I? 0.8 2.6 + 0.5 3.9 z!I 1.f 9.5 + 5.1
6.0 3.9 3.0 2.2 4.1 9.6
ri: 1.8 i 1.2 rt 0.7 -t- 0.6 t 1.4 J- 5.3
rt i: + t ir
0.3 0.2 0.1 0.1 0.2 0.9
210
M. L. CHIPMANet al
Table 3. Annual severe crash rates per driver, driver-km, and driver-day (with 95% confidence limits) Denominator
Age
Men
Women
Per 100 drivers
16-19 20-24 25-59 60-69 70-79 80-t
4.09 3.42 2.43 1.37 1.73 2.41
I!Z0.24 I!I 0.14 + 0.07 + 0.05 Yc0.09 + 0.31
1.78 1.55 1.18 0.70 1.03 2.05
?z 0.18 t 0.10 + 0.05 !z 0.04 + 0.09 + 0.54
Per IO”driver-kilometre
16-19 20-24 25-59 60-69 70-79 80+
3.12 1.78 1.33 0.85 1.24 2.40
i 1.05 I!I 0.54 I 0.34 t 0.17 + 0.68 rt 1.98
2.15 1.36 1.08 1.00 2.00 4.00
It t i i t 2
Per 1O-’driver-days
16-19 20-24 25-59 60-69 70-79 80+
3.67 2.19 1.28 0.85 1.26 3.18
t 1.13 + 0.62 + 0.39 I!K0. I5 + 0.37 + 1.72
2.20 1.46 1.10 0.73 1.36 3.27
+ 0.68 f 0.45 Y!I0.27 + 0.20 I!Z0.46 I!I 1.96
0.69 0.43 0.30 0.26 0.73 2.85
quences. Comparable sets of crash rates, restricted to crashes resulting in death or injury to at least one person, are found in Table 3. Rates for crashes with severe consequences exhibited the same pattern as for all crashes. Per 100 licensed drivers, men had higher rates than women, and there was a steady decline in the rates with age in both sexes, at least to age 80. When the population at risk is modified by the inclusion of time or distance, the rates continue to exhibit a U-shaped curve, with high rates at both extremes of age. Women over 80 and men under 20 have the highest risk per unit of time or distance. As with the rates for all crashes, the age-specific rates for severe crashes exhibit differences between men and women drivers per million kilometres that virtually disappear for older drivers per thousand hours of driving. Total and severe crash rates by region, adjusting for age group and sex, are given in Table 4. There are no significant regional differences when only the
Table 4. Annual crash rates by region of residence Crashes by region
Per 100 drivers
Per IO6 dr-km
Per IO’ dr-day
All Northern Southern rural Southern urban P-value (F test)
5.3 4.8 5.6 0.4126
5.2 3.6 5.2 0.0019
5.5 3.1 5.5 0.0089
Severe Northern Southern rural Southern urban P-value (F test)
1.67 1.66 1.99 0.1969
1.71 I .08 I .95 0.0040
1.62
1.24 1.87 0.0135
number of licensed drivers is considered, but crash rates per million kilometres or per thousand days indicate a significantly lower rate for drivers in rural areas. The rates for all crashes in urban or northern regions are 46% higher than in rural regions when distance is included, and are 75% higher when time is used. Comparable data for injury- and fatality-producing accidents exhibit similar differences, although there is a greater difference between urban and northem regions than was apparent for rates based on distance driven. There is very little evidence of difference by place of residence among the crash rates per 100 drivers. There are significant differences among the three regions for crash rates per million kilometres (P = 0.004) and per thousand driver-days (P = 0.014). The major deviation among the rates is due to the strata of drivers living in rural areas in the southern, more densely settled part ofthe province, whose rates are roughly half to three-quarters the rates of urban or northern strata. DISCUSSION
AND
CONCLUSIONS
It is clear that differences in exposure must be considered in making comparisons of crash risk for different groups of drivers. Furthermore, time and distance-the simplest and most direct measures available-are not equivalent for many useful comparisons. The time taken by drivers to cover a given distance is, among other things, a reflection of perceived risk. In heavy traffic, in poor weather or lighting conditions, or negotiating a twisted section of roadway, people tend to drive more slowly. This reduces the density of potential trafhc conflicts (per minute of driving, say) to a more acceptable level. Should a crash occur, taking more time may reduce the potential for serious injury. This simple relationship is complicated, however, by situations where drivers are caught in very low-risk situations (of serious incidents, at least) for extended periods of time. The urban traffic jam is a typical example. While many potential conflicts are fixed and determined by stable characteristics of the environment, such as intersections, pedestrian crossings, or sharp curves, there are many others that are transient, such as changes in traffic density, parked cars, weather, and lighting conditions. If people take more time to drive through more hazardous situations, time spent driving is likely to be a better measure of exposure to risk than distance; the latter estimate for any given driver or trip will include an unknown mixture of high- and low-conflict road segments. Studies of the characteristics of specific trips (origin-destination, time of day, types of road most frequently used, etc.) might clarify this issue greatly.
Role of
exposure in comparisons
If time is to be used to measure exposure, it must be measured fairly and consistently. In this study, the use of a log encouraged drivers to record times and odometer readings before starting the engine and after parking the vehicle. The beginning and end of any trip is easily measured with an odometer, which stops when the vehicle does, but not with a watch, which does not. When time spent searching for a place to park or time spent entering a stream of heavy traffic from a parking lot is included, this will not alter the odometer figures by much, but may expand trip length appreciably. The result is a reduction in average speed, especially for drivers making many short trips rather than a few long trips in a day. Drivers living in rural areas, who drive more on roads with relatively low densities of fixed traffic conflict, such as stop lights or intersections, and some of the transient conflicts such as parked cars, have lower crash rates. In theory, this should be true of other isolated regions such as northern Ontario. However, a substantial part of the population in this region lives in a few relatively large and urban centres, and both the pattern of exposure and the crash rates reflect the similarity of many northern drivers’ environment with that of urban drivers elsewhere. Among older men and women, crash risk per unit of time behind the wheel was very similar; per unit of distance driven, women appeared to have higher crash risks than men of the same age. This apparent discrepancy is likely due to factors related to average speeds of travel, such as differences in the driving environment, differences in risk tolerance or possibly driving experience. Again, time is likely to be a better measure of exposure than distance to account for such effects. It is clear, however, that without knowledge of both measures of exposure the effects of risk tolerance, environment and similar factors could not be clarified. We had speculated that older drivers would show some increase in vulnerability to injury or death, but this is not apparent in the figures. In part, this is due to the definition of crash severity used in provincial statistics; i.e. the death or injury of at least one person (not necessarily the driver) determines crash severity. Although data exists in the accident report on other
of crash risk
211
people involved in the crashes, including who was killed or injured, it was not part ofthe data considered here. Among younger drivers, men had higher rates than women regardless of which measure of exposure was used; and drivers aged 16-20 of either sex had higher rates than drivers slightly older. These data are consistent with the risks associated with a lack of experience among all new and very young drivers, and perhaps with the acceptance of higher densities oftraffit conflicts, especially among young men drivers.
Acknow/~~~~~~mrn/-Support for this study has come from the Coordinator of Highway Safety Research Grant Program, Ontario Ministry of Transportation, 1990- I99 1.
REFERENCES Armitage, P.; Berry, G. Statistical methods in medical research. 2nd edition. Oxford, U.K.: Blackwell Scientific Publications; 1987. Chipman, M. L. The role of exposure, experience and demerit point levels in the risk of collision. Accid. Anal. Prev. 14:475-483; 1982. Chipman, M. L.; MacGregor, C. G.; Smiley, A. M.; LeeGosselin, M. E. H. Time vs. distance as a measure of exposure in driving surveys. Accid. Anal. Prev. 24:679684; 1992. Hauer, E.; Persaud, B. N.; Smiley, A.; Duncan, D. Estimating the accident potential of an Ontario driver. Accid. Anal. Prev. 23: 133- 152; 199 1. Janke, M. K. Accidents, mileage and the exaggeration of risk. Accid. Anal. Prev. 23: 183-l 88; I99 I. Mayhew, D. R.; Warren, R. A.; Simpson, H. M.; Haas, G. C. Young driver accidents: Magnitude and characteristics of the problem. Ottawa, Ontario: Traffic Injury Research Foundation of Canada; I98 1. Mayhew, D. R.; Simpson, H. M. New to the road. Young drivers and novice drivers: Similar problems and solutions? Ottawa, Ontario: Traffic Injury Research Foundation of Canada; 1990. Risk, A.; Shaoul, J. E. Exposure to risk and the risk of exposure. Accid. Anal. Prev. 14:353-357; 1982. Smiley, A. M.; MacGregor, C.; Lee-Gosselin, M.; Chipman, M. L.; Clifford, L.; Duncan, D. Exposure survey autumn 1988: A study ofthe amount and type ofdriving done by Ontario drivers. SCDO 9 l-l 14. Toronto, Ontario: Safety Co-ordination and Development Office, Ministry of Transportation (Ontario); April 199 I.
‘Department of Preventive Medicine and Biostatistics, University ofToronto, Toronto, Ontario, Canada; ‘Department of Industrial Engineering, University of Toronto, Toronto. Ontario, Canada: jHuman Factors North. Toronto. Ontario, Canada: Wniversit& Laval, Quebec City. Quebec. Canada
Abslraet-Crash
rates based on drivers. driver-kilometres, and driver-days in the denominator were compared, using survey estimates of time and distance driven and the annual frequency of traffic srashes in Ontario. Rates by age, sex, and region were computed for all crashes and for crashes resulting in injury or fatality. Young male drivers remained at high risk for al1 types ~fdenomjnator~ older women hacl high rates when distance was inAded it%the denomjnator. When time spent driving was substituted. men and women drivers over 60 had very similar rates, For ~om~arjsons ofrural residents with urban and northern residents. time and d~stan~egive equivaIent results. These endings suggest that apparent difF&ences in crash risk per kiIometre, whether for older women or for urban drivers. is explained by differences in typical driving speed and environment. Exposure time is better than distance to explain crash risk among drivers and regions with very different driving patterns and environments. When studying the etiology of disease, it is common for epidemiologists to use measures of duration of exposure, such as person-years at risk, as a natural modifier of risk: the value ofsuth measures for the risk of traffic crashes is widety acknowledged: so too, unfortunately, are the probfems of good and retiable estimates. In previous work (fhipman et af. 1992) we exami~~d daily estimates af time spent driving and distance driven to measure exposure to risk. The different patterns among age groups, sex, and region of residence indicated not only appreciable variation in the amount of driving done by these groups in the driving population. but also differences in the average speeds of travel: i.e. some drivers had taken more or less time to travel the same distance. Speed is a function of e~v~r~nmentas weII as the choice ofthe driver_ and is typica!Iy reduced in conditions perceived to be more hazardous. Limited access highways are usuafJy considered “safer” than streets with more opportunities for traffic comlict. yet many cautious drivers avoid this environment. Many deficiencies have been identified in using distance to quantify exposure to crash risk. The rela-
tionship between distance and other aspects ofexposure, which often vary substantially among drivers. is not a simple one. These other aspects include time of day, type of road and speed ~~h~~rnan 1982), the density of traffic conflicts and drivers’ reaction to them {Risk and Shaoul 1982). Other measures or variants ofdistan~e have been considered to measure exposure: Risk and Shaouf f 1982) describe their efforts at identifying and counting hazards on road segments and the close correlation they observed with the frequency of traffic crashes. Janke ( 199 1) has addressed similar issues in examining attempts to modify the estimates of distance (by adding a constant, by treating it as a muttiplicative factor, etc.) to iflustrate the lack of proportionality in the re~at~o~sh~~ between simple distance driven and collision risk. She considered the use of “induced exposure”. based on the characteristics of drivers involved in traffic crashes but not at fault, might be more promising. As the driving populations of Europe and North America shift to include a larger proportion of older people, any increase in crash risk-however measured-in these age groups is viewed with alarm by public health and driver licensing ofhciais ahke (Jan ke I99 i ). The concern is analogous to the more famihar concerns for the increased risks of younger
208
M. L. CHIPMAN et al.
drivers. Whether the units are population, driver-distance, or driver-time, the exposure of drivers at both extremes of age is typically less than the exposure of established, middle-aged drivers, so that crash rates based on exposure can change dramatically (Mayhew et al. 198 1). Thus rates based on the population of drivers typically decrease with increasing age, and are higher for men than women. For rates based on driver-kilometres, there may be a U-shaped curve, highest at the extremes of age? where exposure is lowest, and lowest for the middle-aged driver, as noted by Janke ( 199 1). Thus, when exposure to risk is included in the calculations, the changes in hazard with age would be enlarged for young people, and might be reversed for older drivers. There is, in addition, a growing concern for drivers who are newly licensed and not necessarily also young (Mayhew and Simpson 1990). Examples include women who acquire a driver’s license at a later age than men, and immigrants, a growing proportion of whom arrive in Canada from countries with very different opportunities to drive and very different road environments. Exposure patterns in these new drivers are likely to be different both from those of younger drivers who are also recently licensed and from those of established drivers of comparable age and sex. Comparisons between men and women drivers are plagued by great differences in the type as well as the amount of driving (Chipman 1982) which make interpretation of well-known differences in collision risk difficult. If women cover shorter distances, in different environments, are they at greater, less, or the same risk of trafic crash per kilometre or per hour as men? fn data collected in 1988 from a survey of Ontario drivers, it has been possible to obtain measures of exposure in terms of both time and distance. Previous work has shown that the differences in exposure between men and women drivers, among age groups or among drivers living in urban, rural, or northern areas of Ontario are not the same for time spent driving as they are for distance (Chipman et al. 1992). In this paper we will compare crash rates among drivers by age, sex, and place of residence to see whether time. distance, or some combination is more appropriate to describe drivers’ exposure to the risk of traffic crash. METHODS The survey of Ontario drivers on which these analyses are based was carried out in October 1988 and has been reported elsewhere (Smiley et al. 199 1). Briefly, a random sample, stratified by age group, sex,
and region of the province, was drawn from the file of licensed drivers: the strata included six age groups and the regions of Northern Ontario, rural southern Ontario, and urban southern Ontario. Thirty-six strata were required to include all combinations of age group, sex, and region. The 330-335 drivers selected in each stratum were sent a questionnaire which included (i) questions about how and when they learned to drive and other demographic details and (ii) a trip log to record the driving they did in one or three specihc days of the week. Drivers who provided both time and distance, but whose responses indicated travel at exceptionally low or high speeds, were excluded as providers of dubious data. This criterion is identical to that used in an earlier paper (Chipman et al. 1992). Drivers reporting time or distance but not both were also excluded as providers of potentially dubious data. Drivers reporting zero for both time and distance were included in these analyses, as valid nondrivers in the survey interval. The means of the times spent driving and the distances driven per day were calculated for each stratum. The mean daily distance and time spent driving were used to estimate the annual driver-kilometres and driver days for all drivers in the population of each stratum. Accident data, in the form of annual figures for each stratum, were obtained from provincial records for 1988. Estimates of population size for the denominator came from two sources. For the first source, the population size was calculated from the sampling fraction for each stratum used in the original sampling plan. These data were sufficient to compare drivers from different regions, but were not reliable for other comparisons, especially among age groups. It was apparent, from the pattern of response rates to the survey, that the driver file from which the sample was drawn had not been purged for some time of drivers who had died, moved, or not renewed their license. This problem left regional comparisons relatively unaffected, but resulted in the underestimation of rates for older and younger drivers, since older drivers were more likely to have died and younger drivers were more likely to have moved. For these comparisons other estimates of population size were required. The second source was a previous study that had used the provincial driver records (Hauer et al. i 99 1); crash and population data were available by age and sex (but not region) for drivers who had renewed their license in a specific year. These data, together with crash data and weighted averages of the exposure data to reflect diff‘erences in population size in the three regions, were used for age- and sex-specific crash rates.
Role of exposure in comparisons of crash risk
Annual crash rates by stratum were calculated per 100 drivers, per million driver-kilometres, and per 1,000 driver-days, where a “day” is taken as 24 hours. All crashes, whether resulting in fatality, personal injury, or property damage were considered; the analyses were repeated considering only fatal and inju~-producing events. Age- and sex-specific crash rates were calculated dim&y, with a standard error and 95% confidence interval derived for the ratio estimate of crashes per driver-exposure as described by Armitage and Berry f 1987). Adjusted crash rates based on the linear regression model were computed for each region by type of crash; regions were compared controlling for age and sex of drivers in each stratum. The comparisons were tested using standard F or t-tests as appropriate. RESULTS There was a total of 3, t 58 (85.7%) survey respondents with ‘“non-dubious” data, from the 3,586 respondents to this survey. The numbers and the means for daily time and distance of these drivers are shown in Table 1. The numbers of drivers with “non-dubious” data were highest for middle-aged drivers and dropped most markedly for drivers 80 years old or more. Since the samples selected were of equal size in each stratum, this is largely a reflection of differences in response rate, which was 36% overall. but varied from 13% to 54% in individual strata (Smiley et al. 199 I >. As noted in ear-her work (Chipman et aI_ 19X?), both time and distance vary dramaticalfy between age groups and between men and women drivers. Between regions, drivers in the north and urban areas ofthe southern part ofthe province appear similar, with drivers in rural parts of southern Ontario
209
spending both more time on the road and covering greater distances. ‘The rates by age group and sex for each denominator are shown in Table 2 for all crashes. Traditional crash rates, per 100 licensed drivers, exhibit the familiar pattern of high values for young men, decreasing or remaining stable with increasing age. Rates for drivers over 80 (a highly select group in Ontario, due to stringent licensing requirements for drivers ofthis age) are the only ones to reach or exceed the rates for young drivers. Rates are substantially higher at each age for men than for women. When distance is included in the denominator, the pattern remains the same for young drivers but changes for older drivers. Most noteworthy is that rates are now consistently (and substantially) higher for older women than for older men. The confidence intervals are wider for these rates than for the simple basic rates: the addition of variable exposure data to the denominator has affected the precision of each estimate, The rates per thousand driver-days exhibit a similar pattern among age groups for each sex, with retatively high rates for the very young and the very old. The difference between men and women drivers of the same age, however, is much reduced for older drivers and more marked for younger drivers. These rates are for all crashes, regardless of severity. The increased frailty of elderly people and the associated increase in the risk of injury even in relatively minor colhsions, together with the association of speed with more severe outcomes, led us to consider that these patterns might be different if comparisons were restricted to crashes with severe conseTable 2. Annual total crash rates per driver, driver-km, and driverday (with 95% confidence limits)
Table I, Summary ofdaily time and distanceestimates or region -Drivers Sex and age groups Men 16-19 20-74 25-B 60-69 70-79 go+ Women 16 19 20-24 25-59 60-69 70-79 HI+ Regions of Ontario Northern Southern rural Southern urban
by age, sex. Denominator
Time (min)
Distance (km)
256 234 338 349 260 76 301 275 318 355 245 91
43.9 61.3 74.9 64.0 54.0 29.9 31.8 42.0 42.2 38.3 29.8 24.8
35.9 52.7 50.2 44.3 38.1 27.5 22.6 31.3 29.9 19.2 14.2 14.1
1024 f I64 970
45.8 54.8 46.6
32.8 44.0 31.2
Age
Men
Women
Per 100 drivers
16-19 20-24 25-59 60-69 K-79 xl)+
11.6 9.5 7.1 4.2 5.3 7.2
+ 0.4 f 0.2 ?!I0.t + 0.1 -t 0.2 i 0.5
4.8 4.1 3.2 2.1 3.1 6.0
Per Id driver-kifometres
16-19 20-24 25-59 60-69 IO-79 so+
8.8 4.9 3.9 3.1 3.1 5.9
ri: 2.9 I I.5 5 0.5 f 0.6 -t 1.7 I 4.8
5.8 3.6 2.3 4.1 5.4 IO.1
i- I.8 t 1.1 4 0.8 IL I.0 + I.9 I 6.9
Per IO’ driver-days
16-19 20-24 25-59 60-69 X-79 8Oi
IO.4 * 3.2 6.1 + I.7 3.6 I? 0.8 2.6 + 0.5 3.9 z!I 1.f 9.5 + 5.1
6.0 3.9 3.0 2.2 4.1 9.6
ri: 1.8 i 1.2 rt 0.7 -t- 0.6 t 1.4 J- 5.3
rt i: + t ir
0.3 0.2 0.1 0.1 0.2 0.9
210
M. L. CHIPMANet al
Table 3. Annual severe crash rates per driver, driver-km, and driver-day (with 95% confidence limits) Denominator
Age
Men
Women
Per 100 drivers
16-19 20-24 25-59 60-69 70-79 80-t
4.09 3.42 2.43 1.37 1.73 2.41
I!Z0.24 I!I 0.14 + 0.07 + 0.05 Yc0.09 + 0.31
1.78 1.55 1.18 0.70 1.03 2.05
?z 0.18 t 0.10 + 0.05 !z 0.04 + 0.09 + 0.54
Per IO”driver-kilometre
16-19 20-24 25-59 60-69 70-79 80+
3.12 1.78 1.33 0.85 1.24 2.40
i 1.05 I!I 0.54 I 0.34 t 0.17 + 0.68 rt 1.98
2.15 1.36 1.08 1.00 2.00 4.00
It t i i t 2
Per 1O-’driver-days
16-19 20-24 25-59 60-69 70-79 80+
3.67 2.19 1.28 0.85 1.26 3.18
t 1.13 + 0.62 + 0.39 I!K0. I5 + 0.37 + 1.72
2.20 1.46 1.10 0.73 1.36 3.27
+ 0.68 f 0.45 Y!I0.27 + 0.20 I!Z0.46 I!I 1.96
0.69 0.43 0.30 0.26 0.73 2.85
quences. Comparable sets of crash rates, restricted to crashes resulting in death or injury to at least one person, are found in Table 3. Rates for crashes with severe consequences exhibited the same pattern as for all crashes. Per 100 licensed drivers, men had higher rates than women, and there was a steady decline in the rates with age in both sexes, at least to age 80. When the population at risk is modified by the inclusion of time or distance, the rates continue to exhibit a U-shaped curve, with high rates at both extremes of age. Women over 80 and men under 20 have the highest risk per unit of time or distance. As with the rates for all crashes, the age-specific rates for severe crashes exhibit differences between men and women drivers per million kilometres that virtually disappear for older drivers per thousand hours of driving. Total and severe crash rates by region, adjusting for age group and sex, are given in Table 4. There are no significant regional differences when only the
Table 4. Annual crash rates by region of residence Crashes by region
Per 100 drivers
Per IO6 dr-km
Per IO’ dr-day
All Northern Southern rural Southern urban P-value (F test)
5.3 4.8 5.6 0.4126
5.2 3.6 5.2 0.0019
5.5 3.1 5.5 0.0089
Severe Northern Southern rural Southern urban P-value (F test)
1.67 1.66 1.99 0.1969
1.71 I .08 I .95 0.0040
1.62
1.24 1.87 0.0135
number of licensed drivers is considered, but crash rates per million kilometres or per thousand days indicate a significantly lower rate for drivers in rural areas. The rates for all crashes in urban or northern regions are 46% higher than in rural regions when distance is included, and are 75% higher when time is used. Comparable data for injury- and fatality-producing accidents exhibit similar differences, although there is a greater difference between urban and northem regions than was apparent for rates based on distance driven. There is very little evidence of difference by place of residence among the crash rates per 100 drivers. There are significant differences among the three regions for crash rates per million kilometres (P = 0.004) and per thousand driver-days (P = 0.014). The major deviation among the rates is due to the strata of drivers living in rural areas in the southern, more densely settled part ofthe province, whose rates are roughly half to three-quarters the rates of urban or northern strata. DISCUSSION
AND
CONCLUSIONS
It is clear that differences in exposure must be considered in making comparisons of crash risk for different groups of drivers. Furthermore, time and distance-the simplest and most direct measures available-are not equivalent for many useful comparisons. The time taken by drivers to cover a given distance is, among other things, a reflection of perceived risk. In heavy traffic, in poor weather or lighting conditions, or negotiating a twisted section of roadway, people tend to drive more slowly. This reduces the density of potential trafhc conflicts (per minute of driving, say) to a more acceptable level. Should a crash occur, taking more time may reduce the potential for serious injury. This simple relationship is complicated, however, by situations where drivers are caught in very low-risk situations (of serious incidents, at least) for extended periods of time. The urban traffic jam is a typical example. While many potential conflicts are fixed and determined by stable characteristics of the environment, such as intersections, pedestrian crossings, or sharp curves, there are many others that are transient, such as changes in traffic density, parked cars, weather, and lighting conditions. If people take more time to drive through more hazardous situations, time spent driving is likely to be a better measure of exposure to risk than distance; the latter estimate for any given driver or trip will include an unknown mixture of high- and low-conflict road segments. Studies of the characteristics of specific trips (origin-destination, time of day, types of road most frequently used, etc.) might clarify this issue greatly.
Role of
exposure in comparisons
If time is to be used to measure exposure, it must be measured fairly and consistently. In this study, the use of a log encouraged drivers to record times and odometer readings before starting the engine and after parking the vehicle. The beginning and end of any trip is easily measured with an odometer, which stops when the vehicle does, but not with a watch, which does not. When time spent searching for a place to park or time spent entering a stream of heavy traffic from a parking lot is included, this will not alter the odometer figures by much, but may expand trip length appreciably. The result is a reduction in average speed, especially for drivers making many short trips rather than a few long trips in a day. Drivers living in rural areas, who drive more on roads with relatively low densities of fixed traffic conflict, such as stop lights or intersections, and some of the transient conflicts such as parked cars, have lower crash rates. In theory, this should be true of other isolated regions such as northern Ontario. However, a substantial part of the population in this region lives in a few relatively large and urban centres, and both the pattern of exposure and the crash rates reflect the similarity of many northern drivers’ environment with that of urban drivers elsewhere. Among older men and women, crash risk per unit of time behind the wheel was very similar; per unit of distance driven, women appeared to have higher crash risks than men of the same age. This apparent discrepancy is likely due to factors related to average speeds of travel, such as differences in the driving environment, differences in risk tolerance or possibly driving experience. Again, time is likely to be a better measure of exposure than distance to account for such effects. It is clear, however, that without knowledge of both measures of exposure the effects of risk tolerance, environment and similar factors could not be clarified. We had speculated that older drivers would show some increase in vulnerability to injury or death, but this is not apparent in the figures. In part, this is due to the definition of crash severity used in provincial statistics; i.e. the death or injury of at least one person (not necessarily the driver) determines crash severity. Although data exists in the accident report on other
of crash risk
211
people involved in the crashes, including who was killed or injured, it was not part ofthe data considered here. Among younger drivers, men had higher rates than women regardless of which measure of exposure was used; and drivers aged 16-20 of either sex had higher rates than drivers slightly older. These data are consistent with the risks associated with a lack of experience among all new and very young drivers, and perhaps with the acceptance of higher densities oftraffit conflicts, especially among young men drivers.
Acknow/~~~~~~mrn/-Support for this study has come from the Coordinator of Highway Safety Research Grant Program, Ontario Ministry of Transportation, 1990- I99 1.
REFERENCES Armitage, P.; Berry, G. Statistical methods in medical research. 2nd edition. Oxford, U.K.: Blackwell Scientific Publications; 1987. Chipman, M. L. The role of exposure, experience and demerit point levels in the risk of collision. Accid. Anal. Prev. 14:475-483; 1982. Chipman, M. L.; MacGregor, C. G.; Smiley, A. M.; LeeGosselin, M. E. H. Time vs. distance as a measure of exposure in driving surveys. Accid. Anal. Prev. 24:679684; 1992. Hauer, E.; Persaud, B. N.; Smiley, A.; Duncan, D. Estimating the accident potential of an Ontario driver. Accid. Anal. Prev. 23: 133- 152; 199 1. Janke, M. K. Accidents, mileage and the exaggeration of risk. Accid. Anal. Prev. 23: 183-l 88; I99 I. Mayhew, D. R.; Warren, R. A.; Simpson, H. M.; Haas, G. C. Young driver accidents: Magnitude and characteristics of the problem. Ottawa, Ontario: Traffic Injury Research Foundation of Canada; I98 1. Mayhew, D. R.; Simpson, H. M. New to the road. Young drivers and novice drivers: Similar problems and solutions? Ottawa, Ontario: Traffic Injury Research Foundation of Canada; 1990. Risk, A.; Shaoul, J. E. Exposure to risk and the risk of exposure. Accid. Anal. Prev. 14:353-357; 1982. Smiley, A. M.; MacGregor, C.; Lee-Gosselin, M.; Chipman, M. L.; Clifford, L.; Duncan, D. Exposure survey autumn 1988: A study ofthe amount and type ofdriving done by Ontario drivers. SCDO 9 l-l 14. Toronto, Ontario: Safety Co-ordination and Development Office, Ministry of Transportation (Ontario); April 199 I.