- Email: [email protected]
‘Low mileage bias’ and related policy implications—A cautionary note
Accident Analysis and Prevention 40 (2008) 1249–1252
Short communication
‘Low mileage bias’ and related policy implications—A cautionary note Loren Staplin a,∗ , Kenneth W. Gish a , John Joyce b a
b
TransAnalytics, LLC 1722 Sumneytown Pike, Box 328, Kulpsville, PA 19443, USA Maryland Motor Vehicle Administration, 6601 Ritchie Hwy NE, Glen Burnie, MD 21062, USA Received 27 June 2007; received in revised form 26 October 2007; accepted 29 October 2007
Abstract This research examined a relationship asserted in recent literature in the field of traffic safety and injury prevention—that a significant indicator for elevated crash risk among older drivers, and potential trigger for individualized assessment at license renewal, is a low (<3000 km) annual driving distance. Sampling problems in earlier reports, in particular a reliance on self-report measures of both exposure and crash involvement, are highlighted. A pattern of misestimation for those who self-report an extremely low or extremely high number of miles driven is documented, that casts serious doubt upon the effect reported earlier. The present findings underscore the need for objective exposure measures for future analyses of this nature, and impact discussions about the feasibility of this suggested strategy to aid detection of at-risk older drivers by licensing officials. © 2007 Elsevier Ltd. All rights reserved. Keywords: Older driver; Safety; Crash risk; Exposure; Licensing
1. Introduction Licensing officials seek practical policies that can identify individuals at increased crash risk. One group that has been broadly associated with increased crash risk is older drivers. This association has been supported by the familiar “U-shaped curve” indicating a higher fatal and injury crash rate per-miledriven for the oldest (as well as the youngest) driver cohorts (IIHS, 2005). At the same time, there is an increasing prevalence of impairments in key functional abilities needed to drive safely (e.g., vision, cognition) with increasing age (cf. AMA, 2003), and significant relationships between such declines and ‘at-fault’ crashes have been found among a representative sample of older drivers (Staplin et al., 2003). It has been noted that the relationship between age and fatal crashes is influenced by a “frailty bias,” whereby more incidents of a common magnitude will result in death or serious injury for older persons than for younger persons. Even taking this into account, however, analyses have continued to show higher crash rates for older drivers (Li et al., 2003). More recently, researchers have asserted a “low mileage bias” (LMB) to explain the apparent overinvolvement of older drivers in motor vehicle crashes, concluding that only a small subset of seniors are at high risk – those who drive the
∗
Corresponding author. Tel.: +1 215 855 5380; fax: +1 215 855 5381. E-mail address: [email protected] (L. Staplin).
0001-4575/$ – see front matter © 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2007.10.012
fewest miles – and should be candidates for special scrutiny at license renewal (Langford et al., 2006a). A closer examination of LMB addresses the reliability of the data sources that are at the foundation of the supporting analyses, and the impact on analysis outcomes of taking identified limitations in data quality into consideration. Langford et al.’s work above (2006a) and a companion report (Langford et al., 2006b) analyzed travel survey data containing drivers’ selfreports of annual driving distances and crash experience. The authors parsed the driver survey data according to exposure and age. Specifically, drivers were sorted into five age groups (18–20, 21–30, 31–64, 65–74, and 75+), and further segregated into low (<3000 km), medium (3000–14,000 km), and high (>14,000 km) mileage drivers within each group. Graphs were then plotted for each age group-by-mileage combination, based on their selfreported crashes in the prior 1-year (Langford et al., 2006b) or 2-year (Langford et al., 2006a) period. The result describes a figure with three curves, one for low mileage drivers, one for medium mileage drivers, and one for high mileage drivers. Fig. 1 reproduces the curves from Langford et al. (2006b, Table 1), also showing the upper and lower limits of the confidence intervals for the subjective crash rate data. As shown, the low mileage drivers are associated with the highest crash rates, the high mileage drivers are associated with the lowest crash rates, and the medium mileage drivers are in between. Also, the crash rate graph for each of these groups demonstrates a clear downward slope from the 18–20 age group to the 31–64 age group,
1250
L. Staplin et al. / Accident Analysis and Prevention 40 (2008) 1249–1252
Fig. 1. Variation in self-reported crash rates by age, including 95% confidence intervals, for low (<3000 km), medium (3000–14,000 km), and high (>14,000 km) distance drivers, based on self-reported exposure (from Langford et al., 2006a,b).
then levels off; except the plot for low mileage drivers, which spikes upward again for the oldest age group. While this descriptive analysis appears to localize elevated crash risk among older drivers to those who drive the fewest miles, in fact, none of the crash rate comparisons between the 75+ age group and their younger counterparts were statistically significant. This result was attributed to the wide confidence intervals associated with low sample sizes in the ∼47,500-driver sample analyzed by Langford et al. (2006b); the ∼1000-driver sample in Langford et al. (2006a) was presumably even more problematic in this respect. Of greater concern, however, is the lack of an objective gauge of the accuracy of the data included in these descriptive analyses. As already noted, all driver exposure data and all crash data included in these analyses were derived from self-reports; in addition, the samples providing these reports were self-selected. The ∼47,500-driver sample was comprised of respondents at the final stage of a multi-year, multistage survey process. In one year (2003) for which the authors provide details about the selection process, 8569 persons out of 83,708 initially queried by mail ultimately contributed data to the analyses. No information is available about the non-respondents to the initial query, hence the representativeness of the analysis sample is unknown. Setting aside evidence of disagreement between drivers’ selfreports of crash involvement and state crash records (cf. McGwin et al., 1998), this brief communication concentrates on the reliability of self-reported exposure data. The consistency of exposure estimates within the same drivers is examined first. Next, a perspective on the reliability of such data is offered by analyses that provide more detailed information about the magnitude and direction of error that has been shown to contaminate self-reported exposure data. The purported LMB is then reexamined in this context. 2. Methods and results Evidence of bias in mileage estimation was identified both in the technical literature and through analyses of databases maintained by the State of Maryland. The state-maintained databases
allowed comparisons between self-reported mileage estimates made by the same individuals (a) at different points in time and under different circumstances, and (b) at the same point in time using alternative estimating procedures. In the first case, the Maryland Motor Vehicle Administration (MVA) has created an Emission Exemption Database (EED) that identifies all individuals (vehicle owners and co-owners) age 70 and older who qualify for an exemption from emission testing, based on their self-reports that the subject vehicle is driven less than 5000 miles per year. This database contains 90,316 drivers who so qualified for this exemption in 2005; to place this number in context, there was a total of 335,787 licensed drivers age 70 and above in 2005. Among the drivers in the EED is a subset of individuals who also were participants in the Maryland Pilot Older Driver Study (see Staplin et al., 2003). This study included a driving history questionnaire, also with a mileage bin of 5000 or less. A comparison of categorical mileage estimations for 331 individuals age 75+ who provided self-reports to both queries in the same timeframe, using the Maryland Soundex number (driver record number) as the matching criterion, was performed for this report. The analyses using the subset of MVA EED drivers who also participated in the Maryland Pilot Older Driver Study (MaryPODS) examined intra-rater consistency, across both survey instruments, for the 331 individuals who self-certified that they drove more or less than 5000 miles per year. Results indicated that, of the 138 individuals who were classified as driving less than 5000 miles per year in the EED, 41 or roughly 30% reported driving more than 5000 miles per year in the MaryPODS survey. It deserves emphasis that there is a financial incentive in being exempt from emissions inspection, while this source of potential bias is absent in the MaryPODS survey. Normally, a known source of bias might exclude a data set; but here it underscores threats to the reliability of self-reported exposure data due to situational influences. In the second case, the aforementioned Maryland Pilot Older Driver Study requested self-reports of driving exposure using two question formats—one based on weekly miles driven and the other on annual miles driven. The internal reliability of drivers’ exposure estimates was gauged by comparing the annual-milesdriven figures with an extension of the miles per week estimates (i.e., multiplied by 52). This multiplication was performed, then the product was divided by the estimate of annual miles driven for each person in the sample (n = 1868). A “percent error” score was yielded by this procedure. Problems with the reliability of self-reported exposure data were found in comparing the weekly versus annual mileage estimates. A discrepancy between the same drivers’ estimates of miles driven when asked the same question in two different ways is shown in Fig. 2. Over 10% of the sample provided responses characterized by over 100% error, and a 50% error rate was demonstrated in over 40% of the responses. Finally, data sources were sought which offered the potential to compare self-reported mileage with objective records of miles traveled for the same drivers. These included studies comparing drivers’ subjective exposure estimates with miles driven as recorded by global positioning system (GPS) devices in their
L. Staplin et al. / Accident Analysis and Prevention 40 (2008) 1249–1252
Fig. 2. Discrepancy between the same drivers’ estimates of miles driven on a weekly versus annual basis.
vehicles, for the same trips (see Ogle, 2005; Battelle, 1997). Unfortunately, the sampling and data reporting strategies in these studies preclude sorting into the age-by-mileage categories of interest in this paper; but both studies demonstrated overestimation (i.e., of self-reported exposure) by the highest mileage drivers and underestimation by the lowest mileage drivers, with underestimation also linked specifically to a travel pattern comprised of frequent but short trips (Ogle, 2005). More helpful are figures from the most recent National Household Travel Survey (NHTS) for which data are available (2001), that provides comparisons of self-reported mileage with odometer readings. Two NHTS data tables were downloaded, PERPUB and VEHPUB. All values from PERPUB were used. Only the values from VEHPUB were used that passed filters designed to exclude records where there are few cars and many drivers, or where an individual was the primary driver for more than one vehicle. Specifically, thresholds of ≥0.5 and ≤1.5 were set for the ratio of number of vehicles to number of drivers for a given household. This provided reasonable assurance that the annual mileage variables analyzed were based on the primary driver of the vehicle. Applying these thresholds retained 11,013 drivers for the present analysis. The mileage variables used were Annualzd (annual mileage based on odometer reading) and AnnMiles (annual mileage based on self-report). Finally, filters were applied to ensure that Annualzd ≥ 0; AnnMiles ≥ 0; and R Age > 0 (age of driver). The NHTS data were sorted into bins corresponding to the Langford et al. age and mileage categories (see Section 1 above) for analysis, and curves expressing the percent error in mileage estimates were plotted for the ‘low,’ ‘medium,’ and ‘high’ exposure groups, as defined previously. Percent error was calculated as [(self-report miles) − (odometer miles)/(self-report miles)] × 100. Analyses using the NHTS data first segregated the downloaded records into the same age bins and mileage groups utilized by Langford et al. Annual vehicle miles traveled (VMT), based on odometer readings, were then compared to selfreported miles. An underestimation bias for the lowest mileage group (ratio of VMT via odometer divided by VMT via selfreport) of approximately 1.8 was found. There is also a slight overestimation for the highest mileage group. Curves depicting
1251
Fig. 3. Magnitude and direction of error in self-reports of driving exposure calculated from National Household Travel Survey data, for individuals in identified age cohorts with low (<3000 km), medium (3000–14,000 km), and high (>14,000 km) annual driving distance.
the percent error in self-reported exposure for each age group of interest are shown in Fig. 3, for low, medium, and high mileage drivers. To a striking degree, the mileage estimation errors revealed in these plots inversely mirror the relationships highlighted by Langford et al. (refer to Fig. 1). This pattern of misestimation, if manifested in the Langford et al. data, would reduce the curves plotted by these authors, and substantially lessen the apparent effect that they attribute to LMB. 3. Discussion This research note addressed an alleged LMB in studies of driver age and crash risk, whereby increased crash risk may be localized to a subset of older individuals who drive the fewest miles. Implications for licensing policy are also advanced in research published recently by Langford et al. (2006a,b), by invoking differences in subjective (self-reported) driving exposure to explain differences in crash rates. While some interesting correlations are reported in the LMB papers, other researchers (e.g., Janke, 1991) who have observed similar phenomena appear to regard such mileage differences as an artifact of the differences in road type, geography, and other aspects of the (chosen) operational contexts that differentiate drivers, whatever their age. However, it is the questionable reliability of the reported exposure differences themselves that is most troublesome for the advocates of LMB. Numerous examples from the technical literature – in addition to the analysis outcomes highlighted herein – attest to the unreliability of subjective exposure estimates, generally. A more specific problem of underestimation by low mileage drivers and overestimation by high mileage drivers has also been indicated in sources identified in this report. While this evidence does not disprove the LMB hypothesis, its credibility must be challenged because of the unstable foundation upon which it rests. A clear recommendation to advance research in this area is to include objective measures of driver exposure in future
1252
L. Staplin et al. / Accident Analysis and Prevention 40 (2008) 1249–1252
risk analyses. But it may be of greatest importance to speak to the assertion that LMB should receive consideration vis-`avis new policies and programs for identifying high-risk drivers among license renewal applicants. Even as research initiatives are announced1 to quantify the safety benefits from brief screens for key visual and cognitive deficits at renewal, proponents of “low mileage bias” suggest that additional requirements for relicensing among seniors, if any, could more efficiently be targeted to those who drive the least. On the surface, the apparent simplicity of this approach is appealing. But on closer examination, a practical application of LMB to the driver licensing process – as well as the scientific basis for LMB – seems dubious. By what means could a licensing authority acquire the needed information to employ a low mileage screen? Certainly not by self-report. But if not, then by what society-wide monitoring system, that would be capable of tracking exposure at the individual level, and that would be politically viable? As unlikely as it is for this capacity to become available to licensing officials, its application also would run counter to the regulatory approach that traditionally reserves sanctions for unwanted behavior. Self-regulation among at-risk drivers is universally promoted as a desirable behavior; yet, with a low mileage screen, those who limit their driving the most would incur greater scrutiny and a higher likelihood of license restriction/revocation than, for example, a cognitively impaired but more active driver who denies or is unaware of his/her limitations.
1 “DMV giving new license tests a spin”, Sacramento Bee, Metro Section, May 21, 2007.
References AMA, 2003. Physician’s Guide to Assessing and Counseling Older Drivers. American Medical Association, Chicago, IL. Battelle Transportation Division, 1997. Global Positioning Systems for Personal Travel Surveys, Lexington Area Travel Data Collection Test. Final Report to Office of Highway Information Management. Federal Highway Administration, Washington, DC. IIHS, 2005. Insurance Institute for Highway Safety analysis of data from the U.S.DOT Fatality Analysis Reporting System and the National Household Travel Survey. Source: http://www.iihs.org/research/fatality facts/ olderpeople.html. Janke, M., 1991. Accidents, mileage, and the exaggeration of risk. Accid. Anal. Prev. 23, 183–188. Langford, J., Koppel, S., Charlton, J., Fildes, B., Newstead, S., 2006a. A reassessment of older drivers as a road safety risk. IATSS Res. 20 (1), 27–37. Langford, J., Methorst, R., Hakamies-Blomqvist, L., 2006b. Older drivers do not have a high crash risk—a replication of low mileage bias. Accid. Anal. Prev. 38, 574–578. Li, G., Braver, E., Chen, L., 2003. Fragility versus excessive crash involvement as determinants of high death rates per vehicle mile of travel among older drivers. Accid. Anal. Prev. 35 (2), 227–235. McGwin, G., Owsley, C., Ball, K., 1998. Identifying crash involvement among older drivers: agreement between self-reports and state records. Accid. Anal. Prev. 30 (6), 781–791. Ogle, J., 2005. Quantitative Assessment of Driver Speeding Behavior Using Instrumented Vehicles. Doctoral Dissertation, Georgia Tech University, Department of Civil Engineering. Staplin, L., Gish, K., Wagner, E., 2003. MaryPODS revisited: updated crash analysis and implications for screening program implementation. J. Saf. Res. 34 (4), 389–397.
Short communication
‘Low mileage bias’ and related policy implications—A cautionary note Loren Staplin a,∗ , Kenneth W. Gish a , John Joyce b a
b
TransAnalytics, LLC 1722 Sumneytown Pike, Box 328, Kulpsville, PA 19443, USA Maryland Motor Vehicle Administration, 6601 Ritchie Hwy NE, Glen Burnie, MD 21062, USA Received 27 June 2007; received in revised form 26 October 2007; accepted 29 October 2007
Abstract This research examined a relationship asserted in recent literature in the field of traffic safety and injury prevention—that a significant indicator for elevated crash risk among older drivers, and potential trigger for individualized assessment at license renewal, is a low (<3000 km) annual driving distance. Sampling problems in earlier reports, in particular a reliance on self-report measures of both exposure and crash involvement, are highlighted. A pattern of misestimation for those who self-report an extremely low or extremely high number of miles driven is documented, that casts serious doubt upon the effect reported earlier. The present findings underscore the need for objective exposure measures for future analyses of this nature, and impact discussions about the feasibility of this suggested strategy to aid detection of at-risk older drivers by licensing officials. © 2007 Elsevier Ltd. All rights reserved. Keywords: Older driver; Safety; Crash risk; Exposure; Licensing
1. Introduction Licensing officials seek practical policies that can identify individuals at increased crash risk. One group that has been broadly associated with increased crash risk is older drivers. This association has been supported by the familiar “U-shaped curve” indicating a higher fatal and injury crash rate per-miledriven for the oldest (as well as the youngest) driver cohorts (IIHS, 2005). At the same time, there is an increasing prevalence of impairments in key functional abilities needed to drive safely (e.g., vision, cognition) with increasing age (cf. AMA, 2003), and significant relationships between such declines and ‘at-fault’ crashes have been found among a representative sample of older drivers (Staplin et al., 2003). It has been noted that the relationship between age and fatal crashes is influenced by a “frailty bias,” whereby more incidents of a common magnitude will result in death or serious injury for older persons than for younger persons. Even taking this into account, however, analyses have continued to show higher crash rates for older drivers (Li et al., 2003). More recently, researchers have asserted a “low mileage bias” (LMB) to explain the apparent overinvolvement of older drivers in motor vehicle crashes, concluding that only a small subset of seniors are at high risk – those who drive the
∗
Corresponding author. Tel.: +1 215 855 5380; fax: +1 215 855 5381. E-mail address: [email protected] (L. Staplin).
0001-4575/$ – see front matter © 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2007.10.012
fewest miles – and should be candidates for special scrutiny at license renewal (Langford et al., 2006a). A closer examination of LMB addresses the reliability of the data sources that are at the foundation of the supporting analyses, and the impact on analysis outcomes of taking identified limitations in data quality into consideration. Langford et al.’s work above (2006a) and a companion report (Langford et al., 2006b) analyzed travel survey data containing drivers’ selfreports of annual driving distances and crash experience. The authors parsed the driver survey data according to exposure and age. Specifically, drivers were sorted into five age groups (18–20, 21–30, 31–64, 65–74, and 75+), and further segregated into low (<3000 km), medium (3000–14,000 km), and high (>14,000 km) mileage drivers within each group. Graphs were then plotted for each age group-by-mileage combination, based on their selfreported crashes in the prior 1-year (Langford et al., 2006b) or 2-year (Langford et al., 2006a) period. The result describes a figure with three curves, one for low mileage drivers, one for medium mileage drivers, and one for high mileage drivers. Fig. 1 reproduces the curves from Langford et al. (2006b, Table 1), also showing the upper and lower limits of the confidence intervals for the subjective crash rate data. As shown, the low mileage drivers are associated with the highest crash rates, the high mileage drivers are associated with the lowest crash rates, and the medium mileage drivers are in between. Also, the crash rate graph for each of these groups demonstrates a clear downward slope from the 18–20 age group to the 31–64 age group,
1250
L. Staplin et al. / Accident Analysis and Prevention 40 (2008) 1249–1252
Fig. 1. Variation in self-reported crash rates by age, including 95% confidence intervals, for low (<3000 km), medium (3000–14,000 km), and high (>14,000 km) distance drivers, based on self-reported exposure (from Langford et al., 2006a,b).
then levels off; except the plot for low mileage drivers, which spikes upward again for the oldest age group. While this descriptive analysis appears to localize elevated crash risk among older drivers to those who drive the fewest miles, in fact, none of the crash rate comparisons between the 75+ age group and their younger counterparts were statistically significant. This result was attributed to the wide confidence intervals associated with low sample sizes in the ∼47,500-driver sample analyzed by Langford et al. (2006b); the ∼1000-driver sample in Langford et al. (2006a) was presumably even more problematic in this respect. Of greater concern, however, is the lack of an objective gauge of the accuracy of the data included in these descriptive analyses. As already noted, all driver exposure data and all crash data included in these analyses were derived from self-reports; in addition, the samples providing these reports were self-selected. The ∼47,500-driver sample was comprised of respondents at the final stage of a multi-year, multistage survey process. In one year (2003) for which the authors provide details about the selection process, 8569 persons out of 83,708 initially queried by mail ultimately contributed data to the analyses. No information is available about the non-respondents to the initial query, hence the representativeness of the analysis sample is unknown. Setting aside evidence of disagreement between drivers’ selfreports of crash involvement and state crash records (cf. McGwin et al., 1998), this brief communication concentrates on the reliability of self-reported exposure data. The consistency of exposure estimates within the same drivers is examined first. Next, a perspective on the reliability of such data is offered by analyses that provide more detailed information about the magnitude and direction of error that has been shown to contaminate self-reported exposure data. The purported LMB is then reexamined in this context. 2. Methods and results Evidence of bias in mileage estimation was identified both in the technical literature and through analyses of databases maintained by the State of Maryland. The state-maintained databases
allowed comparisons between self-reported mileage estimates made by the same individuals (a) at different points in time and under different circumstances, and (b) at the same point in time using alternative estimating procedures. In the first case, the Maryland Motor Vehicle Administration (MVA) has created an Emission Exemption Database (EED) that identifies all individuals (vehicle owners and co-owners) age 70 and older who qualify for an exemption from emission testing, based on their self-reports that the subject vehicle is driven less than 5000 miles per year. This database contains 90,316 drivers who so qualified for this exemption in 2005; to place this number in context, there was a total of 335,787 licensed drivers age 70 and above in 2005. Among the drivers in the EED is a subset of individuals who also were participants in the Maryland Pilot Older Driver Study (see Staplin et al., 2003). This study included a driving history questionnaire, also with a mileage bin of 5000 or less. A comparison of categorical mileage estimations for 331 individuals age 75+ who provided self-reports to both queries in the same timeframe, using the Maryland Soundex number (driver record number) as the matching criterion, was performed for this report. The analyses using the subset of MVA EED drivers who also participated in the Maryland Pilot Older Driver Study (MaryPODS) examined intra-rater consistency, across both survey instruments, for the 331 individuals who self-certified that they drove more or less than 5000 miles per year. Results indicated that, of the 138 individuals who were classified as driving less than 5000 miles per year in the EED, 41 or roughly 30% reported driving more than 5000 miles per year in the MaryPODS survey. It deserves emphasis that there is a financial incentive in being exempt from emissions inspection, while this source of potential bias is absent in the MaryPODS survey. Normally, a known source of bias might exclude a data set; but here it underscores threats to the reliability of self-reported exposure data due to situational influences. In the second case, the aforementioned Maryland Pilot Older Driver Study requested self-reports of driving exposure using two question formats—one based on weekly miles driven and the other on annual miles driven. The internal reliability of drivers’ exposure estimates was gauged by comparing the annual-milesdriven figures with an extension of the miles per week estimates (i.e., multiplied by 52). This multiplication was performed, then the product was divided by the estimate of annual miles driven for each person in the sample (n = 1868). A “percent error” score was yielded by this procedure. Problems with the reliability of self-reported exposure data were found in comparing the weekly versus annual mileage estimates. A discrepancy between the same drivers’ estimates of miles driven when asked the same question in two different ways is shown in Fig. 2. Over 10% of the sample provided responses characterized by over 100% error, and a 50% error rate was demonstrated in over 40% of the responses. Finally, data sources were sought which offered the potential to compare self-reported mileage with objective records of miles traveled for the same drivers. These included studies comparing drivers’ subjective exposure estimates with miles driven as recorded by global positioning system (GPS) devices in their
L. Staplin et al. / Accident Analysis and Prevention 40 (2008) 1249–1252
Fig. 2. Discrepancy between the same drivers’ estimates of miles driven on a weekly versus annual basis.
vehicles, for the same trips (see Ogle, 2005; Battelle, 1997). Unfortunately, the sampling and data reporting strategies in these studies preclude sorting into the age-by-mileage categories of interest in this paper; but both studies demonstrated overestimation (i.e., of self-reported exposure) by the highest mileage drivers and underestimation by the lowest mileage drivers, with underestimation also linked specifically to a travel pattern comprised of frequent but short trips (Ogle, 2005). More helpful are figures from the most recent National Household Travel Survey (NHTS) for which data are available (2001), that provides comparisons of self-reported mileage with odometer readings. Two NHTS data tables were downloaded, PERPUB and VEHPUB. All values from PERPUB were used. Only the values from VEHPUB were used that passed filters designed to exclude records where there are few cars and many drivers, or where an individual was the primary driver for more than one vehicle. Specifically, thresholds of ≥0.5 and ≤1.5 were set for the ratio of number of vehicles to number of drivers for a given household. This provided reasonable assurance that the annual mileage variables analyzed were based on the primary driver of the vehicle. Applying these thresholds retained 11,013 drivers for the present analysis. The mileage variables used were Annualzd (annual mileage based on odometer reading) and AnnMiles (annual mileage based on self-report). Finally, filters were applied to ensure that Annualzd ≥ 0; AnnMiles ≥ 0; and R Age > 0 (age of driver). The NHTS data were sorted into bins corresponding to the Langford et al. age and mileage categories (see Section 1 above) for analysis, and curves expressing the percent error in mileage estimates were plotted for the ‘low,’ ‘medium,’ and ‘high’ exposure groups, as defined previously. Percent error was calculated as [(self-report miles) − (odometer miles)/(self-report miles)] × 100. Analyses using the NHTS data first segregated the downloaded records into the same age bins and mileage groups utilized by Langford et al. Annual vehicle miles traveled (VMT), based on odometer readings, were then compared to selfreported miles. An underestimation bias for the lowest mileage group (ratio of VMT via odometer divided by VMT via selfreport) of approximately 1.8 was found. There is also a slight overestimation for the highest mileage group. Curves depicting
1251
Fig. 3. Magnitude and direction of error in self-reports of driving exposure calculated from National Household Travel Survey data, for individuals in identified age cohorts with low (<3000 km), medium (3000–14,000 km), and high (>14,000 km) annual driving distance.
the percent error in self-reported exposure for each age group of interest are shown in Fig. 3, for low, medium, and high mileage drivers. To a striking degree, the mileage estimation errors revealed in these plots inversely mirror the relationships highlighted by Langford et al. (refer to Fig. 1). This pattern of misestimation, if manifested in the Langford et al. data, would reduce the curves plotted by these authors, and substantially lessen the apparent effect that they attribute to LMB. 3. Discussion This research note addressed an alleged LMB in studies of driver age and crash risk, whereby increased crash risk may be localized to a subset of older individuals who drive the fewest miles. Implications for licensing policy are also advanced in research published recently by Langford et al. (2006a,b), by invoking differences in subjective (self-reported) driving exposure to explain differences in crash rates. While some interesting correlations are reported in the LMB papers, other researchers (e.g., Janke, 1991) who have observed similar phenomena appear to regard such mileage differences as an artifact of the differences in road type, geography, and other aspects of the (chosen) operational contexts that differentiate drivers, whatever their age. However, it is the questionable reliability of the reported exposure differences themselves that is most troublesome for the advocates of LMB. Numerous examples from the technical literature – in addition to the analysis outcomes highlighted herein – attest to the unreliability of subjective exposure estimates, generally. A more specific problem of underestimation by low mileage drivers and overestimation by high mileage drivers has also been indicated in sources identified in this report. While this evidence does not disprove the LMB hypothesis, its credibility must be challenged because of the unstable foundation upon which it rests. A clear recommendation to advance research in this area is to include objective measures of driver exposure in future
1252
L. Staplin et al. / Accident Analysis and Prevention 40 (2008) 1249–1252
risk analyses. But it may be of greatest importance to speak to the assertion that LMB should receive consideration vis-`avis new policies and programs for identifying high-risk drivers among license renewal applicants. Even as research initiatives are announced1 to quantify the safety benefits from brief screens for key visual and cognitive deficits at renewal, proponents of “low mileage bias” suggest that additional requirements for relicensing among seniors, if any, could more efficiently be targeted to those who drive the least. On the surface, the apparent simplicity of this approach is appealing. But on closer examination, a practical application of LMB to the driver licensing process – as well as the scientific basis for LMB – seems dubious. By what means could a licensing authority acquire the needed information to employ a low mileage screen? Certainly not by self-report. But if not, then by what society-wide monitoring system, that would be capable of tracking exposure at the individual level, and that would be politically viable? As unlikely as it is for this capacity to become available to licensing officials, its application also would run counter to the regulatory approach that traditionally reserves sanctions for unwanted behavior. Self-regulation among at-risk drivers is universally promoted as a desirable behavior; yet, with a low mileage screen, those who limit their driving the most would incur greater scrutiny and a higher likelihood of license restriction/revocation than, for example, a cognitively impaired but more active driver who denies or is unaware of his/her limitations.
1 “DMV giving new license tests a spin”, Sacramento Bee, Metro Section, May 21, 2007.
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