Abstract
Federal reporting of crash fatalities has limited age-by-sex stratification, but both age and sex are associated with driving reduction and cessation. We described older driver fatal crash involvement and fatalities using Fatality Analysis Reporting System data to calculate rates (per 100,000 licensed drivers, per 100,000 population) with age-by-sex stratifications. Nationally from 2000 through 2017, 110,422 drivers 65+ were involved in crashes resulting in at least one death within 30 days, and 67,843 of these older drivers died. Involvement and fatality rates per 100,000 licensed drivers in 2017 were lowest for females 65–69 (7.7 and 3.6, respectively) and highest for males age 85+ (34.3 and 25.5, respectively). Females had lower driver fatal crash involvement and fatality rates throughout the lifespan, even when rates generally decreased over time. Elaborating fatal crash trends and rates by age and sex helps to differentiate the public health burden of older driver crashes and fatalities.
Keywords: motor vehicle fatality, crash involvement, driver fatality rates, trend, FARS
Introduction
The US population of older adults (aged 65+) is growing and is expected to account for 20% of the total population by 2030 (Colby & Ortman, 2015). Given the increase in the number of older licensed drivers, and the fact that they accounted for 18% of all traffic fatalities in the US in 2017 (National Center for Statistics and Analysis, 2019), there has been a push to investigate patterns of crash-related injury among older adults to reduce injury and promote safe mobility.
Older driver behavior is known to vary by gender. Driving self-regulation refers to adjusting driving to compensate for declining ability, such as avoiding difficult situations such as night driving (Dickerson, Molnar, Bedard, Eby, Berg-Weger, et al., 2019). Women are more likely to self-regulate their driving compared to men, leading to women having increased rates of driving cessation at younger ages (Kington, Reuben, Rogowski, & Lillard, 1994; Wong, Smith, Sullivan, & Allan, 2016).
Previous research has linked older adults’ age- and health-related functional decline with driving performance and crash risk, despite significant heterogeneity in physical and cognitive ability, health status, and driving history among older adults (Choi, Mezuk, Lohman, Edwards, & Rebok, 2012; Classen, Wang, Crizzle, Winter, & Lanford, 2013; Kandasamy et al., 2018; Lombardi, Horrey, & Courtney, 2017). Thus, age is a limited proxy for comprehensive evaluation of driving fitness (Dickerson, Molnar, Bedard, Eby, Classen, et al., 2019). Given this heterogeneity, broad age categorizations (such as 65+ or 70+) may not be informative although routinely used in epidemiological research due to data availability. Existing information on the aging population of licensed drivers in the US is broad and fails to address the intersection of age and sex with needed detail (Classen et al., 2013). Previous research indicates that older female drivers are at greater risk for injury in crashes (Awadzi, Classen, Hall, Duncan, & Garvan, 2008), so examining fatality rates alone is limited. Therefore, the purpose of this study was to provide a more refined description of both older driver fatal crash involvement and fatality in the US by investigating age-by-sex stratifications to compare rates over time.
Methods
Data Sources
We analyzed fatal crash data from the National Highway Traffic Safety Administration (NHTSA)’s Fatality Analysis Reporting System (FARS) for drivers aged 65+ from 2000–2017 (available at https://www.nhtsa.gov/node/97996/251). FARS is an annual census of police-reported fatal crashes occurring on public roadways in the US, and contains detailed information about the crash, vehicles, and individuals involved. A crash is considered fatal if it results in the death of at least one vehicle occupant or non-occupant within 30 days. Thus, fatal crash involvement does not necessarily mean the driver was killed. State data is compiled from multiple sources by trained personnel and is described in detail elsewhere (National Center for Statistics and Analysis, 2018). The annual number of licensed drivers was obtained from the Federal Highway Administration (FHWA) with the highest age category being 85+ (FHWA, 2019), and population data was from the US Census Bureau, which provided additional detail about the oldest drivers. Data were publicly available and de-identified, and therefore, not subject to institutional review board oversight.
Statistical Analysis
Drivers were grouped into 5-year age categories beginning with age 65 and capped at 90+ due to frequency. Those with unknown sex (n=11) were excluded. Categorical variables describing crash counts were compared using Chi-square analysis. Annual age- and sex-adjusted driver fatal crash involvement and fatality rates were calculated per 100,000 licensed drivers and per 100,000 population. Counts and rates were assessed separately by age and sex. A Poisson regression model was fit to estimate the rate ratio (RR) and 95% confidence interval (CI) for the fatal crash involvement rate and for the driver fatality rate per 100,000 licensed drivers change from 2000 through 2017. Analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC).
Results
From 2000–2017, 110,422 drivers aged 65+ were involved in crashes resulting in at least one death within 30 days; 67,843 of these drivers died (61.4%). Most fatal crash-involved drivers were male (69.6%) and aged 65–69 (29.5%), while driver fatalities were majority male (69.2%) and aged 65–69 (25.4%) (Table 1). For females, the median age for fatal crash involvement was 75 [mean = 75.4; standard deviation (SD) = 7.2], the median age for driver fatality was 76 (mean = 76.5; SD = 7.4), and the oldest driver fatality was 101. For males, the median age for fatal crash involvement was 73 (mean = 74.7; SD = 7.3), the median age for driver fatality was 75 (mean = 75.6; SD = 7.5), and the oldest driver fatality was 103.
Table 1.
Characteristics of older drivers involved in fatal crashes, USA, 2000–2017
| Fatal Crash Involvement | Driver Fatality | |||||
|---|---|---|---|---|---|---|
| N = 110,422 | N = 67,843 | |||||
| n (%) | n (%) | |||||
| Female 33,533 (30.4) |
Male 76,889 (69.6) |
P- value |
Female 20,894 (30.8) |
Male 46,949 (69.2) |
P- value |
|
| Age group | ||||||
| 65–69 | 8,884 (26.5) | 23,699 (30.8) | <.001 | 4,665 (22.3) | 12,576 (26.8) | <.001 |
| 70–74 | 7,665 (22.9) | 18,153 (23.6) | 4,406 (21.1) | 10,344 (22.0) | ||
| 75–79 | 6,906 (20.6) | 14,705 (19.1) | 4,393 (21.0) | 9,364 (20.0) | ||
| 80–84 | 5,709 (17.0) | 11,195 (14.6) | 3,994 (19.1) | 7,701 (16.4) | ||
| 85–89 | 3,259 (9.7) | 6,698 (8.7) | 2,506 (12.0) | 4,982 (10.6) | ||
| 90–94 | 1,110 (3.3) | 2,449 (3.2) | 930 (4.5) | 1,982 (4.2) | ||
| Single ehicle |
8,953 (26.7) | 25,054 (32.6) | <.001 | 5,771 (27.6) | 17,381 (37.0) | <.001 |
| Pedestrian- involved |
2,666 (8.0) | 6,841 (8.9) | <.001 | 59 (0.3) | 170 (0.4) | 0.10 |
Note. P-values comparing male and female frequencies were calculated using the chi-square test. Data source: Fatality Analysis Reporting System.
Overall, 30.8% of older driver fatal crashes were single vehicle, with the percentage higher for males than females (32.6% versus 26.7%, p < 0.001). As age category increased, the percent of single-vehicle fatal crashes decreased (from 33.8% for ages 65–69, to 24.9% for ages 90+, p < 0.001, results not shown). Single-vehicle crashes accounted for 34.1% of older driver fatalities, with the percentage higher for males than females (37.0% versus 27.6%, p < 0.001) and decreasing by age group sexes combined (from 42.2% ages 65–69, to 24.1% ages 90+, p < .001) as well as by sex (results not shown).
Approximately 8.6% (n = 9,507) of older driver fatal crashes involved pedestrians. The percentage of pedestrian-involved crashes was higher for male drivers compared to females (8.9% vs 8.0%, p <0.0001) and decreased by age (10.6% for ages 65–69 to 4.1% for ages 90+, p < 0.0001). The decrease by age held true overall and for each sex (results not shown). Pedestrian-involved fatal crashes resulted in only 229 (0.3%) driver fatalities, with no statistically significant difference by sex (p = 0.10) or age group (p = 0.78).
Fatal Crash Involvement Rates per Licensed Driver
Overall and separately by sex, driver fatal crash involvement rates per 100,000 licensed drivers increased by age and were lower in 2017 compared to 2000 (Table 2). Female rates ranged from 11.0 (age 65–69) to 28.0 (age 85+) in 2000, compared to 7.7 (age 65–69) to 12.4 (age 85+) in 2017. Female rates were lower than the male rates, which ranged from 26.0 (age 65–69) to 52.5 (age 85+) in 2000 and 22.3 (age 65–69) to 34.3 (age 85+) in 2017. The greatest reduction in fatal crash involvement over time was for females aged 80–84, which had a 39% reduction between 2000 and 2017 (RR = 0.61; 95% CI = 0.52–0.74)
Table 2.
Driver fatal crash involvement rate per 100,000 licensed drivers, FARS, 2000–2017
| 2000 | 2017 | Rate Ratio | |||
|---|---|---|---|---|---|
| Age group | N | Rate | N | Rate | (95% CI) |
| Overall | |||||
| 65–69 | 1548 | 18.5 | 2316 | 14.8 | 0.80 (0.75, 0.86) |
| 70–74 | 1586 | 21.2 | 1791 | 15.3 | 0.72 (0.67, 0.77) |
| 75–79 | 1422 | 24.1 | 1292 | 17.0 | 0.71 (0.66,0.76) |
| 80–84 | 1004 | 28.6 | 934 | 19.8 | 0.69 (0.63, 0.76) |
| 85+ | 721 | 35.2 | 894 | 22.5 | 0.64 (0.58, 0.70) |
| Female | |||||
| 65–69 | 462 | 11.0 | 621 | 7.7 | 0.70 (0.62, 0.79) |
| 70–74 | 511 | 13.4 | 524 | 8.7 | 0.65 (0.58, 0.74) |
| 75–79 | 459 | 14.8 | 401 | 10.2 | 0.68 (0.60, 0.78) |
| 80–84 | 359 | 19.4 | 292 | 11.8 | 0.61 (0.52,0.71) |
| 85+ | 218 | 20.0 | 267 | 12.4 | 0.62 (0.52, 0.74) |
| Male | |||||
| 65–69 | 1086 | 26.0 | 1695 | 22.3 | 0.86 (0.80, 0.93) |
| 70–74 | 1075 | 29.5 | 1267 | 22.3 | 0.75 (0.70, 0.82) |
| 75–79 | 963 | 34.1 | 891 | 24.5 | 0.72 (0.65, 0.79) |
| 80–84 | 645 | 38.9 | 642 | 28.7 | 0.74 (0.66, 0.82) |
| 85+ | 503 | 52.5 | 627 | 34.3 | 0.65 (0.58, 0.73) |
Note. Data sources: Fatality Analysis Reporting System and Federal Highway Administration.
Driver Fatality Rates
Licensed driver-based fatality rates.
Overall and separately by sex, fatality rates per 100,000 licensed drivers increased with age and decreased over time although the rates fluctuated yearly (Figure 1). Among females, all age groups showed a general downward trend from 2000–2017, although the slope decreased over time and relatively flattened from 2011 onwards. The greatest change over time occurred among females aged 70–74, with a 45% decrease in fatality rates in 2017 compared to 2000 (RR = 0.55; 95% CI = 0.46–0.64) (Table 3). Among males, rates for all age groups decreased over time, with the greatest reduction (39%) for those aged 85+ (RR = 0.61; 95% CI = 0.54–0.70).
Figure 1. Licensed Driver-based Older Driver Fatality Rates, 2000–2017.
Licensed Driver-based Older Driver Fatality Rates, 2000–2017
Table 3.
Driver fatality rate per 100,000 licensed drivers, FARS, 2000–2017
| 2000 | 2017 | Rate Ratio | |||
|---|---|---|---|---|---|
| Age group | N | Rate | N | Rate | (95% CI) |
| Overall | |||||
| 65–69 | 831 | 9.9 | 1,158 | 7.4 | 0.75 (0.68, 0.82) |
| 70–74 | 926 | 12.4 | 974 | 8.3 | 0.67 (0.61,0.73) |
| 75–79 | 953 | 16.1 | 812 | 10.7 | 0.66 (0.61,0.73) |
| 80–84 | 714 | 20.3 | 630 | 13.3 | 0.66 (0.59, 0.73) |
| 85+ | 573 | 18.8 | 674 | 16.9 | 0.61 (0.54,0.68) |
| Female | |||||
| 65–69 | 268 | 6.4 | 292 | 3.6 | 0.57 (0.48, 0.67) |
| 70–74 | 312 | 8.2 | 268 | 4.5 | 0.55 (0.46, 0.64) |
| 75–79 | 304 | 9.8 | 254 | 6.4 | 0.65 (0.55, 0.77) |
| 80–84 | 255 | 13.8 | 193 | 7.8 | 0.56 (0.47, 0.68) |
| 85+ | 175 | 16.0 | 207 | 9.6 | 0.60 (0.49, 0.73) |
| Male | |||||
| 65–69 | 563 | 13.5 | 866 | 11.4 | 0.85 (0.76, 0.94) |
| 70–74 | 614 | 16.8 | 706 | 12.4 | 0.74 (0.66, 0.82) |
| 75–79 | 649 | 23.0 | 558 | 15.3 | 0.67 (0.60, 0.75) |
| 80–84 | 459 | 27.7 | 437 | 19.5 | 0.70 (0.62, 0.80) |
| 85+ | 398 | 41.5 | 467 | 25.5 | 0.61 (0.54,0.70) |
Note. Data sources: Fatality Analysis Reporting System and Federal Highway Administration.
Population-based fatality rates.
Fatality rates per 100,000 population also increased with age and showed a gradual decrease over time (overall and by sex), with yearly fluctuations by age group (Figure 2). Inclusion of an additional age category (age 90+) increased the instability of yearly rates for the oldest age categorization, particularly for females. Overall, the annual rates were lowest for age 65–69 (range 6.6–9.4) and highest for age 80–84 (range 9.8–14.6) and age 85–89 (range 9.9–14.9). Across the study period, the overall rates per 100,000 population ranged from 7.7 (age 65–69) to 12.1 (age 85–89), while the range for females was 3.8 (age 90+) to 6.5 (age 80–84) and for males the range was 12.0 (age 65–69) to 23.1 (age 85–89).
Figure 2. Population-based Older Driver Fatality Rates, 2000–2017.
Population-based Older Driver Fatality Rates, 2000–2017
Note: Population-based rates have different vertical scales
Discussion
We examined older driver fatal crash involvement and fatality rates by age group and sex. From 2000–2017, over 61% of older drivers involved in fatal crashes (i.e., a crash resulting in at least one occupant or non-occupant fatality within 30 days) died. Fatal crash involvement and driver fatality rates increased with age but significantly decreased throughout the study period across age and sex groups. Fatality rates per 100,000 licensed drivers declined over time and varied by age group, with those aged 85+ experiencing a 39% relative decrease (RR = 0.61) compared to the 25% decrease among those aged 65–69 (RR = 0.75). Female driver fatality rates decreased 35% (age 75–79, RR = 0.65) to 45% (age 70–74, RR = 0.55) from 2000–2017, while male fatality rates decreased 15% (age 65–69, RR = 0.85) to 39% (age 85+, RR = 0.61). As discussed in detail by Cicchino and McCartt (2014), these reductions likely reflect a range of factors including improved overall health, improved emergency medical services, changes in driving patterns, and improvements in vehicle safety over time such as driver assistance devices (Cicchino & McCartt, 2014; NHTSA, 2020). Additionally, observed occupant restraint use has increased over time (Enriquez, 2019).
Average vehicle miles travelled (VMT) per driver is significantly higher for men than women throughout older adulthood, although the gap has narrowed from 2009 to 2017 (McGuckin & Fucci, 2018), and drivers with low VMT are at increased crash risk per miles travelled (Cicchino & McCartt, 2014). This could account for some sex differences in our results. Although older driver behaviors such as self-regulation differ by age and sex (D’Ambrosio, Donorfio, Coughlin, Mohyde, & Meyer, 2008; Kostyniuk & Molnar, 2008; Lombardi et al., 2017), these sex differences have not been widely studied regarding driver fatal crash involvement or driver fatalities. For example, Cicchino and McCartt (2014) examined trends in older driver crash involvement, but limited analysis to ages 70–74, 75–79, and 80+ and did not stratify by sex. Our study expands upon their findings to show that while the overall rate change for fatal crash involvement decreased over time, the amount of change varied by the age-by-sex grouping. Similarly, our results support and expand upon a commentary indicating that the greatest reductions in older driver crash rates are among drivers age 80+ (O’Neill, Walshe, Romer, & Winston, 2019).
When comparing rates, it is important to consider the denominator, or the population at risk. In general, the population-based rates were lower than the rates per licensed driver due to the number of older adults who do not have a driver’s license. Use of the general population defines the public health burden, or the overall societal impact, but does not directly translate to true driving exposure, particularly considering that females tend to cease driving sooner than males and have a longer post-driving lifespan (Foley, Heimovitz, Guralnik, & Brock, 2002). An alternative approach is to use the number of licensed drivers. Variability in state driver licensure requirements differentially impacts older driver fatal crash involvement, although the mechanism is unclear (Tefft, 2014). A potential limitation with using FHWA licensure data is the discrepancy with state data (Curry, Kim, & Pfeiffer, 2014). This is problematic for young driver studies, but the extent of discrepancy for other age groups is unknown. However, even licensure does not necessarily reflect driving exposure in terms of time or distance driven. Furthermore, age categorizations in publicly-available data for driver licensure and estimates of VMT or time travelled cap the oldest age category at 80 or 85, whereas population-based rates can provide additional categories. Further investigation on the most accurate estimate of driving exposure or risk in older drivers is warranted (Shen, Pope, Stamatiadis, & Zhu, 2019).
A study limitation is data availability. FARS only reports fatal crashes and therefore driving exposure was not examined. Another limitation is the relatively small numbers of yearly observations for the oldest age categories when stratified by sex, which may result in unstable annual rates.
Driving patterns are known to differ by age and sex, and the current findings support existing studies by showing that crash characteristics (e.g., single vehicle and pedestrian crashes), crash involvement rates, and fatality rates of older drivers also differ by age and sex. It should be noted that while the sex difference for pedestrian-involved crashes was statistically significant, the <1% difference may not be practically significant. The expanded age-sex stratification revealed that sex differences in driver fatal crash involvement and fatality rates persisted throughout the lifespan, even when rates generally decreased over time. This study also expands the age categorization by using census data. As older adults continue to age, there is an increasing need to expand publicly-available transportation data to accommodate additional age categories. Doing so will provide information about the public health burden of oldest driver fatalities and highlight the importance of understanding how driver mortality risk varies with age.
Funding:
This work was supported by the National Institute on Aging at the National Institutes of Health (grant number R01AG050581) to MZ.
Footnotes
Conflict of Interest: We have no conflicts of interest to declare.
IRB/Human Subjects: This study utilized public, de-identified data not subject to IRB oversight.
References
- Awadzi KD, Classen S, Hall AG, Duncan RP, & Garvan CW (2008). Predictors ofinjury among younger and older adults in fatal motor vehicle crashes. Accident Analysis & Prevention, 40, 1804–1810. 10.1016/j.aap.2008.07.010 [DOI] [PubMed] [Google Scholar]
- Choi M, Mezuk B, Lohman MC, Edwards JD, & Rebok GW (2012). Gender and racial disparities in driving cessation among older adults. J Aging Health, 24(8), 1364–1379. doi: 10.1177/0898264312460574 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Cicchino JB, & McCartt AT (2014). Trends in older driver crash involvement rates and survivability in the United States: An update. Accident Analysis & Prevention, 72, 44–54. 10.1016/j.aap.2014.06.011 [DOI] [PubMed] [Google Scholar]
- Classen S, Wang Y, Crizzle AM, Winter SM, & Lanford DN (2013). Gender differences among older drivers in a comprehensive driving evaluation. Accident Analysis & Prevention, 61, 146–152. doi: 10.1016/j.aap.2012.10.010 [DOI] [PubMed] [Google Scholar]
- Colby SL, & Ortman JM (2015). Projections of the size and composition of the U.S. population: 2014 to 2060 (P25–1143). https://www.census.gov/content/dam/Census/library/publications/2015/demo/p25-1143.pdf
- Curry AE, Kim KH, & Pfeiffer MR (2014). Inaccuracy of Federal Highway Administration licensed driver data: Implications on young driver trends. J Adolesc Health, 55(3), 452–454. doi: 10.1016/j.jadohealth.2014.05.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- D’Ambrosio LA, Donorfio LK, Coughlin JF, Mohyde M, & Meyer J (2008). Gender differences in self-regulation patterns and attitudes toward driving among older adults. J Women Aging, 20(3–4), 265–282. doi: 10.1080/08952840801984758 [DOI] [PubMed] [Google Scholar]
- Dickerson AE, Molnar L, Bedard M, Eby DW, Classen S, & Polgar J (2019). Transportation and aging: an updated research agenda for advancing safe mobility. J Appl Gerontol, 38(12), 1643–1660. doi: 10.1177/0733464817739154 [DOI] [PubMed] [Google Scholar]
- Dickerson AE, Molnar LJ, Bedard M, Eby DW, Berg-Weger M, Choi M,…Silverstein NM (2019). Transportation and aging: an updated research agenda to advance safe mobility among older adults transitioning from driving to non-driving. Gerontologist, 59(2), 215–221. doi: 10.1093/geront/ngx120 [DOI] [PubMed] [Google Scholar]
- Enriquez J (2019). Occupant restraint use in 2018: Results from the NOPUS Controlled Intersection Study (DOT HS 812 781). Washington, DC: National Highway Traffic Safety Administration. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812781 [Google Scholar]
- Federal Highway Administration. (2019). Highway Statistics 2017. Table DL-220: Licensed Drivers, by Sex and Age Group, 1963–2017. Washington, D.C.: US Department of Transportation. https://www.fhwa.dot.gov/policyinformation/statistics/2017/dl220.cfm [Google Scholar]
- Foley DJ, Heimovitz HK, Guralnik JM, & Brock DB (2002). Driving life expectancy of persons aged 70 years and older in the United States. Am J Public Health, 92(8), 1284–1289. doi: 10.2105/AJPH.92.8.1284 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kandasamy D, Betz ME, DiGuiseppi C, Mielenz TJ, Eby DW, Molnar LJ, … Li G (2018). Self-reported health conditions and related driving reduction in older drivers. Occup Ther Health Care, 32(4), 363–379. doi: 10.1080/07380577.2018.1522681 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kington R, Reuben D, Rogowski J, & Lillard L (1994). Sociodemographic and health factors in driving patterns after 50 years of age. Am J Public Health, 84(8), 1327–1329. 10.2105/ajph.84.8.1327 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kostyniuk LP, & Molnar LJ (2008). Self-regulatory driving practices among older adults: health, age and sex effects. Accid Anal Prev, 40(4), 1576–1580. doi: 10.1016/j.aap.2008.04.005 [DOI] [PubMed] [Google Scholar]
- Lombardi DA, Horrey WJ, & Courtney TK (2017). Age-related differences in fatal intersection crashes in the United States. Accid Anal Prev, 99(Pt A), 20–29. doi: 10.1016/j.aap.2016.10.030 [DOI] [PubMed] [Google Scholar]
- McGuckin N, & Fucci A (2018). Summary of travel trends: 2017 National Household Travel Survey (FHWA-PL-18–019). Rockville, MD: Federal Highway Administration.www.fhwa.dot.gov/policyinformation/documents/2017_nhts_summary_travel_trends.pdf [Google Scholar]
- National Center for Statistics and Analysis. (2019). 2017 Older Population Fact Sheet (Traffic Safety Facts. Report No. DOT HS 812 684) Washington, DC: National Highway Traffic Safety Administration. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812684 [Google Scholar]
- National Center for Statistics and Analysis. (2018). Fatality Analysis Reporting System (FARS) Analytical User’s Manual 1975–2017 (DOT HS 812 602). https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812602
- National Highway Traffic Safety Administration. (2020). Newer Cars Are Safer Cars. https://www.nhtsa.gov/newer-cars-are-safer-cars
- O’Neill D, Walshe E, Romer D, & Winston F (2019). Transportation equity, health, and aging: A novel approach to healthy longevity with benefits across the life span. [Commentary]. NAM Perspectives. 10.31478/201912a [DOI] [PMC free article] [PubMed]
- Shen S, Pope CN, Stamatiadis N, & Zhu M (2019). Validation of not-at-fault driver representativeness assumption for quasi-induced exposure using U.S. national traffic databases. J Safety Res, 71, 243–249. 10.1016/j.jsr.2019.09.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tefft BC (2014). Driver license renewal policies and fatal crash involvement rates of older drivers, United States, 1986–2011. Inj Epidemiol, 1(1), 25. doi: 10.1186/s40621-014-0025-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wong IY, Smith SS, Sullivan KA, & Allan AC (2016). Toward the Multilevel Older Person’s Transportation and Road Safety model: A new perspective on the role of demographic, functional, and physchosocial factors. J Gerontol B Psychol Sci Soc Sci, 71(1), 71–86. doi: 10.1093/geronb/gbu099 [DOI] [PubMed] [Google Scholar]