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Road safety in an aging population: risk factors, assessment, interventions, and future directions

Published online by Cambridge University Press:  18 February 2016

Kaarin J. Anstey*
Affiliation:
Centre for Research on Ageing, Health and Wellbeing, The Australian National University, Canberra, Australia
Ranmalee Eramudugolla
Affiliation:
Centre for Research on Ageing, Health and Wellbeing, The Australian National University, Canberra, Australia
Lesley A. Ross
Affiliation:
Department of Human Development and Family Studies, The Pennsylvania State University, State College, USA
Nicola T. Lautenschlager
Affiliation:
Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Melbourne, Australia NorthWestern Mental Health, Melbourne Health, Royal Melbourne Hospital, Melbourne, Australia
Joanne Wood
Affiliation:
School of Optometry and Vision Science and Institute of Health and Biomedical InnovationQueensland University of Technology, Australia
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Extract

With the number of older drivers projected to increase by up to 70% over the next 20 years, preventing injury resulting from crashes involving older drivers is a significant concern for both policy-makers and clinicians. While the total number of fatal crashes per annum has steadily decreased since 2005 in Australia, the rate of fatalities has demonstrated an upward trend since 2010 in drivers aged 65 years and above (8.5 per 100,000), such that it is now on par with the fatality rate in drivers aged 17–25 years (8.0 per 100,000) (Austroads, 2015). Similar statistics are reported for the United States (NHTSA, 2012), implying there is a need for better identification of those older drivers who are unsafe and implementation of strategies that can enhance mobility while maximizing road safety.

Type
Guest Editorial
Copyright
Copyright © International Psychogeriatric Association 2016 

Introduction

With the number of older drivers projected to increase by up to 70% over the next 20 years, preventing injury resulting from crashes involving older drivers is a significant concern for both policy-makers and clinicians. While the total number of fatal crashes per annum has steadily decreased since 2005 in Australia, the rate of fatalities has demonstrated an upward trend since 2010 in drivers aged 65 years and above (8.5 per 100,000), such that it is now on par with the fatality rate in drivers aged 17–25 years (8.0 per 100,000) (Austroads, 2015). Similar statistics are reported for the United States (NHTSA, 2012), implying there is a need for better identification of those older drivers who are unsafe and implementation of strategies that can enhance mobility while maximizing road safety.

The responsibility for ensuring older drivers are safe drivers often falls between the health and road safety sectors, with few countries or jurisdictions providing an overall, integrated approach to assessment, reassessment, and management. Prevalent medical conditions among very old adults (over 85) such as dementia, stroke, and eye disease reduce driving safety and in the case of dementia and macular degeneration will inevitably preclude driving with progression of symptoms.

We argue that there is a need for an evidence-based system of assessment, reassessment, intervention, and license review for older adults. An older driver may present to a psychiatrist, neurologist, optometrist, general practitioner, or other health professional. Reliance solely on health professionals to report crash risk based on clinical judgment may result in patients failing to report important symptoms for fear of license implications. Alternatively, physicians, with little training in relevant evidence-based crash risk assessments, may not recognize deficits associated with unsafe driving. In many countries a system to manage older drivers will need to be multi-sectorial and multi-disciplinary, involving a range of clinicians (including driving specialists) and government licensing authorities (Carr and O'Neill, Reference Carr and O'Neill2015).

Clinical judgment of driving capacity without objective assessment

In most systems, evaluation of fitness to drive (FTD) is largely placed in the hands of physicians. This is consistent across several countries: Canada (Marshall et al., Reference Marshall, Demmings, Woolnough, Salim and Man-Son-Hing2012), the United States (American Medical Association, 2014), the United Kingdom, (Hawley and Galbraith, Reference Hawley and Galbraith2008) and Australia (Sims et al., Reference Sims, Rouse-Watson, Schattner, Beveridge and Jones2012). Given the accessibility of family physicians, and their capacity to screen for a range of medical conditions that potentially impact driving safety, primary care is a logical first point of contact in the process of evaluating FTD. Over the past decade, however, there has been growing recognition that physicians are not confident in making FTD assessments (Lipski, Reference Lipski2002; Jang et al., Reference Jang2007; Marshall et al., Reference Marshall, Demmings, Woolnough, Salim and Man-Son-Hing2012; Sims et al., Reference Sims, Rouse-Watson, Schattner, Beveridge and Jones2012). Surveys conducted on Canadian physicians from a range of specialties (Jang et al., Reference Jang2007; Marshall et al., Reference Marshall, Demmings, Woolnough, Salim and Man-Son-Hing2012) found that only 33% felt confident in their ability to make FTD assessments, and 73%–88% felt they would benefit from further education. Similarly, in the Australia surveys of general practitioners (GPs) found that only 41%–54% felt confident in their ability to assess FTD, and 59%–74% felt they would benefit from further education in this area (Lipski, Reference Lipski2002; Sims et al., Reference Sims, Rouse-Watson, Schattner, Beveridge and Jones2012).

Current recommended practice for FTD assessment in the medical/office-based context includes gathering evidence regarding the patient's cognitive status, presence of fluctuating conditions, visual impairment, musculoskeletal conditions, drugs and alcohol, as well as history of traffic infringements and informant report of driving difficulties (Carr and Ott, Reference Carr and Ott2010; Molnar and Simpson, Reference Molnar and Simpson2010; Carmody et al., Reference Carmody, Traynor and Iverson2012). However, most guidelines for applying this type of clinical data collection are based on expert opinion or consensus statements rather than objective evidence. For example, practice parameters provided by the American Academy of Neurology propose the use of global cognitive measures such as the Mini-Mental Status Examination (MMSE) and the Clinical Dementia Rating Scale (CDR), and a family/caregiver questionnaire of driving history, for identifying patients at risk of unsafe driving (Iverson et al., Reference Iverson, Gronseth, Reger, Classen, Dubinsky and Rizzo2010), with only slightly different protocols suggested by other groups (Canadian Medical Association, 2006; Australian and New Zealand Society for Geriatric Medicine, 2010). Critically, most recommended measures are either not validated against on-road performance, or have poor evidence of association with driving safety risk (Odenheimer et al., Reference Odenheimer, Beaudet, Jette, Albert, Grande and Minaker1994; Reger et al., Reference Reger, Welsh, Watson, Cholerton, Baker and Craft2004; Crizzle et al., Reference Crizzle, Classen, Bédard, Lanford and Winter2012; Joseph et al., Reference Joseph2014). Furthermore, recommendations are not consistent, with some guidelines suggesting a diagnosis of early dementia is sufficient to enforce driving retirement (Iverson et al., Reference Iverson, Gronseth, Reger, Classen, Dubinsky and Rizzo2010; Austroads, 2012), while others suggest this is not sufficient evidence for license cancellation (Australian and New Zealand Society for Geriatric Medicine, 2010).

Few studies have investigated the accuracy or validity of physician assessments of FTD. One study examined the association between physician warnings given to drivers aged over 18 years, and emergency department visits relating to road-traffic incidents in which the patient was a driver (n = 100,075) (Redelmeier et al., Reference Redelmeier, Yarnell, Thiruchelvam and Tibshirani2012). Using a self-matching cross-over design, Redelmeier et al. found that crash-related injury declined by 45% in the year following the physician warning compared to the three years preceding the warning. Older drivers, who constituted 51% of their sample, showed a similar pattern to the rest of the sample with injury rates declining in those aged 60–75 years (3.39 per 1,000 per annum to 2.29), and in those aged 75 years and above (2.65 per 1,000/pa to 0.93 per 1,000/pa) (Redelmeier et al., Reference Redelmeier, Yarnell, Thiruchelvam and Tibshirani2012). However, the methodology used in this study makes it difficult to determine whether the change was caused by license cancellation (which occurred in 10%–30% of cases) or the warning itself, and how the physician warnings were implemented (e.g. driving cessation, self-regulation, or remediation). Importantly, the false positive rate cannot be determined. In fact, Redelmeier and colleagues report that following the physician warning, the patients’ emergency department visits for depression increased from 19 per 1,000/pa to 23 per 1,000/pa, and visits to the responsible physician declined by 29% (Redelmeier et al., Reference Redelmeier, Yarnell, Thiruchelvam and Tibshirani2012) lending some credence to physicians’ concerns that FTD assessments have negative impacts on patient-physician relationship and may reduce visitation rates (Lipski, Reference Lipski2002; Jang et al., Reference Jang2007; Marshall et al., Reference Marshall, Demmings, Woolnough, Salim and Man-Son-Hing2012; Sims et al., Reference Sims, Rouse-Watson, Schattner, Beveridge and Jones2012).

Fox and colleagues (Reference Fox, Bowden, Bashford and Smith1997) examined the association between physician judgment of FTD in 19 patients with Alzheimer's Dementia (AD) (which included medical exam, MMSE, caregiver report, and vision testing), against an on-road assessment conducted in traffic by an occupational therapist (OT). They found that physician prediction of driving competence was not associated with on-road performance (Fox et al., Reference Fox, Bowden, Bashford and Smith1997). Furthermore, 37% of the AD patients were judged as safe to drive by the on-road assessment, suggesting that AD diagnosis may not be sufficient to recommend driving cessation.

A Delphi study on physician consensus in identifying at-risk older drivers (Rapoport et al., Reference Rapoport2014), had 41 physicians rate 26 patient scenarios, and achieved only 27% consensus, which improved to 69% on the fifth iteration. They found the strongest predictors of physician decisions were caregiver concern and abnormal performance on the Clock Drawing Test, and recommended that uncertain cases be referred for on-road assessment (Rapoport et al., Reference Rapoport2014). A systematic review of studies on the validity of in-office FTD assessments for a range of chronic diseases (e.g. Parkinson's Disease, AD, Sleep Apnoea, Stroke etc.) concluded that the clinical tests employed in these studies were not consistently related to measures of driving performance (Marino et al., Reference Marino2013).

Based on current evidence, it is therefore unclear whether physician assessment of FTD provides an accurate prediction of driving safety. Although physician warnings have the potential to influence rates of crash related injury, their specificity, reliability, and accuracy needs to be evaluated in order to minimize negative effects of unnecessary or premature license cancellation. Clear, consistent guidelines and validated in-office screening measures are critical for physicians to not only feel confident in their decision-making for further referral, but to provide an accurate first-tier evaluation of risk in a multi-tiered, multi-disciplinary system for assessing FTD (Carmody et al., Reference Carmody, Traynor, Iverson and Marchetti2013).

Risk assessment

Risk assessment for older drivers focusses on cognitive, visual, and motor abilities that are linked to safe driving in large cohort studies (Reger et al., Reference Reger, Welsh, Watson, Cholerton, Baker and Craft2004; Mathias and Lucas, Reference Mathias and Lucas2009; Devos et al., Reference Devos, Akinwuntan, Nieuwboer, Truijen, Tant and De Weerdt2011).

Cognitive function

Common risk assessment tools currently widely used to screen for cognitive impairment fail to discriminate safe from unsafe drivers. For example, a large Canadian study (n = 17,538) found that the MMSE did not predict crashes over two years of follow-up (Joseph et al., Reference Joseph2014). Generic tools and dementia screening instruments do not assess the specific deficits that cause drivers to become unsafe. A consistent body of research has demonstrated that memory deficits are not typically associated with unsafe driving (Reger et al., Reference Reger, Welsh, Watson, Cholerton, Baker and Craft2004; Anstey et al., Reference Anstey, Wood, Lord and Walker2005; Aksan et al., Reference Aksan, Anderson, Dawson, Uc and Rizzo2015), which may explain why patients with early AD may continue to drive safely. In contrast, deficits in executive function, visual selective attention, and processing speed predict crashes in older drivers (Ball et al., Reference Ball2006; Dawson et al., Reference Dawson, Uc, Anderson, Johnson and Rizzo2010; Anstey et al., Reference Anstey, Horswill, Wood and Hatherly2012). As dementia progresses and multiple domains of cognitive function are affected, driving cessation is inevitable. Some specific types of cognitive deficits in non-AD dementia may also preclude safe driving early in the disease. Cognitive assessment for safe driving focusses on key abilities which are evaluated in most older-driver screening batteries e.g. Wood et al. (Reference Wood, Anstey, Kerr, Lacherez and Lord2008).

Visual function

VISUAL TESTS

Visual acuity (VA) is the most common method employed to assess visual function in clinical practice and by licensing authorities. However, studies using a range of sample sizes and methodologies have found only a weak relationship between VA and crash risk (Hofstetter, Reference Hofstetter1976; Gresset and Meyer, Reference Gresset and Meyer1994; Marottoli, Reference Marottoli1998; Ivers et al., Reference Ivers, Mitchell and Cumming1999) or no association (Keeffe et al., Reference Keeffe, Jin, Weih, McCarty and Taylor2002; Rubin et al., Reference Rubin, Ng, Bandeen-Roche, Keyl, Freeman and West2007; Cross et al., Reference Cross2009). High-contrast static VA is unlikely to represent the normal-driving environment that includes both static and moving objects of different sizes and contrast levels. Tests of contrast and motion sensitivity and visual fields may thus provide better measures of visual performance for driving than VA.

VISUAL AGING AND EYE DISEASE

Population studies consistently demonstrate an increase in the prevalence of visual impairment for those aged 65 years and above (Klein et al., Reference Klein, Klein, Linton and De Mets1991; Attebo et al., Reference Attebo, Mitchell and Smith1996; Rubin et al., Reference Rubin1997). For example, the prevalence of bilateral visual impairment, defined as VA worse than the commonly adopted driving standard of 6/12 (or 20/40), increases from 1% for those between 60−69 years, to 26% for those aged 80 years and over (Wang et al., Reference Wang, Foran and Mitchell2000).

An estimated 30 % of the older population have significant cataract in at least one eye (Rochtchina et al., Reference Rochtchina, Mukesh, Wang, McCarty, Taylor and Mitchell2003). Many people live with cataracts for extended periods before cataract removal (Owsley et al., Reference Owsley, Stalvey, Wells and Sloane1999); with around a quarter of these continuing to drive, even if their vision fails to meet the visual standard for driver licensing (Monestam, Reference Monestam1999; Pager et al., Reference Pager, McCluskey and Retsas2004). This has important ramifications for driving safety, given that older drivers with cataracts have 2.5 times higher crash rates than age-matched controls without cataracts (Owsley et al., Reference Owsley, Stalvey, Wells and Sloane1999).

Glaucoma is one of the leading causes of visual field loss in older adults (Ramrattan et al., Reference Ramrattan2001) with approximately 60 million people affected by the disease worldwide, with numbers estimated to increase in the aging population (Quigley, Reference Quigley2011). It has been estimated that 20% of those with glaucoma experience progressive loss of their visual fields even with appropriate treatment (Musch et al., Reference Musch, Gillespie, Lichter, Niziol and Janz2009) and approximately half of those with glaucoma are not aware that they have the condition (Weih et al., Reference Weih, Nanjan, McCarty and Taylor2001). Numerous studies suggest that glaucoma is associated with higher at-fault crash risk than those without glaucoma (e.g. McGwin et al. (Reference McGwin, Huisingh, Jain, Girkin and Owsley2015)).

Finally, age-related macular degeneration (AMD) is the leading cause of irreversible moderate to severe visual impairment in older adults worldwide (Weih et al., Reference Weih, VanNewkirk, McCarty and Taylor2000; Wang et al., Reference Wang, Foran and Mitchell2000). A retrospective study (McGwin et al., Reference McGwin2013) showed that drivers with intermediate AMD, had significantly lower crash rates than drivers with normal vision. The authors suggest that these drivers self-regulate their driving to avoid challenging situations and exercise greater caution on the road, which is reflected in their lower risk for crashes. In contrast, drivers with early and advanced AMD had similar crash rates to the controls. Drivers with early AMD may be less likely to self-regulate their driving, given their better levels of visual function, and thus show similar driving risk profiles to controls. Interpretation of the findings is also difficult for drivers with advanced AMD due to the low numbers of active drivers, as many cease driving in the advanced stages of the condition (Sengupta et al., Reference Sengupta, van Landingham, Solomon, Do, Friedman and Ramulu2014).

Approaches to prevention

There is increasing evidence that some interventions, or combinations of interventions, improve the driving safety and mobility of older adults. Adaptive cognitive training programs (i.e. those that systematically increase difficulty level during training), have transferred to both maintained driving mobility and safety. For example, Speed of Processing Training, which focuses on visually-based processing speed and divided and selective attention, has repeatedly been shown to reduce driving cessation risk across three years and maintain driving mobility across five years as compared to both a no-contact and social-contact control groups (Edwards et al., Reference Edwards, Delahunt and Mahncke2009a; Reference Edwards2009b; Ross et al., Reference Ross, Edwards, O'Connor, Ball, Wadley and Vance2015). Importantly, this training has also demonstrated transfer to driving safety. Ball and colleagues examined the impact of ten hours of three separate cognitive training interventions (Speed of Processing, Reasoning, and Memory) compared to a no-contact control group on crashes across six years (Ball et al., Reference Ball, Edwards, Ross and McGwin2010). Results revealed that the Speed of Processing and Reasoning training interventions resulted in over a 40% reduction in at-fault state-reported crashes. When training focused on memory performance, which is not typically predictive of driving outcomes, there was no impact on driving safety.

In addition to improving cognitive function, there is growing evidence that improving visual function related to eye disease has important benefits for road safety. A growing body of work suggests that cataract removal improves driving safety (Subzwari et al., Reference Subzwari, Desapriya, Scime, Babul, Jivani and Pike2008). In one study, cataract surgery resulted in a 50% reduction in crash rates, compared to those who did not have surgery (Owsley et al., Reference Owsley, McGwin, Sloane, Wells, Stalvey and Gauthreaux2002) and an Australian study found that cataract surgery resulted in a 12.7% reduction in crashes' corresponding to $4.3 million in savings (Meuleners et al., Reference Meuleners, Hendrie, Lee, Ng and Morlet2012). Relatedly, Mennemeyer and colleagues (Reference Mennemeyer, Owsley and McGwin2013) extended this work using a US data simulation model and found that earlier removal of cataracts, versus current treatment, reduced crashes by 21%, thus illustrating the important, and often under-investigated, impact of the timing of interventions.

Interestingly, educational interventions are one of the most commonly used forms of rehabilitation; however, current evidence suggests that educational interventions alone have a minimal impact on driving safety. Owsley and colleagues found that visually impaired participants reported avoiding more challenging driving situations after an educational intervention (Owsley et al., Reference Owsley, Stalvey and Phillips2003); however, the intervention had no impact on driving safety (as measured by crashes) two years later (Owsley et al., Reference Owsley, McGwin, Phillips, McNeal and Stalvey2004). Similarly, Bédard and colleagues (Reference Bédard, Isherwood, Moore, Gibbons and Lindstrom2004), examined a widely-used educational intervention and found that the training did not result in significant improvements during an on-road evaluation. There is some evidence that educational programs combined with other components, such as on-road training (Marottoli et al., Reference Marottoli2007b; Bédard et al., Reference Bédard2008) may improve on-road driving.

Finally, the improvement of physical abilities related to driving may also be an avenue for improved function. Marottoli and colleagues (Reference Marottoli2007a) found that a graduated physical therapist-guided exercise program translated to improvement of on-road driving performance. Although not an exhaustive review of driving safety interventions, the studies discussed here provide support that there are an increasing number of potential domains, and combinations of domains, that may be targeted to enhance driving safety. Further discussions of driving safety and mobility should include not only assessment, but importantly, potential rehabilitation through evidence-based interventions.

Other older road users – pedestrians and cyclists

Discussion of road safety in aging often omits walking and cycling which are cost-effective methods of maintaining mobility and increasing physical activity for older adults. Numerous studies report that older adults have the highest risk of mortality of all pedestrians when involved in vehicle collisions (e.g. Koepsell et al., Reference Koepsell2002). While this is partly due to older adults’ increased frailty, simulation studies indicate that aging affects gap selection, in particular, the ability to weigh-up estimated vehicle time of arrival and one's own walking speed when selecting a gap in the traffic to cross the road (Oxley et al., Reference Oxley, Ihsen, Fildes, Charlton and Day2005; Lobjois and Cavallo, Reference Lobjois and Cavallo2009; Holland and Hill, Reference Holland and Hill2010; Dommès and Cavallo, Reference Dommès and Cavallo2011). Mobility-impaired older adults show additional difficulty in judging safe crossing gaps to compensate for their rate of walking (Holland and Hill, Reference Holland and Hill2010). Age-related cognitive changes such as decline in executive functions, including decision making, and attentional control, are speculated to contribute to unsafe crossing behavior in older adults (Oxley et al., Reference Oxley, Ihsen, Fildes, Charlton and Day2005; Lobjois and Cavallo, Reference Lobjois and Cavallo2009; Dommès and Cavallo, Reference Dommès and Cavallo2011). The effects of neurodegenerative diseases such as AD and Parkinson's disease further increase risk for older pedestrians (Gorrie et al., Reference Gorrie, Brown and Waite2008). Studies suggest that signs of neuropathology (e.g. brain accumulation of neurofibrillary tangles) is associated with risk of older pedestrian crash fatalities (Gorrie et al., Reference Gorrie, Rodriguez, Sachdev, Duflou and Waite2006), particularly in areas with low complexity traffic (Gorrie et al., Reference Gorrie, Brown and Waite2008).

Cycling in older adults has received very little research interest. However, a Swedish study of older bicyclists (n = 456) treated for injury over a ten year period found that the most frequent cause of injury was due to falls when mounting or dismounting the cycle (20%) or due to irregular road surfaces (13%), with only 6% due to vehicle collisions (Scheiman et al., Reference Scheiman, Moghaddas, Björnstig, Bylund and Saveman2010). Interestingly, poor balance and falls risk are also associated with reduced executive function in older adults (e.g. Muir et al., Reference Muir, Gopaul and Odasso2012), although no study has yet examined cognitive factors in cycling safety. Along with road safety education, cognitive remediation targeting executive functions may help improve older adults’ safety when cycling or walking within their neighborhoods.

The design of crossings and intersections can also contribute to the elevated risk experienced by older pedestrians (Koepsell et al., Reference Koepsell2002). Koepsell and colleagues reported that unless marked crossings are accompanied by traffic signals or signage, they pose much greater risk to older pedestrians relative to un-marked crossings. As such, urban designers may need to take into account how aging affects the ability to safely negotiate traffic, and introduce greater traffic control, separation of pedestrians and vehicles, and pedestrian visibility (Koepsell et al., Reference Koepsell2002; Retting et al., Reference Retting, Ferguson and McCartt2003; Michael et al., Reference Michael, Green and Farquhar2006).

Thus, understanding how aging and age-associated disease impacts pedestrian safety is important for effective promotion of health behaviors, encouraging mobility and independence in aging, and designing age-friendly neighborhoods.

Future directions

The range of factors that may impact on the capacity to drive safely spans several disciplines. Future strategies for road safety among older adults need to encompass all types of road users, as neurological and sensory deficits impact on the use of public transport, pedestrian safety, and cycling as well as the capacity to access community transport. In most countries there is currently a shortage of driver-trained OTs, and other medical specialists who may be involved in assessing FTD. The ideal scenario is that evidence-based screening and general assessment methods become available which are inexpensive, culturally appropriate, and can be conducted by health care providers with different discipline backgrounds and in different settings. This will allow specialist driving assessment services to focus on complex cases. Evidence-based screening needs to be part of a framework of older driver safety that includes clear referral pathways to interventions, full assessment, and support for driving cessation and alternative transport. Interventions to improve older driver skill and safety are being developed and future investment in research and implementation of these will enhance the overall mobility and quality of life in our aging population.

Conflict of interest

NT Lautenschlager is the Editor-in-Chief of International Psychogeriatrics and therefore this editorial was assigned to a Deputy Editor of International Psychogeriatrics for the editorial process.

References

Aksan, N., Anderson, S. W., Dawson, J., Uc, E. and Rizzo, M. (2015). Cognitive functioning differentially predicts different dimensions of older drivers’ on-road safety. Accident Analysis & Prevention, 75, 236244.Google Scholar
American Medical Association (2014). Physician's Guide to Assessing and Counselling Older Drivers, 2003. Chicago, IL: American Medical Association.Google Scholar
Anstey, K. J., Horswill, M. S., Wood, J. M. and Hatherly, C. (2012). The role of cognitive and visual abilities as predictors in the multifactorial model of driving safety. Accident; Analysis and Prevention, 45, 766774.CrossRefGoogle ScholarPubMed
Anstey, K. J., Wood, J., Lord, S. and Walker, J. G. (2005). Cognitive, sensory and physical factors enabling driving safety in older adults. Clinical Psychology Review, 25, 4565.Google Scholar
Attebo, K., Mitchell, P. and Smith, W. (1996). Visual acuity and the causes of visual loss in Australia: the Blue Mountains Eye Study. Ophthalmology, 103, 357364.CrossRefGoogle ScholarPubMed
Australian and New Zealand Society for Geriatric Medicine (2010). Position statement number 11: driving and dementia. Australasian Journal on Ageing, 29, 137141.Google Scholar
Austroads (2012). Assessing Fitness to Drive for Commercial and Private Vehicle Drivers: Medical Standards for Licensing and Clinical Management Guidelines. Sydney: Austroads.Google Scholar
Austroads (2015). Review of the National Road Safety Strategy. Research Report AP-R477-15. Sydney: Austroads.Google Scholar
Ball, K. K. et al. (2006). Can high-risk older drivers be identified through performance-based measures in a department of motor vehicles setting? Journal of the American Geriatrics Society, 54, 7784.Google Scholar
Ball, K., Edwards, J. D., Ross, L. A. and McGwin, G. Jr. (2010). Cognitive training decreases motor vehicle collision involvement of older drivers. Journal of the American Geriatrics Society, 58, 21072113.Google Scholar
Bédard, M. et al. (2008). The combination of two training approaches to improve older adults' driving safety. Traffic Injury Prevention, 9, 7076.CrossRefGoogle ScholarPubMed
Bédard, M., Isherwood, I., Moore, E., Gibbons, C. and Lindstrom, W. (2004). Evaluation of a re-training program for older drivers. Canadian Journal of Public Health, 95, 295298.CrossRefGoogle ScholarPubMed
Canadian Medical Association (2006). Determining medical fitness to operate motor vehicles. CMA Driver's Guide, 7th edn. Ottawa: CMA.Google Scholar
Carmody, J., Traynor, V. and Iverson, D. (2012). Dementia and driving: an approach for general practice . Australian Family Physician, 41, 230.Google Scholar
Carmody, J., Traynor, V., Iverson, D. and Marchetti, E. (2013). Driving, dementia and Australian physicians: primum non nocere? Internal Medicine Journal, 43, 625630.Google Scholar
Carr, D. B. and O'Neill, D. (2015). Mobility and safety issues in drivers with dementia. International Psychogeriatrics/IPA, 27, 16131622.CrossRefGoogle ScholarPubMed
Carr, D. B. and Ott, B. R. (2010). The older adult driver with cognitive impairment: “It's a very frustrating life”. JAMA, 303, 16321641.CrossRefGoogle Scholar
Crizzle, A. M., Classen, S., Bédard, M., Lanford, D. and Winter, S. (2012). MMSE as a predictor of on-road driving performance in community dwelling older drivers. Accident Analysis & Prevention, 49, 287292.Google Scholar
Cross, J. M. et al. (2009). Visual and medical risk factors for motor vehicle collision involvement among older drivers. British Journal of Ophthalmology, 93, 400404.Google Scholar
Dawson, J. D., Uc, E. Y., Anderson, S. W., Johnson, A. M. and Rizzo, M. (2010). Neuropsychological predictors of driving errors in older adults. Journal of the American Geriatrics Society, 58, 10901096.CrossRefGoogle ScholarPubMed
Devos, H., Akinwuntan, A. E., Nieuwboer, A., Truijen, S., Tant, M. and De Weerdt, W. (2011). Screening for fitness to drive after stroke a systematic review and meta-analysis. Neurology, 76, 747756.Google Scholar
Dommès, A. and Cavallo, V. (2011). The role of perceptual, cognitive, and motor abilities in street‐crossing decisions of young and older pedestrians. Ophthalmic & Physiological Optics, 31, 292301.CrossRefGoogle Scholar
Edwards, J. D. et al. (2009b). The longitudinal impact of cognitive speed of processing training on driving mobility. The Gerontologist, 49, 485494.CrossRefGoogle ScholarPubMed
Edwards, J. D., Delahunt, P. B. and Mahncke, H. W. (2009a). Cognitive speed of processing training delays driving cessation. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 64, 12621267.Google Scholar
Fox, G. K., Bowden, S. C., Bashford, G. M. and Smith, D. S. (1997). Alzheimer's disease and driving: prediction and assessment of driving performance. Journal of the American Geriatrics Society, 45, 949953.CrossRefGoogle ScholarPubMed
Gorrie, C. A., Brown, J. and Waite, P. M. (2008). Crash characteristics of older pedestrian fatalities: dementia pathology may be related to ‘at risk’ traffic situations. Accident Analysis & Prevention, 40, 912919.CrossRefGoogle Scholar
Gorrie, C. A., Rodriguez, M., Sachdev, P., Duflou, J. and Waite, P. M. (2006). Increased neurofibrillary tangles in the brains of older pedestrians killed in traffic accidents. Dementia and Geriatric Cognitive Disorders, 22, 2026.CrossRefGoogle ScholarPubMed
Gresset, J. and Meyer, F. (1994). Risk of accidents among elderly car drivers with visual acuity equal to 6/12 or 6/15 and lack of binocular vision. Opthalmic & Physiological Optics, 14, 3337.Google Scholar
Hawley, C. A. and Galbraith, N. (2008). Medical education on fitness to drive: a survey of all UK medical schools. Postgraduate Medical Journal, 84, 635638.CrossRefGoogle ScholarPubMed
Hofstetter, H. W. (1976). Visual acuity and highway accidents . Journal of the American Optometric Association, 47, 887893.Google ScholarPubMed
Holland, C. and Hill, R. (2010). Gender differences in factors predicting unsafe crossing decisions in adult pedestrians across the lifespan: a simulation study. Accident Analysis & Prevention, 42, 10971106.Google Scholar
Ivers, R. Q., Mitchell, P. and Cumming, R. G. (1999). Sensory impairment and driving: the Blue Mountains Eye Study. American Journal of Public Health, 89, 8587.Google Scholar
Iverson, D., Gronseth, G., Reger, M., Classen, S., Dubinsky, R. and Rizzo, M. (2010). Practice parameter update: evaluation and management of driving risk in dementia report of the quality standards subcommittee of the American academy of neurology. Neurology, 74, 13161324.Google Scholar
Jang, R. W. et al. (2007). Family physicians’ attitudes and practices regarding assessments of medical fitness to drive in older persons. Journal of General Internal Medicine, 22, 531543.Google Scholar
Joseph, P. G. et al. (2014). The Mini-Mental State Examination, clinical factors, and motor vehicle crash risk. Journal of the American Geriatrics Society, 62, 14191426.Google Scholar
Keeffe, J. E., Jin, C. F., Weih, L. M., McCarty, C. A. and Taylor, H. R. (2002). Vision impairment and older drivers: who's driving? British Journal of Ophthalmology, 86, 11181121.CrossRefGoogle ScholarPubMed
Klein, R., Klein, B. E., Linton, K. L. and De Mets, D. L. (1991). The Beaver Dam Eye Study: visual acuity. Ophthalmology, 98, 13101315.Google Scholar
Koepsell, T. et al. (2002). Crosswalk markings and the risk of pedestrian–motor vehicle collisions in older pedestrians. JAMA , 288, 21362143.CrossRefGoogle ScholarPubMed
Lipski, P. S. (2002). A survey of general practitioners' attitudes to older drivers on the New South Wales Central Coast. Australasian Journal on Ageing, 21, 98100.CrossRefGoogle Scholar
Lobjois, R. and Cavallo, V. (2009). The effects of aging on street-crossing behavior: from estimation to actual crossing. Accident Analysis & Prevention, 41, 259267.Google Scholar
Marino, M. et al. (2013). Interventions to evaluate fitness to drive among people with chronic conditions: systematic review of literature. Accident Analysis & Prevention, 50, 377396.Google Scholar
Marottoli, R. A. et al. (2007a). A randomized trial of a physical conditioning program to enhance the driving performance of older persons. Journal of General Internal Medicine, 22, 590597.Google Scholar
Marottoli, R. A. et al. (2007b). A randomized trial of an education program to enhance older driver performance. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 62A, 11131119.Google Scholar
Marshall, S., Demmings, E. M., Woolnough, A., Salim, D. and Man-Son-Hing, M. (2012). Determining fitness to drive in older persons: a survey of medical and surgical specialists. Canadian Geriatrics Journal, 15, 101.Google Scholar
Mathias, J. and Lucas, L. (2009). Cognitive predictors of unsafe driving in older drivers: a meta-analysis. International Psychogeriatrics, 21, 637653.Google Scholar
McGwin, G. Jr. et al. (2013). Examining the association between age-related macular degeneration and motor vehicle collision involvement: a retrospective cohort study. British Journal of Ophthalmology, 97, 11731176.Google Scholar
McGwin, G., Huisingh, C., Jain, S. G., Girkin, C. A. and Owsley, C. (2015). Binocular visual field impairment in glaucoma and at-fault motor vehicle collisions. Journal of Glaucoma, 24, 138143.Google Scholar
Mennemeyer, S. T., Owsley, C. and McGwin, G. (2013). Reducing older driver motor vehicle collisions via earlier cataract surgery. Accident Analysis & Prevention, 61, 203211.CrossRefGoogle ScholarPubMed
Meuleners, L. B., Hendrie, D., Lee, A. H., Ng, J. Q. and Morlet, N. (2012). The effectiveness of cataract surgery in reducing motor vehicle crashes: a whole population study using linked data. Ophthalmic Epidemiology, 19, 2328.CrossRefGoogle ScholarPubMed
Michael, Y. L., Green, M. K. and Farquhar, S. A. (2006). Neighborhood design and active aging. Health & Place, 12, 734740.Google Scholar
Molnar, F. J. and Simpson, C. S. (2010). Approach to assessing fitness to drive in patients with cardiac and cognitive conditions. Canadian Family Physician, 56, 11231129.Google Scholar
Monestam, E. (1999). Impact of cataract surgery. A population based approach. Acta Ophthalmologica Scandinavica, 77, 729.Google Scholar
Muir, S. W., Gopaul, K. and Odasso, M. M. M. (2012). The role of cognitive impairment in fall risk among older adults: a systematic review and meta-analysis . Age and Ageing, 41, 299308.Google Scholar
Musch, D. C., Gillespie, B. W., Lichter, P. R., Niziol, L. M. and Janz, N. K. (2009). Visual field progression in the collaborative initial glaucoma treatment study the impact of treatment and other baseline factors. Ophthalmology, 116, 200207.Google Scholar
NHTSA (2012). Traffic Safety Facts 2012: Older Population. (pp. http://www-nrd.nhtsa.dot.gov/Pubs/812005.pdf). Washington (DC): National Highway Traffic Safety Administration, Department of Transportation.Google Scholar
Odenheimer, G. L., Beaudet, M., Jette, A. M., Albert, M. S., Grande, L. and Minaker, K. L. (1994). Performance-based driving evaluation of the elderly driver: safety, reliability, and validity. Journal of Gerontology, 49, M153–M159.Google Scholar
Owsley, C., McGwin, G., Phillips, J. M., McNeal, S. F. and Stalvey, B. T. (2004). Impact of an education program on the safety of high-risk, visually impaired, older drivers. American Journal of Preventive Medicine, 26, 222229.Google Scholar
Owsley, C., McGwin, G. Jr., Sloane, M., Wells, J., Stalvey, B. T. and Gauthreaux, S. (2002). Impact of cataract surgery on motor vehicle crash involvement by older adults. JAMA, 288, 841849.Google Scholar
Owsley, C., Stalvey, B. T. and Phillips, J. M. (2003). The efficacy of an educational intervention in promoting self-regulation among high-risk older drivers. Accident Analysis and Prevention, 35, 393400.Google Scholar
Owsley, C., Stalvey, B., Wells, J. and Sloane, M. E. (1999). Older drivers and cataract: driving habits and crash risk. Journal of Gerontology: Medical Sciences, 54A, M203–M211.Google Scholar
Oxley, J. A., Ihsen, E., Fildes, B. N., Charlton, J. L. and Day, R. H. (2005). Crossing roads safely: an experimental study of age differences in gap selection by pedestrians. Accident Analysis & Prevention, 37, 962971.Google Scholar
Pager, C. K., McCluskey, P. J. and Retsas, C. (2004). Cataract surgery in Australia: a profile of patient-centred outcomes. Clinical and Experimental Ophthalmology, 32, 388392.Google Scholar
Quigley, H. A. (2011). Glaucoma. Lancet, 377, 13671377.Google Scholar
Ramrattan, R. S. et al. (2001). Prevalence and causes of visual field loss in the elderly and associations with impairment in daily functioning: the Rotterdam Study. Archives of Ophthalmology, 119, 17881794.CrossRefGoogle ScholarPubMed
Rapoport, M. J. et al. (2014). Developing physician consensus on the reporting of patients with mild cognitive impairment and mild dementia to transportation authorities in a region with mandatory reporting legislation. The American Journal of Geriatric Psychiatry, 22, 15301543.Google Scholar
Redelmeier, D. A., Yarnell, C. J., Thiruchelvam, D. and Tibshirani, R. J. (2012). Physicians' warnings for unfit drivers and the risk of trauma from road crashes. New England Journal of Medicine, 367, 12281236.Google Scholar
Reger, M. A., Welsh, R. K., Watson, G., Cholerton, B., Baker, L. D. and Craft, S. (2004). The relationship between neuropsychological functioning and driving ability in dementia: a meta-analysis. Neuropsychology, 18, 85.Google Scholar
Retting, R. A., Ferguson, , , S. A. and McCartt, A. T. (2003). A review of evidence-based traffic engineering measures designed to reduce pedestrian-motor vehicle crashes. American Journal of Public Health, 93, 14561463.CrossRefGoogle ScholarPubMed
Rochtchina, E., Mukesh, B. N., Wang, J. J., McCarty, C. A., Taylor, H. R. and Mitchell, P. (2003). Projected prevalence of age-related cataract and cataract surgery in Australia for the years 2001 and 2021: pooled data from two population-based surveys. Clinical and Experimental Ophthalmology, 31, 233236.Google Scholar
Ross, L. A., Edwards, J. D., O'Connor, M. L., Ball, K. K., Wadley, V. G. and Vance, D. E. (2016). The transfer of cognitive speed of processing training to older adults' driving mobility across 5 years. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 71 (1), 8797. doi: 10.1093/geronb/gbv022.Google Scholar
Rubin, G. S. et al. (1997). A comprehensive assessment of visual impairment in a population of older Americans. The SEE Study. Salisbury Eye Evaluation Project. Investigative Ophthalmology and Visual Science, 38, 557568.Google Scholar
Rubin, G. S., Ng, E. S., Bandeen-Roche, K., Keyl, P. M., Freeman, E. E. and West, S. K. (2007). A prospective, population-based study of the role of visual impairment in motor vehicle crashes among older drivers: the SEE study. Investigative Ophthalmology & Visual Science, 48, 14831491.Google Scholar
Scheiman, S., Moghaddas, H. S., Björnstig, U., Bylund, P.-O. and Saveman, B.-I. (2010). Bicycle injury events among older adults in Northern Sweden: a 10-year population based study. Accident Analysis & Prevention, 42, 758763.CrossRefGoogle ScholarPubMed
Sengupta, S., van Landingham, S. W., Solomon, S. D., Do, D. V., Friedman, D. S. and Ramulu, P. Y. (2014). Driving habits in older patients with central vision loss. Ophthalmology, 121, 727732.CrossRefGoogle ScholarPubMed
Sims, J., Rouse-Watson, S., Schattner, P., Beveridge, A. and Jones, K. (2012). To drive or not to drive: assessment dilemmas for GPs. International Journal of Family Medicine. doi: 10.1155/2012/417512.Google Scholar
Marottoli, R. A. et al. (1998). Development of a test battery to identify older drivers at risk for self-reported adverse driving events. Journal of the American Geriatrics Society, 46, 562568.Google Scholar
Subzwari, S., Desapriya, E., Scime, G., Babul, S., Jivani, K. and Pike, I. (2008). Effectiveness of cataract surgery in reducing driving-related difficulties: a systematic review and meta-analysis. Injury Prevention, 14, 324328.Google Scholar
Wang, J. J., Foran, S. and Mitchell, P. (2000). Age-specific prevalence and causes of bilateral and unilateral visual impairment in older Australians: the blue mountains eye study. Clinical and Experimental Ophthalmology, 28, 268273.Google Scholar
Weih, L. M., Nanjan, M., McCarty, C. A. and Taylor, H. R. (2001). Prevalence and predictors of open-angle glaucoma: results from the visual impairment project. Ophthalmology, 108, 19661972.Google Scholar
Weih, L. M., VanNewkirk, M. R., McCarty, C. A. and Taylor, H. R. (2000). Age-specific causes of bilateral visual impairment. Archives of Ophthalmology, 118, 264269.Google Scholar
Wood, J. M., Anstey, K. J., Kerr, G. K., Lacherez, P. F. and Lord, S. (2008). A multidomain approach for predicting older driver safety under in-traffic road conditions. Journal of the American Geriatrics Society, 56, 986993.Google Scholar