Assessment of cognitive screening tests as predictors of driving cessation: A prospective cohort study of a median 4-year follow-up.

BACKGROUND: Assessing fitness to drive and predicting driving cessation remains a challenge for primary care physicians using standard screening procedures. The objective of this study was to prospectively evaluate the properties of neuropsychological screening tests, including the Trail Making Test (TMT), Clock Drawing Test (CDT), Montreal Cognitive Assessment (MoCA), Useful Field of View (UFOV), and Timed Up and Go (TUG) test, in predicting driving cessation for health reasons in drivers older than 70 years of age. DESIGN AND METHODS: This prospective cohort study, with a median follow-up of 4 years for drivers of 70 years old or older with an active driving license in Switzerland, included 441 participants from a driving refresher course dedicated to volunteer senior drivers. Cases were drivers reported in the national driving registry who lost their license following a health-related accident, who were reported as unfit to drive by their physician or voluntarily ceased driving for health reasons. Survival analysis was used to measure the hazard ratio of driving cessation by adjusting for age and sex and to evaluate the predictive value of combining 3 or more positive tests in predicting driving cessation during a 4-year follow-up.
RESULTS: A total of 1738 person-years were followed-up in the cohort, with 19 (4.3%) having ceased driving for health reasons. We found that participants with a TMT-A < 54 sec and TMT-B < 150 sec at baseline had a significantly lower cumulative hazard of driving cessation in 4 years than those with slower performance (adjusted HR 3, 95% CI: 1.16-7.78, p = 0.023). Participants who performed a CDT ≥ 5 had a significantly lower cumulative hazard of driving cessation (adjusted HR 2.89, 95% CI: 1.01-7.71, p = 0.033). Similarly, an MoCA score ≥ 26, TUG test <12 sec or a UFOV of low risk showed a lower but not significant cumulative risk at a median follow-up of 4 years. When using tests as a battery, those with three or more positive tests out of five were 3.46 times more likely to cease driving (95% CI: 1.31-9.13, p = 0.012).
CONCLUSIONS: The CDT and the TMT may predict driving cessation in a statistically significant way, with a better performance than the UFOV and MoCA tests during a median 4-year follow-up. Combining tests may increase the predictability of driving cessation. Although our results are consistent with current evidence, they should be interpreted with precaution; more than 95% of the participants above the set threshold were able to continue driving for 4 years without any serious incident.

Introduction

Background/rationale

Fitness to drive in the geriatric population and the potential effects of cognitive and physical decline remain a difficult challenge for primary care physicians [1]. Research has shown that maintaining out-of-home mobility is of great importance for people moving to old age from late midlife [2]. Il is well established the association between driving cessation and functional dependency, depressive disorders, social dysfunction and mortality, with a considerable individual and societal impact [3]. As many adverse health problems have been related to driving cessation in later life, predicting and evaluating accurately the decline of driving capacity of older drivers is of critical importance [4]. Supporting and stimulating out-of-home mobility in the elder population, detecting and preventing a functional decline and possible future driving cessation, depends on individual screening strategies, as well as on transport policy and social policy measures [5].

Concerning the individual screening strategies, many clinical tests to evaluate the driving aptitude of patients, including cognitive, mental, motor and vision tests, have been proposed [1]. Among the promising neuropsychological tests, the Trail Making Test (TMT-A and TMT-B) and the useful field of view (UFOV) were estimated to be predictors of unsafe driving in a meta-analysis [6]. In older drivers, the TMT has a 63,6% sensitivity, 64,9% specificity, low positive predictive value (9,5%) and high negative predictive value (96,9%) for poor driving performance in a translational study [7]. Furthermore, TMT can be a useful tool in evaluating driving performance, as it can estimate various functions, such as visual scanning, executive function and graphomotor speed [8].

The UFOV, a computer-based test that evaluates visual processing speed and attention [9], has been developed and investigated as a tool to detect poor driving performance and has been correlated with increased risks of on-road accidents [10]. Normative values, based on measures taken from drivers referred to specialized centers for testing, have been used to define benchmarks for clinical practice [11]. It is, however, not clear how the instrument has managed to distinguish cognitive decline from visual reduction in contrast sensitivity or visual acuity, which also affects test and driving performance [12]. The literature also fails to clearly distinguish the old from the new version of the test, making it difficult to clearly assess its ability to predict on-road events [13].

Based on the current literature, cognitive tests alone have not shown a high predictive value in predicting unfit drivers and driving cessation [14]. Cognitive evaluation tests such as the Montreal Cognitive Assessment (MoCA) are considered to be useful screening tools to detect declining driving performance, but their predictive properties have shown low sensitivity (84.5%) and specificity (50%) with a cutoff score ≤25 [15]. Furthermore, a poor clock drawing test (CDT) performance has been correlated with a decline in driving aptitude evaluated by a driving simulation in a prospective cohort study [16]. A CDT score less than 5 out of 7 points was associated with significantly more driving errors and hazardous driving, indicating the need for a formal driving evaluation [16]. More recent studies show that the CDT could have limited screening predictive value as a solitary screening tool of driving performance [17]. The Mini Mental State Examination (MMSE) score has shown a small effect in predicting unsafe driving in older drivers [6]. Concerning the Timed Up and Go (TUG) test, which evaluates the ability to walk and balance safely and efficiently, the current literature shows that it could be a predictor of driving cessation (OR 12.60, CI 2.74–57.89; p < 0.01) [18]. A modified version of the TUG test has demonstrated high sensitivity for detecting fall risk in elderly individuals with good predictive values, rendering this test a possible predictor for elderly functional decline [19].

Although vision abilities, health indicators and physical capacities are usually considered to determine the driving aptitude, instrumental functional performance and cognitive speed of processing, indicators such as the UFOV were shown to be better predictors of driving cessation in a 3-year study [20]. In another 2-year cohort study, age and crash history were shown to indicate high-risk drivers [21]. Despite multiple tests evaluating cognitive decline and driving cessation [22], there is weak evidence in the current literature on the added value of these tests in screening procedures for predicting driving cessation for health reasons.

Objectives

The primary objective of our study was to prospectively evaluate to what extent the TMT, the CDT, the MoCA test and the UFOV test can predict driving cessation in drivers aged 70 years or more in a Cox proportional hazard regression model. A secondary objective was to evaluate the predictive value of combining 3 or more positive tests in predicting driving cessation during a median 4-year follow-up.

Methods

Study design

We designed and conducted a prospective cohort study, the GARAge study, in collaboration with the State Driver and Vehicle Licencing Agency and the Swiss Automobile Club. All active drivers 70 years or older from predefined French-speaking regions of Switzerland were invited for a refresher course, and all drivers who participated in the course were invited to participate in the study to minimize bias. The median follow-up of these drivers was 4 years.

Setting

The inclusion of participants took place between 2011 and 2013 in French-speaking regions of Switzerland (Lausanne, northern Vaud, Valais, Vevey, Montreux, Aigle and Entremont). Driving cessation was recorded by the official authorities, the State Driver and Vehicle Agency. Data were collected and registered in an independent way to eliminate bias through a median follow-up period of 4 years.

Participants

All drivers aged 70 years of age or more who were residents of the seven defined regions of Switzerland (n = 16,858) were invited to a driving refresher course and to participate in the study. A total of 1,004 drivers agreed to participate in the refresher course. The participants were included in the study between May 2011 and September 2013 with a variable follow-up period ranging from 3 to 5 years and a median follow-up of 4 years. A total of 441 drivers fulfilled the inclusion criteria and agreed to participate. To participate, drivers had to be aged 70 years or more, have a valid Swiss driver’s license, and not be institutionalized.

All participants were informed in detail about the procedures of the cohort and the data management. They were provided and signed a written informed consent form before inclusion in the study, in accordance with the ethical standards of the amended Declaration of Helsinki of 2008, Seoul. The participants voluntarily completed a driving test administered by the TSC (Auto Club Suisse) on the road during the day. Each participant completed a questionnaire with personal and health-related information, as well as information about driving habits. The study protocol was validated by the cantonal commission of human research ethics of the Vaud region, Switzerland (www.cer-vd.ch) and registered under the reference number CE 157/11.

Exposure

We used basic and widely accepted scores (CDT, MoCA, TMT, UFOV) to assess the functional cognitive and visual capacities of older individuals and indirectly assess and predict their driving decline or cessation.

Clock drawing test (CDT)

We calculated the CDT score using Freund’s methods and the established 7-point scale [16]. Correct time indication is awarded up to 3 points, correct numbers are awarded up to 2 points and correct spacing is awarded up to 2 points. A positive CDS was defined as a score <5, a cutoff that showed significant positive and negative predictive values in previous studies [16]. Each score was evaluated twice, independently blinding the assessor to any other clinical information. Discrepancies were solved by an additional person.

Montreal cognitive assessment (MoCA)

The MoCA was used to estimate the cognitive and functional status of the participants [23]. A cutoff of <26 was retained as a sensitive reference value for the diagnosis of mild cognitive impairment (MCI), following the current literature [15].

Trail making test (TMT)

The TMT test consists of 2 parts. TMT-A calculates the time needed to connect 25 circles in ascending number order up to 25. In the TMT-B the participant has to connect 13 numbers and 12 letters alternatively in ascending numerical and alphabetical order [8]. Based on previous literature, we used the commonly accepted cutoffs of ≥54 sec for TMT-A or ≥150 sec for TMT-B [7].

Useful field of view (UFOV)

The UFOV test is a computer-based visual and cognitive assessment and consists of 3 subtests. UFOV-1 tests processing speed, UFOV-2 tests processing speed during a divided attention task, and UFOV-3 tests processing speed for a selective attention task [11]. The reaction speed of the participants is measured in milliseconds (14–500 ms), and lower scores indicate better and faster performance on the tasks [24]. The participants were classified as low, moderate or high risk depending on their performance using the set standards from the UFOV. The dichotomization for survival analysis was accomplished by dividing the drivers into a moderate- to very-high-risk group and a low-risk group.

Timed up and go (TUG)

We used a modified TUG test, as it is more adapted for clinical settings as a quick, reliable and easily performed functional evaluation tool for the elderly population [25]. It has been shown to be one of the best performance tests for evaluating balance and functional impairment [26]. Mobility status is considered a strong predictor of TUG performance, and values below 12 seconds are used as the normal cutoff for elderly people 65 to 85 years old [27]. Cognitive impairment does not seem to affect the reliability of TUG test scores, and the variability of the results increases with the time needed to execute the test [28].

Data sources/measurement

All participants were evaluated at baseline by independent staff, and information about medical status and driving events was recorded. Cognitive tests (TMT, CDT, MoCA test), ophthalmologic tests (UFOV) and mobility tests were performed for each participant. Data were collected on paper and in electronic form and double checked to minimize systematic errors. Data were collected and registered in an independent way to eliminate bias through a follow-up period of 4 years.

The on-road driving evaluation was performed by twelve certified driving instructors who participated in the study, either as self-employed or as employees of the Swiss Automobile Club. The routes were standardized for participants from the same region, were validated by the Swiss National Council for Road Security and were adapted the current Switzerland traffic control and examination standards. In order to minimize bias, all driving instructors were blinded to the psychometric and functional characteristics of the participants. Details about the methods used for the evaluation of driving performance can be found in our previous publications [7].

Case definition

The outcome was defined as driving cessation caused by accident, voluntary cessation or cessation following medical advice. The outcome was recorded in an objective way, as it was provided by the official authorities and extracted from the State Driver and Vehicle Licencing Agency from the 12th of July 2016 to the 16th of August 2016 to minimize bias. Due to this method, the median follow-up of the study participants was 4 years. After the end of the trial, the dataset was locked and analyzed by an independent statistician. The statistical methods were predefined before the study and the multiplicity of analysis was taken into account. To reduce publication bias, all the results were analyzed and published.

Sample size

Sample size was estimated using the log-rank test (Freedman method). Significant level was set at 0.05, and power was set at 0.8. The study was powered to detect an HR of 2, with an expected 90% of control subjects continuing to drive versus 80% for control. Expecting a ratio of 2 to 1 for control vs cases and a loss to follow-up of 5%, 483 participants should be recruited. In total, 441 drivers were included in the follow-up and the final analysis, which rendered our study sample underpowered for the expected results.

Statistical methods

Hazard ratios were measured using Cox proportional hazards regression. We tested the assumption of proportionality using the Nelson Aalen estimate for the cumulative hazard function, concluding that the assumption was met. Patients who moved out of the canton, died or ceased driving for another reason were censored. We ran a secondary Cox regression model adjusting for age and sex. Furthermore, using Breslow methods, we verified whether the incidence of events was constant over time. We transformed all continuous variables to range from 0 to 1 for the 25th and 75th percentiles of the studied population.

Following the current literature, we considered the MoCA, TMT, UFOV, CDT, and TUG as a battery of tests [29]. This would allow us to address the secondary objective of the study and evaluate the hypothesis of the predictive power of regrouping at least three positive tests regarded as a single test, as recommended by the literature [30]. Those with three or more positive tests were compared to those with two or fewer positive tests using an unadjusted and an adjusted Cox proportional hazard regression analysis [31].

The study protocol defined the statistical methods before the statistical analysis, which was performed by the STATA16 program. All data and statistical analysis files are available in Zenodo under the https://doi.org/10.5281/zenodo.4717755, following the current guidelines.

Results

Baseline characteristics/descriptive data

All 16,858 active drivers, aged 70 years or more, of French-speaking regions of Switzerland were invited to participate in a driving refresher course. From 1,004 course participants, 441 were included in the median 4-year follow-up (Fig 1).

Fig 1

Cohort flow chart of study participants.

From 1,004 course participants, 441 were included in the median 4-year follow-up. MoCA: MoCA: Montreal Cognitive Assessment, EPFL: École Polytechnique Fédérale Lausanne, n = sample size.

The baseline characteristics of the sample were recorded at inclusion. Of the 441 participants, 216 (49.0%) were considered exempted from any health condition known to affect driving. Population baseline characteristics and health profiles are provided in .

Concerning the driving distance per week, the majority of the participants (53.3%) declared driving more than 200 km per week. The remaining 46.7% of the drivers declared a distance less than 150 km per week. A total of 73.2% of the participants had no accident during the previous 2 years, and 20.4% had 1 accident. The remaining 6.4% had 2 or more accidents on their driving records. The on-road driving performance was estimated to be excellent for 47%, good for 26.5%, moderate for 21.2% and poor for 5.3% of the participants.

The CDT was found to be positive at baseline in 15% of the drivers (66 participants), with a positive test defined as a score less than 5. The mean MoCA score was 26.4 (95% CI: 26.2–26.7) at baseline, and the modified MoCA score was 23.6 (95% CI: 23.4–23.9). The TUG test was normal for all individuals at baseline (8.5 sec, 95% CI: 8.2–8.7).

Survival

A total of 1738.5 person-years were observed within the study. The hazard regression survival analysis showed that the risk of driving cessation in a median follow-up of 4 years was 2.89 times higher for drivers who had a CDT<5 at baseline than for those with a score of CDT>5 (hazard ratio 2.89, 95% CI: 1.01–7.71, p = 0.033) (Fig 2A). Drivers who had a TMT-A ≥54 sec or TMT-B ≥150 sec at baseline had a 3 times higher risk of driving cessation than those with lower TMT scores (hazard ratio 3, 95% CI: 1.16–7.78, p = 0.023) (Fig 2B). The risk of driving cessation in a median follow-up of 4 years was 1.9 times higher for drivers who had a moderate-to-high UFOV risk at baseline than for those with a low UFOV risk (hazard ratio 1.9 (95% CI: 0.75–4.82, p = 0.178) (Fig 2C) and 1.24 times higher for drivers who had an MoCA score < 26 at baseline than for those with a higher MoCA score (hazard ratio 1.24, 95% CI: 0.48–3.18, p = 0.660) (Fig 2D).

Fig 2

Cox proportional hazards regression of driving cessation depending on baseline functional status.

Cox proportional hazards regression of driving cessation during a median 4-year follow-up depending on baseline: a) CDT score, b) TMT-A and TMT-B score, c) UFOV, and d) MoCA score. CDT: Clock Drawing Test, TMT: Trail Making Test, UFOV: Useful Field of View, MoCA: Montreal Cognitive Assessment.

Unadjusted analysis

The unadjusted hazard of driving cessation was found to be 2.89 times higher for drivers who with a CDT<5 at baseline than for those with a CDT>5 (hazard ratio 2.89, 95% CI: 1.1–7.62, p = 0.031), as shown in Table 2. The risk of driving cessation was estimated to be 3.44 times higher for drivers who had a TMT-A ≥54 sec or a TMT-B ≥150 sec at baseline than for those who had lower TMT scores (hazard ratio 3.44, 95% CI: 1.35–8.76, p = 0.009). The difference was statistically significant between drivers with low CDT and TMT score performance at baseline regarding the prospective risk of driving cessation.

Table 2

Cox proportional hazards regression for driving cessation.

		Unadjusted analysisa		Adjusted analysisb
	Prevalence (n)	HR (95%CIc)	P-valued	HR (95%CIc)	P-valued
• CDT < 5	15% (66)	2.89 (1.1–7.62)	0.031	2.89 (1.01–7.71)	0.033
• MoCA < 26	29.7% (131)	1.41 (0.56–3.59)	0.467	1.24 (0.48–3.18)	0.660
• TMT (A ≥54 sec. or B ≥150 sec.)	35.4% (156)	3.44 (1.35–8.76)	0.009	3 (1.16–7.78)	0.023
• TUG (≥12 sec.)	10.9% (48)	1.95 (0.56–6.74)	0.292	1.83 (0.52–6.4)	0.343
• UFOV moderate-to-high risk	29.3% (129)	2.29 (0.93–5.63)	0.072	1.9 (0.75–4.82)	0.178
Battery at least 3 positive testse	14% (62)	3.46 (1.31–9.13)	0.012	3.12 (1.15–8.55)	0.026

aUnadjusted Cox proportional hazard regression for driving cessation for CDT, MoCA, TMT, TUG and UFOV at a median 4-year follow-up.

bAdjusted Cox proportional hazard regression for CDT, MoCA, TMT, TUG and UFOV, adjusted for sex and age category, in a median 4-year follow-up

cCI: Confidence Interval.

dP-value of Cox regression–Breslow method for ties, evaluating the hazard ratio of drivers with low performance in the respective tests compared to high-performance drivers.

eBattery at least three positive tests out of five (MoCA, TMT, UFOV, CDT, TUG) considered as a single test.

CDT: Clock Drawing Test. MoCA: Montreal Cognitive Assessment. TMT: Trail Making Test. TUG: Timed Up and Go test. UFOV: Useful Field of View.

The risk of driving cessation in a median follow-up of 4 years was calculated as 2.29 times higher for drivers who had a moderate-to-high UFOV risk at baseline than for those who had a low UFOV risk (hazard ratio 2.29, 95% CI: 0.93–5.63, p = 0.072). Furthermore, we found that the risk of driving cessation was 1.41 times higher for drivers who had an MoCA score < 26 at baseline than for those who had an MoCA score in the normal range (hazard ratio 1.41, 95% CI: 0.56–3.59, p = 0.467). The Cox proportional hazards regression of driving cessation did not show a statistically significant difference based on UFOV or MoCA scores at baseline.

We analyzed the predictive value of a battery of tests, as it would be impossible to test a model including multiple tests simultaneously, as only 4.3% of the drivers ceased driving for health reasons. Suspicious cases were defined arbitrarily by considering at least three positive tests out of five (MoCA, TMT, UFOV, CDT, TUG) regarded as a single test. We found that the cumulative hazard of driving cessation was significantly high in the unadjusted analysis for drivers who had a battery of at least three positive tests (unadjusted HR 3.46, 95% CI: 1.31–9.13, p = 0.012) (Fig 3).

Fig 3

Cox proportional hazards regression of driving cessation depending on baseline battery testing.

Cox proportional hazards regression of driving cessation during a median 4-year follow-up depending on a battery by considering at least three positive tests out of five (CDT, TMT, UFOV, MoCA, TUG) regarded as a single test. CDT: Clock Drawing Test, TMT: Trail Making Test, UFOV: Useful Field of View, MoCA: Montreal Cognitive Assessment. TUG: Time Up and Go test.

Adjusted analysis

We adjusted the Cox proportional hazard regression analysis by taking into consideration age and sex (Table 2). The adjusted analysis suggests that the hazard ratio for driving cessation in older drivers of the same age, same sex and with a CDT score < 5, compared to those with a score ≥5, is 2.89 (95% confidence interval 1.01 to 7.71) during a median 4-year follow-up. This average 2.89-fold elevation in hazard for driving cessation is statistically significant, as the confidence interval, even though relatively large, does not include the value 1 and the p value is small (p = 0.033). Therefore, there is evidence of a difference in the hazard of the outcome between the two groups, in favor of the group performing better on the CDT, when adjusting for age and sex. The hazard ratio for driving cessation is 3 times higher for drivers with a TMT-A score ≥54 sec or a TMT-B ≥150 sec, when adjusting for age and sex, than for those with better TMT times. The 95% confidence interval ranges between 1.16 and 7.78, showing a statistically significant difference (p = 0.023). Although the risk of driving cessation seemed higher for drivers with an MoCA score <26 at baseline (HR 1.24, 95% CI: 0.48–3.18, p = 0.660), for drivers with a TUG test ≥12 sec (HR 1.83, 95% CI: 0.52–6.4, p = 0.394) and for drivers with a UFOV of moderate-to-high risk (HR 1.9, 95% CI: 0.75–4.82, p = 0.178), there was no evidence for a statistically significant difference.

Concerning the battery testing analysis, the cumulative hazard of driving cessation was significantly high in the adjusted analysis for drivers who had a battery of at least three positive tests (adjusted HR 3.12, 95% CI: 1.15–8.55, p = 0.026). The adjusted analysis is consistent with the results of the unadjusted analysis with similar hazard ratios and confidence intervals, and the observed associations were independent of sex and age.

Discussion

The median 4-year follow-up of the study participants showed that the CDT and the TMT may predict driving cessation in a statistically significant way, with a better performance than the UFOV and MoCA tests. The participants with a TMT-A < 54 sec or a TMT-B < 150 sec had a significantly lower cumulative hazard of driving cessation than those with a TMT-A ≥ 54 sec or a TMT-B ≥ 150 sc. The participants with a CDT score > 5 had a significantly lower cumulative hazard of driving cessation than those with a CDT score < 5. Similarly, an MoCA score ≥ 26 or a UFOV of low risk showed a lower cumulative risk at a median 4-year follow-up than an MoCA score <26 or a UFOV of moderate-to-high risk, although there was no evidence of a statistically significant difference, perhaps due to the limited power of the study.

Strengths and limitations

One of the strengths of the current study was the representativeness of the sample, as we invited all active drivers aged 70 years or older who were registered by the official automobile authorities in a considerable region of Switzerland. We finally included in the follow-up those interested in the cognitive screening and the refresher course, elements that render our sample a representative sample of the elderly driver population in the primary care ambulatory setting of Switzerland. The inclusion procedure in the cohort minimizes any possible selection bias and contributes to the internal and external validity of the study.

Another strength of the current study was the prospective follow-up of a median of 4 years, which diminishes bias. The outcome of driving cessation was registered in an objective way by independent personnel in the setting of the official driving authorities, the State Driver and Vehicle Licencing Agency in Switzerland. The data were registered in an independent database with the information provided by the official authorities concerning driving cessation due to medical decisions or voluntary cessation. The independent and objective source of information concerning the outcome of the cohort renders the results more plausible, minimizing any potential source of bias and thus increasing the external validity of the study.

One of the major limitations of the study was that we did not adjust for confounding factors other than age and sex. Other residual confounding factors may exist, and the results should be interpreted with precaution. The outcome of interest, driving cessation, could have been based on medical decisions relying on tests similar to those defining exposure. Less subjective outcomes such as on-road accidents or deaths would have been more relevant but require including many more participants. The study was not powered to evaluate these outcomes. Furthermore, the current study does not provide any evidence of a causal relationship between the evaluated cognitive tests and driving cessation but shows a risk association depending on the baseline performance of basic primary care tests, such as the MoCA, the CDT, the UFOV and the TMT, in a prospective way. We can consider the results as an indicator of the strength of associations between initial tests and driving cessation during a median 4-year follow-up. The strength and significance level of the association is therefore by no means an indicator of the clinical importance of the tests. Indeed, for the studied tests, the number of false positives would be a better indicator of the avoidable societal burden from the procedure.

The fact that our study was underpowered consists of another major limitation of the results and statistical significance. The sample size estimated using the log-rank test (Freedman method) for a significance level of 0.05 and a power of 0.8 to detect an HR of 2 was 483 participants. We finally included 441 drivers in the follow-up, and the final analysis could have influenced the expected results. A low MoCA test and an UFOV of moderate-to-high risk seem to increase the risk of driving cessation, although they do not reach the expected level of statistical significance, perhaps due to the lack of power of the study. Furthermore, driving cessation events were rare, rendering the current prospective cohort study relatively underpowered.

We should mention that our results do not take into consideration the specific clinical situation of driving cessation and the reasons for medical decisions to stop driving for elderly participants. We independently evaluated driving cessation either voluntarily or due to medical decisions, independent of the medical reasons that could have led to this decision. This lack of information was due to the absence of medical details that led to driving cessation by the official authorities’ registries. Furthermore, our study was underpowered to detect any potential effect and relationship with medical reasons for driving cessation. It is important to note that health status can change over time and results from clinical tests might be affected. The study therefore does not rule out that participants exposure status might have changed over time.

Aside from the number of motor vehicle accidents, the number of citations (warnings or traffic tickets), the driving errors or traffic violations, could contain valuable information of fitness to drive. Current research shows that older drivers’ driving errors may not be related to functional performance in healthy older adults [32]. One of the limitations of the current study is that we do not have information about the driving risks or the behavior of the participants. As this analysis was not included in the objectives of our study, the sample size was not powered enough in order to take into account the potential driving errors or violations and our results should be interpreted by taking into account this limitation. Furthermore, like most neuropsychological tests, the TUG is not only dependent of cognitive functions, as pain and reduced mobility can also affect results. This is the case for all tests, and neuropsychological tests need to be interpreted with care when assessing fitness to drive.

Another limitation of the current study, which is important for the evaluation of the external validity of the results, is the age limit of the study participants. The Cox proportional analysis and the results apply a sample of elderly drivers aged 70 years or more. We do not know whether the cognitive tests used in our study can play a predictive role or show an association with driving cessation prospectively in younger drivers. Thus, the results of this study cannot be applied to populations younger than 70 years old. Furthermore, our sample included patients from rural remote areas as well as cities, and this be considered when assessing the external validity limitations of the results, as driving cessation may be easier in rural areas. Public transports are highly developed in Switzerland even in rural areas making transportation available to most citizens.

Concerning battery testing, the combination of one or more tests can enhance the specificity of prediction, as shown by previous literature [30]. As most tests used in the current study are independent, our estimate of hazard and predictive values can be considered valid. However, a limitation of the current study is that the CDT is a part of the MoCA test and their combination in battery testing could influence the results. Even in this case of possible “convergence”, as explained by Parikh et al. [30], the use of three tests would have minimal clinical significance but should be taken into consideration in the interpretation of the battery Cox proportional hazard analysis.

Interpretation

Although our results are consistent with current evidence, they should be interpreted with precaution, taking into consideration the limitations of the current study. The results of the study show an association between low performance on the baseline tests of the MoCA, UFOV, CDT and TMT and a relatively higher risk of driving cessation prospectively. Recent studies show the importance of working memory and other cognitive functions, as patients with MCI have a higher prevalence of driving cessation [33]. Poor performance on diagnostic tests, such as the digit span backward test, has been shown to be associated with prospective driving cessation (OR: 0.493, 95% CI: 0.258–0.939) [33], although other tests, such as the MMSE, have not shown a significant effect [34].

Recent studies suggest that the MoCA could be a valuable tool for identifying fitness to drive, especially when adjusting for factors such as age, sex and walking speed [35]. These results are in accordance with our findings that show a 1.24-fold higher risk for driving cessation for elderly individuals who have an MoCA score < 26 at baseline than for those with a higher MoCA score, although we did not reach statistically significant results. A reason may be that our study was underpowered or a different cutoff should be considered, as suggested by sensitivity analysis showing an abnormal MoCA cutoff score < 28 [35].

Poor clinical evaluation by screening tools, such as the MMSE, TMT and CDT, seems to be independently associated with unsafe driving, defined as committing hazardous errors and/or traffic violations, and with restricted driving, defined as committing traffic or rule violations under certain driving conditions in a retrospective cross-sectional study [36]. Our prospective cohort study supports this hypothesis, as a driver with a CDT score <5 at baseline may have a 2.89 times higher risk of prospective driving cessation at 4 years than a driver with a CDT score >5 (p = 0.033).

Furthermore, recent studies show that drivers with a higher TMT test are associated with unsafe or restricted driving in a statistically significant way [36], while others argue that the TMT may not be specific enough in clinical practice to predict driving cessation without other investigations [7, 37]. Slow psychomotor speed and visual perception were associated with stopping or reducing driving in a one-year prospective cohort study with a significant OR of 1.15 (95% CI: 1.03–1.28) [38]. Our findings are in line with the current evidence, showing that a driver with a TMT-A ≥54 sec or a TMT-B ≥150 sec at baseline may have a threefold risk of driving cessation compared to those with lower TMT scores (p = 0.023). However, there is lack of evidence supporting the use of neuropsychological tests for screening purposes in assessing fitness to drive. Our study suggests most tests to have low predictive value for correctly classifying patients in risk levels for future driving cessation. Adapted ecological on-road tests might be more valuable in correctly assuming patients are unfit to drive, taking into account the screening limitations and methods to overcome them [39].

Additionally, evidence shows that instrumental cognitive and functional status is important when assessing fitness to drive, in association with visual and constructional functioning and visuospatial abilities [34]. A ten-year follow-up prospective cohort study with 1248 participants showed that slower speed of processing as measured by the UFOV test could be a predictor of driving cessation (HR 1.76, p<0.01) [40]. Our findings seem to be in accordance with previous evidence, suggesting that a low-risk UFOV may be associated with a lower risk of driving cessation, although the results did not reach statistical significance (HR 1.9, p = 0.178).

The current literature on the evaluation of a battery of neurocognitive tests for the prediction of driving cessation is limited. Our findings that the combination of at least three out of five positive tests (MoCA, TMT, UFOV, CDT, TUG) may be associated with an increased hazard of driving cessation in the adjusted and unadjusted analysis (p<0.05) could be a useful tool for risk assessment in the primary care setting, taking into consideration the limitations mentioned above.

According to current evidence, neuropsychological and visuospatial tests can play a major role in screening elderly drivers 70 years old or older, but they should not be considered discriminating or decisive for their actual or future driving capacity [41]. They can show an association between low cognitive test performance and driving cessation prospectively, but this association is not direct and not causal. Thus, we can conclude that no test seems to independently predict driving cessation with reliable predictive values, and more specific tests and on-road driving performance evaluations should be taken into consideration by the official authorities when determining driving authorization in the elderly population.

Practical implications

The results of our study can be generalized to the elderly Swiss population by taking into consideration the baseline characteristics of the participants of the study. The participants were drivers 70 years old or older with an active driving license from French-speaking Switzerland areas. The sample can be considered representative of the general population, as we invited all the individuals who had active driving licenses following the official authority records, minimizing the risk of selection bias. Thus, our sample can be generalized to drivers requiring systematic screening in the primary care setting in ambulatory health care, where the abovementioned tests are accessible and can easily be performed.

Although tests such as the TMT, UFOV, MoCA and CDT may be associated with future driving cessation, individually or in batteries of at least three positive tests, we do not have any evidence that these tests can independently predict driving cessation with reliable predictive values [42]. There is a lack of evidence on the validity of neuropsychological tests to correctly identify fitness to drive, therefore they only provide indicators of associations without giving indications of validity [43]. Further research is needed to define or develop specific tests in association with on-road driving performance evaluations that could be taken into consideration by the official authorities when determining driving authorisation in the elderly population.

Conclusion

Our study confirms previous findings that neuropsychological tests may be associated with driving cessation. However, predicting driving cessation by cognitive and visual tools in the primary care setting remains a challenge. The TMT, the CDT, the MoCA test and the UFOV could be considered possible tools to estimate the risk of driving cessation in drivers 70 years old or older. Although the CDT and the TMT seem to predict driving cessation in a statistically significant way, with a better performance than the UFOV and MoCA tests, the association remains weak for a 4-year period, and further research is required before setting cutoff values to impose driving cessation based on these tests alone. A battery of at least three positive tests may also be associated with a higher hazard of driving cessation. Although our results are consistent with current evidence, they should be interpreted with precaution, taking into consideration the limitations of the study. Further research is needed to define specific screening tests in association with on-road evaluations as possible and reliable predictors of driving cessation in elderly patients.

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

2 Jun 2021

PONE-D-21-15340

Assessment of cognitive screening tests as predictors of driving cessation: a prospective cohort study of a median 4-year follow-up

PLOS ONE

Dear Dr. Kokkinakis,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 17 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Abiodun E. Akinwuntan, PhD, MPH, MBA

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for offering an opportunity to review an article titled “Assessment of cognitive screening tests as predictors of driving cessation: a prospective cohort study of a median 4-year follow-up”. This article investigated the driving cessation predictability based on various clinical tests in drivers aged 70 or higher. Overall, the manuscript is well written. However, there are still some concerns as stated below. Therefore, I would like to recommend a major revision for the current manuscript.

Abstract

Well written.

Introduction

1. Background information of clinical tests were well described.

2. Lack of discussion on driving cessation based on previous literature. For example, readers may also want to know when driving cessation usually happens, what the relationship between driving cessation and individual health looks like, what would the impact of driving cessation looks like from an individual perspective as well as societal perspective, etc. If authors can include an in-depth discussion (perhaps, create one new paragraph), it would enhance the current manuscript and help readers catch the importance of the manuscript more easily.

3. Study objectives are clearly presented.

Methods

1. All the clinical tests utilized in the study were clearly demonstrated.

2. Other methodologies are well described.

Results

1. It seems like driving performance (Table 1) was assessed by driving evaluation officers (?). Until Table 1 in the Results, I was not aware of how driving performance was assessed. If there are any relevant information how it was determined, the authors may want to add a short description in the methods.

2. Aside from the number of motor vehicle accidents, sometimes the number of citations (warnings, or traffic tickets) could contain valuable information of fitness to drive. If there was any, try to add them in the results. If not, the authors may want to add this point in the limitation section.

3. In the Table 1, participant classification depending on the three outcome categories of UFOV outcomes (low, moderate, high) are not presented. Was there any reason? Otherwise, this information will help readers understand the demographics.

4. The authors divided the whole group into two groups, healthy and unhealthy. What made the authors classify those two groups? While I was reading the paragraph (Lines 238 to 248). I was expecting to see any analysis (i.e., a survival analysis depending on healthy vs unhealthy status), but I do not think there is any in the current manuscript.

Discussion

Good summary of findings and good discussion of strengths, limitations, interpretation, and practical implications.

Conclusion

Well-written

Minor comments

Line 52: need a space between 1.16 and -.

Line 192: What is the class III?

Line 472: MoCA

Reviewer #2: Summary:

This study is a prospective cohort study of older drivers aged 70 and older from Switzerland who volunteered to partake in an education session in relation to driving, then complete an on road driving evaluation and associated neuropsychology based testing. The participants were then followed through the motor licensing Bureau to establish whether driving cessation had occurred for the reasons of either cessation due to health reasons, cessation due to physician recommendation, or health related collision. The mean time of follow-up for participants was 4 years. Hazard ratios were used to identify that certain predictors including Trail making test part A and B as well as the clock drawing test were correlated with higher likelihood of driving cessation. A combination of the neuropsychology tests also identified drivers who were more likely to cease driving.

Major Comments:

1. Relevance or implications of the study results: This study has a very significant number of participants with the important outcome of driving cessation. However, one of the limitations of this study is the ability to interpret the results and apply them meaningfully at the individual level. Overall the authors describe only 4.3% of participants stopping driving due to medical reasons. The time for follow-up is at a mean time of 4 years from time of baseline assessment. Given the age of the population and the prolonged time course, it is not clear that the health status of these drivers would be stable enough such that their evaluation up to 4 years earlier would have contributed information to them ceasing driving- for instance new health events may have contributed to the cessation. The authors would need to make a clear how this information advances the field for assisting clinicians or older drivers themselves in making decisions in relation to driving cessation. For instance, whether "healthy" or “unhealthy” the idea of stopping driving based on these measures does not appear very effective for influencing decision-making. There clearly is a correlation with lower values on the neurocognitive tests however providing direction to the clinician or patient based on these tests seems unclear.

Review:

Title: Overall the title is clear and describes the study well.

Abstract: The abstract is accurate. It does reflect the content within the article and is consistent with the information. It is coherent, readable and structured.

Introduction: The background information is relevant to the study at hand. There is some commentary regarding tests (e.g. TUG test) that are not specifically neurocognitive in nature such as the timed up and go test which deviates from the overall aim of this manuscript/paper. The authors in the background do comment on sensitivity and specificity rates of tools in relation to driving prediction–this manuscript will not delve into the sensitivity or specificity or predictive ability of these tests specifically. Further the background does not touch upon the idea of the duration for which screening or assessment measures are valid. This is relevant and that the study will look at 4 years for the median time of follow-up.

Methods: The study design is a prospective cohort study where participants volunteered. There was no further testing after initial assessment at 1 year and a data collection in the form of driving cessation results was collected at the meantime approximately 4 years post onset of study. The study population and recruitment were well described. The statistical analyses were appropriate. One comment can be made upon the definition of "healthy and unhealthy" where it is noted that through this volunteer study that the authors classify the majority of participants as "unhealthy".

Results: The results of the study are clearly described. There is some overlap of the text and the tables but not to a significant degree. The tables are appropriate. The figures are quite blurry and difficult to read and interpret and overall have poor quality.

Discussion: Discussion overall is appropriate for the aims and results of the study. The authors do comment on pertinent literature. The main limitation of the discussion section is in relation to the value and utility of these results. While the authors indicate there is a correlation of these tests with eventual cessation of driving, they do not elaborate as to how this might further management of patients from either the clinician or patient perspective.

In relation to limitations of the study while the authors indicate that there is good representation of the population, other factors such as general health conditions or even if the drivers were rural versus urban living with a higher need for the ability to drive versus use of public transportation systems available to urban dwellers.

Conclusions: These do reflect to the findings of the study. They describe an association of the neuropsychology test with driving cessation.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Sanghee Moon

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Submitted filename: Reviewers comment.docx

Click here for additional data file.

28 Jul 2021

Modifications following the Reviewer Comments:

Reviewer #1 ( Sanghee Moon ):

1) “Introduction - Lack of discussion on driving cessation based on previous literature. For example, readers may also want to know when driving cessation usually happens, what the relationship between driving cessation and individual health looks like, what would the impact of driving cessation looks like from an individual perspective as well as societal perspective, etc. If authors can include an in-depth discussion (perhaps, create one new paragraph), it would enhance the current manuscript and help readers catch the importance of the manuscript more easily.”

Thank you for this very constructive and useful comment. We took into consideration your proposal and adapted the text by adding a paragraph (Lines 70 – 81). We highlighted that research has shown that maintaining out-of-home mobility is of great importance for people moving to old age from late midlife, as well as the association between driving cessation and functional dependency, depressive disorders, social dysfunction and mortality, with a considerable individual and societal impact. As many adverse health problems have been related to driving cessation in later life, predicting and evaluating accurately the decline of driving capacity of older drivers is of critical importance. Supporting and stimulating out-of-home mobility in the elder population, detecting and preventing a functional decline and possible future driving cessation, depends on individual screening strategies, as well as on transport policy and social policy measures.

Indeed, including this discussion, it would enhance the current manuscript and help readers catch the importance of the research subject more easily.

2) “Results - It seems like driving performance (Table 1) was assessed by driving evaluation officers (?). Until Table 1 in the Results, I was not aware of how driving performance was assessed. If there are any relevant information how it was determined, the authors may want to add a short description in the methods.”

As described in our previous publications (Vaucher et al. 2014, DOI: https://doi.org/10.1186/1471-2318-14-123), we specified how driving performance was assessed and we added a short description in the methods, as proposed (Lines 203-210). The on-road driving evaluation was performed by twelve certified driving instructors who participated in the study, either as self-employed or as employees of the Swiss Automobile Club. The routes were standardized for participants from the same region, were validated by the Swiss National Council for Road Security and were adapted the current Switzerland traffic control and examination standards. In order to minimize bias, all driving instructors were blinded to the psychometric and functional characteristics of the participants. Details about the methods used for the evaluation of driving performance can be found in our previous publications.

3) “Results - Aside from the number of motor vehicle accidents, sometimes the number of citations (warnings, or traffic tickets) could contain valuable information of fitness to drive. If there was any, try to add them in the results. If not, the authors may want to add this point in the limitation section.”

Thank you for this very important comment. Indeed, aside from the number of motor vehicle accidents, the number of citations (warnings or traffic tickets), the driving errors or traffic violations, could contain valuable information of fitness to drive. However, one of the limitations of the current study is that we do not have information about the driving risks or the behavior of the participants. As this analysis was not in the objectives of our study, the sample size was not powered enough in order to take into account the potential driving errors or violations. We added a related paragraph in the limitations section, as our results should be interpreted by taking into account this limitation (Lines 407 - 414).

4) “Results - In the Table 1, participant classification depending on the three outcome categories of UFOV outcomes (low, moderate, high) are not presented. Was there any reason? Otherwise, this information will help readers understand the demographics”

Indeed, this information is very important for the internal validity, the consistency and the external validity of the study. We added the participant baseline UFOV classification in the Table 1. This information will effectively contribute to a better understanding of the demographics.

5) “Results - The authors divided the whole group into two groups, healthy and unhealthy. What made the authors classify those two groups? While I was reading the paragraph (Lines 238 to 248). I was expecting to see any analysis (i.e., a survival analysis depending on healthy vs unhealthy status), but I do not think there is any in the current manuscript.”

Thank you for this important comment contributing to enhance the internal validity of the study. Separation of healthy and non-healthy population was done to test psychometric properties of the clock drawing task. However, this analysis is irrelevant for this publication and all referencing to the separation of groups has been removed.

6) “Line 52: need a space between 1.16 and -.”

The space has been added as indicated.

7) “Line 192: What is the class III?”

This section has been erased, as mentioned in the answer to the 5th question.

8) “Line 472: MoCA”

The word was corrected as indicated.

Reviewer #2:

1) “Major Comment: Relevance or implications of the study results: This study has a very significant number of participants with the important outcome of driving cessation. However, one of the limitations of this study is the ability to interpret the results and apply them meaningfully at the individual level. Overall the authors describe only 4.3% of participants stopping driving due to medical reasons. The time for follow-up is at a mean time of 4 years from time of baseline assessment. Given the age of the population and the prolonged time course, it is not clear that the health status of these drivers would be stable enough such that their evaluation up to 4 years earlier would have contributed information to them ceasing driving- for instance new health events may have contributed to the cessation. The authors would need to make a clear how this information advances the field for assisting clinicians or older drivers themselves in making decisions in relation to driving cessation. For instance, whether "healthy" or “unhealthy” the idea of stopping driving based on these measures does not appear very effective for influencing decision-making. There clearly is a correlation with lower values on the neurocognitive tests however providing direction to the clinician or patient based on these tests seems unclear.”

Thank you very much for these comments and proposals that helped us enhance the consistency of the manuscript. All referencing to healthy or unhealthy drivers have been removed. This notion was used to test psychometric properties of the clock drawing test at baseline only. For the other analysis, the aim is to test the predictive properties of specific tests in correctly forecasting driving cessation. As such, other events affecting health might occur and change exposure status. This is however also the case in real life situations where fitness to drive is evaluated only every two years. The reviewer’s comment nevertheless is very relevant, and we have added a small statement in the limitation section.

Changes: “Health status can change over time and results from clinical tests might be affected. The study therefore does not rule out that participants exposure status might have changed over time” (Lines 403 – 406).

2) “Introduction: There is some commentary regarding tests (e.g. TUG test) that are not specifically neurocognitive in nature such as the timed up and go test which deviates from the overall aim of this manuscript/paper.”

The TUG test is considered as a clinical test for executive functions. Like most neuropsychological tests, the TUG is not only dependent of cognitive functions, as pain and reduced mobility can also affect results. This is the case for all tests, and neuropsychological tests need to be interpreted with care when assessing fitness to drive. This limitation was added in the methods section (Lines 414 – 416).

3) “Introduction: The authors in the background do comment on sensitivity and specificity rates of tools in relation to driving prediction. This manuscript will not delve into the sensitivity or specificity or predictive ability of these tests specifically.”

Sensitivity and specificity require having a gold standard to rely on. Driving cessation is not a gold standard for unfitness to drive. Systematic reviews on neuropsychological tests have shown low validity (Mathias et al. 2009, Bennett et al. 2016). However, these studies rely on outcomes that cannot be considered as gold standard of fitness to drive (e.i. on-road driving test, simulator driving test, driver problems). In our study, some people stop driving for other reasons or feel unfit without being unfit. We therefore only provide indicators of associations without giving indications of validity. This lack of evidence on the validity of neuropsychological tests to correctly identify fitness to drive was added to the discussion in the “practical application” section (Lines 500 - 507).

4) “Introduction: Further the background does not touch upon the idea of the duration for which screening, or assessment measures are valid. This is relevant and that the study will look at 4 years for the median time of follow-up.”

This comment is very relevant as it points out that exposure status could change over time. The manuscript has a pragmatic approach given that most countries screen drivers for fitness to drive at a given frequency (in Switzerland, once every two years) and that health events can also occur between visits. This study aims to test the predictive ability of these screening procedures over what actually happens in the following years. The limitation of the study in correctly classifying participants in the survival analysis has been added. The practical implications of this limitation has been added to the discussion. We focused on the difficulties of correctly assessing the state of health and its effects on driving over time. Screening constitutes a high risk of falsely preventing people from driving. Driving cessation is therefore conditional, and the health status affecting driving needs to be documented (screening leading to further investigations on cause, on-road evaluation).

5) “Methods: One comment can be made upon the definition of "healthy and unhealthy" where it is noted that through this volunteer study that the authors classify the majority of participants as "unhealthy".”

Notions of healthy and unhealthy have been removed (see answer to comment 1).

6) “Results: The figures are quite blurry and difficult to read and interpret and overall have poor quality.”

Thank you for your useful observation that allowed us to optimize the quality of the figures. Their quality seemed lower in TIFF format after using the PACE tool during our first submission.

Changes: all figures have been provided in vectorized format using Adobe Illustrator as recommended by the Plos One instructions to the authors. They were adapted and exported to EPS format per Plos One requirements in order to achieve high publication quality figure presentation. Furthermore, all figures are also immediately available in TIFF format, adapted by PACE tool.

7) “Discussion: Discussion overall is appropriate for the aims and results of the study. The authors do comment on pertinent literature. The main limitation of the discussion section is in relation to the value and utility of these results. While the authors indicate there is a correlation of these tests with eventual cessation of driving, they do not elaborate as to how this might further management of patients from either the clinician or patient perspective.”

A small paragraph was added on the lack of evidence supporting the use of neuropsychological tests for screening purposes in assessing fitness to drive. Our study suggests most tests to have low predictive value for correctly classifying patients in risk levels for future driving cessation. Adapted ecological on-road tests might be more valuable in correctly assuming patients are unfit to drive. Furthermore, it seems important to discuss how screening has limitations and how to overcome them. This was added to the discussion (Lines 465 – 470).

8) “Discussion: In relation to limitations of the study while the authors indicate that there is good representation of the population, other factors such as general health conditions or even if the drivers were rural versus urban living with a higher need for the ability to drive versus use of public transportation systems available to urban dwellers.”

This point is relevant and is indeed worth mentioning. It is less an internal validity issue rather than an external validity. Our sample included patients from rural remote areas as well as cities. Driving cessation is easier in rural areas in Switzerland. Compared to other countries such as Canada or Australia, driving cessation is less likely to isolate people. Public transports are highly developed in Switzerland making transportation available to most citizens. This was added to the discussion in the limitation section (Lines 422 – 426).

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

9 Aug 2021

Assessment of cognitive screening tests as predictors of driving cessation: a prospective cohort study of a median 4-year follow-up

PONE-D-21-15340R1

Dear Dr. Kokkinakis,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Abiodun E. Akinwuntan, PhD, MPH, MBA

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors properly addressed reviewer's concerns and suggestions. I recommend to accept this manuscript in its present form. Thank you.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Sanghee Moon

12 Aug 2021

PONE-D-21-15340R1

Assessment of cognitive screening tests as predictors of driving cessation: a prospective cohort study of a median 4-year follow-up

Dear Dr. Kokkinakis:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Abiodun E. Akinwuntan

Academic Editor

PLOS ONE

Table 1

Baseline characteristics–population description.

Variables	All participants (n = 441)
	Mean (95% CI)/% (n)
Age (years)	76 (75.5–76.4)
Sex (%male)	61.2% (270)
Visual acuity (decimal)	1.02 dec.(1–1.04)
Visual field (degreeso)	182.9o (181.3–184.6)
Timed Up and Go (sec)	8.5 sec (8.2–8.7)
MoCAca([0–30] points)	26.4 (26.2–26.7)
MoCAmodB([0–29] points)	23.6 (23.4–23.9)
Driving distance per week
≥200 km	53.3% (234)
150–199 km	12.3% (54)
100–149 km	19.4% (85)
50–99 km	10.7% (47)
<50 km	4.3% (19)
Accidents previous 2 years
0	73.2% (323)
1	20.4% (90)
2	4.3% (19)
3	1.1% (5)
4	0.23% (1)
5	0.45% (2)
8	0.23% (1)
Driving performance
Excellent	47% (197)
Good	26.5% (111)
Moderate	21.2% (89)
Poor	5.3% (22)
Clock drawing (Freund)
1	0.23% (1)
2	1.4% (6)
3	5.4% (24)
4	7.9% (35)
5	22.9% (101)
6	36.7% (162)
7	25.4% (112)
Clock drawing test
Positive (<5)	15% (66)
Negative (≥5)	85% (375)
UFOV
Low risk	70.8% (312)
Moderate risk	20.9% (92)
High risk	5.7% (23)

Baseline characteristics of all drivers included in the cohort follow-up.

aMoCA = Montreal Cognitive Assessment.

bMoCAmod = Modified MoCA (without TMT or education level).