Evidence map›Paper›PMID 40195578›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2025

Validation of the Candrive Older Driver Risk Stratification Tool for Assessing Medical Fitness-to-Drive in Older Australian Drivers.

Judith L Charlton, Sjaan Koppel, Amanda Stephens, Michel Bedard, Jennifer Howcroft, Peteris Darzins, Marilyn Di Stefano, Sylvain Gagnon, Isabelle Gelinas, Malcolm Man-Son-Hing and 6 more

Abstract readValidation Study
In one paragraph

Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Judith L CharltonMonash University Accident Research Centre, Monash University, Clayton, Australia.
Sjaan KoppelMonash University Accident Research Centre, Monash University, Clayton, Australia.
Amanda StephensMonash University Accident Research Centre, Monash University, Clayton, Australia.
Michel BedardLakehead University, Thunder Bay, Canada.ORCID 0000-0001-7924-5657
Jennifer HowcroftDepartment of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada.
Peteris DarzinsEastern Health, Melbourne, Victoria, Australia.
Marilyn Di StefanoVictorian Government Department of Transport and Planning, Melbourne, Australia.
Sylvain GagnonSchool of Psychology, Faculty of Social Sciences, University of Ottawa, Ottawa, Ontario, Canada.
Isabelle GelinasSchool of Physical and Occupational Therapy, McGill University, Montreal, Quebec, Canada.
Malcolm Man-Son-HingIsland Health, Campbell River, British Columbia, Canada.
Anita MyersSchool of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada.
Gary NaglieDepartment of Medicine and Marottoli Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada.
Michelle M PorterFaculty of Kinesiology and Recreation Management, and Centre on Aging, University of Manitoba, Winnipeg, Canada.ORCID 0000-0002-6333-6404
Mark RapoportDepartment of Psychiatry, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.
Brenda VrkljanSchool of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada.
Shawn MarshallDepartment of Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada.

Funding

Australian Research Council Linkage grant LP 100100078CIHR Grant 90429
6 · The paper itself

Abstract

backgroundAssessing older drivers' fitness-to-drive is challenging, with decisions impacting mobility and health. This study aimed to validate the Candrive older driver risk stratification tool for screening medical fitness-to-drive in an independent cohort of older adults from the Ozcandrive 8-year prospective study.

methodsA convenience sample of drivers aged 75 and older residing in Melbourne, Australia completed the Candrive assessments. Their vehicles were instrumented to collect vehicle and global positioning system data, including trip distance. The first 4 years of Ozcandrive data were analyzed. The primary outcome measure was self-reported at-fault collisions, adjusted per 10 000 km driven. Collision risk status was modeled using Generalized Estimating Equations with Poisson regression using predetermined Candrive risk stratification tool predictor variables.

resultsA total of 257 older drivers (70.8% male) were recruited with an average age at study enrollment of 79.7 years (standard deviation = 3.5). Of the 755 adjusted person-years of driving, 74.1% were in the Low risk category (vs original sample, Candrive: 74.8%) and 10.5% were in the Low-Medium risk category (Candrive: 9.3%). Only 15.4% were in the Medium-High risk category (Candrive: 15.9%), where the relative risk for self-reported at-fault collisions was 1.79 (95% confidence interval = 1.06-3.03) compared to the Low risk category.

conclusionsThis study demonstrates an association between self-reported at-fault collisions and Candrive risk stratification tool scores. This result is promising given the primary outcome measure differed from the original Candrive study that used police-reported, at-fault collisions, and supports Candrive risk stratification tool's use by healthcare providers when initiating fitness-to-drive conversations.

Indexed as

Accidents, TrafficAutomobile DrivingGeriatric AssessmentAgedAged, 80 and overAustraliaFemaleHumansMaleProspective StudiesRisk AssessmentDriving issuesGeriatric assessmentMedical fitness-to-drivePrimary care

Identifiers

PMID40195578
PMCPMC12095997

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.