Vision screening of older drivers for preventing road traffic injuries and fatalities
Bibliographic record
Abstract
BACKGROUND: Demographic data in North America, Europe, Asia, Australia and New Zealand suggest a rapid growth in the number of persons over the age of 65 years as the baby boomer generation passes retirement age. As older adults make up an increasing proportion of the population, they are an important consideration when designing future evidence-based traffic safety policies, particularly those that lead to restrictions or cessation of driving. Research has shown that cessation of driving among older drivers can lead to negative emotional consequences such as depression and loss of independence. Older adults who continue to drive tend to do so less frequently than other demographic groups and are more likely to be involved in a road traffic crash, possibly due to what is termed the "low mileage bias". Available research suggests that older driver crash risk estimates based on traditional exposure measures are prone to bias. When annual driving distances are taken in to consideration, older drivers with low driving distances have an increased crash risk, while those with average or high driving distances tend to be safer drivers when compared to other age groups. In addition, older drivers with lower distance driving tend to drive in urban areas which, due to more complex and demanding traffic patterns, tend to be more accident-prone. Failure to control for actual annual driving distances and driving locations among older drivers is referred to as "low mileage bias" in older driver mobility research. It is also important to note that older drivers are more vulnerable to serious injury and death in the event of a traffic crash due to changes in physiology associated with normal ageing. Vision, cognition, and motor functions or skills (e.g., strength, co-ordination, and flexibility) are three key domains required for safe driving. To drive safely, an individual needs to be able to see road signs, road side objects, traffic lights, roadway markings, other vulnerable road users, and other vehicles on the road, among many other cues-all while moving, and under varying light and weather conditions. It is equally important that drivers must have appropriate peripheral vision to monitor objects and movement to identify possible threats in the driving environment. It is, therefore, not surprising that there is agreement among researchers that vision plays a significant role in driving performance. Several age-related processes/conditions impair vision, thus it follows that vision testing of older drivers is an important road safety issue. The components of visual function essential for driving are acuity, static acuity, dynamic acuity, visual fields, visual attention, depth perception, and contrast sensitivity. These indices are typically not fully assessed by licensing agencies. Also, current vision screening regulations and cut-off values required to pass a licensing test vary from country to country. Although there is a clear need to develop evidence-based and validated tools for vision screening for driving, the effectiveness of existing vision screening tools remains unclear. This represents an important and highly warranted initiative to increase road safety worldwide. OBJECTIVES: To assess the effects of vision screening interventions for older drivers to prevent road traffic injuries and fatalities. SEARCH METHODS: For the update of this review we searched the Cochrane Injuries Group's Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library), MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP) and ISI Web of Science: (CPCI-S & SSCI). The searches were conducted up to 26 September 2013. SELECTION CRITERIA: Randomised controlled trials (RCTs) and controlled before and after studies comparing vision screening to non-screening of drivers aged 55 years and older, and which assessed the effect on road traffic crashes, injuries, fatalities and any involvement in traffic law violations. DATA COLLECTION AND ANALYSIS: Two review authors independently screened the reference lists for eligible articles and independently assessed the articles for inclusion against the criteria. If suitable trials had been available, two review authors would have independently extracted data using a standardised extraction form. MAIN RESULTS: No studies were found that met the inclusion criteria for this review. AUTHORS' CONCLUSIONS: Most countries require a vision screening test for the renewal of an individual's driver's licence. There is, however, lack of methodologically sound studies to assess the effects of vision screening tests on subsequent motor vehicle crash reduction. There is a need to develop valid and reliable tools of vision screening that can predict driving performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".