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. 2008 Nov;23(11):1796-803.
doi: 10.1007/s11606-008-0777-8. Epub 2008 Sep 9.

Risks and advantages of detecting individuals unfit to drive: a Markov decision analysis

Sandy Leproust  1 , Emmanuel Lagarde, Louis-Rachid Salmi
Affiliations

Risks and advantages of detecting individuals unfit to drive: a Markov decision analysis

Sandy Leproust et al. J Gen Intern Med. 2008 Nov.

Abstract

Background: Many countries have initiated legislation to detect individuals who are unfit to drive, without any evidence that positive effects of these screening procedures outweigh negative effects.

Objective: To measure the potential effectiveness of a screening program to detect individuals unfit to drive.

Design: Markov decision analysis was used to compare no screening to two potential screening strategies.

Participants: Hypothetical cohorts of 10,000 45-year-old, 65-year-old, 75-year-old and 85-year-old individuals seen in primary care practices.

Interventions: Within the screening strategies: a clinical test without on-road confirmatory testing; a clinical test with on-road confirmatory testing, and an imposed driving cessation for patients with a positive test.

Measurements: For each strategy, we compared for two conditions (sleep disorders and dementia) the numbers of crash-related consequences prevented and of adverse events induced (primary objective) and measured the gain in quality-adjusted life years (secondary objective).

Results: For sleep disorders, on-road confirmatory annual testing was the preferred strategy. Whatever the medical condition and age when screening starts, no screening was always better than single-test screening without an on-road confirmatory testing. In sensitivity analyses, these baseline conclusions were only affected by extreme values of test specificity.

Conclusion: Because of the expected difficult application and cost of road tests and annual screening by clinicians, the most acceptable strategy from public health, clinical, and individual points of view is likely to be no screening.

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Figures

Figure 1
Figure 1
Simplified Markov decision analysis model (for illustrative purposes, only one cycle-tree is shown). *only related to screening; †only related to no screening; MC = medical condition, FP = false positive, TP = true positive; square = decision node, circles = chance nodes, with “M” = Markov nodes; [+] = continuation of tree not shown.
See this image and copyright information in PMC

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