The Accuracy of Evaluations by Drug Recognition Experts in Canada

Beirness, Douglas James; Beasley, Erin; Lecavalier, Jacques

doi:10.1080/00085030.2009.10757598

Cited by 16 publications

(9 citation statements)

References 1 publication

(1 reference statement)

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“…Nevertheless, focusing initial attention on the key signs and symptoms identified in this research may enhance the validity, effectiveness, and efficiency of drug detection and identification by DREs and may lead to a more effective and efficient DEC program, improved enforcement of drug-impaired driving, and greater acceptance of the DEC program by the courts. The findings from the present study add to the accumulating evidence of the validity and accuracy of the DEC program (Beirness et al 2007(Beirness et al , 2009Smith et al 2002) and will help to further support the program and the work of its DREs.…”

Section: Discussionsupporting

confidence: 58%

“…A review of existing laboratory and field evaluation studies on the DEC program reported the overall accuracy of DEC evaluations made by trained DREs on impaired drivers to be more than 80% (Beirness et al 2007). A study of 1,349 DEC evaluations completed by DREs in Canada reported an overall accuracy rate of 95% (Beirness et al 2009), with some drug classes being more difficult to detect than others. Taken together, these research findings (Beirness et al 2007(Beirness et al , 2009 provide confidence in the use of the DEC procedure to detect persons impaired by substances other than alcohol.…”

Section: Introductionmentioning

confidence: 99%

“…A study of 1,349 DEC evaluations completed by DREs in Canada reported an overall accuracy rate of 95% (Beirness et al 2009), with some drug classes being more difficult to detect than others. Taken together, these research findings (Beirness et al 2007(Beirness et al , 2009 provide confidence in the use of the DEC procedure to detect persons impaired by substances other than alcohol. However, as encouraging as the results are, they also indicate that the DEC program is not perfect.…”

Section: Introductionmentioning

confidence: 99%

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Predicting categories of drugs used by suspected drug-impaired drivers using the Drug Evaluation and Classification Program tests

Porath

Beirness

2019

Traffic Injury Prevention

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Objective: The purpose of this study was to statistically determine which combination(s) of drugrelated signs and symptoms from the Drug Evaluation and Classification (DEC) protocol best predict the drug category used by the suspected drug-impaired driver. Methods: Data from 1,512 completed DEC evaluations of suspected impaired drivers subsequently found to have ingested central nervous system (CNS) depressants, CNS stimulants, narcotic analgesics, and cannabis were analyzed using a multinomial logistic regression procedure. A set of evaluations completed on drug-free subjects was also included. The relative importance of clinical, behavioral, and observational measures in predicting drug categories responsible for impairment was also examined. Results: Thirteen drug-related indicators were found to significantly contribute to the prediction of drug category, including being under the care of a doctor or dentist, condition of the eyes, condition of the eyelids, mean pulse rate, assessment of horizontal gaze nystagmus (HGN), convergence, performance on the One Leg Stand (OLS) Test, eyelid tremors, pupil size in darkness, reaction to light, presence of visible injection sites, systolic blood pressure, and muscle tone. Indicators related to the appearance and physiological response of the eye contributed the most to the prediction of drug category, followed closely by clinical indicators and performance on the psychophysical tests. Conclusions: The findings from this study suggest that drug recognition experts (DREs) should be careful to review a set of key signs and symptoms when determining the category of drug used by suspected drug-impaired drivers. Drug use indicators related to the appearance and physiological response of the eye were found to contribute the most to the prediction of the drug category responsible for the impairment. These results could help form the basis of a core set of indicators that DREs could initially consult to form their opinion of drug influence. This in turn may enhance the validity, effectiveness, and efficiency of drug detection and identification by DREs and lead to a more effective and efficient DEC program, improved enforcement of drugimpaired driving, and greater acceptance of the DEC program by the courts.

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Section: Discussionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting categories of drugs used by suspected drug-impaired drivers using the Drug Evaluation and Classification Program tests

Porath

Beirness

2019

Traffic Injury Prevention

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“…• An officer trained in drug identification has a 95% accuracy rate in identifying the type of drug causing impairment [21].…”

Section: Detection Of Cannabis Use By Impaired Driversmentioning

confidence: 99%

The Quality, Readability, and Accuracy of the Information on Google About Cannabis and Driving: Quantitative Content Analysis

Josey¹,

Gaid²,

Bishop³

et al. 2023

JMIR Infodemiology

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Background The public perception of driving under the influence of cannabis (DUIC) is not consistent with current evidence. The internet is an influential source of information available for people to find information about cannabis. Objective The purpose of this study was to assess the quality, readability, and accuracy of the information about DUIC found on the internet using the Google Canada search engine. Methods A quantitative content analysis of the top Google search web pages was conducted to analyze the information available to the public about DUIC. Google searches were performed using keywords, and the first 20 pages were selected. Web pages or web-based resources were eligible if they had text on cannabis and driving in English. We assessed (1) the quality of information using the Quality Evaluation Scoring Tool (QUEST) and the presence of the Health on the Net (HON) code; (2) the readability of information using the Gunning Fox Index (GFI), Flesch Reading Ease Scale (FRES), Flesch-Kincaid Grade Level (FKGL), and Simple Measure of Gobbledygook (SMOG) scores; and (3) the accuracy of information pertaining to the effects of cannabis consumption, prevalence of DUIC, DUIC effects on driving ability, risk of collision, and detection by law enforcement using an adapted version of the 5Cs website evaluation tool. Results A total of 82 web pages were included in the data analysis. The average QUEST score was 17.4 (SD 5.6) out of 28. The average readability scores were 9.7 (SD 2.3) for FKGL, 11.4 (SD 2.9) for GFI, 12.2 (SD 1.9) for SMOG index, and 49.9 (SD 12.3) for FRES. The readability scores demonstrated that 8 (9.8%) to 16 (19.5%) web pages were considered readable by the public. The accuracy results showed that of the web pages that presented information on each key topic, 96% (22/23) of them were accurate about the effects of cannabis consumption; 97% (30/31) were accurate about the prevalence of DUIC; 92% (49/53) were accurate about the DUIC effects on driving ability; 80% (41/51) were accurate about the risk of collision; and 71% (35/49) were accurate about detection by law enforcement. Conclusions Health organizations should consider health literacy of the public when creating content to help prevent misinterpretation and perpetuate prevailing misperceptions surrounding DUIC. Delivering high quality, readable, and accurate information in a way that is comprehensible to the public is needed to support informed decision-making.

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“…Psychophysical and visual examinations have been previously shown to produce reliable indicators of cannabis intoxication in impaired driving cases (Hartman et al, 2016), but less is known about the effectiveness of standard impairment testing in the specific population of medical cannabis users. drug impairment in Canada (Beirness et al, 2009;Porath-Waller & Beirness, 2010;Smith et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Observational study of visual testing efficacy in detecting cannabis usage

Olla

Abumeeiz

Elliott³

et al. 2021

JCSWB

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Drug recognition and examination programs are widely used to detect drug impairment in motor vehicle operators. Visual tests are a key assessment in the detection of cannabis-related impairment. Participants were recruited via social media from the medical cannabis community in Southwestern Ontario, Canada. Twenty-two participants completed the full observational trial design. The majority (n = 13 or 59.1%) were male, with a mean age of 36 years (SD = 9.4; range: 24–59). Participants underwent the following protocol: 1) First round of testing (vital signs, bio sample collection, visual tests, subjective data, neurocognitive testing) (Baseline phase); 2) Consumption of cannabis via inhalation; 3) Second round of testing 30 minutes following consumption (THC phase); 4) Additional rounds of testing at 90, 150, and 210 minutes following consumption (Recovery phase). Visual assessment data and vital signs did not follow typical patterns associated with acute cannabis intoxication. With blood THC levels more than double the Canadian legal limit (5 ng/mL), visual testing results were not diagnostic for cannabis impairment, as participants maintained normal pupil sizes and normal ocular convergence patterns. Visual testing is a key component in standardized examinations used for detecting cannabis-related impairment in Canadian drivers; however, our data indicate that visual testing may not be an effective diagnostic tool for the specific population of medical cannabis users.

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The Accuracy of Evaluations by Drug Recognition Experts in Canada

Cited by 16 publications

References 1 publication

Predicting categories of drugs used by suspected drug-impaired drivers using the Drug Evaluation and Classification Program tests

Predicting categories of drugs used by suspected drug-impaired drivers using the Drug Evaluation and Classification Program tests

The Quality, Readability, and Accuracy of the Information on Google About Cannabis and Driving: Quantitative Content Analysis

Observational study of visual testing efficacy in detecting cannabis usage

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