35 resultados para Heavy retroactions


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Introduction: THC-COOH has been proposed as a criterion to help to distinguish between occasional from regular cannabis users. However, to date this indicator has not been adequately assessed under experimental and real-life conditions. Methods: We carried out a controlled administration study of smoked cannabis with a placebo. Twenty-three heavy smokers and 25 occasional smokers, between 18 and 30 years of age, participated in this study [Battistella G et al., PloS one. 2013;8(1):e52545]. We collected data from a second real case study performed with 146 traffic offenders' cases in which the whole blood cannabinoid concentrations and the frequency of cannabis use were known. Cannabinoid levels were determined in whole blood using tandem mass spectrometry methods. Results: Significantly high differences in THC-COOH concentrations were found between the two groups when measured during the screening visit, prior to the smoking session, and throughout the day of the experiment. Receiver operating characteristic (ROC) curves were determined and two threshold criteria were proposed in order to distinguish between these groups: a free THC-COOH concentration below 3 μg/L suggested an occasional consumption (≤ 1 joint/week) while a concentration higher than 40 μg/L corresponded to a heavy use (≥ 10 joints/month). These thresholds were successfully tested with the second real case study. The two thresholds were not challenged by the presence of ethanol (40% of cases) and of other therapeutic and illegal drugs (24%). These thresholds were also found to be consistent with previously published experimental data. Conclusion: We propose the following procedure that can be very useful in the Swiss context but also in other countries with similar traffic policies: If the whole blood THC-COOH concentration is higher than 40 μg/L, traffic offenders must be directed first and foremost toward medical assessment of their fitness to drive. This evaluation is not recommended if the THC-COOH concentration is lower than 3 μg/L. A THC-COOH level between these two thresholds can't be reliably interpreted. In such a case, further medical assessment and follow up of the fitness to drive are also suggested, but with lower priority.

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AIMS: Managing patients with alcohol dependence includes assessment for heavy drinking, typically by asking patients. Some recommend biomarkers to detect heavy drinking but evidence of accuracy is limited. METHODS: Among people with dependence, we assessed the performance of disialo-carbohydrate-deficient transferrin (%dCDT, ≥1.7%), gamma-glutamyltransferase (GGT, ≥66 U/l), either %dCDT or GGT positive, and breath alcohol (> 0) for identifying 3 self-reported heavy drinking levels: any heavy drinking (≥4 drinks/day or >7 drinks/week for women, ≥5 drinks/day or >14 drinks/week for men), recurrent (≥5 drinks/day on ≥5 days) and persistent heavy drinking (≥5 drinks/day on ≥7 consecutive days). Subjects (n = 402) with dependence and current heavy drinking were referred to primary care and assessed 6 months later with biomarkers and validated self-reported calendar method assessment of past 30-day alcohol use. RESULTS: The self-reported prevalence of any, recurrent and persistent heavy drinking was 54, 34 and 17%. Sensitivity of %dCDT for detecting any, recurrent and persistent self-reported heavy drinking was 41, 53 and 66%. Specificity was 96, 90 and 84%, respectively. %dCDT had higher sensitivity than GGT and breath test for each alcohol use level but was not adequately sensitive to detect heavy drinking (missing 34-59% of the cases). Either %dCDT or GGT positive improved sensitivity but not to satisfactory levels, and specificity decreased. Neither a breath test nor GGT was sufficiently sensitive (both tests missed 70-80% of cases). CONCLUSIONS: Although biomarkers may provide some useful information, their sensitivity is low the incremental value over self-report in clinical settings is questionable.

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AIM: To estimate the statistical interactions between alcohol policy strength and the person-related risk factors of sensation-seeking, antisocial personality disorder and attention-deficit/hyperactivity disorder related to heavy alcohol use. DESIGN: Cross-sectional survey. SETTING: Young Swiss men living within 21 jurisdictions across Switzerland. PARTICIPANTS: A total of 5701 Swiss men (mean age 20 years) participating in the Cohort Study on Substance Use Risk Factors (C-SURF). MEASUREMENTS: Outcome measures were alcohol use disorder (AUD) as defined in the DSM-5 and risky single-occasion drinking (RSOD). Independent variables were sensation-seeking, antisocial personality disorder (ASPD), attention-deficit/hyperactivity disorder (ADHD) and an index of alcohol policy strength. FINDINGS: Alcohol policy strength was protective against RSOD [odds ratio (OR) = 0.91 (0.84-0.99)], while sensation-seeking and ASPD were risk factors for both RSOD [OR = 1.90 (1.77-2.04); OR = 1.69 (1.44-1.97)] and AUD [OR = 1.58 (1.47-1.71); OR = 2.69 (2.30-3.14)] and ADHD was a risk factor for AUD [OR = 1.08 (1.06-1.10)]. Significant interactions between alcohol policy strength and sensation-seeking were identified for RSOD [OR = 1.06 (1.01-1.12)] and AUD [OR = 1.06 (1.01-1.12)], as well as between alcohol policy strength and ASPD for both RSOD [OR = 1.17 (1.03-1.31)] and AUD [OR = 1.15 (1.02-1.29)]. These interactions indicated that the protective effects of alcohol policy strength on RSOD and AUD were lost in men with high levels of sensation-seeking or an ASPD. No interactions were detected between alcohol policy strength and ADHD. CONCLUSION: Stronger alcohol legislation protects against heavy alcohol use in young Swiss men, but this protective effect is lost in individuals with high levels of sensation-seeking or having an antisocial personality disorder.

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BACKGROUND AND AIMS: Evidence-based and reliable measures of addictive disorders are needed in general population-based assessments. One study suggested that heavy use over time (UOT) should be used instead of self-reported addiction scales (AS). This study compared UOT and AS regarding video gaming and internet use empirically, using associations with comorbid factors. DESIGN: Cross-sectional data from the 2011 French Survey on Health and Consumption on Call-up and Preparation for Defence-Day (ESCAPAD), cross-sectional data from the 2012 Swiss ado@internet.ch study and two waves of longitudinal data (2010-13) of the Swiss Longitudinal Cohort Study on Substance Use Risk Factors (C-SURF). SETTING: Three representative samples from the general population of French and Swiss adolescents and young Swiss men, aged approximately 17, 14 and 20 years, respectively. PARTICIPANTS: ESCAPAD: n =22 945 (47.4% men); ado@internet.ch: n =3049 (50% men); C-SURF: n =4813 (baseline + follow-up, 100% men). MEASUREMENTS: We assessed video gaming/internet UOT ESCAPAD and ado@internet.ch: number of hours spent online per week, C-SURF: latent score of time spent gaming/using internet] and AS (ESCAPAD: Problematic Internet Use Questionnaire, ado@internet.ch: Internet Addiction Test, C-SURF: Gaming AS). Comorbidities were assessed with health outcomes (ESCAPAD: physical health evaluation with a single item, suicidal thoughts, and appointment with a psychiatrist; ado@internet.ch: WHO-5 and somatic health problems; C-SURF: Short Form 12 (SF-12 Health Survey) and Major Depression Inventory (MDI). FINDINGS: UOT and AS were correlated moderately (ESCAPAD: r = 0.40, ado@internet.ch: r = 0.53 and C-SURF: r = 0.51). Associations of AS with comorbidity factors were higher than those of UOT in cross-sectional (AS: .005 ≤ |b| ≤ 2.500, UOT: 0.001 ≤ |b| ≤ 1.000) and longitudinal analyses (AS: 0.093 ≤ |b| ≤ 1.079, UOT: 0.020 ≤ |b| ≤ 0.329). The results were similar across gender in ESCAPAD and ado@internet.ch (men: AS: 0.006 ≤ |b| ≤ 0.211, UOT: 0.001 ≤ |b| ≤ 0.061; women: AS: 0.004 ≤ |b| ≤ 0.155, UOT: 0.001 ≤ |b| ≤ 0.094). CONCLUSIONS: The measurement of heavy use over time captures part of addictive video gaming/internet use without overlapping to a large extent with the results of measuring by self-reported addiction scales (AS). Measuring addictive video gaming/internet use via self-reported addiction scales relates more strongly to comorbidity factors than heavy use over time.