scholarly journals Validation of a multisubstance online Timeline Followback assessment

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Renée Martin‐Willett ◽  
Timothy Helmuth ◽  
Median Abraha ◽  
Angela D. Bryan ◽  
Leah Hitchcock ◽  
...  
Keyword(s):  
2013 ◽  
Author(s):  
S. M. Robinson ◽  
L. C. Sobell ◽  
M. B. Sobell ◽  
G. I. Leo

2003 ◽  
Author(s):  
David C. Hodgins ◽  
Karyn Makarchuk
Keyword(s):  

2021 ◽  
Author(s):  
Ayesha Sania ◽  
Nicolo Pini ◽  
Morgan Nelson ◽  
Michael Myers ◽  
Lauren Shuffrey ◽  
...  

Abstract Background — Missing data are a source of bias in epidemiologic studies. This is problematic in alcohol research where data missingness is linked to drinking behavior. Methods — The Safe Passage study was a prospective investigation of prenatal drinking and fetal/infant outcomes (n=11,083). Daily alcohol consumption for last reported drinking day and 30 days prior was recorded using Timeline Followback method. Of 3.2 million person-days, data were missing for 0.36 million. We imputed missing data using a machine learning algorithm; “K Nearest Neighbor” (K-NN). K-NN imputes missing values for a participant using data of participants closest to it. Imputed values were weighted for the distances from nearest neighbors and matched for day of week. Validation was done on randomly deleted data for 5-15 consecutive days. Results — Data from 5 nearest neighbors and segments of 55 days provided imputed values with least imputation error. After deleting data segments from with no missing days first trimester, there was no difference between actual and predicted values for 64% of deleted segments. For 31% of the segments, imputed data were within +/-1 drink/day of the actual. Conclusions — K-NN can be used to impute missing data in longitudinal studies of alcohol use during pregnancy with high accuracy.


2019 ◽  
Vol 3 (s1) ◽  
pp. 154-155
Author(s):  
James Keoni Morris ◽  
Julia E. Swan ◽  
Josh L. Gowin ◽  
Melanie L. Schwandt ◽  
Nancy Diazgranados ◽  
...  

OBJECTIVES/SPECIFIC AIMS: This study attempts to evaluate the drinking patterns and traits of individuals who partake in high intensity drinking, defined as binge drinking at 2 or more times the minimum binge count (4 drinks for females, 5 drinks for males). METHODS/STUDY POPULATION: We analyzed data from non-treatment seeking volunteers enrolled in NIAAA screening protocols. The sample included 706 males and 474 females ranging in age from 18 to 91. Subjects were assigned to one of four groups (Non-Binge, Level 1, Level 2, Level 3) based on the highest binge session reported in their Timeline Followback questionnaire. The criteria for each group were different for males and females based on the current NIAAA definitions of binge drinking. The cutoffs for females were 0-3 drinks for Non-Binge, 4-7 drinks for Level 1, 8-11 drinks for Level 2, and 12+ drinks for Level 3. The male drink cutoffs were 0-4, 5-9, 10-14, and 15+ respectively. We looked at various drinking measures (Timeline Followback, Self-Reported Effects of Alcohol (SRE), Alcohol Use Disorders Identification Test (AUDIT)) and trait measures (UPPS-P Impulsivity Scale, Barratt’s Impulsiveness Scale, Buss Perry Aggression Questionnaire) to identify mean differences between groups. RESULTS/ANTICIPATED RESULTS: There were significant differences in drinking patterns between the groups for both males and females. Number of drinking days, average drinks per drinking day, and number of heavy drinking days all increased as binge level increased. There were also significant differences between groups in males for trait measures. Level 2 and Level 3 bingers scored significantly higher on impulsivity and aggression than the Level 1 and Non-Binge groups. Ongoing analyses are examining differences among binge groups on other measures including SRE and AUDIT. Future analyses will explore potential mechanisms underlying the relationships between trait measures and binge drinking using structural equation modeling. DISCUSSION/SIGNIFICANCE OF IMPACT: This study found significant differences between high-intensity drinkers, or “super bingers”, and lighter binge and non-binge drinkers. Super bingers showed an overall heavier drinking pattern across measures. The elevated aggression, impulsivity, and overall heavy drinking patterns of super bingers suggest a behavioral profile that makes this group in particular at higher risk for developing alcohol use disorder and related problems. These traits and behaviors may also help identify targets for treatment interventions for alcohol use disorder.


2014 ◽  
Vol 21 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Gerardo Flórez ◽  
Pilar A. Saiz ◽  
Paz García-Portilla ◽  
Francisco J. De Cos ◽  
Sonia Dapía ◽  
...  

Aim: This cohort study examined how predictors of alcohol dependence treatment outcomes work together over time by comparing pretreatment and posttreatment predictors. Methods: A sample of 274 alcohol-dependent patients was recruited and assessed at baseline, 6 months after treatment initiation (end of the active intervention phase), and 18 months after treatment initiation (end of the 12-month research follow-up phase). At each assessment point, the participants completed a battery of standardized tests [European Addiction Severity Index (EuropASI), Obsessive Compulsive Drinking Scale (OCDS), Alcohol Timeline Followback (TLFB), Fagerström, and International Personality Disorder Examination (IPDE)] that measured symptom severity and consequences; biological markers of alcohol consumption were also tested at each assessment point. A sequential strategy with univariate and multivariate analyses was used to identify how pretreatment and posttreatment predictors influence outcomes up to 1 year after treatment. Results: Pretreatment variables had less predictive power than posttreatment ones. OCDS scores and biological markers of alcohol consumption were the most significant variables for the prediction of posttreatment outcomes. Prior pharmacotherapy treatment and relapse prevention interventions were also associated with posttreatment outcomes. Conclusions: The findings highlight the positive impact of pharmacotherapy during the first 6 months after treatment initiation and of relapse prevention during the first year after treatment and how posttreatment predictors are more important than pretreatment predictors.


1996 ◽  
Vol 42 (1) ◽  
pp. 49-54 ◽  
Author(s):  
Linda C. Sobell ◽  
Joanne Brown ◽  
Gloria I. Leo ◽  
Mark B. Sobell
Keyword(s):  

2017 ◽  
Vol 21 (S2) ◽  
pp. 228-242 ◽  
Author(s):  
Jean J. Schensul ◽  
Toan Ha ◽  
Stephen Schensul ◽  
Avina Sarna ◽  
Kendall Bryant

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