Using the Timeline Followback to determine time windows representative of annual alcohol consumption with problem drinkers

• Published in: Addictive behaviors Vol. 33; no. 9; pp. 1123 - 1130
• Main Authors: Vakili, Shervin, Sobell, Linda Carter, Sobell, Mark B, Simco, Edward R, Agrawal, Sangeeta
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-09-2008; Elsevier Science Ltd
• Subjects: Addictions; Alcohol; Alcohol Drinking; Alcohol use; Alcohol-Related Disorders - prevention & control; Alcohol-Related Disorders - therapy; Behavior; Canada; Drinking; Drinking time-intervals; Epidemiologic Methods; Female; Humans; Male; Middle Aged; Patient Compliance - statistics & numerical data; Problem drinkers; Psychiatry; Self Disclosure; Studies; Timeline Followback; Canada; Toronto Ontario Canada; Alcohol; Drinking; Timeline Followback; Problem drinkers; Drinking time-intervals
• ISSN: 0306-4603; 1873-6327
• DOI: 10.1016/j.addbeh.2008.03.009

Abstract When assessing individuals with alcohol use disorders, measurement of drinking can be a resource intensive activity, particularly because many research studies report data for intervals ranging from 6 to 12 months prior to the interview. This study examined whether data from shorter assessment intervals is sufficiently representative of longer intervals to warrant the use of shorter intervals for clinical and research purposes. Participants were 825 problem drinkers (33.1% female) who were recruited through media advertisements to participate in a community-based mail intervention in Toronto, Canada. Participants' Timeline Followback (TLFB) reports of drinking were used to investigate the representativeness of different time windows for estimating annual drinking behavior. The findings suggest that for aggregated reports of drinking and with large sample (e.g., surveys), a 1-month window can be used to estimate annual consumption. For individual cases (e.g., clinical use) and smaller samples, a 3-month window is recommended. These results suggest that shorter time windows, which are more time and resource efficient, can be used with little to no loss in the accuracy of the data.