In:
Medical Decision Making, SAGE Publications, Vol. 39, No. 7 ( 2019-10), p. 765-780
Abstract:
Background. There is a lack of data on alcohol consumption over time. This study characterizes the long-term drinking patterns of people with lifetime alcohol use disorders who have engaged in treatment or informal care. Methods. We developed multinomial logit models using the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) to estimate short-term transition probabilities (TPs) among the 4 World Health Organization drinking risk levels (low, medium, high, and very high risk) and abstinence by age, sex, and race/ethnicity. We applied an optimization algorithm to convert 3-year TPs from NESARC to 1-year TPs, then used simulated annealing to calibrate TPs to a propensity-scored matched set of participants derived from a separate 16-year study of alcohol consumption. We validated the resulting long-term TPs using NESARC-III, a cross-sectional study conducted on a different cohort. Results. Across 24 demographic groups, the 1-year probability of remaining in the same state averaged 0.93, 0.81, 0.49, 0.51, and 0.63 for abstinent, low, medium, high, and very high-risk states, respectively. After calibration to the 16-year study data ( N = 420), resulting TPs produced state distributions that hit the calibration target. We find that the abstinent or low-risk states are very stable, and the annual probability of leaving the very high-risk state increases by about 20 percentage points beyond 8 years. Limitations. TPs for some demographic groups had small cell sizes. The data used to calibrate long-term TPs are based on a geographically narrow study. Conclusions. This study is the first to characterize long-term drinking patterns by combining short-term representative data with long-term data on drinking behaviors. Current research is using these patterns to estimate the long-term cost effectiveness of alcohol treatment.
Type of Medium:
Online Resource
ISSN:
0272-989X
,
1552-681X
DOI:
10.1177/0272989X19873627
Language:
English
Publisher:
SAGE Publications
Publication Date:
2019
detail.hit.zdb_id:
2040405-0
