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Determining the Sex-Specific Distributions of Average Daily Alcohol Consumption Using a Machine Learning Method: Is There a Separate Distribution for People With Alcohol Dependence?
Huan Jiang
University of Toronto
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0898-2339
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
It remains unclear whether alcohol use disorders (AUDs) can be characterized by specific levels of average daily alcohol consumption. The aim of the current study was to model the distributions of average daily alcohol consumption among those who consume alcohol and those with alcohol dependence, the most severe AUD, using various machine learning techniques.
Methods
Data from Wave 1 and Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions were used in the current analyses. Clustering algorithms were applied in order to group a set of data points that represent the average daily amount of alcohol consumed. Gaussian Mixture Models (GMMs) were then used to estimate the likelihood of a data point belonging to one of the mixture distributions. Individuals were assigned to the clusters which had the highest posterior probabilities from the GMMs, and their treatment utilization rate was examined for each of the clusters.
Results
Modeling alcohol consumption via machine learning technique was feasible. The clusters identified did not point to alcohol dependence as a separate cluster characterized by a higher level of alcohol consumption. Among both females and males with alcohol dependence, daily alcohol consumption was relatively low.
Conclusions
Overall, we found little evidence for clusters of people with the same drinking distribution, which could be characterized as clinically relevant for people with alcohol use disorders as currently defined.
Keywords
Alcohol consumption, machine learning, survey, Gaussian Mixture Models, clustering, alcohol use disorders, treatment utilization
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This preprint is under consideration at Population Health Metrics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Determining the Sex-Specific Distributions of Average Daily Alcohol Consumption Using a Machine Learning Method: Is There a Separate Distribution for People With Alcohol Dependence?
Huan Jiang
University of Toronto
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0898-2339
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 12 Jan, 2021
No community comments so far
First submitted
On 03 Jan, 2021
Editor assigned
On 03 Jan, 2021
Submission checks complete
On 03 Jan, 2021
Editor invited
On 03 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Population Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
It remains unclear whether alcohol use disorders (AUDs) can be characterized by specific levels of average daily alcohol consumption. The aim of the current study was to model the distributions of average daily alcohol consumption among those who consume alcohol and those with alcohol dependence, the most severe AUD, using various machine learning techniques.
Methods
Data from Wave 1 and Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions were used in the current analyses. Clustering algorithms were applied in order to group a set of data points that represent the average daily amount of alcohol consumed. Gaussian Mixture Models (GMMs) were then used to estimate the likelihood of a data point belonging to one of the mixture distributions. Individuals were assigned to the clusters which had the highest posterior probabilities from the GMMs, and their treatment utilization rate was examined for each of the clusters.
Results
Modeling alcohol consumption via machine learning technique was feasible. The clusters identified did not point to alcohol dependence as a separate cluster characterized by a higher level of alcohol consumption. Among both females and males with alcohol dependence, daily alcohol consumption was relatively low.
Conclusions
Overall, we found little evidence for clusters of people with the same drinking distribution, which could be characterized as clinically relevant for people with alcohol use disorders as currently defined.
Figure 1
Figure 2
Figure 3