Patterns of pharmacotherapies used to treat alcohol use disorders: A population-based administrative data study
Bibliographic record
Abstract
IntroductionAlcohol use disorders (AUDs) – mental and/or physical health diagnoses due to harmful alcohol consumption – are associated with compromised quality of life for the individual. Over the past two decades, pharmacotherapies have been developed to treat alcohol addiction and may help mitigate the harmful outcomes linked with excessive alcohol. Objectives and ApproachThe objectives were to examine the pharmacotherapy dispensation patterns among individuals with an AUD and their mental health comorbidities. We used ICD codes from medical claims and hospital discharge data to identify anyone with a physical / mental health diagnosis due to harmful alcohol consumption – AUD, April 1, 1996-March 31, 2015. We identified mental health comorbidities using administrative health records. Drug dispensation data were used to identify all first-time prescriptions for acamprosate, naltrexone, or disulfiram occurring after an initial AUD diagnosis. Generalized linear models tested for predictors of receiving a prescription and to identify differences in mental health comorbidities. ResultsWe identified 53,556 treatment niave individuals with an AUD who were eligible to receive one of these three prescriptions. 493 of these received a prescription for acamprosate, naltrexone, or disulfiram. The majority of prescriptions came from general practitioners from urban centers. Those with a prescription were significantly more likely to have a comorbid mood or anxiety diagnosis. Those with a prescription were more likely to have a physician visit for a mental health issue a year to two years before diagnosis compared with those who did not have a prescription; and, they were more likely to be dispensed a selective serotonin, a sedative, and an anti-anxiety medication prior to receiving an AUD diagnosis. Conclusion/ImplicationsDrug therapies to aid in the recovery from AUD are being underutilized. Diagnosis of and treatment for mental health disorders is more common among those dispensed these medications. Programs that study clinicians’ use of AUD-targeted drug therapies should be considered, while psychiatric services in addiction care require significant improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".