Prevalence of potentially serious alcohol–medication interactions in older adults in a community pharmacy setting: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Previous prevalence estimates of POtentially Serious Alcohol-Medication INteractions in Older adults (POSAMINO) are based on in-home inventories of medications; however, this method is associated with under-reporting of medications when compared with dispensing records. This study aims to estimate the prevalence of POSAMINO among community-dwelling older adults using drug dispensing data from the community pharmacy setting. DESIGN: Cross-sectional study. SETTING: Irish Community Pharmacy. PARTICIPANTS: 1599 consecutive older adults presenting with a prescription to 1 of 120 community pharmacies nationwide; community-dwelling, aged ≥65 years, able to speak and understand English, with no evidence of cognitive impairment. The mean age of sample was 75.5 years (SD 6.5); 55% (n=884) female. MEASURES: 38 POSAMINO criteria were identified using participants' pharmacy dispensing records linked to self-reported alcohol consumption (beverage-specific quantity and frequency measures) over the last 12 months. RESULTS: The overall prevalence of POSAMINO in the study population was 28%, with 10% at risk of at least one POSAMINO criteria and 18% at risk of two or more. Exposure to POSAMINO most commonly involved cardiovascular agents (19%) and central nervous system agents (15%). Exposure to a higher number of POSAMINO criteria was associated with younger age (adjusted incident rate ratio (AIRR): 0.97; 95% CI: 0.95 to 0.98), male sex (AIRR: 0.55; 95% CI: 0.45 to 0.67) and a higher number of comorbidities (AIRR: 1.05; 95% CI: 1.01 to 1.13). CONCLUSION: This study adds to the growing body of evidence, which suggests that older adults are vulnerable to potentially serious alcohol-medication interactions, particularly those involving cardiovascular and central nervous system agents, increasing their risk of orthostatic hypotension, gastrointestinal bleeds and increased sedation. Application of the POSAMINO criteria at the point of prescribing may facilitate the risk stratification of older adults and prioritise alcohol screening and brief alcohol interventions in those at greatest risk of harm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".