Potentially inappropriate prescribing in two populations with differing socio-economic profiles: a cross-sectional database study using the PROMPT criteria
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to establish the prevalence of potentially inappropriate prescribing (PIP) in middle-aged adults (45-64 years) in two populations with differing socio-economic profiles, and to investigate factors associated with PIP, using the PROMPT (PRescribing Optimally in Middle-aged People's Treatments) criteria. METHODS: A retrospective cross-sectional study was conducted using 2012 data from the Enhanced Prescribing Database (EPD), covering the full population in Northern Ireland and the Health Services Executive Primary Care Reimbursement Service (HSE-PCRS) database, covering the most socio-economically deprived third of the population in this age group in the Republic of Ireland. The prevalence for each PROMPT criterion and overall prevalence of PIP were calculated. Logistic regression was used to investigate the association between PIP and gender, age group and polypharmacy. RESULTS: This study included 441,925 patients from the EPD and 309,748 patients from the HSE-PCRS database. Polypharmacy was common in both datasets (46.7 % in the HSE-PCRS and 20.3 % in the EPD). The prevalence of PIP was 42.9 % (95%CI 42.7, 43.1) in the HSE-PCRS and 21.1 % (95%CI 21.0, 21.2) in the EPD. Age group, female gender and polypharmacy were significantly associated with PIP in both populations (p < 0.05) and polypharmacy had the strongest association. CONCLUSIONS: PIP is common amongst middle-aged people with the risk of PIP increasing with polypharmacy. Differences in the prevalence of polypharmacy and PIP between the two populations may relate to heterogeneity in healthcare services and different socio-economic profiles, with higher rates of multimorbidity and associated polypharmacy in more deprived groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".