Prevalence and therapeutic intensity of dispensed drug groups for individuals with multiple medications: a register-based study of 2.2 million individuals
Bibliographic record
Abstract
To assess the prevalence and the therapeutic intensity of dispensed drug groups for individuals receiving multiple medications. The individual-based data of all dispensed outpatient prescriptions in Sweden in 2006 were analysed. Five or more dispensed drugs (DP ≥ 5) during a 12-month period were applied as an indicator of multiple medications. The drugs were categorized according to the second level of the World Health Organization's Anatomic, Therapeutic, Chemical classification. The defined daily dosage per individual during 12 months was applied as an indicator of the therapeutic intensity. For the 2.2 million individuals with DP ≥ 5, the drug groups with the highest prevalences were antibacterials (48.2%), analgesics (40.3%), psycholeptics (35.9%), antithrombotic agents (33.4%) and beta-blocking agents (31.7%). As examples, the level of prevalence increased with age for analgesics, psycholeptics, antithrombotic agents and diuretics, and decreased with age for antibacterials, drugs for obstructive airway diseases and antihistamines for systemic use. Substantial differences in the level of prevalence between women and men were observed for several drug groups; for example, thyroid therapy (13.3 vs 3.6%), psychoanaleptics (26.3 vs 18.2%), drugs used in diabetes (9.1 vs 15.7%) and lipid-modifying agents (18.1 vs 30.7%). Generally, the therapeutic intensity increased with the increasing number of dispensed drugs. For a third of the most common drug groups, the therapeutic intensity increased with an increasing age above the 60–69-year age group. The number of drugs taken not only increases the potential risks associated with multiple drug use, but also increases the potential burden of an increased therapeutic intensity, especially for older people. The reported findings may enlighten physicians and healthcare stakeholders concerning the complex patterns of multiple drug use in the entire population and the associated expenses. The findings may also be used as a base for interventions aiming to bring about the most appropriate and balanced prescription of medicines to individuals with multiple diseases.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".