Measuring cumulative anticholinergic medicines burden in older Australian women
Bibliographic record
Abstract
ABSTRACT ObjectivesAnticholinergic medicines burden is common, can have negative impacts, and is problematic to identify. Many medicines used by older women have anticholinergic effects. Importantly for older women, where multimorbidity and use of multiple medicines is common, even when anticholinergic effect of an individual medicine is small, the anticholinergic effects of multiple medicines may be additive, constituting cumulative anticholinergic burden. This study describes medicines contributing to and predictors of anticholinergic burden among community-dwelling older Australian women. ApproachRetrospective longitudinal analysis of data from the Australian Longitudinal Study on Women’s Health linked to Pharmaceutical Benefits Scheme medicines data from 1 January 2008 to 30 December 2010; for 3694 women born in 1921–1926.Anticholinergic medicines were assigned anticholinergic potency levels 0 to 3, according to the Anticholinergic Drug Scale. Anticholinergic Drug Scale ratings for all medicines used by each woman were summed across each six months to give an Anticholinergic Drug Scale score. Commonly used medicines were identified for women with high ADS scores (defined as 75th percentile of scores). Predictors of high ADS scores were analysed using generalised estimating equations. ResultsDuring 2008-2010, 1126 (59.9%) of women used at least one anticholinergic medicine. Median Anticholinergic Drug Scale score was 4. Most anticholinergic medicines used by women who had a high anticholinergic burden (Anticholinergic Drug Scale score > 9) had a low anticholinergic potency (Anticholinergic Drug Scale level 1). Increasing age, cardiovascular disease, and number of other medicines used were predictive of a higher anticholinergic burden. ConclusionHigh anticholinergic medicines burden in this group was driven by use of multiple lower anticholinergic potency medicines rather than use of higher potency medicines. While we might expect that doctors would readily identify anticholinergic burden risk for those using high potency medicines, they may be less likely to identify this risk for users of multiple low potency anticholinergic medicines. The paper will also discuss how GPs view these findings, and how to translate them into the prescribing setting.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".