Understanding the Extent of Polypharmacy and its Association With Health Service Utilization Among Persons With Cancer and Multimorbidity: A Population-Based Retrospective Cohort Study in Ontario, Canada
Bibliographic record
Abstract
Background: Cancer often co-occurs with other chronic conditions, which may result in polypharmacy. Polypharmacy is associated with adverse outcomes, including increased health service utilization. Objectives: This study examines the overall prevalence of polypharmacy (5 or more medications) among adults with cancer and multimorbidity, as well as the association of both minor polypharmacy (5-9 medications) and hyper-polypharmacy (10 or more medications) on high use of emergency room visits and hospitalizations, while controlling for age, sex, and type and stage of cancer. Methods: This retrospective longitudinal study used linked health administrative databases and included persons 18 years and older diagnosed with cancer between April 2010 and March 2013 in Ontario, Canada. Data on the number of health service utilizations at or above the 90th percentile (high users), was collected up to March 2014 and multivariate logistic regression was used to determine the impact of polypharmacy. Results: The prevalence of polypharmacy was 46% prior to cancer diagnosis, and 57% one year after diagnosis. Polypharmacy prior to and after cancer diagnosis increased with the level of multimorbidity, increasing age, but did not differ by sex. It was also highest in persons with lung cancer (52.4%) and those diagnosed with stage 4 cancer (51.3%). Minor polypharmacy increased the odds of being a high user of emergency rooms (1.16; 99% CI: 1.09-1.24) and hospitalizations (1.03; 0.98-1.09) and the odds of high use was greater with hyper-polypharmacy (1.41; 1.33-1.51) and (1.23; 1.17-1.29) respectively. Conclusion: Polypharmacy is highly prevalent and is associated with high health service utilization among adults with cancer and multimorbidity
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".