Exploring the associations between polypharmacy and COVID-19-related hospitalisations and deaths: a population-based cohort study among older adults in Quebec, Canada
Bibliographic record
Abstract
OBJECTIVES: To study the association between polypharmacy and the risk of hospitalisation and death in cases of COVID-19 in the population over the age of 65. DESIGN: Population-based cohort study. SETTING: Quebec Integrated Chronic Disease Surveillance System, composed of five medico-administrative databases, in the province of Quebec, Canada. PARTICIPANTS: 32 476 COVID-19 cases aged over 65 whose diagnosis was made between 23 February 2020 and 15 March 2021, and who were covered by the public drug insurance plan (thus excluding those living in long-term care). We counted the number of different medications they claimed between 1 April 2019 and 31 March 2020. OUTCOME MEASURES: Robust Poisson regression was used to calculate relative risk of hospitalisation and death associated with the use of multiple medications, adjusting for age, sex, chronic conditions, material and social deprivation and living environment. RESULTS: Of the 32 476 COVID-19 cases included, 10 350 (32%) were hospitalised and 4146 (13%) died. Compared with 0-4 medications, polypharmacy exposure was associated with increased hospitalisations, with relative risks ranging from 1.11 (95% CI 1.04 to 1.19) for those using 5-9 medications to 1.62 (95% CI 1.51 to 1.75) for those using 20+. Similarly, the risk of death increased with the number of medications, from 1.13 (95% CI 0.99 to 1.30) for those using (5-9 medications to 1.97 (95% CI 1.70 to 2.27) (20+). Increased risk was mainly observed in younger groups. CONCLUSIONS: Polypharmacy was significantly associated with the risk of hospitalisations and deaths related to COVID-19 in this cohort of older adults. Polypharmacy may represent a marker of vulnerability, especially for younger groups of older adults.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".