The impact of age, sex, and gender on polypharmacy and potential prescribing cascades: Lessons from five databases
Bibliographic record
Abstract
BACKGROUND: Few studies describe how gender-related factors may contribute to polypharmacy and prescribing cascades. Describing these patterns using cross-national comparisons can improve the robustness of findings and provide lessons on the importance of considering age, sex, and gender in pharmacological research. The aim of the study was to explore the intersection of age, sex, and gender with polypharmacy and co-prescribing suggesting a potential prescribing cascade. METHODS: In this cross-sectional descriptive study, we assessed polypharmacy and calcium channel blocker and diuretic co-prescribing suggesting a prescribing cascade in patients aged ≥65 years from five international secondary databases: population-level community and nursing home (ICES, Maccabi Healthcare Services), clinical trial (SENATOR), and patient registry (Report-AGE, SHELTER). The intersection of age, sex, and gender was explored. RESULTS: All databases provided age and sex; none included gender-identity data. Gender-related sociocultural factors, socioeconomic status (SES) measured as income and educational attainment, and marital status were not uniformly collected. Compared with males, females had lower income, has less educational attainment, and were more frequently widowed. Polypharmacy was more common in men. Co-prescribing suggesting a prescribing cascade was more frequent in females in four databases and was also more frequent in lower SES and unmarried groups (significant in ICES (community and nursing home) and Maccabi (community), with a nonsignificant trend in Maccabi (nursing home) and three remaining databases). Using two population-level databases, the prevalence of co-prescribing suggesting a prescribing cascade was highest among females 85 years and older who were also in the lower SES group (11.0% ICES and 14.6% Maccabi). Gender disparity was highest in this group (ICES Differential Prevalence = 3.0%, Maccabi Differential Prevalence = 3.8%). CONCLUSION: Older adults with lower SES experienced polypharmacy or co-prescribing suggesting a prescribing cascade more frequently than those with higher SES. Within the lower SES groups, females more frequently than males had evidence of co-prescribing suggesting a prescribing cascade. Considering the role of sex and gender-related sociocultural factors may help to better understand some contributors to polypharmacy and prescribing cascades. The research applications are highlighted in our five lessons learned.
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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 | 0.027 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".