The co-occurrence of multimorbidity and polypharmacy among middle-aged and older adults in Canada: A cross-sectional study using the Canadian Longitudinal Study on Aging (CLSA) and the Canadian Primary Care Sentinel Surveillance Network (CPCSSN)
Bibliographic record
Abstract
BACKGROUND: There is an increasing prevalence of multiple conditions (multimorbidity) and multiple medications (polypharmacy) across many populations. Previous literature has focused on the prevalence and impact of these health states separately, but there is a need to better understand their co-occurrence. METHODS AND FINDINGS: This study reported on multimorbidity and polypharmacy among middle-aged and older adults in two national datasets: the Canadian Longitudinal Study on Aging (CLSA) and the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Using consistent methodology, we conducted a cross-sectional analysis of CLSA participants and CPCSSN patients aged 45 to 85 years as of 2015. When multimorbidity was defined as two or more conditions, the prevalence was 66.7% and 52.0% in the CLSA and CPCSSN cohorts, respectively. The prevalence of polypharmacy was 14.9% in the CLSA cohort and 22.6% in the CPCSSN cohort when defined as five or more medications. Using the same cut-points, the co-occurrence of multimorbidity and polypharmacy was similar between the two cohorts (CLSA: 14.3%; CPCSSN: 13.5%). Approximately 20% of older adults (65 to 85 years) were living with both multimorbidity and polypharmacy (CLSA: 21.4%; CPCSSN: 18.3%), as compared to almost 10% of middle-aged adults (45 to 64 years) living with this co-occurrence (CLSA: 9.2%; CPCSSN: 9.9%). Across both cohorts and age groups, females had consistently higher estimates of multimorbidity, polypharmacy and the co-occurrence of multimorbidity and polypharmacy. CONCLUSIONS: This study found that multimorbidity and polypharmacy are not interchangeable in understanding population health needs. Approximately one in five older adults in the CLSA and CPCSSN cohorts were living with both multimorbidity and polypharmacy, double the proportion in the younger cohorts. This has implications for future research, as well as health policy and clinical practice, that aim to reduce the occurrence and impact of multimorbidity and unnecessary polypharmacy to enhance the well-being of aging populations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".