A Systematic Review of Prevalence Studies on Multimorbidity: Toward a More Uniform Methodology
Bibliographic record
Abstract
PURPOSE: We sought to identify and compare studies reporting the prevalence of multimorbidity and to suggest methodologic aspects to be considered in the conduct of such studies. METHODS: We searched the literature for English- and French-language articles published between 1980 and September 2010 that described the prevalence of multimorbidity in the general population, in primary care, or both. We assessed quality of included studies with a modified version of the Strengthening the Reporting of Observational Studies in Epidemiology checklist. Results of individual prevalence studies were adjusted so that they could be compared graphically. RESULTS: The final sample included 21 articles: 8 described studies conducted in primary care, 12 in the general population, and 1 in both. All articles were of good quality. The largest differences in prevalence of multimorbidity were observed at age 75 in both primary care (with prevalence ranging from 3.5% to 98.5% across studies) and the general population (with prevalence ranging from 13.1% to 71.8% across studies). Apart from differences in geographic settings, we identified differences in recruitment method and sample size (primary care: 980-60,857 patients; general population: 1,099-316,928 individuals), data collection, and the operational definition of multimorbidity used, including the number of diagnoses considered (primary care: 5 to all; general population: 7 to all). This last aspect seemed to be the most important factor in estimating prevalence. CONCLUSIONS: Marked variation exists among studies of the prevalence of multimorbidity with respect to both methodology and findings. When undertaking such studies, investigators should carefully consider the specific diagnoses included and their number, as well as the operational definition of multimorbidity.
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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 | 0.197 | 0.447 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.011 |
| Bibliometrics | 0.055 | 0.039 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".