Prevalence of multimorbidity in community settings: A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Background: With ageing world populations, multimorbidity (presence of two or more chronic diseases in the same individual) becomes a major concern in public health. Although multimorbidity is associated with age, its prevalence varies. This systematic review aimed to summarise and meta-analyse the prevalence of multimorbidity in high, low- and middle-income countries (HICs and LMICs). Methods: Studies were identified by searching electronic databases (Medline, Embase, PsycINFO, Global Health, Web of Science and Cochrane Library). The term ‘multimorbidity’ and its various spellings were used, alongside ‘prevalence’ or ‘epidemiology’. Quality assessment employed the Newcastle-Ottawa scale. Overall and stratified analyses according to multimorbidity operational definitions, HICs/LMICs status, gender and age were performed. A random-effects model for meta-analysis was used. Results: Seventy community-based studies (conducted in 18 HICs and 31 LMICs) were included in the final sample. Sample sizes ranged from 264 to 162,464. The overall pooled prevalence of multimorbidity was 33.1% (95% confidence interval (CI): 30.0–36.3%). There was a considerable difference in the pooled estimates between HICs and LMICs, with prevalence being 37.9% (95% CI: 32.5–43.4%) and 29.7% (26.4–33.0%), respectively. Heterogeneity across studies was high for both overall and stratified analyses ( I 2 > 99%). A sensitivity analysis showed that none of the reviewed studies skewed the overall pooled estimates. Conclusion: A large proportion of the global population, especially those aged 65+, is affected by multimorbidity. To allow accurate estimations of disease burden, and effective disease management and resources distribution, a standardised operationalisation of multimorbidity is needed.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".