Community prevalence and dyad disease pattern of multimorbidity in China and India: a systematic review
Bibliographic record
Abstract
BACKGROUND: Driven by the increasing life expectancy, China and India, the two most populous countries in the world are experiencing a rising burden of multimorbidity. This study aims to explore community prevalence and dyad patterns of multimorbidity in China and India. METHODS: We conducted a systematic review of five English and Chinese electronic databases. Studies involving adults 18 years or older at a community level, which reported multimorbidity prevalence and/or patterns were included. A modified Newcastle-Ottawa Scale was used for quality assessment. Despite large heterogeneity among reported studies, a systematic synthesis of the results was conducted to report the findings. RESULTS: From 13 996 studies retrieved, 59 studies met the inclusion criteria (46 in China, 9 in India and 4 in both). The median prevalence of multimorbidity was 30.7% (IQR 17.1, 49.4), ranging from 1.5% to 90.5%. There was a large difference in multimorbidity prevalence between China and India, with median prevalence being 36.1% (IQR 19.6, 48.8) and 28.3% (IQR 8.9, 56.8), respectively. Among 27 studies that reported age-specific prevalence, 19 studies found multimorbidity prevalence increased with age, while 8 studies observed a paradoxical reduction in the oldest age group. Of the 34 studies that reported sex-specific prevalence, 86% (n=32) observed a higher prevalence in females. The most common multimorbidity patterns from 14 studies included hypertensive diseases combined with diabetes mellitus, arthropathies, heart diseases and metabolic disorders. All included studies were rated as fair or poor quality. CONCLUSION: Multimorbidity is highly prevalent in China and India with hypertensive diseases and other comorbidities being the most observed patterns. The overall quality of the studies was low and there was a lack of representative samples in most studies. Large epidemiology studies, using a common definition of multimorbidity and national representative samples, with sex disaggregation are needed in both countries. PROSPERO REGISTRATION NUMBER: CRD42020176774.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".