A Systematic Review of the Patterns of Associative Multimorbidity in Asia
Bibliographic record
Abstract
Patients with multimorbidity are commonly seen in primary care. An increasing number of multimorbidity patterns are being reported in the Western literature with a few from Asia. The main objective of this systematic review was to describe patterns of associative multimorbidity, defined as associations beyond chance or patterns of diseases, in the Asian population. We searched Medical Literature Analysis and Retrieval System Online (MEDLINE (Ovid)), Excerpta Medica Database (EMBASE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science (Clarivate Analytics), and Scopus (Elsevier) databases from their inception to April 22, 2019 using medical subject headings, keywords in titles, abstracts, and text. We used the Modified Newcastle-Ottawa Scale for risk-of-bias assessment. Eight articles from China, India, Indonesia, and Japan met the inclusion criteria. Patterns of associative multimorbidity were reported as dyadic/triadic disease combinations or disease clusters. The most common multimorbidity pattern, "cardiovascular and metabolic diseases," was identified in six of eight articles. The other four multimorbidity patterns are comprised of "mental health problems," "degenerative diseases," pulmonary diseases," and "cancer diseases." The eight articles showed methodological heterogeneity in terms of the list of chronic diseases, ascertainment of multimorbidity, statistical methods, and study populations. This systematic review identified five common patterns of associative multimorbidity in Asia. "Cardiovascular and metabolic diseases" and "mental diseases" were two patterns that were similarly reported in the Western world. Alignment of the definition of multimorbidity and the statistical methodology are needed to identify the unique patterns of multimorbidity in Asia so that clinical practice guidelines on multimorbidity can be developed for the Asian population.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".