Critical review of multimorbidity outcome measures suitable for low-income and middle-income country settings: perspectives from the Global Alliance for Chronic Diseases (GACD) researchers
Bibliographic record
Abstract
OBJECTIVES: There is growing recognition around the importance of multimorbidity in low-income and middle-income country (LMIC) settings, and specifically the need for pragmatic intervention studies to reduce the risk of developing multimorbidity, and of mitigating the complications and progression of multimorbidity in LMICs. One of many challenges in completing such research has been the selection of appropriate outcomes measures. A 2018 Delphi exercise to develop a core-outcome set for multimorbidity research did not specifically address the challenges of multimorbidity in LMICs where the global burden is greatest, patterns of disease often differ and health systems are frequently fragmented. We, therefore, aimed to summarise and critically review outcome measures suitable for studies investigating mitigation of multimorbidity in LMIC settings. SETTING: LMIC. PARTICIPANTS: People with multimorbidity. OUTCOME MEASURES: Identification of all outcome measures. RESULTS: We present a critical review of outcome measures across eight domains: mortality, quality of life, function, health economics, healthcare access and utilisation, treatment burden, measures of 'Healthy Living' and self-efficacy and social functioning. CONCLUSIONS: Studies in multimorbidity are necessarily diverse and thus different outcome measures will be appropriate for different study designs. Presenting the diversity of outcome measures across domains should provide a useful summary for researchers, encourage the use of multiple domains in multimorbidity research, and provoke debate and progress in the field.
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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 | 0.254 | 0.536 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".