Core outcome sets for trials of interventions to prevent and to treat multimorbidity in low- and middle-income countries: the COSMOS study
Bibliographic record
Abstract
ABSTRACT Introduction The burden of multimorbidity is recognised increasingly in low- and middle-income countries (LMICs), creating a strong emphasis on the need for effective evidence-based interventions. A core outcome set (COS) appropriate for the study of multimorbidity in LMIC contexts does not presently exist. This is required to standardise reporting and contribute to a consistent and cohesive evidence-base to inform policy and practice. We describe the development of two COS for intervention trials aimed at the prevention and treatment of multimorbidity in LMICs. Methods To generate a comprehensive list of relevant prevention and treatment outcomes, we conducted a systematic review and qualitative interviews with people with multimorbidity and their caregivers living in LMICs. We then used a modified two-round Delphi process to identify outcomes most important to four stakeholder groups with representation from 33 countries (people with multimorbidity/caregivers, multimorbidity researchers, healthcare professionals, and policy makers). Consensus meetings were used to reach agreement on the two final COS. Registration: https://www.comet-initiative.org/Studies/Details/1580 . Results The systematic review and qualitative interviews identified 24 outcomes for prevention and 49 for treatment of multimorbidity. An additional 12 prevention, and six treatment outcomes were added from Delphi round one. Delphi round two surveys were completed by 95 of 132 round one participants (72.0%) for prevention and 95 of 133 (71.4%) participants for treatment outcomes. Consensus meetings agreed four outcomes for the prevention COS: (1) Adverse events, (2) Development of new comorbidity, (3) Health risk behaviour, and (4) Quality of life; and four for the treatment COS: (1) Adherence to treatment, (2) Adverse events, (3) Out-of-pocket expenditure, and (4) Quality of life. Conclusion Following established guidelines, we developed two COS for trials of interventions for multimorbidity prevention and treatment, specific to LMIC contexts. We recommend their inclusion in future trials to meaningfully advance the field of multimorbidity research in LMICs. KEY MESSAGES What is already known on this topic? Although a Core Outcome Set (COS) for the study of multimorbidity has been previously developed, it does not include contributions from low- and middle-income countries (LMICs). Given the important differences in disease patterns and healthcare systems between high-income country (HIC) and LMIC contexts, a fit-for-purpose COS for the study of multimorbidity specific to LMICs is urgently needed. What this study adds Following rigorous guidelines and best practice recommendations for developing COS, we have identified four core outcomes for including in trials of interventions for the prevention and four for the treatment of multimorbidity in LMIC settings. The outcomes ‘Adverse events’ and ‘Quality of life (including Health-related quality of life)’ featured in both prevention and treatment COS. In addition, the prevention COS included ‘Development of new comorbidity’ and ‘Health risk behaviour’, whereas the treatment COS included ‘Adherence to treatment’ and ‘Out-of-pocket expenditure’ outcomes. How this study might affect research, practice, or policy The multimorbidity prevention and treatment COS will inform future trials and intervention study designs by helping promote consistency in outcome selection and reporting. COS for multimorbidity interventions that are context-sensitive will likely contribute to reduced research waste, harmonise outcomes to be measured across trials, and advance the field of multimorbidity research in LMIC settings to enhance health outcomes for those living with 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 | 0.318 | 0.358 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".