Determinants of multimorbidity in low‐ and middle‐income countries: A systematic review of longitudinal studies and discovery of evidence gaps
Bibliographic record
Abstract
Multimorbidity-the coexistence of at least two chronic health conditions within the same individual-is an important global health challenge. In high-income countries (HICs), multimorbidity is dominated by non-communicable diseases (NCDs); whereas, the situation may be different in low- and middle-income countries (LMICs), where chronic communicable diseases remain prominent. The aim of this systematic review was to identify determinants (including risk and protective factors) and potential mechanisms underlying multimorbidity from published longitudinal studies across diverse population-based or community-dwelling populations in LMICs. We systematically searched three electronic databases (Medline, Embase, and Global Health) using pre-defined search terms and selection criteria, complemented by hand-searching. All titles, abstracts, and full texts were independently screened by two reviewers from a pool of four researchers. Data extraction and reporting were according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Methodological quality and risk of bias assessment was performed using the Newcastle-Ottawa Scale for cohort studies. Data were summarized using narrative synthesis. The search yielded 1782 records. Of the 52 full-text articles included for review, 8 longitudinal population-based studies were included for final data synthesis. Almost all studies were conducted in Asia, with only one from South America and none from Africa. All studies were published in the last decade, with half published in the year 2021. The definitions used for multimorbidity were heterogeneous, including 3-16 chronic conditions per study. The leading chronic conditions were heart disease, stroke, and diabetes, and there was a lack of consideration of mental health conditions (MHCs), infectious diseases, and undernutrition. Prospectively evaluated determinants included socio-economic status, markers of social inequities, childhood adversity, lifestyle behaviors, obesity, dyslipidemia, and disability. This review revealed a paucity of evidence from LMICs and a geographical bias in the distribution of multimorbidity research. Longitudinal research into epidemiological aspects of multimorbidity is warranted to build up scientific evidence in regions beyond Asia. Such evidence can provide a detailed picture of disease development, with important implications for community, clinical, and interventions in LMICs. The heterogeneity in study designs, exposures, outcomes, and statistical methods observed in the present review calls for greater methodological standardisation while conducting epidemiological studies on multimorbidity. The limited evidence for MHCs, infectious diseases, and undernutrition as components of multimorbidity calls for a more comprehensive definition of multimorbidity globally.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".