Identifying clusters of multimorbid disease and differences by age, sex, and socioeconomic status: A systematic review
Bibliographic record
Abstract
BACKGROUND: The prevalence of multimorbidity has been growing due to the ageing population and increasingly unhealthy lifestyles. There is interest in identifying clusters of disease and how they are influenced. AIMS: This systematic review aims to (i) investigate the most common clusters in the adult population with multimorbidity (ii) identify methods used to define clusters (iii) examine if clusters differ based on age, sex and socioeconomic status. METHODS: We searched Medline, Embase, SCOPUS, Web of Science Core Collection, and CINAHL using concepts of multimorbidity and clustering techniques to identify relevant papers. Secondary data, including commonly reported clustering techniques, identified clusters, and other characteristics were extracted. All studies were quality assessed using the Newcastle-Ottawa Bias scale. RESULTS: From a total of 24,231 papers, 125 were included in the review. There was a total of 918 different clusters identified, which were categorized into 59 broad groups. A cardiometabolic cluster appeared most frequently within the identified studies and across age strata. The most common clustering technique was Latent Class Analysis (n = 51). Disease cluster prevalence appeared to differ based on age, whereas no differences could be identified by sex. CONCLUSION: Across the 125 papers identified, irrespective of clustering method, a relatively common set of clusters of disease were found. The Cardiometabolic cluster was the most frequently identified cluster across all age groups. Studies that stratified participants by age or sex identified distinct clusters within each subgroup, which differed from those observed in clusters formed from the general adult population (18+).Latent class analysis was the most common clustering technique within this review, but it was not explored if different clustering methods led to different clusters. Further work is needed to distinguish the most prevalent clusters within specific stratified cohorts of different ages, sex, and socioeconomic status; nonetheless, data strongly suggests that there are different clusters that arise dependent on stratifications. With the expected increasing burden of multimorbidity, healthcare services may need to think about the most prevalent disease combinations within certain strata and how joint-specialist services can be tailored to treat those common conditions.
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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 | 0.018 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".