Multimorbidity at sea level and high-altitude urban and rural settings: The CRONICAS Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To characterize the prevalence and clustering of multimorbidity in four diverse geographical settings in Peru. METHODS: Multimorbidity, defined as having ≥2 chronic conditions, was studied in adults aged ≥35 years in four diverse settings in Peru: Lima, Tumbes, and urban and rural Puno. Six of these conditions (alcohol disorder, asthma, chronic obstructive pulmonary disease, depression, diabetes, and hypertension) were cataloged as objectively ascertained chronic conditions and paired in dyads to explore clusters of multimorbidity. RESULTS: We analyzed data from 2890 adults, mean age 55.2 years, 49% males. Overall, 19.1% of participants had multimorbidity, ranging from 14.7% in semi-urban Tumbes to 22.8% in Lima. The dyads with the highest coexistence (approximately 20%) were observed in hypertension and diabetes in Tumbes, whereas the dyads with lowest coexistence (approximately 1%) were those involving asthma in all study sites. In terms of clusters, Tumbes showed a predominance of hypertension and diabetes, urban and rural Puno a predominance of depression and alcohol disorders, and Lima a higher degree of coexistence of all of the six conditions than in the other clusters. CONCLUSION: Multimorbidity is common and the pattern of clusters is highly heterogeneous. The conditions to prioritize will vary in each setting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".