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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".