[Risk factors for cardiovascular and chronic diseases in a coffee-growing population].
Bibliographic record
Abstract
OBJECTIVES: Estimating the prevalence of cardiovascular and chronic disease risk factors in a Colombian coffee-growing population. METHODS: This cross-sectional study was carried out from February to November 2007. Multistage conglomerate sampling of 55 rural areas in 13 municipalities led to 516 people being surveyed. The questionnaires used were recommended by PAHO (anthropometric and biochemical measurements). The resulting data was subjected to univariate and bivariate descriptive analysis using 95 % CI, significance tests and comparison with previous studies. RESULTS: There was 21.1 % (19.2-23.3 95 %CI) current smoker prevalence, 31.2 % sedentarism (27.8-32.6 95 % CI), 86.3 % people consumed less than 5 portions of fruit and vegetables per day (84.4-87.9 95 % CI), 2.2 % had high alcohol consumption level (1.6-3.2 95 %CI), 26.2 % suffered from hypertension (23.9-28.6 95 % CI), 4.6 % diabetes (3.6-5.8 95 % CI), 62.1 % hyperlipidaemia (59.5-64.7 95 % CI) and 42.9 % (40.4-45.5 95 % CI) were overweight or obese. 85 % had at least 2 or more risk factors simultaneously. Sedentarism, diabetes, hyperlipidaemia and being overweight /obese was greater in females (p<0.001). Alcohol consumption and smoking were greater in males (p<0.001). Age, civil state, education, income and health system were related to the risk factors being studied. CONCLUSIONS: The study provided fresh knowledge concerning the lack of available information regarding rural Latin-American populations. Compared to the second Colombian study of chronic disease risk factors (ENFREC II), no important advances were found regarding a reduction of the prevalence of risk factors. Further studies are required for going deeper into social determinants and health systems explaining this study's findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".