Self-Reported Prevalence of Chronic Non-Communicable Diseases Concerning Socioeconomic and Educational Factors: Analysis of the PURE-Ecuador Cohort
Bibliographic record
Abstract
Background: The changing epidemiological landscape, marked by the increasing prominence of Non-Communicable Chronic Diseases (NCDs), underscores the need for studies that identify and analyze these conditions and their associated risk factors. This secondary analysis aims to describe the association between socioeconomic and educational characteristics and the prevalence of self-reported NCDs among participants in the PURE-Ecuador cohort in urban and rural populations of the Metropolitan District of Quito (MDQ), Ecuador. Methods: This secondary analysis is part of the Prospective Urban Rural Epidemiological (PURE) study. Data were collected from February to December 2018, including 2028 participants aged 35 to 70 years from different urban and rural areas of the MDQ. Data collection utilized standardized questionnaires administered in face-to-face interviews. Pearson's chi-square tests and multivariate logistic regression were used to assess associations. Results: The self-reported prevalence of hypertension was 16.2%, rising to 32.7% in individuals over 60 years old. The prevalence of diabetes mellitus was 6.7%, coronary heart disease 1.3%, stroke 1.6%, heart failure 1.3%, COPD 0.4%, asthma 1.3%, and cancer 1.9%. Multimorbidity affected 5.9% of participants, with the highest rates in obese and older individuals (≥60 years). Adherence to medications was high for hypertension and diabetes mellitus but varied substantially between communities. Conclusions: The secondary analysis revealed significant disparities in the prevalence and management of NCDs in MDQ. The prevalence of self-reported NCDs in Quito, Ecuador, is significantly associated with age and body mass index (BMI). Older individuals, particularly those over 60 years, and obese participants demonstrated higher rates of NCDs and multimorbidity. While socioeconomic factors such as education and income showed some associations with NCD prevalence, these were less pronounced after adjusting for other variables. These findings highlight the importance of age-specific and obesity-focused interventions in addressing the burden of NCDs in this population.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".