Socio-demographic caracteristics and prevalence of risk factors in a hypertensive and diabetics population: a cross-sectional study in primary health care in Brazil
Bibliographic record
Abstract
BACKGROUND: Systemic arterial hypertension and diabetes mellitus, and their related morbidity and mortality, are currently the most common public health problems and also a higher burden of disease in Brazil. They represent a real challenge for primary health care. This study describes the methodology and baseline data of an adult population with hypertension and diabetes attending in primary health care. METHODS: It is a cross sectional study which presents data from a longitudinal research. 3784 adults were randomly selected from the registry of a health service in Porto Alegre, Brazil. The eligibility criteria were: confirmed diagnosis of hypertension and/or diabetes, consulted at least once in the prior 3 years and 18 years of age or older. Home data collection consisted of a questionnaire with information on demographic, medical history, life style and socio-economic factors. RESULTS: A total of 2482 users were interviewed (response rate of 71 %). The median age was 64 (IQR = 55.7) and the majority were women (68 %), and married (52 %). Whereas 66.5 % (CI 95 % 64.5-68.3) of the sample had only hypertension, 6.5 % (CI 95 % 5.5-7.5) had diabetes and 27.1 % (CI 95 % 25.3-28.8) had both diseases. The prevalence of diseases increased with age and with fewer years of study (p < 0.05). Subjects with both diseases had significantly more associated comorbidities. CONCLUSIONS: Hypertension and diabetes are more prevalent in older individuals, especially women, and less educated people. People suffering with both chronic conditions simultaneously are more likely to have additional comorbidities.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".