A cross-sectional study of socioeconomic status and cardiovascular disease risk among participants in the Prospective Urban Rural Epidemiological (PURE) Study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVDs) are a challenge to populations and health systems worldwide. It is projected that by 2020 about a third of all deaths globally will be caused by CVDs, and that they will become the single leading cause of death by 2030. Empirical evidence suggests that there is socioeconomic patterning in the distribution and prevalence of risk factors for CVD, but the exact nature of this relationship in South Africa remains unclear. OBJECTIVE: To examine the association between socioeconomic status (SES) and risk factors for CVD in a cohort of adult South Africans living in rural and urban communities. METHOD: This was a cross-sectional analytical study of baseline data on a population-based cohort of 1 976 SA men and women aged 35 - 70 years who were part of the Cape Town arm of the Prospective Urban and Rural Epidemiology (PURE) Study. RESULTS: We found a complex association between SES and CVD risk factors, its pattern differing between urban and rural participants. Marital status showed the most consistent association with CVD risk in both groups: widowed participants living in urban communities were more likely to be hypertensive as well as diabetic, while single participants in both locations were more likely to use alcohol and tobacco products. Level of education was the only SES variable that had no significant association with any CVD risk factor in either study group. All measured SES variables were significantly different between urban and rural participants (p<0.05), with diabetes, obesity and alcohol use significantly more prevalent in urban than in rural participants (p<0.05) while hypertension and tobacco use were not (p≥0.05). CONCLUSIONS: In this cohort of South Africans, there were significant associations between SES and CVD risk, with marked differences in these associations between rural and urban locations. These findings highlight the need to consider SES and area of residence when designing interventions for CVD prevention and control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".