Demographic, behavioral, and cardiovascular disease risk factors in the Saudi population: results from the Prospective Urban Rural Epidemiology study (PURE-Saudi)
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is the major cause of death in Saudi Arabia. We aimed to assess associated demographic, behavioral, and CVD risk factors as part of the Prospective Urban Rural Epidemiology (PURE) study. METHODS: PURE is a global cohort study of adults ages 35-70 years in 20 countries. PURE-Saudi study participants were recruited from 19 urban and 6 rural communities randomly selected from the Central province (Riyadh and Alkharj) between February 2012 and January 2015. Data were stratified by age, sex, and urban vs rural and summarized as means and standard deviations for continuous variables and as numbers and percentages for categorical variables. Proportions and means were compared between men and women, among age groups, and between urban and rural areas, using Chi-square test and t-tests, respectively. RESULTS: The PURE-Saudi study enrolled 2047 participants (mean age, 46.5 ± 9.12 years; 43.1% women; 24.5% rural). Overall, 69.4% had low physical activity, 49.6% obesity, 34.4% unhealthy diet, 32.1% dyslipidemia, 30.3% hypertension, and 25.1% diabetes. In addition, 12.2% were current smokers, 15.4% self-reported feeling sad, 16.9% had a history of periods of stress, 6.8% had permanent stress, 1% had a history of stroke, 0.6% had heart failure, and 2.5% had coronary heart disease (CHD). Compared to women, men were more likely to be current smokers and have diabetes and a history of CHD. Women were more likely to be obese, have central obesity, self-report sadness, experience stress, feel permanent stress, and have low education. Compared to participants in urban areas, those in rural areas had higher rates of diabetes, obesity, and hypertension, and lower rates of unhealthy diet, self-reported sadness, stress (several periods), and permanent stress. Compared to middle-aged and older individuals, younger participants more commonly reported an unhealthy diet, permanent stress, and feeling sad. CONCLUSION: These results of the PURE-Saudi study revealed a high prevalence of unhealthy lifestyle and CVD risk factors in the adult Saudi population, with higher rates in rural vs urban areas. National public awareness programs and multi-faceted healthcare policy changes are urgently needed to reduce the future burden of CVD risk and mortality.
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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) | 0.000 | 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 | 0.001 |
| 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".