Prevalence of Undiagnosed Cardiovascular Risk Factors in Adults Aged 20 - 40: A Cross-Sectional Study in 2016 in Jeddah, Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVDs) are the first leading cause of death worldwide. In Saudi Arabia, CVDs are the major killers with a mortality rate of 46%. CVD risk factors are not exclusive to old populations. Thus, the purpose of this study was to approximately find the prevalence of these risk factors, particularly high blood pressure (HBP), high blood glucose (HBG), obesity, and smoking. METHODS: This cross-sectional was conducted in May 2016 and took place in the Ambulatory Care Center of King Abdulaziz Medical City, Jeddah. We used a non-probability convenience sampling technique where only individuals aged 20 - 40 who were free of medical illnesses were included. We excluded pregnant women and people on medications that might interfere with our measurements. We obtained a brief history and measured blood pressure, blood glucose, height and weight. Data analysis was done in form of frequencies. Chi-square test was utilized to compare qualitative variables. P < 0.05 was used to determine statistical significance. RESULTS: A total of 507 participants were included (76.3% males and 23.7% females). All participants were between 20 and 40 with a mean age of 31.6 ± 6.06 SD. We found the prevalence of undiagnosed HBP to be 8.3% and males showed a significantly higher percentage (P < 0.001) when compared to females. HBG prevalence was only 0.6%. Regarding body mass index, the prevalence of overweight and obesity together was 66.3% and males showed significantly higher percentage in falling in this category (P < 0.001). Smoking prevalence was 37.9% with a significantly higher percentage among males (P < 0.001). CONCLUSION: CVD risk factors are apparently quite common in young adults. Efforts must be made to increase the public awareness regarding these risk factors. CVDs are not exclusive to old people. Thus, the public should appreciate this fact in order to prevent these risk factors by establishing healthy life-styles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".