The Prevalence and Predictors of Sickle Cell Anemia in the Saudi Arabia General Population: Findings from a Cross-Sectional Study
Bibliographic record
Abstract
Background/Objectives: Despite the high incidence of sickle cell anemia in Saudi Arabia, little is known about the sociodemographic characteristics, behavioral risk factors, and concomitant conditions of the condition. We performed this study to measure the prevalence of sickle cell anemia and its associated predictors among Saudi residents. Methods: This cross-sectional study was conducted in 48 primary healthcare centers across Saudi Arabia. A total of 14,239 Saudi residents were included through multi-stage random sampling. Data on sociodemographic variables, behavioral factors, and comorbidities were collected using a validated and reliable questionnaire. Univariate and multivariate logistic regression analyses were performed to identify the predictors of sickle cell anemia, with the statistical significance set at a p-value of <0.05. All analyses were carried out using SPSS version 26 for Windows. Results: Overall, the prevalence of sickle cell anemia was found to be 3.2% among Saudi residents. There was a positive association between insurance coverage and sickle cell anemia (AOR: 1.87; 95% CI: 1.52, 2.31). The odds of sickle cell anemia were 1.39 times higher among diabetic than non-diabetic individuals (AOR: 1.39; 95% CI: 1.01, 1.91). There were positive associations between sickle cell anemia and hypertension (AOR: 1.70; 95% CI: 1.23, 2.35), high cholesterol (AOR: 2.38; 95% CI: 1.74, 3.24), and heart disease (AOR: 8.05; 95% CI: 6.05, 10.71). Conclusions: Our findings indicate significant associations between sickle cell anemia and insurance coverage, smoking, obesity, diabetes mellitus, hypertension, hypercholesterolemia, and heart disease. While the overall prevalence of sickle cell anemia in our study was relatively modest, the Saudi Arabian government should prioritize the objective quantification of the disease burden across the population to effectively mitigate its consequences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".