Socio‐economic inequalities in diabetes prevalence in the Kingdom of Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Rising prevalence of non-communicable diseases, including diabetes in the Middle East, is a major public health concern of the 21st century. However, there is a paucity of literature to understand and measure socio-economic inequalities in diabetes prevalence in this region, including the Kingdom of Saudi Arabia (KSA). METHODS: This study investigated socio-economic inequalities in diabetes prevalence in the KSA using data from the Saudi Arabia Health Interview Survey. Concentration curve, concentration index, and multivariate logistic regression were used to measure and examine income- and education-related inequalities in diabetes prevalence. RESULTS: The results showed significant socio-economic inequalities in the prevalence of diabetes through analysing a nationally representative sample of the KSA population. Diabetes prevalence was concentrated among the poor and among people with less education. In addition, education-related inequality was higher than income-related inequality. CONCLUSIONS: The findings of this study are important for policymakers to combat both the increasing prevalence of and socio-economic inequalities in diabetes. The government should promote health education programmes and increase the level of public awareness of diabetes management, especially among the lower educated population in the KSA.
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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 | 0.000 | 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 | 0.000 | 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".