The burden of rheumatoid arthritis in the Middle East and North Africa region, 1990–2019
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease. The present study reported the burden of RA in the Middle East and North Africa (MENA) region from 1990 to 2019 by age, sex, and socio-demographic index (SDI). Publicly available data from the Global Burden of Disease (GBD) 2019 study was used to report the modelled point prevalence, annual incidence, and disability-adjusted life-years (DALYs) of RA, as counts and age-standardised rates with their corresponding 95% uncertainty intervals (UIs). In 2019, RA had an age-standardised point prevalence of 120.6 per 100,000 population (107.0-135.7) and an annual incidence rate of 5.9 (5.2-6.6) in MENA, which have increased 28.3% and 25.2%, respectively, since 1990. In 2019, the number of DALYs due to RA in the region was 103.6 thousand (74.2-136.7), with an age-standardised rate of 19.0 (13.9-24.9) DALYs per 100,000 population, which has increased by 18.6% since 1990 (6.7-28.2). The highest point prevalence was found in females aged 50-54, and in males aged 45-49. The highest number of DALYs was observed in the 50-54 age group. The MENA DALY rate was lower than the global rate (19.0 vs. 39.6 per 100,000), but the rate was higher in all age groups in 2019, when compared with 1990. In addition, from 1990 to 2019 an increased burden from RA was associated with an increase in SDI. In line with global trends, the burden of RA in the MENA region showed a steady increase from 1990 to 2019. This highlights the increasing need for updating the available health data to design more accurate guidelines to enable the early detection and treatment of RA in the MENA countries.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".