The Burden of Gout and Its Attributable Risk Factors in the Middle East and North Africa Region, 1990 to 2019
Bibliographic record
Abstract
OBJECTIVE: This study reported the burden of gout and its attributable risk factors in the Middle East and North Africa (MENA) region between 1990 and 2019 by age, sex, and sociodemographic index (SDI). METHODS: Data on the prevalence, incidence, and years lived with disability (YLD) due to gout were obtained from the Global Burden of Disease 2019 study for the 21 countries in the MENA region, from 1990 to 2019. RESULTS: In 2019, the regional age-standardized point prevalence and annual incidence rates of gout were 509.1 and 97.7 per 100,000 population, which represent a 12% and 11.1% increase since 1990, respectively. Moreover, in 2019 the regional age-standardized YLD rate was 15.8 per 100,000 population, an 11.7% increase since 1990. In 2019, Qatar and Afghanistan had the highest and lowest age-standardized YLD rates, respectively. Regionally, the age-standardized point prevalence of gout increased with age up to the oldest age group, and it was more prevalent among males in all age groups. In addition, there was an overall positive association between SDI and the burden of gout between 1990 and 2019. In 2019, high BMI (46.1%) was the largest contributor to the burden of gout in the MENA region. CONCLUSION: There were large intercountry variations in the burden of gout, but in general, it has increased in MENA over the last 3 decades. This increase is in line with the global trends of gout. However, the age-standardized YLD rate change was higher in MENA than at the global level.
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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 | 0.002 |
| 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".