Global, Regional, and National Prevalence of Gout From 1990 to 2019: Age-Period-Cohort Analysis With Future Burden Prediction
Bibliographic record
Abstract
BACKGROUND: Gout is a common and debilitating condition that is associated with significant morbidity and mortality. Despite advances in medical treatment, the global burden of gout continues to increase, particularly in high-sociodemographic index (SDI) regions. OBJECTIVE: To address the aforementioned issue, we used age-period-cohort (APC) modeling to analyze global trends in gout incidence and prevalence from 1990 to 2019. METHODS: Data were extracted from the Global Burden of Disease Study 2019 to assess all-age prevalence and age-standardized prevalence rates, as well as years lived with disability rates, for 204 countries and territories. APC effects were also examined in relation to gout prevalence. Future burden prediction was carried out using the Nordpred APC prediction of future incidence cases and the Bayesian APC model. RESULTS: The global gout incidence has increased by 63.44% over the past 2 decades, with a corresponding increase of 51.12% in global years lived with disability. The sex ratio remained consistent at 3:1 (male to female), but the global gout incidence increased in both sexes over time. Notably, the prevalence and incidence of gout were the highest in high-SDI regions (95% uncertainty interval 14.19-20.62), with a growth rate of 94.3%. Gout prevalence increases steadily with age, and the prevalence increases rapidly in high-SDI quantiles for the period effect. Finally, the cohort effect showed that gout prevalence increases steadily, with the risk of morbidity increasing in younger birth cohorts. The prediction model suggests that the gout incidence rate will continue to increase globally. CONCLUSIONS: Our study provides important insights into the global burden of gout and highlights the need for effective management and prophylaxis of this condition. The APC model used in our analysis provides a novel approach to understanding the complex trends in gout prevalence and incidence, and our findings can inform the development of targeted interventions to address this growing health issue.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".