Global, regional, and national burden of gout, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021
Bibliographic record
Abstract
BACKGROUND: Gout is an inflammatory arthritis manifesting as acute episodes of severe joint pain and swelling, which can progress to chronic tophaceous or chronic erosive gout, or both. Here, we present the most up-to-date global, regional, and national estimates for prevalence and years lived with disability (YLDs) due to gout by sex, age, and location from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, as well as forecasted prevalence to 2050. METHODS: Gout prevalence and YLDs from 1990 to 2020 were estimated by drawing on population-based data from 35 countries and claims data from the USA and Taiwan (province of China). Nested Bayesian meta-regression models were used to estimate prevalence and YLDs due to gout by age, sex, and location. Prevalence was forecast to 2050 with a mixed-effects model. FINDINGS: In 2020, 55·8 million (95% uncertainty interval 44·4-69·8) people globally had gout, with an age-standardised prevalence of 659·3 (525·4-822·3) per 100 000, an increase of 22·5% (20·9-24·2) since 1990. Globally, the prevalence of gout in 2020 was 3·26 (3·11-3·39) times higher in males than in females and increased with age. The total number of prevalent cases of gout is estimated to reach 95·8 million (81·1-116) in 2050, with population growth being the largest contributor to this increase and only a very small contribution from the forecasted change in gout prevalence. Age-standardised gout prevalence in 2050 is forecast to be 667 (531-830) per 100 000 population. The global age-standardised YLD rate of gout was 20·5 (14·4-28·2) per 100 000 population in 2020. High BMI accounted for 34·3% (27·7-40·6) of YLDs due to gout and kidney dysfunction accounted for 11·8% (9·3-14·2). INTERPRETATION: Our forecasting model estimates that the number of individuals with gout will increase by more than 70% from 2020 to 2050, primarily due to population growth and ageing. With the association between gout disability and high BMI, dietary and lifestyle modifications focusing on bodyweight reduction are needed at the population level to reduce the burden of gout along with access to interventions to prevent and control flares. Despite the rigour of the standardised GBD methodology and modelling, in many countries, particularly low-income and middle-income countries, estimates are based on modelled rather than primary data and are also lacking severity and disability estimates. We strongly encourage the collection of these data to be included in future GBD iterations. FUNDING: Bill & Melinda Gates Foundation and the Global Alliance for Musculoskeletal Health.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".