Global, regional, and national burden of gout, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

Summary 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.

OR cross sectional OR epidemiol* OR survey OR population-based OR population based OR population study OR population sample OR cohort OR follow-up OR follow up OR longitudinal OR regist*) AND (list of names of all GBD countries).
Exclusion criteria were:

Section 2. Severity distribution meta-analysis
To calculate the severity distribution of gout, we used three studies on the distribution of the number of gout attacks per year and fitted a lognormal curve using a least squared differences method.In the absence of data on the proportion of gout cases who have chronic polyarticular gout, we assumed the proportion is equal to those who would have 52 attacks a year (ie, weekly) or more as implied by the lognormal curve.The average number of attacks was estimated from the lognormal fit: 5.66 (95% UI 5.14-6.18).From two studies we derived an average duration of attacks of 6.1 days (5.4-6.8) by simple averaging.The resulting proportion of time symptomatic for acute gout was taken as the multiplication of these two estimates divided by the number of days in a year: 9.4% (8.0-10.9).

Distribution of cases by frequency
Section 3. Tables and figures

Risk factors
High BMI is defined as greater than 20-25 kg/m 2 while kidney dysfunction is defined as estimated glomerular filtration rate less than 60 ml/min/1.73m 2 or albumin to creatinine ratio greater than or equal to 30 mg/g.

Forecasting validation
Validation testing was conducted using estimates for osteoarthritis (OA) from 1990 to 2010 to project prevalence from 2010 to 2019 by age, sex, location, and year.The projections were then compared to the GBD OA prevalence results for this period by calculating the root mean squared error (RMSE) and bias (calculated as the median value of all predicted minus observed values by age, sex, location and year).Four tests were conducted: OA hip, OA knee, OA hand and OA other sites.In all the four tests the model RMSE was <0.0001.

Measure name
Total sources

•
Sub-populations clearly not representative of the national population • Not a population-based study • Low sample size (less than 150) • Review rather than original studies For GBD 2019, 15 additional studies shared through the GBD Collaborator Network were added.In addition, data from USA claims data for 2000 and 2010-2014 by state and Taiwan (province of China) claims data from 2016 were included.

Table S1 . MR-BRT # crosswalk adjustment factors for gout
Adjustment factor is the transformed Beta coefficient in normal space and can be interpreted as the factor by which the alternative case definition is adjusted to reflect what it would have been if measured as the reference. *

Supplemental Table S5. Super-regional, regional, and national breakdown of locations with data sources for gout
https://ghdx.healthdata.org/gbd-2021/sources?components=5&causes=632&locations=214, and are listed in this appendix from page 23.The GBD model incorporated data containing all measures listed in TableS4.