Global, regional and national burdens of gout in the young population from 1990 to 2019: a population-based study

Objective To use data from the Global Burden of Disease (GBD) Study 2019 to report the global, regional and national rates and trends of annual incidence, point prevalence and years lived with disability (YLD) for gout in adolescents and young adults aged 15–39 years. Methods We conducted a serial cross-sectional study of gout burden in the young population aged 15–39 years using data from GBD Study 2019. We extracted rates per 100 000 population of incidence, prevalence and YLD of gout, then calculated their average annual percentage changes (AAPCs) at the global, regional and national level between 1990 and 2019 by sociodemographic index (SDI). Results The global gout prevalent cases in individuals aged 15–39 years was 5.21 million in 2019, with the annual incidence substantially increasing from 38.71 to 45.94 per 100 000 population during 1990–2019 (AAPC 0.61, 95% CI 0.57 to 0.65). This substantial increase was observed in all SDI quintiles (low, low-middle, middle, high-middle and high) and every age subgroup (15–19, 20–24, 25–29, 30–34 and 35–39 years). Males accounted for 80% of the gout burden. High-income North America and East Asia were facing a substantial increase in gout incidence and YLD simultaneously. Elimination of high body mass index can reduce 31.74% of the gout YLD globally in 2019, which varied from 6.97% to 59.31% regionally and nationally. Conclusion Gout incidence and YLD in the young population grew simultaneously and substantially in both developed and developing countries. Improving representative national-level data on gout, interventions for obesity and awareness in young populations are strongly suggested.

Supplement Data1. Ta ble 1 Incidence of gout and years living with gout disability and their average annual percentage changes from 1990 to 2019 in individuals aged 15-39 years at the global and regional levels. Table 1. Incidence of gout and years living with gout disability and their average annual percentage changes from 1990 to 2019 in individuals aged 15-39 years at the global and regional levels. Incidence

Supplement Data 2. Overview, data source, and modeling for Global Burden of Disease 2019
Overview The Global Burden of Disease (GBD) is an approach to global descriptive epidemiology. 1 It is a systematic, scientific effort to quantify the comparative magnitude of health loss due to diseases, injuries, and risk factors by age, sex, and geography for specific points in time. Institute for Health Metrics and Evaluation (IHME) serves as the coordinating center for the GBD and affiliated projects. Published in The Lancet in October 2020, GBD 2019 provides, for the first time, an independent estimation of population for each of 204 countries and territories and for the globe using a standardized, replicable approach, as well as a comprehensive update on fertility and migration. 1 GBD 2019 incorporates major data additions and improvements and methodological refinements. Mortality and life expectancy estimates have expanded to a total of 990 locations at the most detailed level, and new causes have been added to the fatal and nonfatal cause lists, for a total of 369 diseases and injuries (http://www.healthdata.org/gbd/about/protocol). GBD 2019 estimated each epidemiological quantity of interest-incidence, prevalence, mortality, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs)-for 23 age groups; males, females, and both sexes combined; and 204 countries and territories that were grouped into 21 regions and seven super-regions. The GBD 2019 location hierarchy now includes all WHO member states. The GBD disease and injury analytical framework generated estimates for every year from 1990 to 2019. Diseases and injuries were organized into a levelled cause hierarchy from the three broadest causes of death and disability at Level 1 to the most specific causes at Level 4. Within the three Level 1 causes-communicable, maternal, neonatal, and nutritional diseases; noncommunicable diseases; and injuries-there are 22 Level 2 causes, 174 Level 3 causes, and 301 Level 4 causes (including 131 Level 3 causes that are not further disaggregated at Level 4). In total, 364 causes are nonfatal and 286 are fatal. 1

Data sources
The GBD estimation process is based on identifying multiple relevant data sources for each disease or injury, including censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Each of these types of data is identified from a systematic review of published studies, searches of government and international organization websites, published reports, primary data sources such as the Demographic and Health Surveys, and contributions of datasets by GBD collaborators. Aa total of 86,249 sources were used in this analysis, including 19,354 sources reporting deaths, 31,499 reporting incidence, 1973 reporting prevalence, and 26,631 reporting other metrics. Each newly identified and obtained data source is given a unique identifier by a team of librarians and included in the Global Health Data Exchange (GHDx). The GHDx makes publicly available the metadata for each source included in GBD as well as the data, where allowed by the data provider. Additional metadata for each source are available in the online GBD citation tool, http://ghdx.healthdata.org/gbd-results-tool.

Modeling
For most diseases and injuries, processed data are modeled using standardized tools to generate estimates of each quantity of interest by age, sex, location, and year. 1  more details on these general GBD methods. [2][3][4] Briefly, CODEm is a highly systematized tool to analyze cause of death data using an ensemble of different modeling methods for rates or cause fractions with varying choices of covariates that perform best with out-of-sample predictive validity testing. DisMod-MR is a Bayesian meta-regression tool that allows evaluation of all available data on incidence, prevalence, remission, and mortality for a disease, enforcing consistency between epidemiological parameters. Previous studies showed that DisMod-MR can produce robust and valid estimates compared with real surveillance data. 5 ST-GPR is a set of regression methods that borrow strength between locations and over time for single metrics of interest, such as risk factor exposure or mortality rates. 1 BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s)

Supplement Data 3. Gout case definition, input data, and methodology in Global
Burden of Disease 2019.

Definition and data process
Gout was categorized under the musculoskeletal disorders in the Global Burden of Disease 2019. Gout is a rheumatic disease that is characterised by deposition of monosodium urate (MSU) crystals in the synovial fluid of joints and in other tissues, causing inflammation. The crystal formation is caused by elevated urate levels in extracellular fluids. GBD uses the case definition of primary gout given by the American College of Rheumatology, generally referred to as ARA 1977 survey criteria requiring the presence of MSU crystals in joint fluid or the presence of a tophus proven to contain MSU crystals and at least six of 12 gout symptoms or findings (>1 attack of acute arthritis, development of maximal inflammation within a day, attack of monoarticular arthritis, observation of joint erythema, pain or swelling in the first MTP joint, unilateral attack involving the first MTP joint, unilateral attack involving tarsal joint, suspected tophus, hyperuricemia, asymmetrical swelling within a joint on X-ray and negative culture of joint fluid for microorganisms during attack of joint inflammation) to make a diagnosis. The ICD-10 code for gout is M10 and the ICD9 code is 274.

Input data
The last systematic review was conducted in GBD 2013 for studies published between 1980 to 2009 using the following search terms on MEDLINE, EMBASE, CINAHL, CAB Abstracts, WHO Library (WHOLIS), and OpenSIGLE. For prevalence and incidence, the following search terms were used: (gout* OR hyperuricemia) AND (prevalen* OR inciden* OR cross-sectional 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:

Age and sex splitting
Reported estimates of prevalence were split by age and sex where possible. First, if studies reported prevalence for broad age groups by sex (eg, prevalence in 15-to 65-year-old males and females separately), and also by specific age groups for both sexes combined (eg, prevalence in 15-to 30-yearolds, then in 31-to 65-year-olds, for males and females combined), age-specific estimates were split by sex using the reported sex ratio and bounds of uncertainty. Second, prevalence data for both sexes that could not be split using a within-study ratio were split using a sex ratio derived from a meta-analysis of existing sex-specific data using MR-BRT. The female to male ratio was 0.33 (0.33 to 0.34). Finally, after the application of bias adjustments, where studies reported estimates across age groups 25 years or more, these were split into five-year age groups using the prevalence age pattern estimated by DisMod-MR 2.1 in GBD 2017.

Data adjustment
We used study covariates for studies relying on self-reported diagnoses and those identifying sources through a diagnostic code in administrative data, which include gout ICD codes as well as read codes used in the UK health system. We used MR-BRT to adjust alternative case definition and claims data in the USA from the year 2000 and from 2010 onward and for Taiwan claims data to the reference case definition. Matched data was based off of age, sex, year, and location. The mean and standard error for the coefficients were calculated using the MR-BRT crosswalk adjustment method. Betas and exponentiated values (which can be interpreted as an odds ratio) for these covariates are shown in the

Modeling strategy
Prior settings included assuming the excess mortality rate and remission of gout did not exceed 0.01 and 0.2, respectively, and that there was no incidence or prevalence of gout before the age of 15 years. We have made no substantive changes in the modeling strategy from GBD 2017, with the exception of increasing the coefficient of variation from 0.4 at the Global, Super Region, and Region levels to 0.8 to allow the model to better follow the data. We included the summary exposure variable (SEV) scalar for gout which summarises exposure to risks estimated in GBD to impinge on gout, ie, low glomerular filtration rate, as a country covariate. We set bounds of 0.75 to 1.25 as the SEV is constructed in a way that if our risk estimates are accurate the value should be 1.

Severity and Disability
The basis of the GBD disability weight (DW) survey assessments are lay descriptions of sequelae highlighting major functional consequences and symptoms. The lay descriptions and disability weights for gout severity levels are shown below.
Severity distribution, details on the severity levels for gout in GBD 2019 and the associated disability weight (DW) with that severity. 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 (5.14-6.18). From two studies we derived an average duration of attacks of 6.1 (5.4-6.8) days 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%).