Global, Regional, and National Prevalence of Gout From 1990 to 2019: Age-Period-Cohort Analysis With Future Burden Prediction

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.

Keywords: Bayesian age-period-cohort analysis; Global Burden of Disease Study 2019; Norped age-period-cohort analysis; age-period-cohort analysis; gout; prediction; prevalence.

Figure 1

The age effect for gout is shown with all sociodemographic index (SDI) quantiles on total prevalence cases (number) and prevalence rate per 100,000 population from (A) 1990 to (B) 2019. The age effect is shown with all SDI quantiles on years lived with disability (YLD; number) and the YLD rate per 100,000 population from (C) 1990 to (D) 2019. The global tendency under the age effect on both gender.

Figure 2

The period effect for gout is shown with all sociodemographic index (SDI) quantiles on (A) total prevalence cases (number) and age-standardized prevalence rate per 100,000 population and (B) years lived with disability (YLD; number) and standardized YLD rate per 100,000 population. The global tendency under the age effect on both sexes is shown as line charts.

Figure 3

(A) The all-age prevalence for gout in 2019 in 204 countries and territories. (B) Net drift of gout prevalence from 1990 to 2019 in 204 countries and territories.

Figure 4

Age-period-cohort effects on gout prevalence by sociodemographic index (SDI) quintiles. (A) Age effects are shown by the fitted longitudinal age curves of prevalence (per 100,000 person-years) adjusted for period deviations. (B) Period effects are shown by the relative risk of prevalence (prevalence rate ratio) and computed as the ratio of age-specific rates from 1990 to 1994 (the referent period) to 2015-2019. (C) Cohort effects are shown by the relative risk of prevalence and computed as the ratio of age-specific rates from the 1925 cohort to the 2015 cohort, with the referent cohort set at 1960. The dots and shaded areas denote prevalence rates and prevalence rate ratios, respectively, and their corresponding 95% CIs.

Figure 5

Favorable (A) and unfavorable (B) age-period-cohort effects on exemplar countries across sociodemographic index (SDI) quintiles. Age distribution of deaths shows the relative proportion of morbidity from each age group from 1990 to 2019. Local drifts indicate the annual percentage change in prevalence rate (%) across 5-year age groups (from 0-4 to 65-69 years). Age effects are represented by the fitted longitudinal age curves of prevalence (per 100,000 person-years) adjusted for period deviations. Period effects are represented by the relative risk of prevalence (prevalence rate ratio) and computed as the ratio of age-specific rates in each period compared with the referent period from 1990 to 1994. Cohort effects are represented by the relative risk of prevalence (prevalence rate ratio) and computed as the ratio of age-specific rates in each cohort compared with the referent 1960 cohort. The shaded areas indicate the corresponding 95% CIs of each point estimate. See high-resolution image in Multimedia Appendix 1.

Figure 6

Trends in gout by Nordpred age-period-cohort prediction model in (A) global scale. To minimize the scale bias, the plots were separated as follows: (B) global scale, (C) high–sociodemographic index (SDI) quantiles, (D) high-middle–SDI quantiles, (E) middle-SDI quantiles, (F) low-middle–SDI quantiles, and (G) low-SDI quantiles.

Figure 7

Trends in gout by Bayesian age-period-cohort prediction model in (A) global scale, (B) high–sociodemographic index (SDI) quantiles, (C) high-middle–SDI quantiles, (D) middle-SDI quantiles, (E) low-middle–SDI quantiles, and (F) low-SDI quantiles.