Adolescent Type 1 Diabetes Incidence and Prevalence Rising Globally Since 1990

The global incidence and prevalence of type 1 diabetes (T1D) among children ≤14 years have risen by 32.04% and 41.47%, respectively, from 1990 to 2021, due largely to population growth and epidemiological improvements.1

T1D is the most common chronic endocrine pathology among children. Given that common treatments include diet, physical activity, insulin medication, and proper self-control, these processes may be difficult, particularly in children. This creates not only a health burden on the child, but a psychological burden on both them and the parents.2

Investigators conducted a retrospective, observational analysis of trends in incidence, prevalence, disability-adjusted life years (DALYs), and mortality of T1D in children aged 0-14 years. Data was sourced from the Global Burden of Disease (GBD) 2021 database, which aims to systematically assess the impact of individual diseases, injuries, and risk factors at regional and global levels. Regression models were also used to evaluate health inequality between regions with different sociodemographic index (SDI) levels.1

“We utilized the latest data from GBD 2021 to systematically analyze global, regional, and national trends in the incidence and health inequities of childhood T1D from 1990 to 2021, aiming to reveal global patterns and trends; explore the impact of socioeconomic, geographical, and environmental factors on incidence and mortality; and assess health inequities, ultimately providing scientific evidence for global public health decision-making and contributing to the development of more precise and effective disease prevention and control strategies,” wrote Jing Xie, department of pharmacy, Zhongda Hospital, School of Medicine, Southeast University, and colleagues.1

The team analyzed global trends via descriptive statistical methods based on aggregated data, calculating global incidence, prevalence, DALYs, and mortality of T1D. Investigators compared these values to those from 1990, calculating the percentage change. Geographic differences and temporal trends were examined using spatial data analysis to isolate regions showing increasing or decreasing trends in T1D. Age distribution was monitored via stratified analysis by age group, aiming to differentiate trends by age.1

Investigators also utilized a regression model to examine the linear relationships of T1D incidence, prevalence, DALYs, and mortality with SDI. Decomposition model-based attribution analysis was used to quantify the contribution of factors like population growth, epidemiological changes, and fertility rates to changes in T1D incidence. The team examined healthcare inequality as well, using regression analysis to determine the relationship between health inequality and SDI. Bayesian age-period-cohort models were also used to predict future trends of T1D incidence between 2022 and 2050.1

Ultimately, investigators recorded the numbers of T1D incidence, prevalence, DALYs, and deaths in 2021 as 222.31 thousand (95% CI, 159.86 to 297.12), 1629.48 thousand (1197.42 to 2143.32), 437.85 thousand (95% CI, 316.73 to 543.73), and 4.28 thousand (95% CI, 2.99 to 5.35), respectively. These data reflected increases of 32.04% and 41.47% and decreases of -18.33% and -24.83%, respectively, from 1990.1

In 2021, the all-age incidence, prevalence, DALY, and mortality rates for T1D were 11.05 (95% CI, 7.95 to 14.77), 80.99 (95% CI, 59.52 to 106.53), 21.76 (95% CI, 15.74-27.03), and 0.21 (95% CI, 0.15-0.27), respectively, per 100,000 people. This indicated increases of 14.14% and 22.29% and decreases of -29.4% and -35.02% from 1990. Similar trends were noticed across all SDI quintiles, with limited variation.1

Despite these distinct results, investigators noted 3 major limitations inherent to the design. First, the GBD data on which the study was structured may suffer from uneven reporting or insufficient data in certain regions. The SDI also has inherent limitations as a health inequality measure, as it fails to capture contextual determinants like cultural norms, health policy efficiency, and healthcare distribution. Lastly, they noted that concentration indices may fail to reflect dynamic health inequalities like disparities born of inadequate healthcare resources and policy gaps.1

“These limitations highlight the need for future research to adopt more sophisticated indicators to evaluate health inequality, including specific analyses of health care access, the impact of health policies, and sociocultural barriers,” wrote Lie and colleagues.1

References

1. Xie, J., Li, W., Li, X. et al. Global, regional, and national epidemiology of type 1 diabetes in children from 1990 to 2021: trend and health inequality analyses based on the Global Burden of Disease Study 2021. Diabetol Metab Syndr 17, 337 (2025). https://doi.org/10.1186/s13098-025-01905-3

2. Henríquez-Tejo R, Cartes-Velásquez R. Impacto psicosocial de la diabetes mellitus tipo 1 en niños, adolescentes y sus familias. Revisión de la literatura [Psychosocial impact of type 1 diabetes mellitus in children, adolescents and their families. Literature review]. Rev Chil Pediatr. 2018;89(3):391-398. doi:10.4067/S0370-41062018005000507