The Burden of Diabetes in the Southeastern Coastal Region of China From 1990 to 2019 and Projections for 2030: A Systematic Analysis of the 2019 Global Burden of Disease Study

Diabetes Metab Res Rev. 2025 Jan;41(1):e70031. doi: 10.1002/dmrr.70031.

Abstract

Aim: This study examined the diabetes burden in Fujian Province, China, from 1990 to 2019, comparing it with China and global levels to inform policymakers.

Materials and methods: We used data from GBD 2019 to analyse diabetes prevalence, death, and disability-adjusted life-years (DALYs). We assessed the average annual percentage change (AAPC) and estimated the impact of 17 risk factors. An age-period-cohort model evaluated age, period, and cohort effects on diabetes metrics. Bayesian models forecasted prevalence and DALYs for 2020-2030, with frontier analysis linking DALYs to per capita GDP.

Results: In 2019, Fujian Province had approximately 2,359,179 diabetes cases with a prevalence rate of 4423.82 (95% UI 4004.12-4864.55) per 100,000 and an age-standardised DALYs of 475.00 (375.63-589.49) per 100,000, both lower than China and global averages. From 1990 to 2019, Fujian Province's age-standardised mortality rate remained higher than the China average, but the gap narrowed compared with 1990. Elderly males showed a pronounced increase in mortality. The period effect indicated a turning point during 2005-2009. DALYs increased among men and decreased among women over cohorts. By 2030, the DALYs rate is projected to decrease by 6.59%. Frontier analysis showed that compared with the same economic level, the effective difference in diabetes disease burden in Fujian Province was small, but there was room for improvement.

Conclusion: From 1990 to 2019, Fujian Province's age-standardised diabetes prevalence slightly increased, while mortality and DALYs declined. Significant gender and age disparities existed, highlighting the need for targeted strategies for elderly males. Fujian Province's success in diabetes management can provide a model for other regions.

Keywords: age‐period‐cohort modeling; epidemiology; forecast; global burden of disease; trend.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Bayes Theorem
  • Child
  • Child, Preschool
  • China / epidemiology
  • Cost of Illness
  • Diabetes Mellitus* / epidemiology
  • Disability-Adjusted Life Years
  • Female
  • Follow-Up Studies
  • Forecasting
  • Global Burden of Disease* / trends
  • Humans
  • Infant
  • Infant, Newborn
  • Male
  • Middle Aged
  • Prevalence
  • Prognosis
  • Quality-Adjusted Life Years
  • Risk Factors
  • Young Adult