Bayesian Age-Period-Cohort Prediction of Mortality of Type 2 Diabetic Kidney Disease in China: A Modeling Study

作者：Xiaoming Wu, Jianqiang Du, Linchang Li, Wangnan Cao, Shengzhi Sun · 发表于：Frontiers in Endocrinology · 年份：2021 · DOI：10.3389/fendo.2021.767263 · 被引用次数：60 · 研究领域：Insurance, Mortality, Demography, Risk Management、Chronic Kidney Disease and Diabetes、Chronic Disease Management Strategies

Background: The burden of type 2 diabetic kidney disease (DKD) continues to rise in China. We analyzed time trends in DKD mortality and associations with age, period, and birth cohort from 1990 to 2019, made projections up to 2030, and examined the drivers of deaths from DKD. Methods and Findings: The number of DKD deaths in China from 1990 to 2019 was obtained from the GBD 2019. We used age-period-cohort modeling to estimate age, period, and cohort effects in DKD mortality between 1990 and 2019. We calculated net drift (overall annual percentage change), local drift (annual percentage change in each age group), longitudinal age curves (expected longitudinal age-specific rates), period, and cohort relative risks. We used Bayesian age-period-cohort analysis with integrated nested Laplace approximations to project future age-specific DKD death cases from 2020 to 2030. We used a validated decomposition algorithm to attribute changes in DKD deaths to population growth, population aging, and epidemiologic changes from 1990 to 2030. From 1990 to 2019, the age-standardized mortality rate of DKD in China was relatively stable, but the absolute number of DKD deaths showed a noticeable increasing trend. The overall annual percentage change (net drift) was -0.75% (95% confidence interval, CI: -0.93 to -0.57) for males and -1.90% (95% CI, -2.19 to -1.62) for females. The age-specific annual percentage changes (local drifts) were below zero in all age groups from 1990 to 2019 except for m...