Temporal trend analysis of the global prevalence of vision impairment and blindness attributable to diabetes from 1990 to 2021.
This study aimed to systematically assess the burden and temporal patterns of vision impairment due to diabetes mellitus (DM), including moderate vision loss (MVL), severe vision loss (SVL), and blindness, and to identify the demographic drivers of future changes.
An age-period-cohort (APC) model was used to evaluate the effects of age, period, and birth cohort on diabetes-related vision impairment, with analyses stratified by diabetes type, including type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Decomposition analysis was further conducted to quantify the relative contributions of population aging, population growth, and epidemiological changes to projected changes in prevalent case numbers through 2030.
the age-standardized prevalence rates (ASPRs) of blindness due to type 1 diabetes mellitus (T1DM) continued to rise [males: average annual percentage change (AAPC) = 0.82% (95%CI 0.40-1.23); females: AAPC = 1.31% (95%CI 0.86-1.77)]. The age effect indicated that older populations were at higher risk. Meanwhile, the cohort effect revealed that in regions with high Sociodemographic Index (SDI), high-middle SDI, and middle SDI, later-born cohorts had a significantly higher risk of diabetes-related vision impairment compared to earlier-born cohorts. Globally and in most regions, population growth was the main driver of increased in the number of prevalent MVL and SVL cases, whereas epidemiological change contributed most to increases in prevalent blindness cases. However, population aging had a particularly substantial impact in East Asia, High-income Asia Pacific, Central Europe, Eastern Europe, and Western Europe.
The global burden of vision impairment due to DM increased continuously. Older populations represented high-risk groups, and the burden varied by sex, diabetes type, and socioeconomic development, with T2DM contributing most to the overall increase in diabetes-related vision impairment. Although population growth and epidemiological changes were major contributors, the impact of population aging in certain regions was also noteworthy.
An age-period-cohort (APC) model was used to evaluate the effects of age, period, and birth cohort on diabetes-related vision impairment, with analyses stratified by diabetes type, including type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Decomposition analysis was further conducted to quantify the relative contributions of population aging, population growth, and epidemiological changes to projected changes in prevalent case numbers through 2030.
the age-standardized prevalence rates (ASPRs) of blindness due to type 1 diabetes mellitus (T1DM) continued to rise [males: average annual percentage change (AAPC) = 0.82% (95%CI 0.40-1.23); females: AAPC = 1.31% (95%CI 0.86-1.77)]. The age effect indicated that older populations were at higher risk. Meanwhile, the cohort effect revealed that in regions with high Sociodemographic Index (SDI), high-middle SDI, and middle SDI, later-born cohorts had a significantly higher risk of diabetes-related vision impairment compared to earlier-born cohorts. Globally and in most regions, population growth was the main driver of increased in the number of prevalent MVL and SVL cases, whereas epidemiological change contributed most to increases in prevalent blindness cases. However, population aging had a particularly substantial impact in East Asia, High-income Asia Pacific, Central Europe, Eastern Europe, and Western Europe.
The global burden of vision impairment due to DM increased continuously. Older populations represented high-risk groups, and the burden varied by sex, diabetes type, and socioeconomic development, with T2DM contributing most to the overall increase in diabetes-related vision impairment. Although population growth and epidemiological changes were major contributors, the impact of population aging in certain regions was also noteworthy.
Authors
Lin Lin, Yang Yang, Lin Lin, Zhao Zhao, Wang Wang, Wang Wang, Ou Ou, Zhang Zhang, Zeng Zeng, Liang Liang, Ye Ye
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