Global, regional and national burden of blindness and vision loss attributable to diabetic retinopathy, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021

Yujie Pan; Yunkuo Li; Mengzhao Cui; Guangyu He; Guixia Wang

doi:10.1111/dom.16588

. 2025 Jul 22;27(10):5464–5477. doi: 10.1111/dom.16588

Global, regional and national burden of blindness and vision loss attributable to diabetic retinopathy, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021

Yujie Pan ¹, Yunkuo Li ², Mengzhao Cui ¹, Guangyu He ², Guixia Wang ^2,^✉

PMCID: PMC12409291 PMID: 40693378

Abstract

Aims

Diabetes is increasingly reported as a cause of blindness and vision loss. However, the trends in the burden of blindness and vision loss attributed to diabetic retinopathy (DR) have yet to be fully elucidated.

Materials and Methods

Utilizing the latest data from the Global Burden of Disease Study 2021, we extracted prevalence and years lived with disability (YLD) data for these conditions, including their respective age‐standardized rate (ASR) indicators. The data were categorized by time, location, age and sociodemographic index (SDI). This study conducted comprehensive analyses over a span of 32 years (1990–2021) to identify trends in blindness and vision loss attributed to DR, employing advanced statistical methods such as estimated annual percentage change (EAPC), health inequity analysis (slope index and concentration index), decomposition analysis, frontier analysis, and predictive modelling using the Bayesian age‐period‐cohort method.

Results

From 1990 to 2021, the global burden of blindness and vision loss attributed to DR (measured by prevalence and YLD) increased rapidly, and this trend was projected to remain stable until 2046. The age‐standardized prevalence rates (ASPR) and age‐standardized YLD rates (ADYR) in all five SDI regions exhibited an upward trend. Notably, the high and high‐middle SDI regions surpassed global levels, with their EAPC and 95% CI values all greater than 0. In 2021, the prevalence cases, YLD cases, prevalence rates and YLD rates for females across all age groups were generally higher than those for males, and were approximately 1.4 times those of males. Health inequality analysis indicates that over the past 32 years, there have been significant disparities in the distribution of prevalence rates and YLD rates associated with the SDI across 204 countries and regions. Decomposition analysis on a global and cross‐SDI regional scale indicated that ageing, population growth and epidemiological changes had all increased the burden of prevalence and YLD. The frontier analysis showed that high SDI regions had greater potential for improvement. In 2021, compared with the relatively stable trend of type 1 diabetes, the prevalence and YLD rates of blindness and vision loss attributable to type 2 diabetes rapidly increased with age.

Conclusions

Blindness and vision loss attributed to DR pose significant global health and economic challenges. It is imperative for health system managers to formulate strong strategies to address these growing issues effectively.

Keywords: blindness and vision loss, diabetes, GBD, prevalence, YLD

1. INTRODUCTION

Blindness and vision loss are significant global health issues, ranking third in importance after anaemia and hearing impairment. ¹ Per World Health Organization estimates, over 2.2 billion individuals globally suffer from vision impairment, with almost half of these cases being preventable or untreated. ² Frequent causes of vision impairment, such as glaucoma, cataracts, diabetic retinopathy, age‐related macular degeneration and presbyopia, are more prevalent among individuals over the age of 60. ³ Recognizing vision as an integral component of overall health is essential. Growing evidence reveals that blindness and vision loss impact not only sight, but also physical, cognitive and mental well‐being, while amplifying disparities in employment, healthcare access and income. ⁴ Additionally, research has shown a correlation between low self‐esteem and vision impairment. ² The evident global burden has sparked concerns and attention towards eye health.

Diabetes is a widespread and long‐lasting metabolic disorder marked by high blood sugar levels due to irregularities in insulin production or response. ⁵ Ocular complications in diabetic patients, such as diabetic retinopathy (DR), diabetic cataract, diabetic macular oedema, diabetic maculopathy, diabetic optic neuropathy and the indirect impact of other systemic complications, often accompany blindness and vision loss, particularly due to DR. ⁶ , ⁷ , ⁸ , ⁹ DR is a common complication of diabetes, with a global prevalence of 20%–35% among diabetic patients. Globally, the prevalence of DR among adults is anticipated to surge, with projections indicating a 25.9% rise to 129.84 million by 2030, and a 55.6% increase to 160.50 million by 2045. ¹⁰ , ¹¹ This sharp increase will place a considerable burden on both society and the economy. Notably, DR is the leading cause of blindness and vision loss in the working‐age population. ¹² Furthermore, DR is an irreversible eye disease leading to blindness, which can be easily prevented but is challenging to treat. ¹³ Many researchers are diligently exploring the mysteries behind the comprehensive breakdown of retinal microvasculature and neurons, as well as the continuous deterioration of vision caused by DR. DR is generally classified into background retinopathy (non‐proliferative DR) and proliferative DR, with diabetic macular oedema potentially developing at any stage. ¹⁴ In the non‐proliferative DR stage, retinal microvascular damage and leakage, combined with ischaemia and hypoxia in the proliferative DR stage, and the abnormal proliferation of neovascularization, along with subsequent neuroretinal degeneration, collectively lead to blindness and vision loss. ¹⁵ , ¹⁶ Additionally, studies have indicated that the transforming growth factor‐β signalling family plays a significant role in the pathogenesis of DR, due to its crucial involvement in maintaining retinal vascular homeostasis, pericyte differentiation and endothelial cell barrier function. ¹⁶ In summary, there are numerous connections between diabetes and blindness as well as vision loss; however, there is a lack of sufficient research support.

To date, the global burden of blindness and vision loss attributed to DR has not been quantified. Furthermore, the differences in analysis methods, observation periods and model settings in existing studies make comparison of results very complex. To understand the current burden, its time trends, and to develop effective prevention policies, comprehensive, standardized, integrated, dynamic and forward‐looking global health assessments are essential. The 2021 Global Burden of Disease (GBD) study provides a unique opportunity to use standardized methods and comprehensive data to assess the impact of the burden of blindness and vision loss attributed to DR from global, regional and temporal perspectives. This study leveraged data from the GBD 2021 database to fulfil the following key objectives: (1) Conducted a comprehensive assessment of the global, regional and national burden and trends of blindness and vision loss attributed to DR from 1990 to 2021. (2) Evaluate the age and sex patterns of blindness and vision loss attributed to DR. (3) Further evaluate health inequalities, the contributions of ageing, population growth, and epidemiological changes to the disease burden, and the improvement potential related to SDI through health inequality analysis, decomposition analysis and frontier analysis. (4) Compare the contributions of type 1 and type 2 diabetes to the burden of blindness and vision loss. (5) Project the global disease burden of blindness and vision loss attributed to DR until 2046.

2. MATERIALS AND METHODS

2.1. Data resource and disease definition

The blindness and vision loss attributed to DR data analysed in this study come from the GBD 2021, which offers the most recent epidemiological estimates on the burden of 371 diseases and injuries across 21 regions and 204 countries and territories from 1990 to 2021. ¹⁷ These data are accessible for free through the Global Health Data Exchange (https://ghdx.healthdata.org/gbd-2021/sources), with detailed information on data, methodologies, and statistical modelling available in previous reports. ¹⁸

The International Classification of Diseases‐10 codes for blindness and vision loss range from H25‐H28.8, H31‐H36.8, H40‐H40.9, H42‐H42.8, to H46‐H54.9, encompassing conditions like glaucoma, age‐related macular degeneration, cataracts, near vision loss, refractive disorders, and other visual impairments. ¹⁹ Based on the Snellen chart, blindness and vision loss can be classified into these severity levels: (1) Moderate: visual acuity ≥6/60 and <6/18, (2) severe: visual acuity ≥3/60 and <6/60, and (3) blindness: visual acuity less than 3/60 or less than 10% of the visual field around central fixation. ²⁰ In the GBD 2021, diabetes was defined based on a fasting blood glucose level of 7 mmol/L (126 mg/dL) or above, or the use of insulin or diabetes medication. ²¹ Notably, according to the Global Incidence, Prevalence, Years Lived with Disability (YLDs), Disability‐Adjusted Life‐Years (DALYs), and Healthy Life Expectancy (HALE) for 371 Diseases and Injuries in 204 Countries and Territories and 811 Subnational Locations, 1990–2021: A Systematic Analysis for the Global Burden of Disease Study 2021, as defined in Appendix 1 (Methods Appendix), vision loss attributed to diabetes in the GBD specifically refers to vision impairment resulting from diabetic retinopathy. ¹⁷

2.2. Sociodemographic index

The sociodemographic index (SDI) is a comprehensive indicator introduced by the Institute for Health Metrics and Evaluation in 2015 to evaluate the development level of countries or regions, highlighting the link between social progress and health outcomes. It is calculated as the geometric mean of three factors: total fertility rate for individuals under 25 years old, mean education level for those aged 15 years and older and lag‐distributed income per capita. SDI ranges from 0 to 1, with higher values signifying greater levels of socioeconomic development. In the GBD 2021, 204 countries and territories were categorized into five SDI regions: low, low‐middle, middle, high‐middle and high. ¹⁷

2.3. Health inequality analysis

Health inequalities are quantifiable differences in health outcomes among population subgroups, characterized by variations in social, economic, geographic or demographic factors. ²² , ²³ The slope index and concentration index, which are commonly used to measure absolute and relative inequalities, were utilized to evaluate health disparities in prevalence and Years Lived with Disability (YLD) of blindness and vision loss attributed to DR. ²² The slope index is calculated using a weighted regression analysis across all age groups, linked to a relative positional measure based on the SDI. This is represented by the midpoint of the population's cumulative distribution when ranked by SDI. ²⁴ The concentration index is derived by calculating the area under the Lorenz concentration curve, established using relative cumulative scores and cumulative population distribution based on SDI. ²⁵

2.4. Decomposition analysis

Decomposition analysis is a valuable statistical method for understanding the factors influencing health outcomes over time. Widely used in epidemiology and public health, it quantifies the relative impacts of demographic changes, epidemiological shifts and socioeconomic factors on variations in disease burden. ²⁶ To identify the key factors driving changes in the burden of blindness and vision loss attributed to DR from 1990 to 2021, we conducted a decomposition analysis. This quantified the individual contributions of population growth, ageing and epidemiological shifts to prevalence and YLD.

2.5. Frontier analysis

Frontier analysis is a powerful method used to evaluate and improve the efficiency of health care systems by comparing performance with top performing entities. This method enables the quantitative assessment of the relationship between the burden of blindness and vision loss attributed to DR and levels of sociodemographic development. ²⁷ Using non‐parametric data envelopment analysis, we generated a non‐linear frontier to represent the minimal burden achievable, given the developmental status. ²⁸ Using data from 1990 to 2021, we integrated age‐ standardized prevalence rates (ASPR) and age‐ standardized YLD rates (ADYR) with the SDI. A frontier analysis was then conducted to evaluate the potential for reducing the burden of blindness and vision loss attributed to DR across different stages of national development. To strengthen the robustness of frontier analysis, we applied a bootstrap technique with 1000 replications.

2.6. Predictive analysis

The Bayesian age‐period‐cohort (BAPC) model and the Integrated Nested Laplace Approximations (INLA) framework were utilized to forecast future trends. The INLA framework facilitated the approximation of marginal posterior distributions in conjunction with the BAPC model, effectively circumventing the convergence and mixing challenges typically encountered with conventional Bayesian methods that depend on Markov chain Monte Carlo sampling techniques. Utilizing GBD data spanning 1990 to 2021 and population projections from the World Health Organization, the BAPC model demonstrates its ability to accurately forecast future trends. ²⁹ , ³⁰

2.7. Statistical analysis

Numbers of prevalence, YLD and their corresponding rates were the primary measures used to quantify the burden of blindness and vision loss attributed to DR. Each rate is expressed per 100 000 individuals, accompanied by a 95% uncertainty interval (UI) based on the GBD algorithm. To analyse the dynamics of blindness and vision loss attributed to DR, estimated annual percentage change (EAPC) was calculated to identify temporal trends in disease burden. The 95% confidence intervals (CI) of EAPCs were determined through linear modelling. If both the upper limit of the EAPC and its 95% CI are negative, the corresponding rate demonstrates a decreasing trend. Conversely, if both the lower limit of an EAPC and its 95% CI are positive, the corresponding rate demonstrates an increasing trend. All computations were carried out using R Studio, version 4.4.2 (R Project for Statistical Computing). Statistical significance was determined with two‐sided p values, with p < 0.05 considered as significant.

3. RESULTS

3.1. Global, regional and national burden and trends of blindness and vision loss attributed to DR from 1990 to 2021

Figure 1 and Table S1 presented the changes in the prevalence cases, YLD cases, prevalence rates and YLD rates of blindness and vision loss attributed to DR by sex and SDI regions from 1990 to 2021. Figure S1 and Table S2 illustrated the prevalence, YLD, age‐standardized rates of prevalence and YLD per 100 000 population in 2021, along with the EAPC and percentage change from 1990 to 2021 for blindness and vision loss attributed to DR on global and regional levels. Globally, the prevalence cases, YLD cases, prevalence rates and YLD rates of blindness and vision loss attributed to DR increased rapidly from 1990 to 2021, with females experiencing a higher disease burden than males (Table S1 and Figure 1). The prevalence cases rose from 1 927 881 in 1990 to 5 836 466 in 2021, and YLD cases increased from 144 666 in 1990 to 472 664 in 2021, representing percentage changes of 203% and 227%, respectively (Tables S1 and S2; Figure 1A,B and S1A). Meanwhile, the prevalence rates grew from 36.1 per 100 000 people to 74 per 100 000 people, and YLD rates increased from 2.7 per 100 000 people to 6 per 100 000 people (Table S1; Figure 1C,D). In comparison, the age‐standardized prevalence rates and YLD rates exhibited a similar upward trend, with an EAPC of 1.17 (95% CI: 1.11 to 1.24) for ASPR and 1.49 (95% CI: 1.41 to 1.58) for ASYR (Table S2 and Figure S1B).

Temporal trends in global disease burden of blindness and vision loss attributed to DR by SDI quintiles and sex from 1990 to 2021. (A) Prevalence cases. (B) YLD cases. (C) Prevalence rates. (D) YLD rates. DR, diabetic retinopathy; SDI, sociodemographic index; YLD, years lived with disability.

From 1990 to 2021, the pattern of rising prevalence cases, YLD cases, prevalence rates, and YLD rates of blindness and vision loss attributed to DR in five SDI regions was similar to the global pattern, with a gender preference (Table S1 and Figure 1). In 2021, the absolute number of prevalent cases and YLD cases in the middle SDI region were 2 293 806 and 185 991, respectively, both accounting for about 40% of the global total (Table S1; Figure 1A,B). Interestingly, in high SDI regions, the gender disparity in prevalence cases and YLD cases was the most pronounced, with female cases being approximately twice that of male cases (Table S1; Figure 1A,B). From 1990 to 2021, except for the low SDI regions where prevalence rates and YLD rates remained relatively stable, the other four SDI regions steadily increased, with high SDI, high‐middle SDI and middle SDI consistently being above the global level each year (Figure 1C,D). In contrast, the age‐standardized prevalence rates and YLD rates in all five SDI regions showed an upward trend, but only the high SDI and high‐middle SDI regions exceeded the global level, with their EAPC and 95% CI values all greater than 0 (Table S2 and Figure S1B).

From 1990 to 2021, all GBD regions exhibited consistent positive growth in ASPR and ASYR across the 21 GBD regions (EAPC > 0) (Table S2 and Figure S1B). Notably, in 2021, the ASPR and ASYR rankings for Central Latin America and Tropical Latin America were first and second, respectively, each being approximately twice the global level (Table S2). However, High‐income North America saw the fastest growth in ASPR and ASYR, with an EAPC of 2.09 (95% CI: 1.76 to 2.43) for ASPR and 3.05 (95% CI: 2.59 to 3.52) for ASYR, followed by western Europe (Table S2 and Figure S1B). In contrast, High‐income Asia Pacific showed a notable performance with the slowest growth in ASPR and ASYR, with an EAPC of 0.21 (95% CI: 0.11 to 0.32) for ASPR and 0.04 (95% CI: −0.09 to 0.16) for ASYR (Table S2 and Figure S1B).

Figure 2 and Table S3 showed the distribution of ASPR in 2021, ASYR in 2021, EAPC in ASPR from 1990 to 2021 and EAPC in ASYR from 1990 to 2021 for blindness and vision loss attributed to DR across 204 countries and regions. In 2021, out of 204 countries and regions, 13 reported prevalence cases exceeding 100 000, cumulatively accounting for 69.71% of the global prevalence cases of blindness and vision loss attributed to DR (Table S3). Among these, China led by a significant margin, with 1 373 476 cases, accounting for 23.52% of the global prevalence cases. Meanwhile, in 2021, out of 204 countries and regions, 7 reported YLD cases exceeding 10 000, cumulatively accounting for 62.46% of the global YLD cases, with India ranking first (Table S3). Notably, India and China, the two population giants, claimed the top two rankings in terms of prevalence cases and YLD cases. In 2021, 69 countries and regions were identified as having ASPR above the global average, with the top five being Palestine, Libya, Bahrain, Saudi Arabia and Mexico. Additionally, in 2021, 70 countries and regions were identified as having ASYR above the global average, with the top five being Libya, Marshall Islands, Palestine, Mexico and Mauritius (Table S3; Figure 2A,C). From 1990 to 2021, ASPR and ASYR both showed an upward trend in 192 countries and regions, with 131 countries and regions experiencing significant increases (EAPC in ASPR and EAPC in ASYR both >1) (Table S3; Figure 2B,D). From 1990 to 2021, Luxembourg (EAPC in ASPR = 3.88, 95% CI: 3.73 to 4.03), Niger (EAPC in ASPR = 3.71, 95% CI: 3.29 to 4.14), Cambodia (EAPC in ASPR = 3.57, 95% CI: 3.15 to 4), Seychelles (EAPC in ASPR = 3.36, 95% CI: 3.17 to 3.56) and Mauritius (EAPC in ASPR = 3.21, 95% CI: 2.78 to 3.64) observed the most significant upward trends in ASPR (Table S3 and Figure 2B). Meanwhile, Cambodia (EAPC in ASYR = 5.53, 95% CI: 4.99 to 6.07), Luxembourg (EAPC in ASYR = 5.14, 95% CI: 4.89 to 5.39), Seychelles (EAPC in ASYR = 4.92, 95% CI: 4.57 to 5.26), Mauritius (EAPC in ASYR = 4.48, 95% CI: 3.86 to 5.1), and Micronesia (EAPC in ASYR = 4.4, 95% CI: 3.59 to 5.22) observed the largest increases in ASYR (Table S3 and Figure 2D). In contrast, Singapore had the most significant downward trends in ASPR and ASYR, with an EAPC of −0.71 (95% CI: −0.8 to −0.62) for ASPR and −1.21 (95% CI: −1.34 to −1.08) for ASYR (Table S3; Figure 2B,D).

The global distribution of the disease burden of blindness and vision loss attributed to DR. (A) ASPR in 2021. (B) EAPC in ASPR from 1990 to 2021. (C) ASYR in 2021. (D) EAPC in ASYR from 1990 to 2021. ADYR, age‐standardized YLD rates; ASPR, age‐standardized prevalence rates; ASYR, age‐standardized YLD rates; DR, diabetic retinopathy; EAPC, estimated annual percentage change; YLD, years lived with disability.

3.2. Age and sex patterns

The global data for blindness and vision loss attributed to DR in 2021 showed a very distinct gender characteristic: the prevalence cases, YLD cases, prevalence rates, and YLD rates for females across all age groups were generally higher than those for males and were approximately 1.4 times those of males (Table S1; Figure S2A,B). Additionally, in 2021, the age group with the highest number of prevalence cases and YLD cases among women was 65–69 years. At the same time, the age groups with the highest number of prevalence cases and YLD cases among men were 65–69 years and 60–64 years, respectively. Notably, the prevalence cases and YLD cases for the age groups 55–59, 60–64, 65–69 and 70–74 ranked in the top four across all age groups, accounting for approximately 54.76% and 53.16% of the global totals (Table S1; Figure S2A,B). In terms of the trends in prevalence rates and YLD rates, after the age of 35–39 years, the prevalence rates and YLD rates for both males and females increased with age (Figure S2A,B). To further quantify the long‐term dynamic changes in disease burden across different age groups, we introduced the age structure of EAPC in ASPR and EAPC in ASYR globally and in the 5 SDI regions from 1990 to 2021 (Table S4; Figure S2C,D). Globally, the ASPR and ASYR exhibited an upward trend before the ages of 55 years and 60 years, respectively, after which they declined, eventually stabilizing after the age of 75 years. The pattern of change in ASPR and ASYR in high SDI regions was similar to the global pattern, but the upward trend in ASPR and ASYR in high SDI regions was more pronounced (with larger absolute EAPC values). In contrast, the performance in low SDI and middle SDI regions was better, with the absolute values of EAPC in all age groups being lower than the global level.

3.3. Health inequality analysis

The health inequality analysis of blindness and vision loss attributed to DR showed significant differences in the distribution of prevalence rates and YLD rates associated with SDI across 204 countries and regions (Figure 3). From 1990 to 2021, the slope index of prevalence rates increased from 26 to 71, while the slope index of YLD rates rose from 1.81 to 5.40 (Figure 3A,C). These results indicated that, compared with countries with the lowest SDI, countries with the highest SDI had, on average, 71 more cases of blindness and vision loss attributed to DR per 100 000 people in prevalence rates and 5.40 more in YLD rates. This suggests that the disease burden is more concentrated in high‐SDI countries. The concentration index for prevalence rates and YLD rates showed an increasing trend from 1990 to 2021 (Figure 3B,D). Although the wealth gap in some regions has narrowed, the global inequality in blindness and vision loss attributed to DR remains a persistent issue, particularly in high‐SDI countries.

The global health inequality analysis of blindness and vision loss attributed to DR in 1990 and 2021. (A) Regression curves of prevalence rates. (B) Concentration curves of prevalence rates. (C) Regression curves of YLD rates. (D) Concentration curves of YLD rates. DR, diabetic retinopathy; SDI, sociodemographic index; YLD, years lived with disability.

3.4. Decomposition analysis

To study the impact of population growth, ageing, and epidemiological changes on the prevalence and YLD trends of blindness and vision loss attributed to DR from 1990 to 2021, decomposition analysis was conducted (Figure 4). Ageing, population and epidemiological changes contributed to 35.21%, 35.31% and 29.48% of the global prevalence growth, respectively (Table S5; Figure 4). The influence of these factors on prevalence cases differed by country and region. Ageing had the most significant impact in middle SDI regions (51.08%) and high‐middle SDI regions (47.9%), indicating it was the primary factor driving the increase in the burden of blindness and vision loss due to diabetes in these areas. Population growth played a significant role in low SDI regions (68.46%) and low‐middle SDI regions (42.83%), highlighting its predominant influence on the escalating disease burden in these regions. Ageing, population and epidemiological changes accounted for 32.04%, 33.09% and 34.87% of the global YLD growth, respectively (Table S5; Figure 4). Compared with prevalence, epidemiological changes were the primary determinant for the global increase in YLD. In high SDI regions, epidemiological changes even contributed significantly by 44.47%. The contribution of ageing was most apparent in middle SDI regions (47.46%) and high‐middle SDI regions (44.36%), while population growth had the most significant impact in low SDI regions (65.79%) and low‐middle SDI regions (38.82%). The patterns of ageing and population growth in YLD revealed similar trends to those observed in prevalence.

Decomposition analysis of prevalence and YLD change in blindness and vision loss attributed to DR according to population‐level determinants change from 1990 to 2021 at the global level and by SDI. (A) Decomposition analysis of blindness and vision loss attributed to DR change in prevalence. (B) Decomposition analysis of blindness and vision loss attributed to DR change in YLD. The black dot represents the total change contributed by all three components. A positive value for each component indicates a positive contribution, while a negative value indicates a negative contribution. DR, diabetic retinopathy; SDI, sociodemographic index; YLD, years lived with disability.

3.5. Frontier analysis

Using data from 1990 to 2021, frontier analysis was conducted based on the relationship between ASPR and ASYR of blindness and vision loss attributed to DR and SDI, to explore the ideal situation for countries to control the disease burden under the corresponding SDI conditions each year (Table S6; Figure 5). In countries with an SDI between 0.2 and 0.5, the effective difference values of ASPR and ASYR were relatively small. However, as the SDI increased to above 0.5, the effective difference values of ASPR and ASYR rapidly increased (Figure 5). In the frontier analysis results, countries with lower SDI closest to the frontier fit line are highlighted in blue, while those with higher SDI furthest from the frontier fit line are indicated in red. Additionally, the 15 countries with the greatest deviation from the frontier fit line, regardless of SDI, are marked in black (Figure 5). In the frontier analysis based on ASPR and SDI, the top five countries with the greatest effective differences from the frontier fit line (effective differences: 199.88–259.26) had SDI levels between 0.63 and 0.82. These countries included Palestine, Libya, Saudi Arabia, Bahrain and Mexico (Table S6; Figure 5). In the frontier analysis based on ASYR and SDI, the top five countries with the greatest effective differences from the frontier fit line (effective differences: 18.42–21.30) had SDI levels between 0.57 and 0.73. These countries included Libya, the Marshall Islands, Palestine, Mexico and Mauritius (Table S6; Figure 5). The frontier analysis indicated that countries with lower SDI had relatively smaller room for improvement, whereas those with higher SDI demonstrated greater potential for reducing the disease burden.

Frontier analysis explores the relationship between ASPR as well as ASYR of blindness and vision loss attributed to diabetic retinopathy (DR) and SDI in 204 countries and territories. (A) Frontier analysis results of ASPR from 1990 to 2021. (B) Frontier analysis of ASPR for 2021. (C) Frontier analysis results of ASYR from 1990 to 2021. (D) Frontier analysis of ASYR for 2021. ADYR, age‐standardized YLD rates; ASPR, age‐standardized prevalence rates; ASYR, age‐standardized YLD rates; SDI, sociodemographic index; YLD, years lived with disability.

3.6. Comparative burden of blindness and vision loss in type 1 versus type 2 diabetes

The heterogeneity of the burden of blindness and vision loss attributable to type 1 and type 2 diabetes is revealed through multidimensional comparisons, including classification, age groups (excluding invalid data before the age of 24 years) and time trends, as illustrated in Figure S3. In 2021, the number of prevalent cases and YLD cases of blindness and vision loss attributable to type 2 diabetes was mainly concentrated in the age groups of 55–59, 60–64, 65–69 and 70–74 years, with relatively high absolute case numbers. Additionally, in 2021, the prevalence and YLD rate of blindness and vision loss attributable to type 2 diabetes increased significantly with age (Figure S3). In comparison, the pattern of blindness and vision loss attributable to type 1 diabetes showed different trends: the number of prevalent cases and YLD cases did not vary greatly across age groups, and the prevalence and YLD rate remained relatively stable with age (Figure S3). To better understand the dynamic changes in the burden of blindness and vision loss attributable to type 1 and type 2 diabetes, we also presented the EAPC in ASPR and ASYR from 1990 to 2021. We found that the ASPR and ASYR of blindness and vision loss attributable to type 2 diabetes showed a rapid upward trend across age groups (Figure S3). The rapid increase in blindness and vision loss attributable to type 2 diabetes, as compared with the relatively stable trend due to type 1 diabetes, suggests that differentiated prevention and control strategies are needed.

The Snellen chart, as the gold standard for classifying the severity of vision loss, allowed for a detailed analysis of the disease burden (including moderate vision loss, severe vision loss, and blindness). Figure S4 quantified the changes in the global burden of different severities of vision loss attributable to type 1 and type 2 diabetes from 1990 to 2021. Analysis by type 1 and type 2 diabetes revealed that the trends in the burden of different severities of vision loss due to type 1 and type 2 diabetes were similar, but the burden from type 2 diabetes always remained higher (Table S7; Figure S4). Stratified analysis by different severities of vision loss showed that, from 1990 to 2021, the number of prevalent cases and prevalence rates of moderate vision loss attributable to type 1 and type 2 diabetes increased significantly, while the increases in severe vision loss and blindness were relatively slow. Additionally, from 1990 to 2021, the number of YLD cases and YLD rates of blindness attributable to type 1 and type 2 diabetes increased significantly, while the increases in moderate vision loss and severe vision loss were relatively slow (Table S7; Figure S4). This revealed the three‐dimensional associations among classification, severity of vision impairment, and time trends, providing a basis for differentiated interventions.

3.7. Predictive analysis

Based on the BAPC model, the global burden of blindness and vision loss attributed to DR (classified by type) was projected from 1990 to 2046 (Figure S5). The projections indicated that the ASPR of type 1 diabetes was expected to decline in the future. In contrast, the ASPR of type 2 diabetes was expected to stabilize. Additionally, the trends in ASPR for blindness and vision loss attributable to type 1 and type 2 diabetes remained generally similar to those for type 2 diabetes, which might be due to the much greater burden of blindness and vision loss attributable to type 2 diabetes compared with type 1 diabetes (Figure S5).

4. DISCUSSION

Blindness and vision loss attributed to DR present significant global public health challenges, drawing substantial research interest because of their noteworthy epidemiological characteristics. This comprehensive 32‐year analysis includes data from 204 countries across six continents, offering a detailed depiction of the disease burden across diverse populations and time periods. Epidemiological indicators showed that the burden of blindness and vision loss attributed to DR in 2021 was higher than in 1990. The number of prevalent cases increased from 1 927 881 in 1990 to 5 836 466 in 2021, and the number of YLD cases rose from 144 666 in 1990 to 472 664 in 2021. Considering the potential heterogeneity in disease burden caused by population age structure, we also referred to ASPR and ASYR. From 1990 to 2021, both ASPR and ASYR showed an upward trend, reflecting considerable room for improvement in health awareness, treatment methods, and medical technology. Effective medical awareness among both health care providers and patients is essential. For doctors, it is crucial to recognize that addressing the global diabetes epidemic requires not only a cure for the disease itself but also effective treatments for its major complications, such as blindness and vision loss. ³¹ For patients, early detection and proper management are universally acknowledged as critical to mitigating vision loss associated with diabetes. ³² , ³³ In a survey study, one‐third of patients with type 1 diabetes and half of patients with type 2 diabetes had not had an eye examination within five years. ³¹ Efforts to avert blindness and vision impairment attributable to diabetes are primarily concentrated on managing diabetes and hypertension. This is because uncontrolled blood sugar levels and high blood pressure have consistently been linked to the worsening and progression of these conditions. ³⁴ The epidemiological characteristics of blindness and vision loss attributed to DR varied across different regions of the world. The ASPR and ASYR in the five SDI regions all showed an upward trend, but only the high SDI and high‐middle SDI regions exceeded the global level, with their EAPC and 95% CI values all greater than 0. The prevalence detection of diseases relies on sufficient medical personnel, diagnostic facilities, public health awareness and economic capacity. In developed regions, the extensive health care resources and the strong inclination of individuals to seek medical care may result in higher disease detection rates. ³⁵ Additionally, factors such as diet habits and the prevalence of obesity might also play a role in increasing the disease burden. ³⁶

The burden of blindness and vision loss attributed to DR varied among countries within each region, closely linked to the distribution of public health policies, medical resources and the effectiveness of surveillance and reporting systems. India and China, the two most populous countries, ranked first and second in the number of prevalent cases and YLD cases. This was likely related to their large population base, as the combined population of the two countries accounted for nearly 35% of the global total (approximately 2.8 billion). Even with similar prevalence rates, the absolute number of cases was inevitably much higher than in other countries. Additionally, rapid urbanization, high‐sugar and high‐fat diets, sedentary lifestyles and rising obesity rates also contributed to the prevalence of diabetes and its complications. ³⁷ , ³⁸ , ³⁹ It is noteworthy that health care policies, insufficient screening resources for diabetes‐related complications, and inadequate treatment for vision impairment may also contribute to this condition. ⁴⁰ Therefore, when assessing the disease burden at the national level, multiple factors must be considered, including absolute case numbers, crude prevalence and YLD rates, as well as ASPR and ASYR. Additionally, the EAPC provides valuable insights into the temporal trends of these indicators. Through this comprehensive analysis, we can gain a more holistic understanding of the disease burden across different countries, thereby providing a scientific basis for public health policy development. From 1990 to 2021, both ASPR and ASYR exhibited an upward trend in 192 countries and regions. Of these, 131 countries and regions experienced significant increases, with EAPC in ASPR and EAPC in ASYR both greater than 1. In the future, efforts to strengthen primary screening networks, expand health insurance coverage for innovative therapies, utilize AI technology to enhance diagnostic efficiency and increase public education were necessary to effectively reduce the burden of preventable blindness. Interestingly, Singapore has demonstrated the most significant decline in ASPR and ASYR. This positive trend is likely attributable to Singapore's well‐developed multitiered eye care system, extensive screening network coverage, effective implementation of disease prevention strategies, substantial improvements in public health awareness and continuous advancements in medical technology. These factors collectively contribute to Singapore's remarkable success in reducing blindness and vision loss attributed to diabetes, making its experience a valuable reference for other countries.

Globally, approximately 17.7 million more males than females were estimated to have diabetes mellitus. ⁴¹ However, in our study, the gender differences in blindness and vision loss attributed to DR were evident. In all age groups, females had higher prevalence, YLD cases, prevalence rates and YLD rates than males, approximately 1.4 times higher. Throughout their lives, females undergo more significant hormone fluctuations and bodily changes due to reproductive factors compared with males. Females with gestational diabetes who were exposed to high blood sugar levels had an increased risk of blindness and vision loss, and poor blood sugar control after childbirth could lead to long‐term damage. ⁴¹ , ⁴² Additionally, pregnancy itself was a risk factor for the progression of vision loss. Physiological changes, including metabolic, immunologic, vascular and hormonal alterations during pregnancy, can lead to both the onset and worsening of vision loss. ⁴³ Beyond physiological mechanisms, disparities in access to health care resources due to gender inequality further exacerbate the burden on women. For instance, women often delay seeking medical care due to caregiving responsibilities, and in low‐income regions, there is a gender gap in the accessibility of ophthalmic services. ⁴⁴ , ⁴⁵ Therefore, health institutions need to consider integrating a gender perspective into diabetes management guidelines and enhancing community education to increase females' awareness of diabetes complications, breaking down cultural barriers to seeking medical care. Remarkably, the age groups 55–59, 60–64, 65–69 and 70–74 years had the highest prevalence and YLD cases among all age groups, contributing approximately 54.76% and 53.16% to the global totals, respectively. These findings underscored the importance of early retinal examinations and interventions, as well as the need to integrate diabetes treatment with elderly vision care management.

Health inequalities track differences and shifts in health metrics among various population groups. As a critical aspect of social inequity, health disparities present significant obstacles to global health progress. Monitoring and assessing these inequalities is vital for attaining health equity, which remains the central pledge of the WHO. ⁴⁶ , ⁴⁷ For example, in developed countries such as the United States, despite abundant health care resources, many low‐income individuals still struggle to access essential diabetes and vision care services due to high medical costs and incomplete insurance coverage. In contrast, developing countries such as China and India have made significant progress in health care infrastructure development. However, in remote and impoverished areas, health care accessibility remains limited, leading to insufficient early screening and treatment of diabetes‐related complications. ⁴⁸ , ⁴⁹ , ⁵⁰ In our study, spanning from 1990 to 2021, the slope index for prevalence rates surged from 26 to 71, while the slope index for YLD rates climbed from 1.81 to 5.40. The concentration index for prevalence rates and YLD rates showed an increasing trend from 1990 to 2021. This suggests an increase in health disparities concerning the prevalence and YLD rates of blindness and vision loss attributed to DR among various socioeconomic groups. Although the wealth gap in some regions has narrowed, the global inequality in blindness and vision loss attributed to DR remains a persistent issue, particularly in high‐SDI countries. Our study recommends that future efforts should aim at improving health care accessibility and addressing socioeconomic determinants to further alleviate the burden of blindness and vision loss attributed to DR, thereby promoting health equity. Firstly, a comprehensive mechanism for the dynamic allocation of medical resources should be established to ensure that medical resources are directed towards high‐risk areas, thereby reducing disparities between urban and rural regions. Secondly, the costs of ophthalmic and diabetes treatments should be lowered, with policies in place to provide financial relief for low‐income populations, such as free eye exams and subsidies for diabetes medications. Additionally, the feasibility of implementation in areas with different levels of development should be considered, conducting comparative studies on the effectiveness of SDI gradient interventions and striving to achieve resource sharing of ophthalmic specialists between high and low SDI regions, for instance, by developing a telemedicine collaboration platform. ⁵¹ , ⁵²

Decomposition analysis showed that the contributions of population growth and ageing had a significant impact on the prevalence growth of blindness and vision loss attributed to DR in each SDI region from 1990 to 2021. It is noteworthy that the contribution of ageing to blindness and vision loss attributed to diabetes inherently integrates both age and disease duration effects. The higher prevalence of vision impairment among older populations is partly due to the longer duration of diabetes, as individuals with a disease course of more than 10 years have a higher risk of developing DR compared with those with a disease course of less than 5 years. ¹¹ Therefore, the ‘ageing contribution’ in decomposition analysis reflects not only the pathological impact of prolonged disease duration but also the increasing physiological vulnerability associated with ageing. The global population growth directly led to an increase in the number of people with blindness and vision loss attributed to DR, thereby increasing the prevalence. At the same time, the elderly are a high‐risk group for diabetes and its complications, and the ageing trend will exacerbate the risk of the prevalence of blindness and vision loss attributed to DR. Compared with prevalence, epidemiological changes were the primary determinants for the global increase in YLD. The influence of epidemiological changes greatly affected the global burden of YLD due to blindness and vision loss attributed to DR, indicating that public health and medical efforts still have significant room for improvement. To effectively manage the prevalence and YLD burden of blindness and vision loss attributed to DR, it is essential to continuously focus on preventive and treatment measures for older at‐risk populations. Additionally, optimizing the allocation and utilization of public health resources, considering population dynamics and ageing, is crucial.

The frontier analysis underscored the intricate relationship between SDI and the burden of ASPR and ASYR in blindness and vision loss attributed to DR. It is generally believed that low‐level SDI regions have a higher potential for improvement because economic constraints limit their ability to manage disease burden effectively. However, our research indicated that in countries with an SDI between 0.2 and 0.5, the effective difference values of ASPR and ASYR were relatively minor. As the SDI rose above 0.5, these effective difference values increased significantly. The frontier analysis demonstrated that lower SDI countries had comparatively limited potential for improvement, while higher SDI countries exhibited greater capacity for reducing the disease burden. The ‘paradox’ revealed by frontier analysis—limited improvement potential in low‐SDI countries and greater potential in high‐SDI countries—can be attributed to two key factors: measurement interference due to data quality bias and the inefficiency trap in resource allocation within high‐SDI countries. Despite high SDI countries' continuous increase in medical investments, existing systems may fall into the ‘precision medicine trap’. This term refers to a dilemma in precision medicine where an excessive focus on genetic technologies or individualized treatments leads to the neglect of essential public health measures, cost‐effectiveness and social equity. As a result, resource allocation becomes imbalanced, and the actual health benefits achieved are limited. In high SDI regions, the longer average survival of patients diagnosed with diabetes leads to a cumulative growth in the risk of complications. Additionally, the 24‐h economy and social development model in high SDI areas accelerates the progression of diabetes and vision loss among workers. Additionally, we observed that when the SDI exceeds 0.2, the frontier fit line remains nearly constant across different SDI levels. This suggests that in high‐SDI regions, the benefits of medical advancements are offset by societal risks, such as obesogenic environments and occupational hazards. In these regions, high consumption of sugar and processed foods—leading to elevated obesity and diabetes rates—along with prolonged screen exposure in service‐oriented economies, may exacerbate the progression of DR. Moreover, another plausible explanation is that in high‐SDI countries, the elevated risk of DR is counterbalanced by superior medical infrastructure, resulting in the frontier fit line appearing similar across SDI levels. This indicates that the frontier fit line may not be strictly dependent on SDI, thereby weakening the validity of interpreting it as the ‘optimal level achievable at a given SDI’.

We also focused on the heterogeneity of the burden of blindness and vision loss attributable to type 1 and type 2 diabetes. Type 2 diabetes not only contributed significantly more to the burden of blindness and vision loss than type 1 diabetes, but also showed a rapid increase in the ASPR and ASYR of blindness and vision loss across all age groups. These urgent requirements call for type 2 diabetes‐directed differentiated management, age‐gradient prevention and control systems and time‐sensitive intervention windows to address the vision health threats posed by type 2 diabetes. Predictive analysis projected that by 2046, the future ASPR of type 1 diabetes would decrease. However, the ASPR of type 2 diabetes was expected to remain stable. Future public health strategies should prioritize not only reducing prevalence or YLD rates but also focusing on the long‐term care and management of those affected to achieve sustainable health improvements. Adopting a comprehensive approach that integrates prevention, treatment and extensive management will be crucial for tackling the persistent challenges of blindness and vision loss attributed to DR and for enhancing the overall well‐being of the affected populations.

5. LIMITATION

While the GBD offers crucial global health data and insights on blindness and vision loss attributed to diabetes, it is not without its limitations. First, GBD depends on public health data from various countries and regions, which differ in collection standards, frequency and quality. The wide range of data quality and numerous potential sources of bias lead to discrepancies between GBD estimates and actual blindness and vision loss attributed to DR data. This may result in inaccurate disease burden assessments in certain regions, particularly in low‐income areas. Secondly, the GBD relies on intricate statistical models to estimate disease burden, which are grounded in certain assumptions and simplifications. For instance, these models might presume linear relationships between variables or overlook potential confounding factors, potentially affecting the accuracy of results. Additionally, in the frontier analysis, we found that when the SDI exceeds 0.2, the frontier fit line remains nearly constant across different SDI levels. In high‐SDI countries, the elevated risk of DR may be offset by better medical conditions, resulting in a similar frontier fit line across SDI strata. This represents a possibility that the frontier fit line may not be dependent on SDI level, thereby weakening the rationale for interpreting it as the ‘optimal level achievable at the current SDI level’. Lastly, the GBD did not include other epidemiological indicators of blindness and vision loss attributed to DR, such as incidence and death. This omission may obscure some important epidemiological characteristics.

6. CONCLUSIONS

From 1990 to 2021, the global burden of blindness and vision loss attributed to DR (measured by prevalence and YLD) increased rapidly, and this trend was projected to remain stable until 2046. The ASPR and ASYR in all five SDI regions exhibited an upward trend. Notably, the high and high‐middle SDI regions surpassed the global levels, with their EAPC and 95% CI values all greater than 0. In 2021, the prevalence cases, YLD cases, prevalence rates, and YLD rates for females across all age groups were generally higher than those for males and were approximately 1.4 times those of males. Health inequality analysis indicates that over the past 32 years, there have been significant disparities in the distribution of prevalence rates and YLD rates associated with the SDI across 204 countries and regions. Decomposition analysis on a global and cross‐SDI regional scale indicated that ageing, population growth, and epidemiological changes had all increased the burden of prevalence and YLD. The frontier analysis showed that high SDI regions had greater potential for improvement. In 2021, compared with the relatively stable trend of type 1 diabetes, the prevalence and YLD rates of blindness and vision loss attributable to type 2 diabetes rapidly increased with age.

AUTHOR CONTRIBUTIONS

Y.P. and Y.L. designed the study. Y.P. and M.C. drafted the manuscript. G.H. contributed to the data collection and data analysis. Y.P. and G.W. performed manuscript revision. All authors read and approved the final draft of the manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

PEER REVIEW

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/dom.16588.

Supporting information

Data S1. Supplementary figures.

DOM-27-5464-s002.docx^{(3.5MB, docx)}

Data S2. Supplementary tables.

DOM-27-5464-s001.xlsx^{(141.5KB, xlsx)}

ACKNOWLEDGEMENTS

First, we would like to thank the authors of this article and appreciate their help and dedication from the beginning to the end. Secondly, we want to thank the collaborators of GBD 2021 and their work on the GBD database. Finally, the authors would like to thank all the staff of the editorial department and appreciate all your valuable comments.

Pan Y, Li Y, Cui M, He G, Wang G. Global, regional and national burden of blindness and vision loss attributable to diabetic retinopathy, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021. Diabetes Obes Metab. 2025;27(10):5464‐5477. doi: 10.1111/dom.16588

Funding information The authors declare that no financial support was received for the research, authorship, and/or publication of this article.

DATA AVAILABILITY STATEMENT

The datasets analysed during the current study are available in the Global Burden of Disease Study repository (https://ghdx.healthdata.org/gbd-2021/sources). All data involved in this article are publicly accessible.

REFERENCES

1. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990‐2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1789‐1858. doi: 10.1016/s0140-6736(18)32279-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Luo Y, Wang Q, Wang Y, et al. Trends in the prevalence and burden of blindness and vision loss among adolescents aged 10‐24 years in the Western Pacific Region over 30 years. BMC Public Health. 2024;24:3193. doi: 10.1186/s12889-024-20607-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Gurudas S, Vasconcelos JC, Prevost AT, et al. National prevalence of vision impairment and blindness and associated risk factors in adults aged 40 years and older with known or undiagnosed diabetes: results from the SMART‐India cross‐sectional study. Lancet Glob Health. 2024;12:e838‐e847. doi: 10.1016/s2214-109x(24)00035-4 [DOI] [PubMed] [Google Scholar]
4. Swenor BK, Ehrlich JR. Ageing and vision loss: looking to the future. Lancet Glob Health. 2021;9:e385‐e386. doi: 10.1016/s2214-109x(21)00031-0 [DOI] [PubMed] [Google Scholar]
5. Serikbaeva A, Li Y, Ma S, Yi D, Kazlauskas A. Resilience to diabetic retinopathy. Prog Retin Eye Res. 2024;101:101271. doi: 10.1016/j.preteyeres.2024.101271 [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Yan A, Jones C, Demirel S, Chhablani J. Diabetic macular edema: upcoming therapies. Graefes Arch Clin Exp Ophthalmol. 2024;263:249‐258. doi: 10.1007/s00417-024-06595-7 [DOI] [PubMed] [Google Scholar]
7. Yuan Y, Ji S, Song Y, et al. Global trends in diabetic eye disease research from 2012 to 2021. Neural Regen Res. 2024;19:2310‐2320. doi: 10.4103/1673-5374.391303 [DOI] [PMC free article] [PubMed] [Google Scholar]
8. Lee SH, Tseng BY, Wu MC, Wang JH, Chiu CJ. Incidence and progression of diabetic retinopathy after cataract surgery: a systematic review and meta‐analysis. Am J Ophthalmol. 2025;269:105‐115. doi: 10.1016/j.ajo.2024.08.017 [DOI] [PubMed] [Google Scholar]
9. Li H, Jia W, Vujosevic S, et al. Current research and future strategies for the management of vision‐threatening diabetic retinopathy. Asia Pac J Ophthalmol. 2024;13:100109. doi: 10.1016/j.apjo.2024.100109 [DOI] [PubMed] [Google Scholar]
10. Teo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta‐analysis. Ophthalmology. 2021;128:1580‐1591. doi: 10.1016/j.ophtha.2021.04.027 [DOI] [PubMed] [Google Scholar]
11. Yau JW, Rogers SL, Kawasaki R, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35:556‐564. doi: 10.2337/dc11-1909 [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet. 2010;376:124‐136. doi: 10.1016/s0140-6736(09)62124-3 [DOI] [PubMed] [Google Scholar]
13. Zhang X, Zhang F, Xu X. Single‐cell RNA sequencing in exploring the pathogenesis of diabetic retinopathy. Clin Transl Med. 2024;14:e1751. doi: 10.1002/ctm2.1751 [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Reddy SK, Devi V, Seetharaman ATM, et al. Cell and molecular targeted therapies for diabetic retinopathy. Front Endocrinol (Lausanne). 2024;15:1416668. doi: 10.3389/fendo.2024.1416668 [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Xiao J, Xu Z. Roles of noncoding RNAs in diabetic retinopathy: mechanisms and therapeutic implications. Life Sci. 2024;357:123092. doi: 10.1016/j.lfs.2024.123092 [DOI] [PubMed] [Google Scholar]
16. Callan A, Jha S, Valdez L, Baldado L, Tsin A. TGF‐β signaling pathways in the development of diabetic retinopathy. Int J Mol Sci. 2024;25:3052. doi: 10.3390/ijms25053052 [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Global incidence, prevalence, years lived with disability (YLDs), disability‐adjusted life‐years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990‐2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2133‐2161. doi: 10.1016/s0140-6736(24)00757-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990‐2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2100‐2132. doi: 10.1016/s0140-6736(24)00367-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Li Y, Wang H, Guan Z, et al. Persistence of severe global inequalities in the burden of blindness and vision loss from 1990 to 2019: findings from the Global Burden of Disease Study 2019. Br J Ophthalmol. 2024;108:301‐309. doi: 10.1136/bjo-2022-321801 [DOI] [PubMed] [Google Scholar]
20. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9:e144‐e160. doi: 10.1016/s2214-109x(20)30489-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Global, regional, and national burden of low back pain, 1990‐2020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023;5:e316‐e329. doi: 10.1016/s2665-9913(23)00098-x [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Han T, Wang T, Ye Y, Ying C, Wang X, Liu S. The global, regional, and national burden of paralytic ileus and intestinal obstruction, 1990 to 2021: a cross‐sectional analysis from the 2021 global burden of disease study. Int J Surg. 2025;111:1773‐1787. doi: 10.1097/js9.0000000000002189 [DOI] [PubMed] [Google Scholar]
23. Hosseinpoor AR, Bergen N, Kirkby K, Schlotheuber A. Strengthening and expanding health inequality monitoring for the advancement of health equity: a review of WHO resources and contributions. Int J Equity Health. 2023;22:49. doi: 10.1186/s12939-022-01811-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Peng J, Xu H, Tang X. Global inequalities in the burden of digestive diseases from 1990 to 2019: findings from the global burden of disease study 2019. Gastroenterology. 2024;166:223‐224.e221. doi: 10.1053/j.gastro.2023.09.024 [DOI] [PubMed] [Google Scholar]
25. Ordunez P, Martinez R, Soliz P, Giraldo G, Mujica OJ, Nordet P. Rheumatic heart disease burden, trends, and inequalities in the Americas, 1990–2017: a population‐based study. Lancet Glob Health. 2019;7:e1388‐e1397. doi: 10.1016/s2214-109x(19)30360-2 [DOI] [PubMed] [Google Scholar]
26. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7:e105‐e125. doi: 10.1016/s2468-2667(21)00249-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Pan H, Zhao Z, Deng Y, et al. The global, regional, and national early‐onset colorectal cancer burden and trends from 1990 to 2019: results from the Global Burden of Disease Study 2019. BMC Public Health. 2022;22:1896. doi: 10.1186/s12889-022-14274-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990‐2015: a novel analysis from the Global Burden of Disease Study 2015. Lancet. 2017;390:231‐266. doi: 10.1016/s0140-6736(17)30818-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Li S, Chen H, Man J, et al. Changing trends in the disease burden of esophageal cancer in China from 1990 to 2017 and its predicted level in 25 years. Cancer Med. 2021;10:1889‐1899. doi: 10.1002/cam4.3775 [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Chen J, Li C, Bu CLN, et al. Global burden of non‐communicable diseases attributable to kidney dysfunction with projection into 2040. Chin Med J (Engl). 2024;138:1334‐1344. doi: 10.1097/cm9.0000000000003143 [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiol Rev. 2013;93:137‐188. doi: 10.1152/physrev.00045.2011 [DOI] [PubMed] [Google Scholar]
32. Ting DS, Cheung GC, Wong TY. Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review. Clin Exp Ophthalmol. 2016;44:260‐277. doi: 10.1111/ceo.12696 [DOI] [PubMed] [Google Scholar]
33. Wong TY, Sun J, Kawasaki R, et al. Guidelines on diabetic eye care: the International Council of Ophthalmology Recommendations for screening, follow‐up, referral, and treatment based on resource settings. Ophthalmology. 2018;125:1608‐1622. doi: 10.1016/j.ophtha.2018.04.007 [DOI] [PubMed] [Google Scholar]
34. Wong TY, Sabanayagam C. Strategies to tackle the global burden of diabetic retinopathy: from epidemiology to artificial intelligence. Ophthalmologica. 2020;243:9‐20. doi: 10.1159/000502387 [DOI] [PubMed] [Google Scholar]
35. Godman B, Basu D, Pillay Y, et al. Review of ongoing activities and challenges to improve the care of patients with type 2 diabetes across Africa and the implications for the future. Front Pharmacol. 2020;11:108. doi: 10.3389/fphar.2020.00108 [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Hruby A, Hu FB. The epidemiology of obesity: a big picture. Pharmacoeconomics. 2015;33:673‐689. doi: 10.1007/s40273-014-0243-x [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Unnikrishnan R, Anjana RM, Mohan V. Diabetes mellitus and its complications in India. Nat Rev Endocrinol. 2016;12:357‐370. doi: 10.1038/nrendo.2016.53 [DOI] [PubMed] [Google Scholar]
38. Tian Y, Qiu Z, Wang F, et al. Associations of diabetes and prediabetes with mortality and life expectancy in China: a National Study. Diabetes Care. 2024;47:1969‐1977. doi: 10.2337/dca24-0012 [DOI] [PubMed] [Google Scholar]
39. Ma RCW. Epidemiology of diabetes and diabetic complications in China. Diabetologia. 2018;61:1249‐1260. doi: 10.1007/s00125-018-4557-7 [DOI] [PubMed] [Google Scholar]
40. Song P, Yu J, Chan KY, Theodoratou E, Rudan I. Prevalence, risk factors and burden of diabetic retinopathy in China: a systematic review and meta‐analysis. J Glob Health. 2018;8:010803. doi: 10.7189/jogh.08.010803 [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Kautzky‐Willer A, Leutner M, Harreiter J. Sex differences in type 2 diabetes. Diabetologia. 2023;66:986‐1002. doi: 10.1007/s00125-023-05891-x [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Pescosolido N, Campagna O, Barbato A. Diabetic retinopathy and pregnancy. Int Ophthalmol. 2014;34:989‐997. doi: 10.1007/s10792-014-9906-z [DOI] [PubMed] [Google Scholar]
43. Chandrasekaran PR, Madanagopalan VG, Narayanan R. Diabetic retinopathy in pregnancy – a review. Indian J Ophthalmol. 2021;69:3015‐3025. doi: 10.4103/ijo.IJO_1377_21 [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Eiland L, Thangavelu T, Drincic A. Has technology improved diabetes management in relation to age, gender, and ethnicity? Curr Diab Rep. 2019;19:111. doi: 10.1007/s11892-019-1231-5 [DOI] [PubMed] [Google Scholar]
45. Barth C, Crestol A, de Lange AG, Galea LAM. Sex steroids and the female brain across the lifespan: insights into risk of depression and Alzheimer's disease. Lancet Diabetes Endocrinol. 2023;11:926‐941. doi: 10.1016/s2213-8587(23)00224-3 [DOI] [PubMed] [Google Scholar]
46. Xie Y, Bowe B, Mokdad AH, et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94:567‐581. doi: 10.1016/j.kint.2018.04.011 [DOI] [PubMed] [Google Scholar]
47. Schlotheuber A, Hosseinpoor AR. Summary measures of health inequality: a review of existing measures and their application. Int J Environ Res Public Health. 2022;19:3697. doi: 10.3390/ijerph19063697 [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Henderson DAG, Donaghy E, Dozier M, et al. Understanding primary care transformation and implications for ageing populations and health inequalities: a systematic scoping review of new models of primary health care in OECD countries and China. BMC Med. 2023;21:319. doi: 10.1186/s12916-023-03033-z [DOI] [PMC free article] [PubMed] [Google Scholar]
49. Hay K, McDougal L, Percival V, et al. Disrupting gender norms in health systems: making the case for change. Lancet. 2019;393:2535‐2549. doi: 10.1016/s0140-6736(19)30648-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
50. Bailey ZD, Krieger N, Agénor M, Graves J, Linos N, Bassett MT. Structural racism and health inequities in the USA: evidence and interventions. Lancet. 2017;389:1453‐1463. doi: 10.1016/s0140-6736(17)30569-x [DOI] [PubMed] [Google Scholar]
51. Mokaya M, Kyallo F, Vangoitsenhoven R, Matthys C. Clinical and patient‐centered implementation outcomes of mHealth interventions for type 2 diabetes in low‐and‐middle income countries: a systematic review. Int J Behav Nutr Phys Act. 2022;19:1. doi: 10.1186/s12966-021-01238-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
52. Sharma M, John R, Afrin S, et al. Cost‐effectiveness of population screening programs for cardiovascular diseases and diabetes in low‐ and middle‐income countries: a systematic review. Front Public Health. 2022;10:820750. doi: 10.3389/fpubh.2022.820750 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1. Supplementary figures.

DOM-27-5464-s002.docx^{(3.5MB, docx)}

Data S2. Supplementary tables.

DOM-27-5464-s001.xlsx^{(141.5KB, xlsx)}

Data Availability Statement

[dom16588-bib-0001] 1. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990‐2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1789‐1858. doi: 10.1016/s0140-6736(18)32279-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0002] 2. Luo Y, Wang Q, Wang Y, et al. Trends in the prevalence and burden of blindness and vision loss among adolescents aged 10‐24 years in the Western Pacific Region over 30 years. BMC Public Health. 2024;24:3193. doi: 10.1186/s12889-024-20607-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0003] 3. Gurudas S, Vasconcelos JC, Prevost AT, et al. National prevalence of vision impairment and blindness and associated risk factors in adults aged 40 years and older with known or undiagnosed diabetes: results from the SMART‐India cross‐sectional study. Lancet Glob Health. 2024;12:e838‐e847. doi: 10.1016/s2214-109x(24)00035-4 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0004] 4. Swenor BK, Ehrlich JR. Ageing and vision loss: looking to the future. Lancet Glob Health. 2021;9:e385‐e386. doi: 10.1016/s2214-109x(21)00031-0 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0005] 5. Serikbaeva A, Li Y, Ma S, Yi D, Kazlauskas A. Resilience to diabetic retinopathy. Prog Retin Eye Res. 2024;101:101271. doi: 10.1016/j.preteyeres.2024.101271 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0006] 6. Yan A, Jones C, Demirel S, Chhablani J. Diabetic macular edema: upcoming therapies. Graefes Arch Clin Exp Ophthalmol. 2024;263:249‐258. doi: 10.1007/s00417-024-06595-7 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0007] 7. Yuan Y, Ji S, Song Y, et al. Global trends in diabetic eye disease research from 2012 to 2021. Neural Regen Res. 2024;19:2310‐2320. doi: 10.4103/1673-5374.391303 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0008] 8. Lee SH, Tseng BY, Wu MC, Wang JH, Chiu CJ. Incidence and progression of diabetic retinopathy after cataract surgery: a systematic review and meta‐analysis. Am J Ophthalmol. 2025;269:105‐115. doi: 10.1016/j.ajo.2024.08.017 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0009] 9. Li H, Jia W, Vujosevic S, et al. Current research and future strategies for the management of vision‐threatening diabetic retinopathy. Asia Pac J Ophthalmol. 2024;13:100109. doi: 10.1016/j.apjo.2024.100109 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0010] 10. Teo ZL, Tham YC, Yu M, et al. Global prevalence of diabetic retinopathy and projection of burden through 2045: systematic review and meta‐analysis. Ophthalmology. 2021;128:1580‐1591. doi: 10.1016/j.ophtha.2021.04.027 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0011] 11. Yau JW, Rogers SL, Kawasaki R, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35:556‐564. doi: 10.2337/dc11-1909 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0012] 12. Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet. 2010;376:124‐136. doi: 10.1016/s0140-6736(09)62124-3 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0013] 13. Zhang X, Zhang F, Xu X. Single‐cell RNA sequencing in exploring the pathogenesis of diabetic retinopathy. Clin Transl Med. 2024;14:e1751. doi: 10.1002/ctm2.1751 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0014] 14. Reddy SK, Devi V, Seetharaman ATM, et al. Cell and molecular targeted therapies for diabetic retinopathy. Front Endocrinol (Lausanne). 2024;15:1416668. doi: 10.3389/fendo.2024.1416668 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0015] 15. Xiao J, Xu Z. Roles of noncoding RNAs in diabetic retinopathy: mechanisms and therapeutic implications. Life Sci. 2024;357:123092. doi: 10.1016/j.lfs.2024.123092 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0016] 16. Callan A, Jha S, Valdez L, Baldado L, Tsin A. TGF‐β signaling pathways in the development of diabetic retinopathy. Int J Mol Sci. 2024;25:3052. doi: 10.3390/ijms25053052 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0017] 17. Global incidence, prevalence, years lived with disability (YLDs), disability‐adjusted life‐years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990‐2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2133‐2161. doi: 10.1016/s0140-6736(24)00757-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0018] 18. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990‐2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403:2100‐2132. doi: 10.1016/s0140-6736(24)00367-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0019] 19. Li Y, Wang H, Guan Z, et al. Persistence of severe global inequalities in the burden of blindness and vision loss from 1990 to 2019: findings from the Global Burden of Disease Study 2019. Br J Ophthalmol. 2024;108:301‐309. doi: 10.1136/bjo-2022-321801 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0020] 20. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9:e144‐e160. doi: 10.1016/s2214-109x(20)30489-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0021] 21. Global, regional, and national burden of low back pain, 1990‐2020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023;5:e316‐e329. doi: 10.1016/s2665-9913(23)00098-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0022] 22. Han T, Wang T, Ye Y, Ying C, Wang X, Liu S. The global, regional, and national burden of paralytic ileus and intestinal obstruction, 1990 to 2021: a cross‐sectional analysis from the 2021 global burden of disease study. Int J Surg. 2025;111:1773‐1787. doi: 10.1097/js9.0000000000002189 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0023] 23. Hosseinpoor AR, Bergen N, Kirkby K, Schlotheuber A. Strengthening and expanding health inequality monitoring for the advancement of health equity: a review of WHO resources and contributions. Int J Equity Health. 2023;22:49. doi: 10.1186/s12939-022-01811-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0024] 24. Peng J, Xu H, Tang X. Global inequalities in the burden of digestive diseases from 1990 to 2019: findings from the global burden of disease study 2019. Gastroenterology. 2024;166:223‐224.e221. doi: 10.1053/j.gastro.2023.09.024 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0025] 25. Ordunez P, Martinez R, Soliz P, Giraldo G, Mujica OJ, Nordet P. Rheumatic heart disease burden, trends, and inequalities in the Americas, 1990–2017: a population‐based study. Lancet Glob Health. 2019;7:e1388‐e1397. doi: 10.1016/s2214-109x(19)30360-2 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0026] 26. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7:e105‐e125. doi: 10.1016/s2468-2667(21)00249-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0027] 27. Pan H, Zhao Z, Deng Y, et al. The global, regional, and national early‐onset colorectal cancer burden and trends from 1990 to 2019: results from the Global Burden of Disease Study 2019. BMC Public Health. 2022;22:1896. doi: 10.1186/s12889-022-14274-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0028] 28. Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990‐2015: a novel analysis from the Global Burden of Disease Study 2015. Lancet. 2017;390:231‐266. doi: 10.1016/s0140-6736(17)30818-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0029] 29. Li S, Chen H, Man J, et al. Changing trends in the disease burden of esophageal cancer in China from 1990 to 2017 and its predicted level in 25 years. Cancer Med. 2021;10:1889‐1899. doi: 10.1002/cam4.3775 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0030] 30. Chen J, Li C, Bu CLN, et al. Global burden of non‐communicable diseases attributable to kidney dysfunction with projection into 2040. Chin Med J (Engl). 2024;138:1334‐1344. doi: 10.1097/cm9.0000000000003143 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0031] 31. Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiol Rev. 2013;93:137‐188. doi: 10.1152/physrev.00045.2011 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0032] 32. Ting DS, Cheung GC, Wong TY. Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review. Clin Exp Ophthalmol. 2016;44:260‐277. doi: 10.1111/ceo.12696 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0033] 33. Wong TY, Sun J, Kawasaki R, et al. Guidelines on diabetic eye care: the International Council of Ophthalmology Recommendations for screening, follow‐up, referral, and treatment based on resource settings. Ophthalmology. 2018;125:1608‐1622. doi: 10.1016/j.ophtha.2018.04.007 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0034] 34. Wong TY, Sabanayagam C. Strategies to tackle the global burden of diabetic retinopathy: from epidemiology to artificial intelligence. Ophthalmologica. 2020;243:9‐20. doi: 10.1159/000502387 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0035] 35. Godman B, Basu D, Pillay Y, et al. Review of ongoing activities and challenges to improve the care of patients with type 2 diabetes across Africa and the implications for the future. Front Pharmacol. 2020;11:108. doi: 10.3389/fphar.2020.00108 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0036] 36. Hruby A, Hu FB. The epidemiology of obesity: a big picture. Pharmacoeconomics. 2015;33:673‐689. doi: 10.1007/s40273-014-0243-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0037] 37. Unnikrishnan R, Anjana RM, Mohan V. Diabetes mellitus and its complications in India. Nat Rev Endocrinol. 2016;12:357‐370. doi: 10.1038/nrendo.2016.53 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0038] 38. Tian Y, Qiu Z, Wang F, et al. Associations of diabetes and prediabetes with mortality and life expectancy in China: a National Study. Diabetes Care. 2024;47:1969‐1977. doi: 10.2337/dca24-0012 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0039] 39. Ma RCW. Epidemiology of diabetes and diabetic complications in China. Diabetologia. 2018;61:1249‐1260. doi: 10.1007/s00125-018-4557-7 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0040] 40. Song P, Yu J, Chan KY, Theodoratou E, Rudan I. Prevalence, risk factors and burden of diabetic retinopathy in China: a systematic review and meta‐analysis. J Glob Health. 2018;8:010803. doi: 10.7189/jogh.08.010803 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0041] 41. Kautzky‐Willer A, Leutner M, Harreiter J. Sex differences in type 2 diabetes. Diabetologia. 2023;66:986‐1002. doi: 10.1007/s00125-023-05891-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0042] 42. Pescosolido N, Campagna O, Barbato A. Diabetic retinopathy and pregnancy. Int Ophthalmol. 2014;34:989‐997. doi: 10.1007/s10792-014-9906-z [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0043] 43. Chandrasekaran PR, Madanagopalan VG, Narayanan R. Diabetic retinopathy in pregnancy – a review. Indian J Ophthalmol. 2021;69:3015‐3025. doi: 10.4103/ijo.IJO_1377_21 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0044] 44. Eiland L, Thangavelu T, Drincic A. Has technology improved diabetes management in relation to age, gender, and ethnicity? Curr Diab Rep. 2019;19:111. doi: 10.1007/s11892-019-1231-5 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0045] 45. Barth C, Crestol A, de Lange AG, Galea LAM. Sex steroids and the female brain across the lifespan: insights into risk of depression and Alzheimer's disease. Lancet Diabetes Endocrinol. 2023;11:926‐941. doi: 10.1016/s2213-8587(23)00224-3 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0046] 46. Xie Y, Bowe B, Mokdad AH, et al. Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016. Kidney Int. 2018;94:567‐581. doi: 10.1016/j.kint.2018.04.011 [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0047] 47. Schlotheuber A, Hosseinpoor AR. Summary measures of health inequality: a review of existing measures and their application. Int J Environ Res Public Health. 2022;19:3697. doi: 10.3390/ijerph19063697 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0048] 48. Henderson DAG, Donaghy E, Dozier M, et al. Understanding primary care transformation and implications for ageing populations and health inequalities: a systematic scoping review of new models of primary health care in OECD countries and China. BMC Med. 2023;21:319. doi: 10.1186/s12916-023-03033-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0049] 49. Hay K, McDougal L, Percival V, et al. Disrupting gender norms in health systems: making the case for change. Lancet. 2019;393:2535‐2549. doi: 10.1016/s0140-6736(19)30648-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0050] 50. Bailey ZD, Krieger N, Agénor M, Graves J, Linos N, Bassett MT. Structural racism and health inequities in the USA: evidence and interventions. Lancet. 2017;389:1453‐1463. doi: 10.1016/s0140-6736(17)30569-x [DOI] [PubMed] [Google Scholar]

[dom16588-bib-0051] 51. Mokaya M, Kyallo F, Vangoitsenhoven R, Matthys C. Clinical and patient‐centered implementation outcomes of mHealth interventions for type 2 diabetes in low‐and‐middle income countries: a systematic review. Int J Behav Nutr Phys Act. 2022;19:1. doi: 10.1186/s12966-021-01238-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom16588-bib-0052] 52. Sharma M, John R, Afrin S, et al. Cost‐effectiveness of population screening programs for cardiovascular diseases and diabetes in low‐ and middle‐income countries: a systematic review. Front Public Health. 2022;10:820750. doi: 10.3389/fpubh.2022.820750 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Global, regional and national burden of blindness and vision loss attributable to diabetic retinopathy, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021

Yujie Pan, PhD

Yunkuo Li, PhD

Mengzhao Cui, PhD

Guangyu He, PhD

Guixia Wang, PhD

Abstract

Aims

Materials and Methods

Results

Conclusions

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1. Data resource and disease definition

2.2. Sociodemographic index

2.3. Health inequality analysis

2.4. Decomposition analysis

2.5. Frontier analysis

2.6. Predictive analysis

2.7. Statistical analysis

3. RESULTS

3.1. Global, regional and national burden and trends of blindness and vision loss attributed to DR from 1990 to 2021

FIGURE 1.

FIGURE 2.

3.2. Age and sex patterns

3.3. Health inequality analysis

FIGURE 3.

3.4. Decomposition analysis

FIGURE 4.

3.5. Frontier analysis

FIGURE 5.

3.6. Comparative burden of blindness and vision loss in type 1 versus type 2 diabetes

3.7. Predictive analysis

4. DISCUSSION

5. LIMITATION

6. CONCLUSIONS

AUTHOR CONTRIBUTIONS

CONFLICT OF INTEREST STATEMENT

PEER REVIEW

Supporting information

ACKNOWLEDGEMENTS

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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