Nationwide incidence and blindness trends in patients with diabetic retinopathy using age-period-cohort analysis

doi:10.21203/rs.3.rs-3245223/v1

Download PDF

Article

Nationwide incidence and blindness trends in patients with diabetic retinopathy using age-period-cohort analysis

https://doi.org/10.21203/rs.3.rs-3245223/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Objectives: To estimate the epidemiologic trends of blindness in patients with diabetic retinopathy (DR) and investigate the age-, period-, and cohort-specific effects on blindness.

Methods: A total of 1.5 million patients with DR aged 20 years or older were included from the Korean National Health Claims database from 2005 to 2019. Non-proliferative DR/ proliferative DR (NPDR/PDR) cohorts were constructed separately. Participants were identified as having a blindness in at least one eye. The incidence of blindness were calculated. Using a log-linear Poisson age-period-cohort (APC) analysis model, each effect on blindness were estimated for each study group.

Results: The incidence of blindness was 2162.36 per 100,000 in the NPDR group and 5339.29 in the PDR group. The blindness rate sharply decreased after 2011, with annual decreases of 5.6% and 4.4% in the NPDR and the PDR groups, respectively. People born between 1920 and 1930 had the highest overallrisk of blindness, with the risk decreasing rapidly after that. For those born after 1980, the risk started to increase in both sexes. Among the APC models, the combination model of age, period, and cohort effects showed the highest explanatory power (0.96).

Conclusions: In this nationwide long-term study, blindness in DR was not due to a single epidemiologic cause but rather a combination of biological age, social determinants, and healthcare policies. The increased risk of blindness in individuals in their 20s and 30s may even increase in the future and should not be ignored. Therefore, vigilance of younger patients are recommended.

Health sciences/Medical research/Epidemiology

Health sciences/Diseases/Eye diseases/Retinal diseases

Diabetes mellitus (DM) is a major global health concern. Due to the global prevalence of diabetes increasing,¹ the incidence of DR has also increased. DR is considered one of the main complications of diabetes and is a leading cause of blindness in the working-age population. A review demonstrated that the annual incidence of DR ranged from 2.2% to 12.7% and progression from 3.4-12.3%.² Shani et al. reported that 30% of patients with diabetes over a 4.5-year period had non-proliferative DR (NPDR).³

Blindness in patients with DR should be considered from a broader perspective, including medical, socioeconomic, and public health perspectives. Blindness may be caused not only by the direct effects of DR itself, such as tractional retinal detachment, but also by the indirect effects of DM, including delayed healing or aggravation of ischemic condition, or secondary infection from poor general condition. Thus, investigations of blindness trend of DR provide clues regarding the etiology of the illness. However, few studies have reported the longitudinal trend of blindness in patients with DR, and a comprehensive understanding of this is lacking.

To overcome it, we used an effective age-period cohort (APC) model. The APC analysis plays an important role in understanding time-varying elements in epidemiology.^4-6 It has three types of time-varying phenomena: age, period, and cohort effects. Age effects refer to the variations in the biological and social processes of aging that are specific to individuals. This is usually denoted by the varying rates of disease across age groups. Period effects refer to external factors that affect all age groups and cohorts at a particular period of time (e.g., floods, war, technology, or medical policy). Cohort effects are variations related to the unique experiences of the same cohort groups as they move across time (characterized population). Each cohort group was composed of participants born in the same year (birth cohort) to evaluate the difference in the risk of a health outcome based on the birth year. It is important to search for patterns of diseases defined by shared temporal experiences or common life experiences (e.g., years of exposure to radiation). Herein, APC analysis was performed using nationally representative data from South Korea from 2005 to 2019 to analyze the epidemiological trends of blindness. Therefore, we aimed to determine the incidence and trends of blindness and elucidate the potential role of age, period, and birth cohort effects in blindness.

Data source and the creation of study groups

We accessed health claims from 2003 to 2019, recorded in the National Health Information Database (NHID) of the National Health Insurance Service (NHIS) of South Korea. The NHID contains data on the personal characteristics, demographics, lifestyle, health checkups, and medical treatment of the Korean population, including insured employees, insured self-employed individuals, and medical aid beneficiaries.⁷ This study included 1.5 million patients with DR (registration code H360) randomly sampled from among the national population. Among all the participants with DR, those who had never received any relevant treatment were classified as the ‘NPDR’ group, and those who had undergone pan-retinal photocoagulation (code S5160) were defined as having ‘more than severe NPDR’ (for convenience, these patients are referred to as the ‘proliferative DR (PDR) group’ later). They were analyzed separately as specific study groups. It was approved by the Institutional Review Board of Seoul National University Hospital (NO. 2011-046-1171) and adhered to the tenets of the Declaration of Helsinki.

Definition of blindness and case selection

Among all the patients with DR in the dataset, adult participants aged 20 years or older with a blindness code (9.9) in at least one eye during a health checkup over the 17-year study period were extracted from the NHIS database (Supplementary Figure 1). To ensure that complex conditions were not included, those who had a previous history of DR during the two-year washout period (2003-2004) and had blindness before the index date of DR were excluded. Participants who had any major history of blindness before the index date of initial blindness, were also excluded (Supplementary Table 1).

Analyses of the overall incidence of blindness

The participants were divided according to age (seven strata) and sex. Patients identified as blind during the 15-year period from January 2005 to December 2019 were included in the final estimates. To compare the trends in blindness in each study group, the age- and sex-specific incidences of blindness in the entire population during the same period were obtained. The cumulative incidence of all blind cases was estimated for the entire population as a reference value. To define South Korea’s population, data from the Population and Housing Census conducted in March 2022 were used (Korean Statistical Information Service, available at https://kosis.kr).

APC model analysis on blindness

The entire study period was divided into five periods (2005-2007,2008-2010, 2011-2013, 2014-2016, 2017-2019). Log-linear models were employed to evaluate the effects of age, period, and birth cohort on blindness in DR. Because age, period, and cohort are continuous variables, their impact on the blindness rate was analyzed in a continuous-time model. A nonlinear regression model was estimated with the blindness rate as the dependent variable for age, year of blindness, and birth cohort. The formulation of the multiplicative APC model for the mortality rate r(a,p) at age a in period p for persons in birth cohort c=p-a is

ln(r(a,p))=f_C0(a)+δ(c-c₀)+g(p)+h(c)

, where f_C0(a) are the age-specific rates in the reference cohort C₀=1958 (mid-point of the cohorts); δ is the slope of the log-linear trend in the cohort (drift); h(c) is the cohort function, interpretable as the log relative risk to cohort C₀, and g(p) is the period function. To obtain identifiability, h(c) and g(p) are detrended with a zero slope, and the average period function is zero. The inclusion of drift in the cohort effect interprets the age effect as a cohort-specific rate of blindness. Drift was extracted using the weight method. The effects of age, period, and birth cohort were nonlinear through the functions f_C0(a), h(c), and g(p). Based on the general form, we established five models in sequence: a one-factor age model, a two-factor age-drift model, an age-period model, an age-cohort model, and an APC model. The drift term in the age-drift model refers to a temporal change in the blindness rate that cannot be identified as a period or cohort effect. By comparing the deviances from the models, the statistical significance of each factor was tested after adjusting for other factors. Since age is known to be a significant factor in blindness, a goodness of fit that considered age was considered valuable. Therefore, the age-adjusted R² was used to evaluate the goodness of fit, as it measures how much variability can be explained by factors other than age. The variability contribution by period was calculated in the same manner as described previously.⁸

Statistics and data availability

A confidence level of 95% was used for this analysis, and all results are presented as the mean ± standard deviation. Analyses were performed using the R programming language with the apc.fit function from the Epi package (http://www.r-project.org) and SAS Enterprise Guide version 6.1 software (SAS Inc., Cary, NC, USA). The raw data used in this study are available from the investigators upon request from the NIHS system.

Demographics

A total of 1,070,966 participants were diagnosed with DR between January 2003 and December 2019. After considering a two-year washout period and identifying incidental cases, 898,988 participants with DR and 143,657 with PDR were enrolled between January 2005 and December 2019. After screening all incidental cases of blindness among the eligible participants, 8,442 cases were excluded because of a history of blindness before the index date of DR diagnosis. The final cohort included 774,062 participants (387,452 males and 386,610 females) in the NPDR group, and 115,212 participants (64,791 males and 50,421 females) in the PDR group. Among all the participants, 611,027 (78.9 %) had type 2 DM in the NPDR group and 75,711 (65.7%) in the PDR group.

The 15-year cumulative incidence of blindness

The incidences of blindness were 22,888 (2.6%) including 16,738 (2.2%) in the NPDR group and 6,150 (5.3%) in the PDR groups. Among blindness cases, 16,858 (73.7 %) had type 2 DM. The mean interval between the index date of initial DR diagnosis and blindness was 3.96 ±3.06 years. The interval was 4.91±3.33, 4.33±3.13, and 3.25±2.69 years for those in their 30s, 50s, and 70s, respectively.

In the NPDR group, 2162.36 (95% CI: 2161.18-2163.54) per 100,000 had blindness (2109.16 [95% CI: 2085.93-2132.39] and 2215.67 [95% CI: 2191.81-2239.53] per 100,000 males and females, respectively) (Table 1). The mean age at diagnosis of blindness was 65.6±11.7 years in males and 68.0±11.1 years in females.

In the PDR group, 5339.29 (95% CI: 5326.86-5351.72) per 100,000 people had blindness (5204.43 [95% CI: 4986.19-5422.66] and 5512.58 [95% CI: 5223.97-5801.20] per 100,000 males and females, respectively). The mean age at diagnosis of blindness was 61.2±10.7 years in males and 63.2±11.0 years in females. Over the past 15 years, the cumulative incidence of blindness in each DR group was significantly higher than the estimated incidence in the entire population (Figure 1, Supplementary Table 2).

Epidemiologic multi-angle trends of blindness

Prior to 2010, the blindness rates in the NPDR group were significantly higher for those aged 60 years and older than for younger age groups (Figure 2). However, these rates have declined sharply in recent years, with the most recent period (2017-2019) showing no remarkable difference in the blindness rates across all age groups. There was no drastic change across the entire period for those in their 20s to 40s. Overall, the age-specific blindness rates were lower in more recent periods than in older periods. In the PDR group, until 2010, the peak incidence was in those in their 60s; after 2014, the peak incidence was in those in their 40s, showing a gradual shift in peak incidence in terms of age. From 2005 to 2013, the blindness rate gradually increased for those aged between 20 and 50 years and then decreased, with similar rates in all age groups in recent years (2017-2019). The rate of blindness increased in those born more recently within the same age group. This trend was more pronounced in the PDR group than in the NPDR group.

Relative risk of APC effects on blindness

In the NPDR group, the relative risk of the effect of age on blindness increased continuously up to those in their 60s (Figure 3). In the PDR group, the relative risk of the age effect on blindness increased rapidly from those in their 20s to 30s and then became moderate in males; it increased rapidly until 49 years and then became moderate in females. The risk of the age effect on blindness was rather small and tended to decrease in both sexes after 70 years old.

The relative risk of blindness in each period began to decrease in 2008 and sharply decreased in 2011 in both sexes in the NPDR group (mean annual decrease:5.6%). In the PDR group, the risk of blindness tended to increase in older periods (2005-2008) and then decreased from 2011, with a sharp decrease after 2014-2016 (mean annual decrease:4.4%).

Relative risk of the cohort effect on blindness differed between the NPDR and PDR groups. In the NPDR group, males born in 1920 and females born in 1930 had the highest risk of blindness, which decreased rapidly for both sexes after that. Individuals of both sexes born after 1980 had an increased risk of blindness. In the PDR group, males born between 1920 and 1930 had the highest risk of blindness, whereas those born between 1970 and 1980 had the lowest risk. Males born after 1980 have an increased risk of developing blindness. In females, the opposite trend was observed; those born in 1960 had the highest risk of blindness, with the risk decreasing sharply for those born subsequently. For those born after 1980, the decline in the rate of risk has slowed significantly.

Goodness of fit for the APC model on blindness

Table 2 presents the goodness of fit (scaled deviance) of the APC models used to assess whether each model had a significant effect on blindness throughout the study period. In all groups, each model showed a high value of adj–R²_A (explanatory power) of 0.8 or higher. After adjusting for age, the period and cohort effects on blindness were statistically significant (p<0.001). Among all the models, the combination model of age, period, and cohort effects showed the highest adj–R²_A(0.96 in the NPDR group and 0.91 in the PDR group).

DR-associated blindness is a health problem of great concern worldwide that must be actively managed. To derive high-quality results on it, it is necessary to broadly consider the epidemiological factors that affect blindness and make decisions based on estimates and predictions obtained through effective statistical model fitting. Many studies have been conducted on this topic due to widespread interest. In Indonesia, the incidence of blindness was revealed to be 4% in patients with DR and 7.7% in patients with vision-threatening DR (VTDR).⁹ In United States, the incidence of blindness was 757/100,000 in VTDR. However, although there may be significant differences between races worldwide due to environmental and genetic differences, few studies on this topic have been conducted in Asian populations. Thus, we performed an epidemiological analysis of blindness in patients with DR. Herein, we investigated age- and sex-specific blindness in patients with DR and the longitudinal trends in blindness rates over 15 years. We also assessed the effects of age, period, and cohort on blindness trends over the study period.

In the present study, approximately 2.2% in the NPDR group and 5.3% in the PDR group had blindness. These rates are lower than those reported in Indonesia but higher than those reported in the United States. The 15-year cumulative incidence was slightly higher in females, except for those in their 20s and 70s. Unsurprisingly, compared with the total number of blindness cases in the entire population, there was a sharp increase in the cumulative incidence from NPDR to PDR. Except for the possibility of underestimation due to follow-up loss at very old ages, the effect of age on blindness in patients with DR increased, similar to the pattern observed in other chronic diseases. However, in the PDR group, blindness was relatively higher in the middle-aged group, suggesting that blindness in PDR was not only due to age-related changes.

The overall blindness rate has shown a clear decreasing trend. This was more evident in the older age groups than in the younger age groups, and the decrease was greater in the PDR group than in the NPDR group. This finding is consistent with a previous study that reported that there has been a decline in the incidence of blindness due to DR, particularly in developed countries.¹ This decline could be due to the improved management of DM and related systemic ischemic conditions and improved access to cataract surgery due to an increased awareness of cataracts,¹⁰ resulting in a gradual decrease in patients with NPDR. In particular, the curve of the declining trend became steeper after 2011. It may be related to the development of health programs that aim to better manage the complications of chronic diseases, such as free complication screening programs and free distribution of glucose testing devices. In addition to these changes, the national insurance coverage for optical coherence tomography and Lucentis^® (ranibizumab) may have further fueled this downward trend, particularly in those with PDR.

We also observed that the risk of blindness varied according to birth year, with distinct patterns observed in both sexes. The risk of blindness in the NPDR group was higher in those born between 1920 and 1940, which may have been influenced by the colonial period. This group shares life experiences of relative childhood malnutrition and environmental deprivation, leading to unhealthy conditions that made them more vulnerable to infection and complications.

Notably, the recent trend in blindness appeared to be somewhat different in the younger age group (20-39 years). In both DR groups, the rates of blindness tended to increase rather than decrease. We observed an increased risk of blindness among individuals born after the 1980s, particularly among males in the PDR group. This may be due to the recent increase in the prevalence of DM across all age groups, accompanied by a remarkable increase in younger age groups with poor eating habits and rapid progression after DR onset.¹¹ It also may have resulted from the fact that the younger age group was relatively more affected by type 1 DM. The prevalence of type 1 DM is gradually increasing and will continue to increase in the future.¹² Based on the fact that almost all patients with type 1 have signs of DR after the age of 20 years and that the prevalence of PDR increases to 50% in those requiring insulin therapy,¹³ PDR in these younger age groups is more likely to be associated with a poor prognosis.

Taken together, we learned that the incidence of blindness in DR is not due to a single epidemiologic cause but rather a combination of biological age, social determinants, and healthcare policies. Based on this, it is expected that the overall incidence of blindness will decrease in the future, but the risk of blindness in individuals in their 20s and 30s may even increase and should not be ignored. Our results suggest that vigilance and associated morbidity management should be implemented for younger patients.

This study has several limitations. First, we could not obtain information regarding the exact cause of the blindness. However, to avoid complex conditions, we have tried to exclude major medical conditions that do not have a strong direct causal relationship with diabetes, but even these conditions will not completely exclude the effect of diabetes. Second, we could not obtain the stage of DR or the presence of macular edema. We focused on identifying trends in blindness across large populations rather than on the detailed stages of DR. Thus, further extensive studies, including all medical information, are required.

In conclusion, this was a long-term nationwide cohort study with a comprehensive approach to blindness in patients with DR. We reported the incidence and trends of blindness and considered various epidemiologic factors that may contribute to the general healthcare burden and be helpful in predicting future trends.

Acknowledgments:

Author contributions: JL and SP conceived and designed the study. JL and SL performed the statistical analyses. JL wrote the first draft of the manuscript, and all authors contributed to the subsequent drafts and approved the final version. JL, KB, SL, and SP verified the data. SP had full access to the study data and confirmed the integrity and accuracy of the data analysis.

Conflict of Interest: No conflicting relationships exist for any of the authors.

Funding Statement: None

Sabanayagam C, Yip W, Ting DS, Tan G, Wong TY. Ten Emerging Trends in the Epidemiology of Diabetic Retinopathy. Ophthalmic Epidemiol 2016; 23(4): 209-222.
Sabanayagam C, Banu R, Chee ML, Lee R, Wang YX, Tan G et al. Incidence and progression of diabetic retinopathy: a systematic review. Lancet Diabetes Endocrinol 2019; 7(2): 140-149.
Shani M, Eviatar T, Komaneshter D, Vinker S. Diabetic Retinopathy -Incidence And Risk Factors In A Community Setting- A Longitudinal Study. Scand J Prim Health Care 2018; 36(3): 237-241.
Chaurasiya D, Gupta A, Chauhan S, Patel R, Chaurasia V. Age, period and birth cohort effects on prevalence of obesity among reproductive-age women in India. SSM Popul Health 2019; 9: 100507.
Murphy CC, Yang YC. Use of age-period-cohort analysis in cancer epidemiology research. Curr Epidemiol Rep 2018; 5(4): 418-431.
Bell A, Jones K. Age, Period and Cohort Processes in Longitudinal and Life Course Analysis: A Multilevel Perspective. In: Burton-Jeangros C, Cullati S, Sacker A, Blane D (eds). A Life Course Perspective on Health Trajectories and Transitions: Cham (CH); 2015. pp 197-213.
Shahbazi F, Doosti-Irani A, Soltanian A, Poorolajal J. National trends and projection of chronic kidney disease incidence according to etiology from 1990 to 2030 in Iran: a Bayesian age-period-cohort modeling study. Epidemiol Health 2023; 45: e2023027.
Choi Y, Kim Y, Park SK, Shin HR, Yoo KY. Age-Period-Cohort Analysis of Female Breast Cancer Mortality in Korea. Cancer Res Treat 2016; 48(1): 11-19.
Sasongko MB, Widyaputri F, Agni AN, Wardhana FS, Kotha S, Gupta P et al. Prevalence of Diabetic Retinopathy and Blindness in Indonesian Adults With Type 2 Diabetes. Am J Ophthalmol 2017; 181: 79-87.
Ryu SY, Kim J, Hong JH, Chung EJ. Incidence and characteristics of cataract surgery in South Korea from 2011 to 2015: A nationwide population-based study. Clin Exp Ophthalmol 2020; 48(3): 319-327.
Group TS. Development and Progression of Diabetic Retinopathy in Adolescents and Young Adults With Type 2 Diabetes: Results From the TODAY Study. Diabetes Care 2021; 45(5): 1049-1055.
Gregory GA, Robinson TIG, Linklater SE, Wang F, Colagiuri S, de Beaufort C et al. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. Lancet Diabetes Endocrinol 2022; 10(10): 741-760.
Klein R, Knudtson MD, Lee KE, Gangnon R, Klein BE. The Wisconsin Epidemiologic Study of Diabetic Retinopathy: XXII the twenty-five-year progression of retinopathy in persons with type 1 diabetes. Ophthalmology 2008; 115(11): 1859-1868.

Table 1. Number of patients with total incident visual deprivation cases and estimated cumulative incidence (per 100,000) in the each diabetic retinopathy (DR) cohort population during 2005 to 2019 in South Korean population

Age group (yrs)	Total		Men		Women		Men-Women ratio
Age group (yrs)	No.	Incidence (95% confidence interval)*	No.	Incidence (95% confidence interval)*	No.	Incidence (95% confidence interval)*	Men-Women ratio
*NPDR*
20-29	142	1404.27 (667.92-2130.62)	83	1626.81 (77.39-3176.29)	59	1177.64 (64.89-2520.18)	1.38
30-39	422	1378.90 (1141.09-1616.73)	253	1366.45 (975.14-1757.78)	169	1397.96 (791.77-2004.16)	0.99
40-49	1,507	1569.61 (1488.73-1650.49)	865	1416.70 (1295.88-1537.54)	642	1836.69 (1596.39-2077.01)	0.77
50-59	3,552	1863.98 (1819.58-1908.40)	1,885	1790.29 (1711.53-1869.06)	1,667	1954.98 (1853.36-2056.62)	0.92
60-69	5,816	2549.32 (2505.95-2592.70)	2,764	2491.59 (2403.40-2579.79)	3,052	2603.96 (2518.6302689.30)	0.96
70-	5,299	2423.65 (2419.24-2428.07)	2,322	2682.68 (2565.40-2799.98)	2,977	2253.90 (2183.45-2324.45)	1.19
Total	16,738	2162.35 (667.92-2130.62)	8,172	2109.16 (77.39-3176.29)	8,566	2215.66 (2191.81-2239.53)	0.95
*More than severe NPDR plus PDR*
20-29	57	2226.95 (0.00-5809.20)	29	2331.18 (0.00-9938.38)	28	2130.04 (0.00-8890.79)	1.09
30-39	268	4141.55 (2192.31-6090.80)	169	4325.56 (1026.18-7624.96)	99	3861.15 (0.00-8611.19)	1.12
40-49	1,067	5204.31 (4514.69-5894.04)	625	4779.01 (3742.96-5815.08)	442	5953.66 (3916.57-7990.75)	0.80
50-59	1,988	5587.40 (5175.64-5999.18)	1,143	5352.12 (4680.70-6023.55)	845	5940.66 (4878.60-7002.73)	0.90
60-69	1,888	5847.73 (5383.50-6311.97)	1,001	5798.86 (4934.22-6663.51)	887	5903.88 (4901.49-6906.28)	0.98
70-	883	4963.18 (4885.57-5040.80)	405	5098.18 (3336.52-6859.86)	478	4854.27 (3467.47-6241.07)	1.05
Total	6,151	5339.28 (5326.86-5351.72)	3,372	5204.42 (4986.19-5422.66)	2,779	5512.58 (5223.97-5801.20)	0.94

* per 100,000 people

Abbreviation: No, number; NPDR, non-proliferative diabetic retinopathy; PDR, proliferative diabetic retinopathy;

Table 2. Summary statistics of various age-period-cohort (APC) models for blindness in participants with non-proliferative diabetic retinopathy (NPDR) or more than severe NPDR (proliferative DR) between 2005 and 2019 in Korean population

Terms in model	Summary Statistics
Terms in model	Deviance (df)	ΔD (Δdf)	Effect	p-value	Adj-R²
*NPDR group*
Age	8859.53 (26)	-	-	-	-
Age + drift	864.15 (25)	7995.38 (1)	δΙA	2.46e-104	0.90
Age + period	391.02 (23)	8468.51 (3)	PΙA	3.90e-11	0.96
Age + cohort	705.05 (20)	8154.48 (6)	CΙA	1.14e-34	0.92
Age + period + cohort	343.37 (18)	361.68 (2)	ㅁPΙA,C	4.12e-80	0.96
		47.65 (5)	ACΙA,P	4.12e-80
*More than severe NPDR (PDR) group*
Age	2713.95 (26)	-	-	-	-
Age + drift	446.95 (25)	2267.00 (1)	δΙA	1.79e-40	0.84
Age + period	267.91 (23)	2446.04 (3)	PΙA	2.69e-04	0.90
Age + cohort	430.96 (20)	2282.99 (6)	CΙA	8.96e-05	0.84
Age + period + cohort	245.40 (18)0	176.56 (2)	ㅁPΙA,C	6.17e-40	0.91
		47.65 (5)	ACΙA,P	6.17e-40

There is no conflict of interest

Download PDF

Editorial decision: revise
08 Jan, 2024
Review #2 received at journal
20 Nov, 2023
Review #1 received at journal
18 Nov, 2023
Reviewer #2 agreed at journal
17 Nov, 2023
Reviewer #1 agreed at journal
03 Nov, 2023
Reviewers invited by journal
03 Nov, 2023
Editor assigned by journal
29 Aug, 2023
Submission checks completed at journal
08 Aug, 2023
First submitted to journal
08 Aug, 2023

You are reading this latest preprint version

Nationwide incidence and blindness trends in patients with diabetic retinopathy using age-period-cohort analysis

Status:

Version 1

Abstract

Figures

Introduction

Methods

Results

Demographics

The 15-year cumulative incidence of blindness

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1