Changing Trends in the Global Burden of Cataract Over the Past 30 Years: Retrospective Data Analysis of the Global Burden of Disease Study 2019
Bibliographic record
Abstract
Background Cataracts now account for the largest proportion of the global burden of blindness and vision loss. Understanding the changing trends in the global burden of cataracts over the past 30 years and the next 15 years is of clear significance for the prevention and control of cataracts in key populations. As far as we know, research on the future burden of cataracts is lacking. Objective This study aims to assess the global burden of cataracts over the past 30 years by using age-period-cohort modeling and to estimate trends in the next 15 years. Methods Data were obtained from the Global Burden of Disease Study 2019, the United Nations Development Programme, and the WHO (World Health Organization) Global Health Observatory data repository. The assessment of trends and disparities in the number and rate of disability-adjusted life years (DALYs) for cataracts from 1990 to 2019 was conducted. The association between the age-standardized DALY rate (ASDR) and the socio-demographic index (SDI), human development index (HDI), national levels of particulate matter <2.5 μm in diameter (PM2.5), and ambient ultraviolet radiation (UVR) was determined using linear regression analysis. Additionally, we used the Nordpred (Harald Fekjær and Bjørn Møller) age-period-cohort model to predict the cataract burden from 2020 to 2034. Results Globally, the number of DALYs due to cataract increased from 3,492,604 (95% uncertainty interval [UI] 2,481,846-4,719,629) in 1990 to 6,676,281 (95% UI 4,761,210-9,006,193) in 2019. The ASDRs due to cataract decreased from 93.17 (95% UI 66.14-125.32) in 1990 to 82.94 (95% UI 59.06-111.75) in 2019, with an average annual percentage change of –0.37 (95% CI –0.44 to –0.3; P<.001). Age, female sex, air pollution, smoking, high fasting plasma glucose levels, and a high body mass index were risk factors for the burden of cataracts. SDI and HDI were negatively correlated with ASDRs of cataracts, while PM2.5 and UVR were positively associated with them. Higher DALY rates were also associated with lower SDI (R2=0.1939; P<.001), lower HDI (R2=0.2828; P<.001), national PM2.5 concentration (R2=0.1874; P<.001), and ambient UVR levels (R2=0.2354; P<.001). The prediction model suggested that the number of DALYs due to cataract will continue to rise globally, while the cataract DALY rate will continue to decrease. Conclusions While the ASDR of cataracts has decreased, there has been a notable increase in the number of DALYs over the past 30 years. Projections suggest that the global burden of cataracts will continue to rise over the next 15 years. To address this challenge, appropriate prevention and treatment policies must be implemented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".