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Global, regional, and national burden of blindness and vision loss attributable to smoking from 1990 to 2021, and forecasts to 2030: findings from the Global Burden of Disease Study 2021
Ó£»¨ÊÓƵ volumeÌý25, ArticleÌýnumber:Ìý440 (2025)
Abstract
Objective
This study aims to systematically elucidate the burden of blindness and vision loss (BVL) attributable to smoking from 1990 to 2021 and to forecast the trends in BVL burden over the next decade.
Methods
We extracted data on years lived with disability (YLDs) and age-standardized YLDs rate (ASYR) related to blindness and vision loss (BVL) caused by smoking, including cataracts and age-related macular degeneration (AMD), from the Global Burden of Disease (GBD) database for the years 1990 to 2021. These data were disaggregated by age, gender, sociodemographic index (SDI), region, and country. Temporal trends in the burden of smoking-induced BVL were evaluated by calculating the average annual percentage changes (AAPCs).
Results
BVL attributable to smoking presents a significant disease burden, with global BVL-related YLDs attributable to smoking increasing from 1990 to 2021, while ASYR showed a declining trend. In 2021, the global BVL-related YLDs and ASYR attributable to smoking were estimated at 284.03 thousand and 3.27 per 100,000 population. The ASYR for cataract and AMD are 2.60 and 0.68 per 100,000, respectively. The burden was notably higher in males than females, highlighting significant gender disparities. Regionally, the highest burdens were observed in South Asia, Southeast Asia, and North Africa. It is expected that the number of global BVL-related YLDs will increase further by 2030.
Conclusion
Smoking has imposed a substantial disease burden on BVL over the past three decades. The burden is predominantly concentrated among males, particularly older individuals and those in low to middle-SDI regions. Moreover, the burden of smoking-induced BVL is expected to continue improving over the next decade.
Introduction
The escalating threat of global blindness has emerged as a significant concern within the public health domain. According to the World Health Organization, as of 2020, approximately 43.3Ìýmillion people worldwide were completely blind, with an additional 295Ìýmillion experiencing varying degrees of visual impairment [1]. Visual health profoundly impacts individual quality of life and socio-economic development [2, 3]. In recent years, major causes of blindness globally include cataracts, age-related macular degeneration (AMD), glaucoma, diabetic retinopathy, and corneal diseases, which are closely related to global economic development levels and the aging population [2, 4].
The risk factors of vision loss include genetic factors, environmental factors, and psychological factors. Among these, smoking stands out as one of important risk factors [5, 6]. Harmful chemicals and free radicals in tobacco smoke can directly or indirectly damage ocular tissues, leading to various severe blinding eye diseases [7,8,9,10,11]. Cataracts and AMD are particularly closely associated with smoking [10, 12]. Numerous studies indicate that smokers are 2–3 times more likely to develop AMD and cataracts compared to non-smokers [7, 10, 13, 14]. Moreover, smoking exacerbates the progression of diabetic retinopathy, optic neuropathy, and other ocular diseases [15]. Therefore, understanding the spatial and temporal trends of the global burden of smoking-related blindness and vision loss (BVL) is crucial for formulating effective prevention and intervention strategies.
A previous study using 2019 Global Burden of Disease (GBD) data briefly mentioned the global prevalence and proportion of cataracts and AMD attributable to smoking from 1990 to 2019 [16]. However, there has been no systematic analysis of the global vision loss caused by smoking, disaggregated by age, gender, region, and Social Development Index (SDI). Such an analysis is essential for a comprehensive understanding of the global BVL burden attributable to smoking. This study aims to utilize the latest 2021 GBD data to systematically evaluate the spatiotemporal distribution trends and age-gender-specific burden of BVL caused by smoking from 1990 to 2021. Additionally, we seek to predict the BVL burden due to smoking over the next decade, providing essential information for developing targeted primary prevention strategies for BVL.
Methods
Data source
Data utilized in this study are derived from the Global Burden of Disease Study 2021 (GBD 2021). GBD 2021 offers a standardized and exhaustive evaluation of the prevalence of 88 risk factors, their relative risks, and the attributable burden of 369 diseases across 204 countries and territories, spanning the period from 1990 to 2021, via a unified and comparable method [17]. Compared with GBD 2019, GBD 2021 enhances the method of considering random variation of sparse data to improve the accuracy of data estimation. In addition, GBD 2021 reports for the first time the impact of the COVID-19 pandemic on the burden of disease [18]. The study extracted the number and rate of years lived with disability (YLDs) attributable to the burden of smoking among various age groups, sexes, Sociodemographic Index (SDI) levels, regions, and countries from 1990 to 2021 using an accessible online platform, the Global Health Data Exchange (GHDx), accessible at . Additionally, the study incorporates the calculation of 95% uncertainty intervals (UIs) for the estimations, which are indicative of the potential random and systematic errors inherent in statistical modeling. These intervals are delineated by the 2.5th and 97.5th percentiles of the ordered set of 1000 estimates. The spatial stratification of the data is categorized into four distinct levels: global, sociodemographic, epidemiologic similarity and geographic proximity, and individual countries or regions. Within the sociodemographic stratum, the SDI—a composite measure reflecting per-capita income, average educational attainment, and total fertility rate—is employed to categorize countries and regions into quintiles: low, low-middle, middle, high-middle, and high SDI. The third stratum, grounded in epidemiologic similarity and geographic proximity, delineates the world into 21 distinct geographic regions, including but not limited to East Asia, Eastern Sub-Saharan Africa, and Western Europe. The final stratum encompasses the 204 discrete countries or regions. The methodology underlying the estimation of the burden of diseases, injuries, and risk factors in GBD 2021 has been reported elsewhere [18]. The current investigation is conducted in accordance with the Guidelines for Accurate and Transparent Health Estimates Reporting, ensuring the integrity and transparency of the health estimates presented [19].
Definitions
In accordance with the methodologies established by the GBD 2021 Risk Factors Collaborators for the assessment of disease burden attributable to risk factors, the GBD 2021 employs a rule-based synthesis of evidence. This approach ensures a consistent quantification of risk across different temporal and demographic contexts. The analytical process encompasses seven distinct steps: (1) estimate the effect size by determining the relative risk (RR) of a specified health outcome in relation to exposure to a particular risk factor; (2) estimate relative risks; (3) determine the theoretical minimum risk exposure level; (4) quantified the levels of exposure for each stratum defined by age, sex, and geographical location within the study’s timeframe; (5) use summary exposure values to represent the age-specific risk-weighted prevalence of exposure; (6) estimate the population-attributable fractions and the attributable burden for various combinations of risk factors, with consideration of mediation effects of different risk factors on one another; (7) calculate attributable burden for each specific combination of age group, sex, location, and year [17]. Smoking exposure is defined as the current or past utilization of any tobacco product, with the exclusion of electronic cigarettes or vaporizers. For individuals who are current smokers, exposure is gauged using two continuous measures: the daily number of cigarettes consumed and the annual cumulative total of cigarettes smoked. For those who have ceased smoking, exposure is estimated based on the number of years that have elapsed since they quit [20]. BVL is defined as a visual acuity of less than 3/60 or less than 10% of the visual field around the central fixation point. The cause-specific BVL includes a spectrum of conditions such as glaucoma, uncorrected refractive error, macular degeneration, diabetic retinopathy, cataract, vitamin A deficiency, trachoma, retinopathy of prematurity, meningitis, encephalitis, onchocerciasis, and a residual category encompassing other forms of vision loss [21]. Among them, smoking-related BVL mainly includes two diseases, cataract and AMD in the current GBD database.
Statistical analysis
In the present study, we delineated the number and age-standardized years lived with disability (YLDs) rate (ASYR) of BVL attributable to smoking. The age-standardized rates (ASR) were calculated using the direct standardization method, with weights applied based on the GBD 2021 World Standard Population. The ASRs were reported per 100,000 population and stratified by location, sex, year, and age group.
Subsequently, we employed the Joinpoint Regression model to calculate the annual percentage change (APC) and the average annual percentage changes (AAPCs) with 95% confidence intervals (CIs), thereby assessing the temporal trends in the ASYR of BVL due to smoking from 1990 to 2021. First, piecewise regression is conducted using a log-linear model (ln y = β*x), and all possible breakpoints are established using the grid search method (GSM). The mean squared errors (MSE) corresponding to each possible scenario are calculated, and the grid point with the minimum MSE is selected as the breakpoint. Second, the optimal model for the breakpoint regression (i.e., the number of breakpoints) is determined using the Monte Carlo permutation test. We set the maximum number of potential breakpoints to 5 and the minimum to 0. The permutation test starts with the number of breakpoints k = 0 and k_max = 5. If k ≠ k_max, then set k = k + 1 and continue the test until the model corresponding to k = k_max is selected as the optimal model. An AAPC value and its 95% CI exceeding zero indicate an increasing trend, whereas an AAPC value and its 95% CI below zero denote a decreasing trend [22]. We further conducted a decomposition analysis to meticulously examine the underlying determinants and their contributions to the increase in smoking-attributable BVL from 1990 to 2021. This analysis encompassed the effects of population growth, demographic aging, and epidemiological shifts [23].
To explore the relationship between ASYR and the SDI across 21 GBD regions and 204 countries, we utilized Pearson’s correlation analysis. Additionally, we investigated SDI-related health inequalities in disease burden, employing the slope index to quantify absolute inequalities and the concentration index to measure relative inequalities. The Slope Index is defined by the gradient of the regression line that correlates the smoking-attributable BVL with ASYR at the national level, weighted by the socioeconomic status of each country. This metric is derived through a regression analysis, where the national-level ASYR for the entire population is regressed against a socioeconomic status-related scale of relative social position. This scale is determined by the midpoint of the cumulative class intervals of the population, ranked according to SDI. Furthermore, the Concentration Index is employed to quantify the relative inequality in the burden of smoking-attributable BVL among countries. It is calculated by fitting a Lorenz concentration curve to the cumulative YLDs and the cumulative population. The concentration index is a numerical measure that integrates the area beneath the curve, with values ranging from − 1 to 1. A negative concentration Index value signifies a higher concentration of the smoking-attributable BVL burden within populations of countries with lower SDI, indicating a socioeconomic gradient in health outcomes related to smoking [24].
Moreover, we applied a Bayesian age-period-cohort (BAPC) model to project the evolving trends up to the year 2030. The age-period-cohort model, a logarithmic linear Poisson model, posits a multiplicative effect of age, period, and cohort on the dependent variable. Each of these factors is assumed to follow a Poisson distribution, with the model employing a link function that is specific to the age-period-cohort framework [25]. For the Bayesian Age-Period-Cohort (BAPC) model utilizing Integrated Nested Laplace Approximations (INLA), the BAPC package (version 0.0.36) was employed as a wrapper for the INLA package (version 22.12.16), which is specifically designed for Age-Period-Cohort (APC) analysis in R software.
All aforementioned analyses were performed using R software (version 4.3.2) and the Joinpoint software (version 5.0.2). Statistical significance was defined as p-values less than 0.05 [26].
Result
Global burden of BVL attributable to smoking from 1990 to 2021
In 2021, the global burden of BVL attributable to smoking was estimated at 284.03 thousand YLDs (95% UI: 198.43 to 393.38) and an ASYR of 3.27 per 100,000 (95% UI: 2.29 to 4.54). Specifically, for cause-specific BVL, the global YLDs and ASYR for cataracts were 225.17 thousand (95% UI: 160.7 to 315.1) and 2.6 per 100,000 (95% UI: 1.85 to 3.63), respectively, while for AMD, these numbers were 58.86 thousand (95% UI: 31.94 to 96.95) and 0.68 per 100,000 (95% UI: 0.37 to 1.11), respectively. Between 1990 and 2021, smoking-related global YLDs for BVL, cataracts, and AMD showed a gradual increase, while ASYR showed a gradual decline, with the AAPC of -1.43% (95% CI: -1.56 to -1.3), -1.44% (95% CI: -1.58 to -1.29), and − 1.39% (95% CI: -1.57 to -1.2), respectively.
The YLDs, ASYR, and AAPC of smoking-related BVL, cataracts, and AMD across different regions from 1990 to 2021 are detailed in TableÌý1. Among the 21 global regions, South Asia, East Asia, Southeast Asia recorded the highest number of YLDs related to smoking-attributable BVL in 2021. In contrast, the Caribbean, Australasia, and Oceania reported the lowest numbers. Regarding ASYR, South Asia, Southeast Asia, and Oceania had the highest rates, while Central Sub-Saharan Africa, High-income Asia Pacific, and Australasia had the lowest. Cataract and AMD also showed a similar trend.
From 1990 to 2021, most regions exhibited a decline in ASYR, with Southern Sub-Saharan Africa (AAPC: -3.41%, 95% CI: -3.47 to -3.35), Central Latin America (AAPC: -3.24%, 95% CI: -3.31 to -3.16), Tropical Latin America (AAPC: -2.73%, 95% CI: -2.83 to -2.63), and South Asia (AAPC: -2.37%, 95% CI: -2.4 to -2.33) showing the most significant decreases. Similar trends were observed in the burden of cataracts and AMD due to smoking.
BVL burden attributable to smoking by countries
In 2021, among the 204 countries globally, China (83.21, 95% UI: 57.06 to 120.03), India (66.62, 95% UI: 46.92 to 95.29), and Indonesia (14.85, 95% UI: 10.34 to 20.72) had the highest number of YLDs attributable to smoking-related BVL. The countries with the highest ASYR were Pakistan (10.9, 95% UI: 7.57 to 15.24), Lebanon (10.84, 95% UI: 7.14 to 15.87), and Cambodia (9.76, 95% UI: 6.57 to 13.76) (Fig.Ìý1A, Supplementary Table 1). Similar patterns were observed for cataracts (Fig.Ìý1B). However, for AMD, the highest ASYR was particularly notable in Nepal, Lebanon, and Yemen (Fig.Ìý1C, Supplementary Table 1).
From 1990 to 2021, the ASYR attributable to smoking-related BVL decreased in the majority of countries (Fig.Ìý1D). Equatorial Guinea (AAPC: -3.94%, 95% CI: -4.01 to -3.86), South Africa (AAPC: -3.9%, 95% CI: -3.99 to -3.81), and Myanmar (AAPC: -3.8%, 95% CI: -3.91 to -3.69) exhibited the most substantial declines in ASYR. Similar trends were observed in the burden of cataracts attributable to smoking, likely due to the large proportion of patients with cataracts in BVL (Fig.Ìý1E). Conversely, the burden of AMD attributable to smoking showed the greatest decline in Kenya, Madagascar, and Iceland (Fig.Ìý1F).
SDI correlation analysis of BVL burden attributable to smoking
The burden of BVL and cataracts attributable to smoking is significantly correlated with the SDI, with correlation coefficients of -0.61 and − 0.62, respectively (Fig.Ìý2A-B). Across all SDI regions from 1990 to 2021, the ASYR for BVL and cataracts initially increased with rising SDI, peaking around an SDI of 0.4, before starting to decline. At the national level in 2021, the burden of smoking-attributable BVL and cataracts in 204 countries and regions was generally negatively correlated with SDI, while the burden of AMD was positively correlated with SDI (Fig.Ìý2D-F). In 2021, the highest ASYR for smoking-attributable BVL and cataracts were observed in low-middle SDI regions, followed by middle SDI regions (TableÌý1, Supplementary Fig.Ìý1). The highest ASYR for AMD was found in High-middle SDI regions, followed by middle SDI regions. From 1990 to 2021, the overall annual change in ASYR for smoking-attributable BVL, cataracts, and AMD showed a declining trend across all SDI regions, with the most pronounced decline in low-middle SDI regions. Additionally, decomposition analysis by SDI regions revealed the relative contributions of population growth, population aging, and epidemiological changes to the increase in YLDs (Supplementary Fig.Ìý2).
Characteristics of smoking-attributable BVL burden by gender and age
In 2021, the burden of smoking-attributable BVL, primarily including cataracts and AMD, was predominantly observed in males (Fig.Ìý3A-C). Interestingly, from 1990 to 2021, the BVL burden decreased in both genders, but the decline was more pronounced in females (AAPC: -7.1%, 95% CI: -1.83 to -1.58) (TableÌý1). For males, the burden of BVL and cataracts was mainly concentrated in low-middle and middle SDI regions (Supplementary Fig.Ìý3A, B). The AMD burden was primarily located in low-middle SDI regions before 1995, but shifted to high-middle SDI regions after 2000 (Supplementary Fig.Ìý3C). For females, the burden of BVL and cataracts was predominantly in low-middle and low-SDI regions (Supplementary Fig.Ìý4A, B), while the AMD burden was mainly in high-middle SDI regions (Supplementary Fig.Ìý4C).
Among different age subgroups, the highest ASYR of BVL, cataracts, and AMD were observed in older adults over 85 years of age, with similar trends in both males and females (Fig.Ìý3D-F).
Temporal joinpoint analysis
Joinpoint regression analysis indicates that from 1990 to 2021, the global ASYR for smoking-attributable BVL (AAPC = -1.43; 95% CI: -1.56 to -1.30; P < 0.001), cataracts (AAPC = -1.44; 95% CI: -1.58 to -1.29; P < 0.001), and AMD (AAPC = -1.39; 95% CI: -1.57 to -1.20; P < 0.001) exhibited overall declining trends (Fig.Ìý4A-C). The ASYR demonstrated varying degrees of decline across different periods, with the most pronounced decrease occurring from 2015 to 2018 (APC = -2.60; 95% CI: -3.63 to -1.55; P < 0.001) (Fig.Ìý4A). Additionally, we observed that the overall decline was slightly more significant in females compared to males. The ASYR for cataracts and AMD showed a similar declining trend (Fig.Ìý4B, C).
Analysis of health inequality in smoking-attributable BVL burden relative to SDI
Our previous analysis revealed a significant negative correlation between SDI and the overall burden of BVL. To further explore this relationship, we conducted a health inequality analysis. The slope index of inequality indicated significant absolute inequality related to SDI in smoking-attributable BVL. However, the health inequality across different SDI regions decreased from 1990 to 2021 (slope index for 1990 ASYR: -0.72, 95% CI: -1.76 to 0.31; 2021 ASYR: -0.41, 95% CI: -1.01 to 0.20) (Fig.Ìý5A). Gender disparities in health inequality were also evident. In 2021, the slope index for males was − 1.23 (95% CI: -2.29 to -0.17), whereas for females, it was 0.31 (95% CI: 0.06 to 0.56), indicating that males were more significantly impacted by SDI (Fig.Ìý5B). The burden of cataracts attributable to smoking displayed a similar trend (Supplementary Fig.Ìý5A, B), whereas the burden of AMD showed a positive correlation with SDI, with the impact being greater in females (Supplementary Fig.Ìý6A, B). The concentration index confirmed the existence of relative inequality in BVL burden. In 1990, the concentration index was − 0.24 (95% CI: -0.28 to -0.19), whereas in 2021, it was − 0.12 (95% CI: -0.17 to -0.08) (Fig.Ìý5C). This indicates that in 1990, the YLD burden was more concentrated among populations with lower SDI, but this health inequality diminished by 2021. The burden of cataracts and AMD showed similar changes (Supplementary Fig.Ìý5C, D, Supplementary Fig.Ìý6C, D). Furthermore, the health inequality for BVL and cataracts was slightly higher in males than in females (Fig.Ìý5D, Supplementary Fig.Ìý5D), while the burden of AMD was relatively higher in females and more concentrated in regions with higher SDI (Supplementary Fig.Ìý6D).
Future trend predictions for smoking-attributable BVL burden
Our analysis indicates that smoking remains a significant global risk factor for vision loss. We now extend our predictions to estimate the future trends in YLDs and ASYR for smoking-attributable BVL, cataracts, and AMD up to the year 2030. Given the increasing population and aging demographics, the overall burden of YLDs for BVL is projected to rise substantially by 2030 (Fig.Ìý6A). Projections for 2030 suggest that global YLDs will rise to 314.59 thousand (95%UI: 0 to 19582403.93). However, the ASYR is expected to continue its downward trend, with an approximately 13% decline. (Fig.Ìý6D). It is noteworthy that the increase in YLDs is predominantly attributed to males, whereas the burden for females is anticipated to plateau. Cataracts and AMD are projected to follow similar trends to BVL (Fig.Ìý6B-C).
Discussion
This study highlights the significant impact of smoking on the global burden of BVL, including cataracts and AMD. Globally, smoking imposes a significant burden on BVL, and over the past 30 years, the disease burden caused by smoking has continually increased, making it one of the primary threats to global vision health [6, 27].In 2021, the global smoking-attributable BVL-related YLDs amounted to 284.03 thousand (95% UI: 198.43 to 393.38), with an ASYR of 3.27 per 100,000 (95% UI: 2.29 to 4.54). Specifically, the YLDs for cataracts and AMD were 225.17 thousand and 58.86 thousand, respectively, with the ASYR for cataracts at 2.6 per 100,000 and for AMD at 0.68 per 100,000. While the global YLDs have been increasing, the ASYR have generally shown a downward trend across many regions, particularly in Southeast Asia and South Asia. Additionally, a significant negative correlation was found between the SDI and the BVL burden, indicating that higher levels of socio-economic development are associated with a reduced smoking-related eye disease burden. These findings underscore the importance of strengthening smoking cessation policies and improving public health interventions, particularly in low and middle SDI regions. Through comprehensive measures, the burden of smoking-related vision loss can be further reduced, particularly within the aging population.
In regions with varying SDI, the burden of eye diseases attributable to smoking exhibits distinct patterns, with a significant inverse relationship between the smoking-attributable burden of BVL, cataracts, and SDI. In low to middle SDI regions, the ASYR of BVL, cataracts, and AMD due to smoking are highest. This could be associated with higher smoking rates and insufficient eye health services in these areas, indicating a need for more public health interventions and smoking control measures. Conversely, in high SDI regions, the burden of eye diseases is relatively lower, which may be attributed to better healthcare conditions and the implementation of smoking control policies. In countries with strict tobacco control policies, the ASYR has shown a significant downward trend, such as in Singapore, Ireland, and South Korea. Moreover, we observe an increase in the burden of vision impairment due to smoking from low to middle SDI stages, potentially linked to rising smoking rates and inadequate health education. However, at higher SDI stages, this burden begins to decline as public health measures and medical conditions improve. At the national level, our analysis reveals that China, India, and Indonesia bear the highest overall burden of BVL caused by smoking. Meanwhile, Pakistan, Lebanon, and Cambodia exhibit the highest ASYR of BVL. This underscores the varying impact of smoking on eye health across different countries, highlighting the necessity for tailored prevention and intervention strategies based on specific national contexts.
Regarding gender differences, we observe that the burden of BVL, cataracts, and AMD attributable to smoking is predominantly concentrated among men. Notably, over the past 30 years, although the disease burden has decreased for both genders, the reduction has been more pronounced among women. This trend reflects advancements in health care and lifestyle changes among women. In terms of age, our findings indicate that the burden of eye diseases due to smoking increases with age. This burden is particularly pronounced among those aged 65 and above, with the 70–74 age group following closely. This trend underscores the global aging population [28]. Additionally, the ASYR of eye diseases rises across age groups, peaking among individuals aged 85 and above. Interestingly, around 2020, we observed a notable inflection point in the disease burden across most age groups, which we hypothesize may be related to the COVID-19 pandemic. This phenomenon highlights the need to maintain continuity and stability in chronic disease management and routine medical services during public health emergencies. Overall, older adults are more susceptible to the harmful effects of smoking due to physiological decline. The cumulative effects of long-term smoking become evident with age, resulting in a significant increase in the burden of eye diseases among the elderly. This finding suggests that public health policies should particularly focus on elderly smokers, providing smoking cessation support and education to reduce tobacco use and, consequently, the incidence of eye diseases.
The highlight of this study lies in its utilization of the latest and most comprehensive GBD database to systematically analyze the burden of smoking-related BVL globally from 1990 to 2021 and to predict its impact trends over the next decade. However, our analysis is not without limitations. The 2021 GBD database includes only two major eye diseases attributable to smoking—cataracts and AMD—but lacks data on other blinding eye diseases, such as glaucoma and diabetic retinopathy, which also contribute to global vision loss. While numerous studies have indicated that cataracts and AMD are the most prevalent causes of smoking-related BVL [6, 29, 30], there is also evidence suggesting a significant association between smoking and the onset of glaucoma and other blinding eye diseases [31]. This evidence implies that our study underestimated the overall burden of smoking-related BVL. In particular, regions with higher prevalence rates of glaucoma and diabetic retinopathy may be disproportionately affected by this data gap, leading to a substantial underestimation of the total burden of smoking-related vision loss. In countries where smoking rates are higher or where these diseases are more prevalent, the lack of comprehensive data on these conditions could result in an underestimate of the disease burden, thereby compromising the accuracy of our understanding of smoking’s impact on global vision loss. Furthermore, in countries with lower SDI, insufficient data collection may exacerbate these biases, further distorting the overall analysis and undermining the validity of the conclusions drawn for these regions. At the same time, the GBD prediction models also have certain limitations. Their accuracy highly depends on the original data, the assumptions of the model, and the accuracy of the predicted covariates, which may introduce inherent biases. Additionally, the prediction models do not account for factors such as developments in medical technology or changes in policies, which may also lead to bias. For future research, we propose expanding the scope to include more smoking-related blinding eye diseases and enhancing international data collaboration. Additionally, advancing data imputation techniques will help reduce biases from missing data. Future studies should also focus on evaluating the effectiveness of public health interventions, particularly smoking control policies, in reducing the burden of eye diseases. Finally, strengthening long-term trend predictions, especially considering the aging population and changes in smoking policies, will provide valuable insights for future public health strategies.
In summary, from 1990 to 2021, despite significant reductions in ASYR in many regions and countries through public health interventions, the increase in total YLDs indicates that smoking remains one of the primary causes of global vision loss. Therefore, it is imperative to further strengthen global tobacco control policies and health education, especially in high-burden and low-SDI regions, to mitigate the health burden of smoking-related vision loss effectively. Additionally, targeted interventions should consider gender and age differences. We must continuously address the health burden among smoking men and develop corresponding strategies, particularly focusing on enhancing tobacco control policies and health education among the elderly. By continually improving public health strategies, we can effectively tackle the challenges of smoking-induced eye health issues and improve ocular health outcomes for populations worldwide.
Data availability
Data is provided within the manuscript or supplementary information files.
References
Blindness GBD, Impairment CV. Vision loss Expert Group of the Global Burden of Disease, trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the global burden of Disease Study. Lancet Glob Health. 2021;9(2):e130–43.
Whitson HE, et al. The combined effect of visual impairment and cognitive impairment on disability in older people. J Am Geriatr Soc. 2007;55(6):885–91.
Webson A. Eye health and the Decade of Action for the Sustainable Development Goals. Lancet Glob Health. 2021;9(4):e383–4.
Blindness GBD, Impairment CV. Vision loss Expert Group of the Global Burden of Disease, 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(2):e144–60.
Larsson SC, Burgess S. Appraising the causal role of smoking in multiple diseases: a systematic review and meta-analysis of mendelian randomization studies. EBioMedicine. 2022;82:104154.
Kelly SP, et al. Smoking and blindness. BMJ. 2004;328(7439):537–8.
Klein R, Klein BE, Moss SE. Relation of smoking to the incidence of age-related maculopathy. The Beaver Dam Eye Study. Am J Epidemiol. 1998;147(2):103–10.
Seddon JM, Widjajahakim R, Rosner B. Rare and common genetic variants, smoking, and body Mass Index: progression and earlier age of developing Advanced Age-Related Macular Degeneration. Volume 61. Investigative Ophthalmology & Visual Science; 2020. 14.
Jonas JB, Cheung CMG, Panda-Jonas S. Updates on the epidemiology of age-related Macular Degeneration. Asia Pac J Ophthalmol (Phila). 2017;6(6):493–7.
Fleckenstein M, Schmitz-Valckenberg S, Chakravarthy U. Age-Related Macular Degeneration: Rev JAMA. 2024;331(2):147–57.
Datta S, et al. The impact of oxidative stress and inflammation on RPE degeneration in non-neovascular AMD. Prog Retin Eye Res. 2017;60:201–18.
Kai JY, et al. Smoking, dietary factors and major age-related eye disorders: an umbrella review of systematic reviews and meta-analyses. Br J Ophthalmol. 2023;108(1):51–7.
Christen WG, et al. A prospective study of cigarette smoking and risk of cataract in men. JAMA. 1992;268(8):989–93.
Christen WG, et al. Smoking cessation and risk of age-related cataract in men. JAMA. 2000;284(6):713–6.
Stratton IM, et al. UKPDS 50: risk factors for incidence and progression of retinopathy in type II diabetes over 6 years from diagnosis. Diabetologia. 2001;44(2):156–63.
Yang X, et al. Global, regional, and national burden of blindness and. Aging. 2021;13(15):19614–42. vision loss due to common eye diseases along with its attributable risk factors from 1990 to 2019: a systematic analysis from the global burden of disease study 2019.
Collaborators GBDRF. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the global burden of Disease Study 2021. Lancet. 2024;403(10440):2162–203.
Collaborators GBDD. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the global burden of Disease Study 2021. Lancet. 2024;403(10440):1989–2056.
Stevens GA, et al. Guidelines for Accurate and Transparent Health estimates reporting: the GATHER statement. Lancet. 2016;388(10062):e19–23.
Zhang L, et al. Global, Regional, and National burdens of Ischemic Heart Disease Attributable to Smoking from 1990 to 2019. J Am Heart Assoc. 2023;12(3):e028193.
Li C, et al. Global, regional, and national burden of blindness and vision loss attributable to high fasting plasma glucose from 1990 to 2019, and forecasts to 2030: a systematic analysis for the global burden of Disease Study 2019. Diabetes Metab Res Rev. 2024;40(4):e3802.
Qu C, et al. Burden of Stroke Attributable to Nonoptimal temperature in 204 countries and territories: a Population-based study, 1990–2019. Neurology. 2024;102(9):e209299.
Rosenberg PS, Check DP, Anderson WF. A web tool for age-period-cohort analysis of cancer incidence and mortality rates. Cancer Epidemiol Biomarkers Prev. 2014;23(11):2296–302.
Lu M, et al. Persistence of severe global inequalities in the burden of Hypertension Heart Disease from 1990 to 2019: findings from the global burden of disease study 2019. Ó£»¨ÊÓƵ. 2024;24(1):110.
Jurgens V, et al. A bayesian generalized age-period-cohort power model for cancer projections. Stat Med. 2014;33(26):4627–36.
Yixian Liu MY, Zeng Z, Wang J, Rong R. Xiaobo Xia., Novel insights into immunopathogenesis and crucial biomarkers between primary open-angle glaucoma and systemic lupus erythematosus. iMetaOmics, 2024. 1(2): p. e27.
Flaxman SR, et al. Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob Health. 2017;5(12):e1221–34.
Xu T, et al. Prevalence and causes of vision loss in China from 1990 to 2019: findings from the global burden of Disease Study 2019. Lancet Public Health. 2020;5(12):e682–91.
Asfar T, Lam BL, Lee DJ. Smoking causes blindness: time for eye care professionals to join the fight against tobacco. Invest Ophthalmol Vis Sci. 2015;56(2):1120–1.
Kelly SP et al. Age related macular degeneration: smoking entails major risk of blindness. BMJ, 2003. 326(7404): pp. 1458-9; author reply 1459-60.
Nishida T, et al. Smoking and progressive retinal nerve fibre layer thinning in glaucoma. Br J Ophthalmol. 2023;107(11):1658–64.
Acknowledgements
The authors are deeply grateful to the generous contributors of the GBD database for their invaluable contribution to data.
Funding
This study was financially supported by grants from the National Natural Science Foundation of China (No.82171058 and 82401261), the National key research and development program of China (No. 2024YFA1108700 & 2024YFA1108704), the China National Postdoctoral Program for Innovative Talents (BX20230434), the China Postdoctoral Science Foundation (No.2023M743952), the Youth Science Foundation of Xiangya Hospital (No. 2023Q12).
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X.X. and R.R. conceived this study and revised the manuscript. Y.Z. and Y.L. performed the bioinformatic analysis and experiments and wrote the manuscript. J.K. and X.C. analyzed the data. All authors read and approved the publication of this manuscript.
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Zeng, Y., Liu, Y., Chen, X. et al. Global, regional, and national burden of blindness and vision loss attributable to smoking from 1990 to 2021, and forecasts to 2030: findings from the Global Burden of Disease Study 2021. Ó£»¨ÊÓƵ 25, 440 (2025). https://doi.org/10.1186/s12889-025-21573-2
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DOI: https://doi.org/10.1186/s12889-025-21573-2