Age-Period-Cohort Analysis of Global Prevalence of Blindness and Vision Loss: Findings From The Global Burden of Disease Study 2019

Age-Period-Cohort Analysis of Global Prevalence of Blindness and Vision Loss: Findings From The Global Burden of Disease Study 2019 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Age-Period-Cohort Analysis of Global Prevalence of Blindness and Vision Loss: Findings From The Global Burden of Disease Study 2019 Chengyao Guo, Yuancun Li, Yingzi Huang, Liu Jing, Kunliang Qiu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2378216/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To quantify age, period, and cohort effect on the global secular trend of prevalence of blindness and vision impairment (BVI) based on the age-period-cohort (APC) model. Methods Data on global BVI were extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 database. Annual percentage change of age-standardized prevalence rate (ASPR) of BVI was estimated by assuming a linear relationship between natural logarithm of ASPR of disease with time. The prevalence of BVI was evaluated from age, period, and cohort effects based on the APC model with intrinsic estimator. Results Global prevalence number of BVI was 353.2 million in 1990 and increased to 713.9 million in 2019, but with an ASPR declined at a speed of -0.14% (95% CI: -7.49–7.8%) per year from 1990 to 2019. The APC model showed that the prevalence of BVI increased with age and period but decreased with cohorts. Changes in each cause (age-related macular degeneration, cataract, glaucoma, refractive disorders, near-vision loss, and other vision loss) are consistent in the overall upward or downward trend of the age, period, and cohort effects. Conclusions Global prevalence of BVI has significant age, period and cohort effects. The risk of vision impairment increases with age and period, however, it decreases with the cohort. Cost-effective prevention and control should be implemented more in the older population at high risk. Blindness and vision loss Age-Period-Cohort Model Global Burden of Disease 2019 study (GBD 2019) Figures Figure 2 Introduction Blindness and vision impairment (BVI) is considered to be major global health problems, which is the leading causes of disability among the population aged 65 + years [ 1 ]. The blindness is defined as presenting best-corrected visual acuity (BCVA) < 3/60 in the better eye and the moderate to severe visual impairment is defined as presenting BCVA < 6/18 but ≥ 3/60 in the better eye by the World Health Organization (WHO) [ 2 , 3 ]. Individuals presenting with vision loss may be involved in age-related macular degeneration (AMD), cataract, glaucoma, uncorrected refractive error, or other ocular diseases. Since most BVI cases are caused by age-related eye diseases, the number of individuals with BVI is speculated to increase along with the aging of the global population [ 4 ]. For policy-making and decreasing socioeconomic burden, it is very important to know the trend of prevalence of BVI among the world population. The age-period-cohort (APC) model is a useful tool in understanding the secular trend in disease prevalence. The APC model is a three-factor multiplicative model, and each aspect of the APC model has a unique effect on the trend in disease prevalence [ 5 ]. The age effect has a relationship with the outcome of time, representing changes in prevalence because of aging processes. The period effect can reflect the burden of disability, morbidity, and mortality at a given time among the whole population. And the cohort effect is associated with changes across groups with the same birth year, reflecting how population health is changing over time [ 6 , 7 ]. These effects can provide epidemiologists and governments with important suggestions on identifying population health determinants and policy planning. Overall, the APC model can eliminate the impact of covariates to avoid substantial bias and provide a complete trend of population health [ 8 ]. Previous studies have already investigated the prevalence and disease burden of BVI in the world population [ 9 , 10 ]. However, it is unclear how the prevalence would change through age, period, or a cohort effect which may indicate the role of environmental and social factors. For this purpose, we performed the APC model to investigate the global trend of changes in BVI to provide new insights into the trend of the disease prevalence by evaluating the unique effects of age, period, and cohort. Materials And Methods Data sources The prevalence data of BVI was downloaded from the Global Burden of Disease Study 2019 (GBD 2019) through the Global Health Data Exchange (GHDx) query tool ( http://ghdx.healthdata.org/gbd-results-tool ) [ 11 ]. The data includes yearly case numbers and the prevalence rate of BVI due to AMD, cataract, glaucoma, refractive disorders, near-vision loss, and other vision loss of different regions from 1990 to 2019. Temporal trend analysis The estimated annual percentage change (EAPC) was used to analyze the temporal trends of age-standardized prevalence rates (ASPR) of BVI. We assumed that the natural logarithm of ASPR has a linear relationship with time, which is ln (ASR) = α + β*year + ε , where α represents intercept and ε represents error term. The coefficient β is the slope of the fitted line, with positive β indicating an increasing trend of ASPR along with the year, and vice versa. EAPC is the exponential function of β with the natural base of e , where EAPC = 100 × (exp(β)-1) . We applied this algorithm to multiple time series and sequentially calculated EAPC for global, regional (socio-demographic index (SDI) and WHO regions), and national ASPR for BVI. For comparison, all 204 countries or territories were sorted according to EAPC from small to large. The analysis was performed by R software (R Foundation for Statistical Computing, Vienna, Austria, version 4.1.0). Age-period-cohort analysis The APC model was used to assess the age, period, and cohort effects separately on prevalence rate of BVI and its causes. The calculation formula of the APC model is as follow: ln(Y apc ) = µ + α*age + β*period + γ*cohort + ε, where ln(Y abc ) represents the natural logarithm of the prevalence rate of BVI; µ, α, β, γ, and ε represents intercept, coefficient of age, coefficient of period, coefficient of cohort, and random effect, respectively. For i th age group (or period, cohort), α i represents the independent effect of i th age group that deviated from the overall age groups, where \(\sum _{1}^{i}{{\alpha }}_{i}=0\) , and exp ( \({{\alpha }}_{i}\) ) is the Relative risk (RR) of i th age group. Ordinary least squares cannot obtain a unique and unbiased estimate of the coefficients due to the perfect collinearity of the three variables (cohort = period - age), which is known as the identification conundrum of the APC model. To solve this problem, the intrinsic estimator (IE) method [ 12 , 13 ] was used in the current study. Based on the estimable functions and the singular value decomposition of matrices, the IE method yields unbiased, robust, and unique age, period, and cohort coefficient estimates. Prior to APC analysis, the prevalence and population data were appropriately divided into successive 10-year age groups (10–19, ..., 90–99 years), and correspondingly consecutive 10-year periods (1990–1999,2000–2009, 2010–2019) and cohorts from 1900 to 2019 (1900–1909, ..., 2010–2019; calculated according to the formula: cohort = period - age). We used “apc_ie” instruction in STATA version 16.0 software (Stata Corp., College Station, TX, USA) to conduct the APC analysis. The dependent variable (cases number) was specified as Poisson distribution, the logarithmic function was assigned as the link function, and the population effect was adjusted by setting the offset option. Age, period, and cohort effects were assigned as independent variables. The coefficients of the age, period and cohort effects were converted into the exponential value which denoted the RR of prevalence in a specific age, period, and cohort relative to each time interval divided. A two-tailed P < 0.05 was considered to be significant. Results Descriptive analysis of BVI prevalence The prevalence number and ASPR of BVI in 1990 and 2019 by economic level, causes, and WHO region stratification were presented in Table 1 . The global prevalence number of all causes was 353.2 million in 1990 and increased to 713.9 million in 2019. Over the past 30 years, a total of 360.7 million new cases were confirmed. Among them, there were 199.5 million male cases and 161.4 million female cases, respectively. ASPR of all causes was 8427.7 per 100,000 population in 1990 and 8686.9 per 100,000 population in 2019, respectively. The EAPC of ASPR of BVI was − 0.14% from 1990 to 2019, with a 95% confidence interval (95%CI) of -7.49–7.8%, which means that the EAPC was not significantly different from 0 at α = 0.05 level (compared with P > 0.05). In the study period, the female-specific burden of blindness and vision impairment was higher than males, no matter from the perspective of prevalence number or ASPR. However, both sexes showed decreasing trend during the past 30 years, the EAPC was − 10.24% and − 6.97% for females and males, respectively. Near vision loss, refraction disorders, and cataract were among the top 3 causes of blindness and visual loss, and all of them showed decreasing trend in terms of EAPC. Table 1 Prevalence number and age-standardized rates of blindness and vision loss t in 1900 and 2019, and estimated trends from 1990 to 2019. Parameters 1990 2019 1990–2019 Prevalence Number No.×10 6 (95% UI) ASPR per 100,000 No. (95% UI) Prevalence Number No.×10 6 (95% UI) ASPR per 100,000 No. (95% UI) EAPC No. ×100% (95% CI) Overall 353.20 (298.70–414.00) 8427.70 (7073.20-9860.80) 713.90 (593.20-841.10) 8686.90 (7270.50-10218.20) -0.14 (-7.49-7.80) Sex Male 162.20 (136.60-191.20) 8178.80 (6878.40-9606.10) 323.60 (268.90-383.80) 8246.10 (6907.10-9727.30) -6.97 (-10.47–3.34) Female 190.90 (161.40-222.50) 8665.30 (7287.90-10114.90) 390.40 (325.00-458.90) 9095.60 (7606.50-10678.70) -10.24 (-14.16–6.14) Socio-demograph ic index Low SDI 36.40 (30.50–42.70) 13631.40 (11426.10-15952.10) 77.20 (65.00-90.80) 13125.50 (10975.80-15371.30) 0.95 (-6.46-8.96) Low-middle SDI 90.80 (76.9-106.5) 13573.10 (11465.90-15791.40) 179.80 (149.20-213.20) 12496.10 (10452.20-14727.00) 3.45 (-3.92-11.39) Middle SDI 111.80 (93.80–132.00) 10117.10 (8442.20-11918.80) 247.00 (204.30-293.60) 9822.80 (8220.10-11612.60) 4.60 (-2.16-11.83) High-middle SDI 85.70 (70.90-102.10) 7997.30 (6669.00-9495.40) 162.50 (133.20-194.60) 8314.60 (6897.60-9882.50) 4.46 (-2.81-12.28) High SDI 28.30 (24.90–32.00) 2967.70 (2628.30-3339.30) 47.10 (41.10–53.80) 3080.60 (2717.30-3478.50) 1.90 (-5.40-9.77) Causes AMD 3.60 (3.00-4.20) 98.80 (83.70-114.60) 7.80 (6.50–9.20) 96.80 (81.30-113.20) -0.13 (-7.48-7.80) Cataract 42.30 (37.70–47.60) 1150.60 (1027.30-1287.40) 97.00 (85.40-109.70) 1207.90 (1065.00-1361.30) -0.15 (-7.51-7.80) Glaucoma 3.90 (3.30–4.50) 111.90 (94.80-130.30) 7.50 (6.30–8.80) 94.70 (80.40-110.90) -10.09 (-13.99–6.02) Refractive disorders 97.60 (87.50-108.50) 2083.70 (1870.50-2309.20) 157.40 (140.90-174.90) 1959.60 (1751.00-2180.00) -10.19 (-14.1–6.1) Near vision loss 227.20 (164.20-298.40) 5613.30 (4081.10-7335.10) 493.20 (358.90-645.50) 5937.80 (4336.40-7772.50) -10.14 (-14.05–6.06) Other vision loss 21.10 (19.10–23.60) 518.40 (466.30-578.10) 38.40 (34.40–43.10) 473.90 (425.70-528.50) -10.17 (-14.08–6.08) WHO regions African Region 29.60 (24.20–35.50) 12015.80 (9845.90-14398.20) 65.20 (53.40–78.10) 11752.20 (9627.40-14052.00) 4.41 (-2.79-12.13) Eastern Mediterranean 20.90 (18.60–23.40) 9850.50 (8699.70-11061.10) 41.40 (36.50–46.60) 8668.00 (7621.70–9762.00) -1.50 (-7.93-5.38) European Region 58.50 (49.40–68.80) 5722.90 (4873.30-6661.60) 79.30 (66.60–93.90) 5555.70 (4722.00-6501.20) 5.21 (-2.69-13.75) Region of the Americas 30.50 (26.80–34.70) 4750.90 (4146.20–5418.00) 61.00 (52.60–70.30) 5104.50 (4427.70-5852.30) -0.45 (-7.34-6.94) South-East Asia Region 115.90 (98.90-134.70) 14722.20 (12588.10-16999.30) 245.50 (205.70-290.30) 13616.90 (11511.90-15908.50) 3.16 (-3.94-10.79) Western Pacific Region 96.60 (77.90-117.30) 7974.30 (6460.70–9666.00) 219.00 (176.10-266.10) 8213.10 (6713.60-9886.20) -0.55 (-7.25-6.64) UI: uncertainty interval, also known as credibility interval, is an interval estimate of the parameter by bayesian method. CI: confidence interval, the difference between UI with CI only philosophical rather than mathematical. ASPR: age-standardized prevalence rate per 100,000 population. EAPC: estimated annual percentage change. AMD: age-related macular degeneration. Table 2. Intrinsic estimates of age, period, and cohort effect for the global prevalence rate of blindness and vision loss. Coefficient (95% CI) Relative risk (95% CI) Z P > |Z| Intercept -2.424 (-2.424, -2.424) 0.089 (0.089, 0.089) -2.8×10 4 0.000 Age 1 ~ 9 -1.319 (-1.319, -1.318) 0.267 (0.267, 0.268) -4424.51 0.000 10 ~ 19 -1.239 (-1.239, -1.238) 0.290 (0.290, 0.290) -5628.80 0.000 20 ~ 29 -1.280 (-1.280, -1.279) 0.278 (0.278, 0.278) -6056.98 0.000 30 ~ 39 -0.751 (-0.752, -0.751) 0.472 (0.472, 0.472) -3975.23 0.000 40 ~ 49 0.103 (0.103, 0.104) 1.109 (1.109, 1.109) 639.70 0.000 50 ~ 59 0.689 (0.689, 0.689) 1.991 (1.991, 1.992) 4913.10 0.000 60 ~ 69 0.990 (0.989, 0.990) 2.690 (2.689, 2.691) 7660.92 0.000 70 ~ 79 1.052 (1.052, 1.053) 2.864 (2.864, 2.865) 7735.81 0.000 80 ~ 89 0.965 (0.965, 0.965) 2.625 (2.624, 2.626) 5647.92 0.000 90 ~ 99 0.790 (0.789, 0.790) 2.203 (2.202, 2.204) 2343.67 0.000 Period 1990 ~ 1999 -0.198 (-0.198, -0.198) 0.820 (0.820, 0.820) -3029.95 0.000 2000 ~ 2009 0.003 (0.003, 0.003) 1.003 (1.003, 1.003) 56.35 0.000 2010 ~ 2019 0.195 (0.195, 0.195) 1.215 (1.215, 1.216) 3202.42 0.000 Cohort 1900 ~ 1909 1.092 (1.091, 1.094) 2.981 (2.977, 2.985) 1631.02 0.000 1910 ~ 1919 0.873 (0.872, 0.873) 2.394 (2.392, 2.395) 2989.74 0.000 1920 ~ 1929 0.678 (0.678, 0.678) 1.970 (1.969, 1.971) 3264.56 0.000 1930 ~ 1939 0.505 (0.504, 0.505) 1.657 (1.656, 1.657) 2821.41 0.000 1940 ~ 1949 0.313 (0.313, 0.314) 1.368 (1.368, 1.369) 1857.73 0.000 1950 ~ 1959 0.101 (0.100, 0.101) 1.106 (1.105, 1.106) 587.51 0.000 1960 ~ 1969 -0.089 (-0.090, -0.089) 0.915 (0.914, 0.915) -483.68 0.000 1970 ~ 1979 -0.272 (-0.273, -0.272) 0.762 (0.761, 0.762) -1350.62 0.000 1980 ~ 1989 -0.463 (-0.464, -0.463) 0.629 (0.629, 0.629) -2232.43 0.000 1990 ~ 1999 -0.684 (-0.684, -0.683) 0.505 (0.505, 0.505) -3285.73 0.000 2000 ~ 2009 -0.909 (-0.909, -0.908) 0.403 (0.403, 0.403) -3487.17 0.000 2010 ~ 2019 -1.145 (-1.146, -1.144) 0.318 (0.318, 0.319) -2410.73 0.000 AIC 138.0355 BIC 3516.6970 AIC: Akaike information criterion; BIC: Bayesian information criterion As for the SDI stratification, ASPR of BVI decreases in a graded manner as SDI levels increase. The specific ASDR numbers are 13125.5 (10975.8-15371.3), 12496.1 (10452.2-14727), 9822.8 (8220.1-11612.6), 8314.6 (6897.6-9882.5), and 3080.6 (2717.3-3478.5) per 100,000 for Low SDI, Low-middle SDI, Middle SDI, High-Middle SDI, and High SDI region, respectively (Table 1 ). Among all six WHO regions, ASDR of BVI among South-East Asia Region (13616.9, 95%UI: 11511.9-15908.5) was the highest in 2019, followed by the African Region (11752.2, 95%UI: 9627.4-14052.0), Eastern Mediterranean region (8668.0, 95%UI: 7621.7–9762.0), Western Pacific Region (8213.1, 95%UI: 6713.6-9886.2), European Region (5555.7, 95%UI: 4722.0-6501.2), and Region of the Americas (5104.5, 95%UI: 4427.7-5852.3). EAPC was the highest in Middle SDI region and European Region, which is 4.6% (-2.16 to 11.83) and 5.21 (-2.69 to 13.75), respectively. But no significant upward or downward trend was found in ASDR of any SDI or WHO region in terms of 95% confidence interval of EAPC. Independent effect of age, period, and cohort on the prevalence of BVI Figure 1 shows the impact of age, period, and cohort effects on the global prevalence rate of BVI. The prevalence rate of BVI increased with age throughout the periods (Fig. 1 A) and cohorts (Fig. 1 B); the prevalence rate decreased as the cohorts declined, but the downward trend varied in different periods (Fig. 1 C). For those born between 1910 and 1999, the same cohort had a higher prevalence in the later periods than in the earlier ones. The independent effect of each variable on the prevalence rate of global BVI was further investigated by the APC model with intrinsic estimators. Figure 2 illustrates the results of the APC model based on the exponential value, and Table 2 shows the detailed value of the independent effect trends of age, period, and cohort. In general, the age and period effect showed an upward trend while the cohort effect showed a downward trend from the perspective of coefficients (Fig. 2 ), which indicated that the advancing age experienced higher vision loss morbidity, while later cohorts experienced lower morbidity. RR reflects the deviation of relative risk of a particular group from the average level (1.0 for age, period, and cohort). After adjusting for covariates, the RR of BVI increased slightly from 0.267 in the 1–9 age group to the 0.278 in the 20–29 age group, and then increased sharply to the peak of 2.864 in the 70–79 age group. However, from 70–79 to 90–99 years, the RR stopped increasing and exhibited a slight decrease to 2.203 in the 90–99 age group. As for the period and cohort effects, the RR of BVI increased from 0.820 in the 1990–1999 period to 1.215 in the 2010–2019 period, while it decreased from 2.981 in the 1900–1909 cohort group to 0.318 in the 2010–2019 cohort group (all P < 0.010). Impact of age, period, cohort on the global prevalence rates of Cause-Specific BVI The age, period, and cohort effects of the prevalence rates of AMD, cataract, glaucoma, near-vision loss, refractive disorders, and other vision loss are shown in Additional f ile 1–3 . Although these pathogeneses differ in global prevalence, changes in each cause are consistent in the overall upward or downward trend of the age, period, and cohort effects. General upward trend of coefficients of age and period of all six vision impairment causes was observed, while general downward trend of coefficients of cohort was seen, with all P < 0.010. More detailed number of coefficients, RR of the three variables were available in Additional f ile 4–9 . As cataract for example, the relative prevalence risk in age group 20–29 is 0.057 (95% CI: 0.057–0.057), and steadily improves to 4.044 (95% CI: 4.039–4.048) in age group 90–99. The relative prevalence risk of cataract increases from 0.698 (95% CI: 0.698–0.698) of period 1990–1999 to 1.41 (95% CI: 1.409–1.410) of period 2010–2019, while relative prevalence risk decreases from 4.627 (95% CI: 4.618–4.637) of cohort 1900–1909 to 0.168 (95% CI: 0.167–0.169) of cohort 1990–1999. Model accuracy analysis The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select a suitable APC model within the data collection in the present study, which aims to minimize bias and evaluate the goodness of the established model [ 14 ]. When selecting the optimal model from a group of candidate models, the model with the least AIC and BIC is selected. The AIC has a value of 138.04 and the BIC has a value of 3516.70 in the current study (the smallest of all model built), suggesting that our APC model is quite suitable. Discussion The current study is the first investigation to estimate the secular trends of prevalence in BVI based on a specific model using age, period, and cohort effects independently. We found that cause-specific and region-specific prevalence data of BVI are continuously increasing from 1990 to 2019. Concerning the age effect, we found that the global prevalence rate of BVI rapidly increased in people older than 20 years but gradually decreased after 80 years old. Regarding the period effect, the whole visual impairment rates rose at a constant speed between 1990 and 2019. As for the birth effect, we demonstrated that the prevalence rate of BVI continually declined across cohorts. Prior studies have investigated the overall trends in vision impairment, revealing that the prevalence of BVI is continuously increasing worldwide, with an estimation of 43.3 million people suffering from blindness and 295 million people suffering from moderate or severe vision loss in 2020 [ 15 ]. Cataract, refractive disorders and near vision loss are the most common eye diseases leading to vision loss and blindness over the past three decades [ 9 ]. Besides, AMD and glaucoma are the other vision-threatening conditions. Consistent with recent research [ 9 , 10 ], an increasing trend in prevalence rates due to BVI was observed in the current study. Fortunately, during the period from 1990 to 2019, the EAPC of all causes for vision loss is negative, except the AMD and cataract, which may be attributed to the effective health policies and healthcare resources investments in recent years [ 16 , 17 ]. Regarding the age effects independently, children and adolescents have much lower visually impaired rates within the study period, suggesting that the BVI is mainly due to age-related eye diseases, such as AMD, cataract, glaucoma, and near vision loss. Prevalence rates rise sharply in people over 30 years of age, peaking between 70 to 79 years, and then decline slowly. Paying more attention to these populations to detect and treat them at an early stage may prevent and delay severe visual impairment, and then reduce the burden on society. In addition, the age effects simply indicate that in each study period, older adults are more likely to be diagnosed with visually impaired eye diseases than the younger population. Due to the age, the effect is a comprehensive estimation within the study period and will not change much in the short term, the results still hold true for all age groups over the next decade, 2020–2029. While controlling for age and cohort effects, we have found that during the time period 1990 to 2019, the average prevalence rate for BVI is about zero, but the rate numbers keep increasing. The increased number of visually impaired cases might be due to the improvements in awareness and diagnosis of eye health in recent years. The high-resolution optical biometry techniques used in clinical practice, such as the optical coherence tomography (OCT) [ 18 ], Zeiss IOL Master 700, and OA 2000 [ 19 ], can improve the detection rate of some eye diseases, including cataract, glaucoma or AMD. Besides, as the global population grows and ages, it is not surprising that the cases of age-related eye diseases are on the rise [ 20 , 21 ]. Aiming to eliminate avoidable blindness and provide eye healthcare services, the program World Vision Day initiated in 2000. All of these can raise people’s awareness of eye care, and improve the rate of eye disease-related visits as well as detection. Concerning the cohort effects, the prevalence rates have a continuous decreased trend as the cohort year increases starting with those born in 1990. Those born in earlier years have a higher prevalence of vision impairment than others. Such changes that occur over time may result from changes in diet and increases in physical activity and time spent outdoors. Besides, global economic growth improved living conditions, and investment in healthcare resources in recent years may also explain the decreased trend [ 22 ]. This study successfully identified age, period, and cohort effects of prevalence rates due to BVI, suggesting that healthcare service plays a vital role in the process of eliminating avoidable blindness and improving life quality for the future, especially for the older population. In addition, the continuously increased prevalence rate of uncorrected refractive errors cannot be negligible. Similar to the rapidly increasing myopia prevalence reported in previous studies [ 23 – 26 ], the present study showed that the prevalence number due to refractive disorders nearly doubled from 97.6 million in 1990 to 157.4 million in 2019. Recently, refractive errors have become a major social public health concern and have brought a great economic burden to society [ 27 ]. Both lifestyle and genetic factors play an important role in the etiology of refractive errors. Myopia-associated genetic variants have facilitated the identification of individuals with a higher risk of developing myopia in population screening [ 28 – 30 ]. Other than the genetic factors, maybe we should pay more attention to the environmental factors, such as lacking outdoor activity or excessive near work for long periods. Because those factors play an important role in the progression of myopia and can be adjusted in daily life. Timely visual screening and correction of refractive errors can effectively reduce the occurrence or development of avoidable vision loss. There are two main limitations need to be noticed for the current study. Firstly, the data used were entirely from the GBD database, and therefore, some possible biases such as data collection bias and assessment bias cannot be avoided. However, we tried to standardize the data before analysis to reduce the bias or errors. Secondly, this is a descriptive study and the trend results are inferred using an updated statistical model based on large-scale population information. The APC model did not adjust some confounding factors, such as lifestyle or education levels, from the raw datasets. However, the effects of some systematic patterns over the regression model have been adjusted separately in the APC method. In summary, this study revealed significant age, period, and cohort effects on prevalence rates for BVI over the past three decades. Age is a risk factor for vision impairment, and more attention should be paid to eye diseases in the elderly. The cohort effect on BVI prevalence reflects the influence of environmental factors (e.g. changes in diet and time spent outdoors) occurring during the early life. The period effect reflects social factors such as the improvement of awareness and detection rate of eye diseases. Assuming that the prevalence rate remains constant, we expect that in the coming decades, as the aging population increases, visually impaired individuals will have greater demand for services. Declarations Ethics approval and consent to participate The study protocol was approved by Human Medical Ethics Committee of the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong (approval number: JSIEC20220501), which is in accordance with the tenets of the Declaration of Helsinki. Participants gave informed consent to participant in the study before taking part. Consent for publication All authors have read this manuscript and consent to publish. Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. Competing interests There are no competing interests in this study. No financial or proprietary interest in any material or method is mentioned. Funding No funding supports this study. Authors' contributions Guo CY was responsible for writing the manuscript, original draft preparation, and conducting the search, and investigation. Li YC was responsible for conceptualization, conducting the search, methodology, visualization, data curation, and writing the manuscript. Qiu KL was responsible for reviewing the manuscript. Huang YZ and Jing L were responsible for conducting the search, and investigation. Zhang MZ was responsible for the supervision, project administration, and reviewing the manuscript. Acknowledgement The authors would like to express their deepest gratitude to the Global Burden of Disease Study 2019 (GBD 2019) database. 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Gateway to Global Aging Data: Resources for Cross-National Comparisons of Family, Social Environment, and Healthy Aging. J Gerontol B Psychol Sci Soc Sci. 2021;76(Suppl 1):5–16. Simoes TC, Borges LF, Parreira DAA, Silva MV, Dos SJ, Meira KC. Chagas disease mortality in Brazil: A Bayesian analysis of age-period-cohort effects and forecasts for two decades. PLoS Negl Trop Dis. 2018;12(9):e0006798. Rosenberg PS. A new age-period-cohort model for cancer surveillance research. Stat Methods Med Res. 2019;28(10–11):3363–91. Liu Y, Ji W, Yin Y, Yang Z, Yang S, Zhou C, et al. An analysis on the trend of AIDS/HIV incidence in Chongqing and Shenzhen, China from 2005–2015 based on Age-Period-Cohort model. Math Biosci Eng. 2021;18(5):6961–77. Carreras G, Gorini G. Time trends of Italian former smokers 1980–2009 and 2010–2030 projections using a Bayesian age period cohort model. Int J Environ Res Public Health. 2013;11(1):1–12. Yang X, Chen H, Zhang T, Yin X, Man J, He Q, et al. Global, regional, and national burden of blindness and 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. Aging. 2021;13(15):19614–42. Sun Y, Chen A, Zou M, Zhang Y, Jin L, Li Y, et al. Time trends, associations and prevalence of blindness and vision loss due to glaucoma: an analysis of observational data from the Global Burden of Disease Study 2017. BMJ Open. 2022;12(1):e053805. Zou J, Sun T, Song X, Liu YM, Lei F, Chen MM, et al. Distributions and trends of the global burden of COPD attributable to risk factors by SDI, age, and sex from 1990 to 2019: a systematic analysis of GBD 2019 data. Respir Res. 2022;23(1):90. Pelzer B, Te GM, Eisinga R, Schmidt-Catran AW. The Non-uniqueness Property of the Intrinsic Estimator in APC Models. Demography. 2015;52(1):315–27. Masters RK, Powers DA, Hummer RA, Beck A, Lin SF. and B.K. Finch, Fitting Age-Period-Cohort Models Using the Intrinsic Estimator: Assumptions and Misapplications. Demography. 2016;53(4):1253–9. Vrieze SI. Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol Methods. 2012;17(2):228–43. 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. Brown DM, Kaiser PK, Michels M, Soubrane G, Heier JS, Kim RY, et al. Ranibizumab versus verteporfin for neovascular age-related macular degeneration. N Engl J Med. 2006;355(14):1432–44. Bourne R, Dineen B, Jadoon Z, Lee PS, Khan A, Johnson GJ, et al. Outcomes of cataract surgery in Pakistan: results from The Pakistan National Blindness and Visual Impairment Survey. Br J Ophthalmol. 2007;91(4):420–6. Minakaran N, de Carvalho ER, Petzold A, Wong SH. Optical coherence tomography (OCT) in neuro-ophthalmology. Eye (Lond). 2021;35(1):17–32. Du YL, Wang G, Huang HC, Lin LY, Jin C, Liu LF, et al. Comparison of OA-2000 and IOL Master 500 using in cataract patients with high myopia. Int J Ophthalmol. 2019;12(5):844–7. Global age-sex. -specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1160–203. Harper S. The Impact of the Covid-19 Pandemic on Global Population Ageing. J Popul Ageing. 2021;14(2):137–42. Gilmore KJ, Pennucci F, De Rosis S, Passino C. Value in Healthcare and the Role of the Patient Voice. Healthc Pap. 2019;18(4):28–35. Hrynchak PK, Mittelstaedt A, Machan CM, Bunn C, Irving EL. Increase in myopia prevalence in clinic-based populations across a century. Optom Vis Sci. 2013;90(11):1331–41. Holden BA, Fricke TR, Wilson DA, Jong M, Naidoo KS, Sankaridurg P, et al. Global Prevalence of Myopia and High Myopia and Temporal Trends from 2000 through 2050. Ophthalmology. 2016;123(5):1036–42. Yang M, Luensmann D, Fonn D, Woods J, Jones D, Gordon K, et al. Myopia prevalence in Canadian school children: a pilot study. Eye (Lond). 2018;32(6):1042–7. Hansen MH, Hvid-Hansen A, Jacobsen N, Kessel L. Myopia prevalence in Denmark - a review of 140 years of myopia research. Acta Ophthalmol. 2021;99(2):118–27. Yang Z, Jin G, Li Z, Liao Y, Gao X, Zhang Y, et al. Global disease burden of uncorrected refractive error among adolescents from 1990 to 2019. BMC Public Health. 2021;21(1):1975. Fan Q, Verhoeven VJ, Wojciechowski R, Barathi VA, Hysi PG, Guggenheim JA, et al. Meta-analysis of gene-environment-wide association scans accounting for education level identifies additional loci for refractive error. Nat Commun. 2016;7:11008. Tedja MS, Wojciechowski R, Hysi PG, Eriksson N, Furlotte NA, Verhoeven V, et al. Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error. Nat Genet. 2018;50(6):834–48. Hysi PG, Choquet H, Khawaja AP, Wojciechowski R, Tedja MS, Yin J, et al. Meta-analysis of 542,934 subjects of European ancestry identifies new genes and mechanisms predisposing to refractive error and myopia. Nat Genet. 2020;52(4):401–7. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additional file 1 Global prevalence of cause-specific blindness and vision loss with age. The overall upward trend was observed in all cause-specific prevalence with age, and the upward trend varies throughout periods. Additionalfile2.docx Additional file 2 Global prevalence of cause-specific blindness and vision loss with age. The overall upward trend was observed in all cause-specific prevalence with age, and the upward trend varies throughout cohorts. Additionalfile3.docx Additional file 3 Global prevalence of cause-specific blindness and vision loss with cohort. The overall downward trend was observed in all cause-specific prevalence within the cohort, and the downward trend varies throughout periods. Additionalfile4.docx Additionalfile5.docx Additionalfile6.docx Additionalfile7.docx Additionalfile8.docx Additionalfile9.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2378216","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":162201458,"identity":"f9fe7c41-a7e3-4968-b96e-6d3ecb50adbb","order_by":0,"name":"Chengyao Guo","email":"","orcid":"","institution":"Joint Shantou International Eye Center of Shantou University, Chinese University of Hong Kong","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chengyao","middleName":"","lastName":"Guo","suffix":""},{"id":162201460,"identity":"1d14c6ec-ed98-4466-b116-f5f54fd9f06f","order_by":1,"name":"Yuancun Li","email":"","orcid":"","institution":"Joint Shantou International Eye Center of Shantou University, Chinese University of Hong Kong","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuancun","middleName":"","lastName":"Li","suffix":""},{"id":162201461,"identity":"5b12544a-f945-41e1-b30c-dd3cfb27578c","order_by":2,"name":"Yingzi Huang","email":"","orcid":"","institution":"Joint Shantou International Eye Center of Shantou University, Chinese University of Hong Kong","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yingzi","middleName":"","lastName":"Huang","suffix":""},{"id":162201464,"identity":"952fcbba-3ecf-4644-9f84-a1fc1527e7ed","order_by":3,"name":"Liu Jing","email":"","orcid":"","institution":"Joint Shantou International Eye Center of Shantou University, Chinese University of Hong Kong","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Jing","suffix":""},{"id":162201467,"identity":"6718acdb-3a41-47ec-9f06-d914f9f15958","order_by":4,"name":"Kunliang Qiu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYDACHubGBxIMEiRpYWw2IFlLG0nqGRj4zhxsq7D4YyHH38B87OMXYrRInm1suyHZJmEscYAtebYMMVoMzjMCtTRIJG5g4DFmJsqJIC0FEn8k6knQAnQYgwSbRIIBUAvjB2K0SJ452CwB9IvhjMNsyczE6ACGWPLBzxJ/6uT525sPM/4gSs8BBgaIF4BWMPMQqwXuBSJtGQWjYBSMgpEGADUfLWADGbkQAAAAAElFTkSuQmCC","orcid":"","institution":"Joint Shantou International Eye Center of Shantou University, Chinese University of Hong Kong","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Kunliang","middleName":"","lastName":"Qiu","suffix":""},{"id":162201470,"identity":"4e47d140-2d88-4f19-9664-451ea84b743f","order_by":5,"name":"Mingzhi Zhang","email":"","orcid":"","institution":"Joint Shantou International Eye Center of Shantou University, Chinese University of Hong Kong","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingzhi","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2022-12-14 13:59:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2378216/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2378216/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":30908475,"identity":"cdc423ba-0ef5-4f2c-8ce7-90b15a5f829c","added_by":"auto","created_at":"2022-12-29 22:39:25","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":124367,"visible":true,"origin":"","legend":"\u003cp\u003eCoefficients of age, period, and cohort effects (APC) of model-based standardized global prevalence rate in blindness and vision loss. A coefficient greater than 0 indicates that it is a risk factor, while a coefficient less than 0 indicates that it is a protective factor.\u003c/p\u003e","description":"","filename":"2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2378216/v1/e3d6f26829ae695a8346857a.jpeg"},{"id":33345407,"identity":"97252bb4-f62a-4549-b8ee-dd752c99cdf4","added_by":"auto","created_at":"2023-02-23 14:00:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":721379,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2378216/v1/433035c4-4e89-4bd5-8206-ad5025cf0a65.pdf"},{"id":30908362,"identity":"1e1fe554-4bad-461e-b516-360572ad19a4","added_by":"auto","created_at":"2022-12-29 22:31:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":100681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1 \u003c/strong\u003eGlobal prevalence of cause-specific blindness and vision loss with age.\u003c/p\u003e\n\u003cp\u003eThe overall upward trend was observed in all cause-specific prevalence with age, and the upward trend varies throughout periods.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2378216/v1/f4a84551f100d97adcb31a8f.docx"},{"id":30908697,"identity":"7f1d48f4-28e4-4e66-bca2-b57059894ecb","added_by":"auto","created_at":"2022-12-29 22:55:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":106075,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2 \u003c/strong\u003eGlobal prevalence of cause-specific blindness and vision loss with age.\u003c/p\u003e\n\u003cp\u003eThe overall upward trend was observed in all cause-specific prevalence with age, and the upward trend varies throughout cohorts.\u003c/p\u003e","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-2378216/v1/38dbcac579b7adcd6f2892a1.docx"},{"id":30908748,"identity":"01564b05-5aeb-4e59-b13e-2c295dc24438","added_by":"auto","created_at":"2022-12-29 23:03:25","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":117369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 3 \u003c/strong\u003eGlobal prevalence of cause-specific blindness and vision loss with cohort.\u003c/p\u003e\n\u003cp\u003eThe overall downward trend was observed in all cause-specific prevalence within the cohort, and the downward trend varies throughout periods.\u003c/p\u003e","description":"","filename":"Additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-2378216/v1/23b87be575f43e9502481cdd.docx"},{"id":30908573,"identity":"1bfbff22-191e-4bc8-a2fc-006b79646c14","added_by":"auto","created_at":"2022-12-29 22:47:25","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18450,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-2378216/v1/c5012d6ae31441f750a6fb42.docx"},{"id":30908480,"identity":"9fc3ff3c-f11f-45ac-8bda-08ca9a4b6964","added_by":"auto","created_at":"2022-12-29 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22:31:25","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":15618,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile7.docx","url":"https://assets-eu.researchsquare.com/files/rs-2378216/v1/42570cc33aed224a4edd649a.docx"},{"id":30908370,"identity":"a6169d8c-978d-4f08-9c2a-4b2cae96ad92","added_by":"auto","created_at":"2022-12-29 22:31:25","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":15579,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile8.docx","url":"https://assets-eu.researchsquare.com/files/rs-2378216/v1/5ba1ac0c7c4a1926e8c1c59c.docx"},{"id":30908481,"identity":"65e80443-7344-4ee3-85b4-a1c9374f7cec","added_by":"auto","created_at":"2022-12-29 22:39:25","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":15456,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile9.docx","url":"https://assets-eu.researchsquare.com/files/rs-2378216/v1/02a3f909039dd0e846b79006.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age-Period-Cohort Analysis of Global Prevalence of Blindness and Vision Loss: Findings From The Global Burden of Disease Study 2019","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBlindness and vision impairment (BVI) is considered to be major global health problems, which is the leading causes of disability among the population aged 65\u0026thinsp;+\u0026thinsp;years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The blindness is defined as presenting best-corrected visual acuity (BCVA)\u0026thinsp;\u0026lt;\u0026thinsp;3/60 in the better eye and the moderate to severe visual impairment is defined as presenting BCVA\u0026thinsp;\u0026lt;\u0026thinsp;6/18 but \u0026ge;\u0026thinsp;3/60 in the better eye by the World Health Organization (WHO) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Individuals presenting with vision loss may be involved in age-related macular degeneration (AMD), cataract, glaucoma, uncorrected refractive error, or other ocular diseases. Since most BVI cases are caused by age-related eye diseases, the number of individuals with BVI is speculated to increase along with the aging of the global population [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For policy-making and decreasing socioeconomic burden, it is very important to know the trend of prevalence of BVI among the world population.\u003c/p\u003e \u003cp\u003eThe age-period-cohort (APC) model is a useful tool in understanding the secular trend in disease prevalence. The APC model is a three-factor multiplicative model, and each aspect of the APC model has a unique effect on the trend in disease prevalence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The age effect has a relationship with the outcome of time, representing changes in prevalence because of aging processes. The period effect can reflect the burden of disability, morbidity, and mortality at a given time among the whole population. And the cohort effect is associated with changes across groups with the same birth year, reflecting how population health is changing over time [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These effects can provide epidemiologists and governments with important suggestions on identifying population health determinants and policy planning. Overall, the APC model can eliminate the impact of covariates to avoid substantial bias and provide a complete trend of population health [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have already investigated the prevalence and disease burden of BVI in the world population [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, it is unclear how the prevalence would change through age, period, or a cohort effect which may indicate the role of environmental and social factors. For this purpose, we performed the APC model to investigate the global trend of changes in BVI to provide new insights into the trend of the disease prevalence by evaluating the unique effects of age, period, and cohort.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThe prevalence data of BVI was downloaded from the Global Burden of Disease Study 2019 (GBD 2019) through the Global Health Data Exchange (GHDx) query tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ghdx.healthdata.org/gbd-results-tool\u003c/span\u003e\u003cspan address=\"http://ghdx.healthdata.org/gbd-results-tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The data includes yearly case numbers and the prevalence rate of BVI due to AMD, cataract, glaucoma, refractive disorders, near-vision loss, and other vision loss of different regions from 1990 to 2019.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTemporal trend analysis\u003c/h3\u003e\n\u003cp\u003eThe estimated annual percentage change (EAPC) was used to analyze the temporal trends of age-standardized prevalence rates (ASPR) of BVI. We assumed that the natural logarithm of ASPR has a linear relationship with time, which is \u003cem\u003eln (ASR) = α\u0026thinsp;+\u0026thinsp;β*year\u0026thinsp;+\u0026thinsp;ε\u003c/em\u003e, where α represents intercept and ε represents error term. The coefficient β is the slope of the fitted line, with positive β indicating an increasing trend of ASPR along with the year, and vice versa. EAPC is the exponential function of β with the natural base of \u003cem\u003ee\u003c/em\u003e, where \u003cem\u003eEAPC\u0026thinsp;=\u0026thinsp;100 \u0026times; (exp(β)-1)\u003c/em\u003e. We applied this algorithm to multiple time series and sequentially calculated EAPC for global, regional (socio-demographic index (SDI) and WHO regions), and national ASPR for BVI. For comparison, all 204 countries or territories were sorted according to EAPC from small to large. The analysis was performed by R software (R Foundation for Statistical Computing, Vienna, Austria, version 4.1.0).\u003c/p\u003e\n\u003ch3\u003eAge-period-cohort analysis\u003c/h3\u003e\n\u003cp\u003eThe APC model was used to assess the age, period, and cohort effects separately on prevalence rate of BVI and its causes. The calculation formula of the APC model is as follow: ln(Y\u003csub\u003eapc\u003c/sub\u003e) = \u0026micro;\u0026thinsp;+\u0026thinsp;α*age\u0026thinsp;+\u0026thinsp;β*period\u0026thinsp;+\u0026thinsp;γ*cohort\u0026thinsp;+\u0026thinsp;ε, where ln(Y\u003csub\u003eabc\u003c/sub\u003e) represents the natural logarithm of the prevalence rate of BVI; \u0026micro;, α, β, γ, and ε represents intercept, coefficient of age, coefficient of period, coefficient of cohort, and random effect, respectively. For \u003cem\u003ei\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e age group (or period, cohort), α\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the independent effect of \u003cem\u003ei\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e age group that deviated from the overall age groups, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{1}^{i}{{\\alpha }}_{i}=0\\)\u003c/span\u003e\u003c/span\u003e, and exp (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\alpha }}_{i}\\)\u003c/span\u003e\u003c/span\u003e) is the Relative risk (RR) of \u003cem\u003ei\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e age group. Ordinary least squares cannot obtain a unique and unbiased estimate of the coefficients due to the perfect collinearity of the three variables (cohort\u0026thinsp;=\u0026thinsp;period - age), which is known as the identification conundrum of the APC model. To solve this problem, the intrinsic estimator (IE) method [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] was used in the current study. Based on the estimable functions and the singular value decomposition of matrices, the IE method yields unbiased, robust, and unique age, period, and cohort coefficient estimates.\u003c/p\u003e \u003cp\u003ePrior to APC analysis, the prevalence and population data were appropriately divided into successive 10-year age groups (10\u0026ndash;19, ..., 90\u0026ndash;99 years), and correspondingly consecutive 10-year periods (1990\u0026ndash;1999,2000\u0026ndash;2009, 2010\u0026ndash;2019) and cohorts from 1900 to 2019 (1900\u0026ndash;1909, ..., 2010\u0026ndash;2019; calculated according to the formula: cohort\u0026thinsp;=\u0026thinsp;period - age). We used \u0026ldquo;apc_ie\u0026rdquo; instruction in STATA version 16.0 software (Stata Corp., College Station, TX, USA) to conduct the APC analysis. The dependent variable (cases number) was specified as Poisson distribution, the logarithmic function was assigned as the link function, and the population effect was adjusted by setting the offset option. Age, period, and cohort effects were assigned as independent variables. The coefficients of the age, period and cohort effects were converted into the exponential value which denoted the RR of prevalence in a specific age, period, and cohort relative to each time interval divided. A two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analysis of BVI prevalence\u003c/h2\u003e \u003cp\u003eThe prevalence number and ASPR of BVI in 1990 and 2019 by economic level, causes, and WHO region stratification were presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The global prevalence number of all causes was 353.2\u0026nbsp;million in 1990 and increased to 713.9\u0026nbsp;million in 2019. Over the past 30 years, a total of 360.7\u0026nbsp;million new cases were confirmed. Among them, there were 199.5\u0026nbsp;million male cases and 161.4\u0026nbsp;million female cases, respectively. ASPR of all causes was 8427.7 per 100,000 population in 1990 and 8686.9 per 100,000 population in 2019, respectively. The EAPC of ASPR of BVI was \u0026minus;\u0026thinsp;0.14% from 1990 to 2019, with a 95% confidence interval (95%CI) of -7.49\u0026ndash;7.8%, which means that the EAPC was not significantly different from 0 at α\u0026thinsp;=\u0026thinsp;0.05 level (compared with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the study period, the female-specific burden of blindness and vision impairment was higher than males, no matter from the perspective of prevalence number or ASPR. However, both sexes showed decreasing trend during the past 30 years, the EAPC was \u0026minus;\u0026thinsp;10.24% and \u0026minus;\u0026thinsp;6.97% for females and males, respectively. Near vision loss, refraction disorders, and cataract were among the top 3 causes of blindness and visual loss, and all of them showed decreasing trend in terms of EAPC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence number and age-standardized rates of blindness and vision loss t in 1900 and 2019, and estimated trends from 1990 to 2019.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1990\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1990\u0026ndash;2019\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrevalence Number\u003c/p\u003e \u003cp\u003eNo.\u0026times;10\u003csup\u003e6\u003c/sup\u003e (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASPR per 100,000\u003c/p\u003e \u003cp\u003e No. (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrevalence Number\u003c/p\u003e \u003cp\u003eNo.\u0026times;10\u003csup\u003e6\u003c/sup\u003e (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eASPR per 100,000\u003c/p\u003e \u003cp\u003e No. (95% UI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEAPC\u003c/p\u003e \u003cp\u003eNo. \u0026times;100%\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e353.20\u003c/p\u003e \u003cp\u003e(298.70\u0026ndash;414.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8427.70\u003c/p\u003e \u003cp\u003e(7073.20-9860.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e713.90\u003c/p\u003e \u003cp\u003e(593.20-841.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8686.90\u003c/p\u003e \u003cp\u003e(7270.50-10218.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003cp\u003e(-7.49-7.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.20\u003c/p\u003e \u003cp\u003e(136.60-191.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8178.80\u003c/p\u003e \u003cp\u003e(6878.40-9606.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e323.60\u003c/p\u003e \u003cp\u003e(268.90-383.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8246.10\u003c/p\u003e \u003cp\u003e(6907.10-9727.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-6.97\u003c/p\u003e \u003cp\u003e(-10.47\u0026ndash;3.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190.90\u003c/p\u003e \u003cp\u003e(161.40-222.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8665.30\u003c/p\u003e \u003cp\u003e(7287.90-10114.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e390.40\u003c/p\u003e \u003cp\u003e(325.00-458.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9095.60\u003c/p\u003e \u003cp\u003e(7606.50-10678.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-10.24\u003c/p\u003e \u003cp\u003e(-14.16\u0026ndash;6.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocio-demograph\u003c/b\u003eic \u003cb\u003eindex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow SDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.40\u003c/p\u003e \u003cp\u003e(30.50\u0026ndash;42.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13631.40\u003c/p\u003e \u003cp\u003e(11426.10-15952.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.20\u003c/p\u003e \u003cp\u003e(65.00-90.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13125.50\u003c/p\u003e \u003cp\u003e(10975.80-15371.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e(-6.46-8.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow-middle SDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.80\u003c/p\u003e \u003cp\u003e(76.9-106.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13573.10\u003c/p\u003e \u003cp\u003e(11465.90-15791.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e179.80\u003c/p\u003e \u003cp\u003e(149.20-213.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12496.10\u003c/p\u003e \u003cp\u003e(10452.20-14727.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003cp\u003e(-3.92-11.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMiddle SDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111.80\u003c/p\u003e \u003cp\u003e(93.80\u0026ndash;132.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10117.10\u003c/p\u003e \u003cp\u003e(8442.20-11918.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e247.00\u003c/p\u003e \u003cp\u003e(204.30-293.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9822.80\u003c/p\u003e \u003cp\u003e(8220.10-11612.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003cp\u003e(-2.16-11.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh-middle SDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.70\u003c/p\u003e \u003cp\u003e(70.90-102.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7997.30\u003c/p\u003e \u003cp\u003e(6669.00-9495.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162.50\u003c/p\u003e \u003cp\u003e(133.20-194.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8314.60\u003c/p\u003e \u003cp\u003e(6897.60-9882.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003cp\u003e(-2.81-12.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh SDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.30\u003c/p\u003e \u003cp\u003e(24.90\u0026ndash;32.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2967.70\u003c/p\u003e \u003cp\u003e(2628.30-3339.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.10\u003c/p\u003e \u003cp\u003e(41.10\u0026ndash;53.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3080.60\u003c/p\u003e \u003cp\u003e(2717.30-3478.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003cp\u003e(-5.40-9.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCauses\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAMD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003cp\u003e(3.00-4.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.80\u003c/p\u003e \u003cp\u003e(83.70-114.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003cp\u003e(6.50\u0026ndash;9.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.80\u003c/p\u003e \u003cp\u003e(81.30-113.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003cp\u003e(-7.48-7.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCataract\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.30\u003c/p\u003e \u003cp\u003e(37.70\u0026ndash;47.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1150.60\u003c/p\u003e \u003cp\u003e(1027.30-1287.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.00\u003c/p\u003e \u003cp\u003e(85.40-109.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1207.90\u003c/p\u003e \u003cp\u003e(1065.00-1361.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003cp\u003e(-7.51-7.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlaucoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003cp\u003e(3.30\u0026ndash;4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.90\u003c/p\u003e \u003cp\u003e(94.80-130.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003cp\u003e(6.30\u0026ndash;8.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.70\u003c/p\u003e \u003cp\u003e(80.40-110.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-10.09\u003c/p\u003e \u003cp\u003e(-13.99\u0026ndash;6.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRefractive disorders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.60\u003c/p\u003e \u003cp\u003e(87.50-108.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2083.70\u003c/p\u003e \u003cp\u003e(1870.50-2309.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e157.40\u003c/p\u003e \u003cp\u003e(140.90-174.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1959.60\u003c/p\u003e \u003cp\u003e(1751.00-2180.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-10.19\u003c/p\u003e \u003cp\u003e(-14.1\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNear vision loss\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227.20\u003c/p\u003e \u003cp\u003e(164.20-298.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5613.30\u003c/p\u003e \u003cp\u003e(4081.10-7335.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e493.20\u003c/p\u003e \u003cp\u003e(358.90-645.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5937.80\u003c/p\u003e \u003cp\u003e(4336.40-7772.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-10.14\u003c/p\u003e \u003cp\u003e(-14.05\u0026ndash;6.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther vision loss\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.10\u003c/p\u003e \u003cp\u003e(19.10\u0026ndash;23.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e518.40\u003c/p\u003e \u003cp\u003e(466.30-578.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.40\u003c/p\u003e \u003cp\u003e(34.40\u0026ndash;43.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e473.90\u003c/p\u003e \u003cp\u003e(425.70-528.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-10.17\u003c/p\u003e \u003cp\u003e(-14.08\u0026ndash;6.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHO regions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAfrican Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.60\u003c/p\u003e \u003cp\u003e(24.20\u0026ndash;35.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12015.80\u003c/p\u003e \u003cp\u003e(9845.90-14398.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.20\u003c/p\u003e \u003cp\u003e(53.40\u0026ndash;78.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11752.20\u003c/p\u003e \u003cp\u003e(9627.40-14052.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003cp\u003e(-2.79-12.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEastern Mediterranean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.90\u003c/p\u003e \u003cp\u003e(18.60\u0026ndash;23.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9850.50\u003c/p\u003e \u003cp\u003e(8699.70-11061.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.40\u003c/p\u003e \u003cp\u003e(36.50\u0026ndash;46.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8668.00\u003c/p\u003e \u003cp\u003e(7621.70\u0026ndash;9762.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003cp\u003e(-7.93-5.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEuropean Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.50\u003c/p\u003e \u003cp\u003e(49.40\u0026ndash;68.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5722.90\u003c/p\u003e \u003cp\u003e(4873.30-6661.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.30\u003c/p\u003e \u003cp\u003e(66.60\u0026ndash;93.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5555.70\u003c/p\u003e \u003cp\u003e(4722.00-6501.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003cp\u003e(-2.69-13.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion of the Americas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.50\u003c/p\u003e \u003cp\u003e(26.80\u0026ndash;34.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4750.90\u003c/p\u003e \u003cp\u003e(4146.20\u0026ndash;5418.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.00\u003c/p\u003e \u003cp\u003e(52.60\u0026ndash;70.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5104.50\u003c/p\u003e \u003cp\u003e(4427.70-5852.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003cp\u003e(-7.34-6.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSouth-East Asia Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.90\u003c/p\u003e \u003cp\u003e(98.90-134.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14722.20\u003c/p\u003e \u003cp\u003e(12588.10-16999.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e245.50\u003c/p\u003e \u003cp\u003e(205.70-290.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13616.90\u003c/p\u003e \u003cp\u003e(11511.90-15908.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003cp\u003e(-3.94-10.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWestern Pacific Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.60\u003c/p\u003e \u003cp\u003e(77.90-117.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7974.30\u003c/p\u003e \u003cp\u003e(6460.70\u0026ndash;9666.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e219.00\u003c/p\u003e \u003cp\u003e(176.10-266.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8213.10\u003c/p\u003e \u003cp\u003e(6713.60-9886.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003cp\u003e(-7.25-6.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUI: uncertainty interval, also known as credibility interval, is an interval estimate of the parameter by bayesian method.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCI: confidence interval, the difference between UI with CI only philosophical rather than mathematical.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eASPR: age-standardized prevalence rate per 100,000 population.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eEAPC: estimated annual percentage change.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eAMD: age-related macular degeneration.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;2. Intrinsic estimates of age, period, and cohort effect for the global prevalence rate of blindness and vision loss.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCoefficient (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRelative risk (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eZ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eP \u0026gt; |Z|\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.424 (-2.424, -2.424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089 (0.089, 0.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.8\u0026times;10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.319 (-1.319, -1.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.267 (0.267, 0.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4424.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026thinsp;~\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.239 (-1.239, -1.238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.290 (0.290, 0.290)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5628.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026thinsp;~\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.280 (-1.280, -1.279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.278 (0.278, 0.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6056.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026thinsp;~\u0026thinsp;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.751 (-0.752, -0.751)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.472 (0.472, 0.472)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3975.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026thinsp;~\u0026thinsp;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.103 (0.103, 0.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.109 (1.109, 1.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e639.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026thinsp;~\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.689 (0.689, 0.689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.991 (1.991, 1.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4913.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.990 (0.989, 0.990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.690 (2.689, 2.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7660.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u0026thinsp;~\u0026thinsp;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.052 (1.052, 1.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.864 (2.864, 2.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7735.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u0026thinsp;~\u0026thinsp;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.965 (0.965, 0.965)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.625 (2.624, 2.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5647.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u0026thinsp;~\u0026thinsp;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.790 (0.789, 0.790)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.203 (2.202, 2.204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2343.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePeriod\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026thinsp;~\u0026thinsp;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.198 (-0.198, -0.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.820 (0.820, 0.820)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3029.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026thinsp;~\u0026thinsp;2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003 (0.003, 0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.003 (1.003, 1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026thinsp;~\u0026thinsp;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.195 (0.195, 0.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.215 (1.215, 1.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3202.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cb\u003eCohort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1900\u0026thinsp;~\u0026thinsp;1909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.092 (1.091, 1.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.981 (2.977, 2.985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1631.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1910\u0026thinsp;~\u0026thinsp;1919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873 (0.872, 0.873)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.394 (2.392, 2.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2989.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1920\u0026thinsp;~\u0026thinsp;1929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.678 (0.678, 0.678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.970 (1.969, 1.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3264.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1930\u0026thinsp;~\u0026thinsp;1939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.505 (0.504, 0.505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.657 (1.656, 1.657)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2821.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1940\u0026thinsp;~\u0026thinsp;1949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.313 (0.313, 0.314)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.368 (1.368, 1.369)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1857.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1950\u0026thinsp;~\u0026thinsp;1959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.101 (0.100, 0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.106 (1.105, 1.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e587.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1960\u0026thinsp;~\u0026thinsp;1969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.089 (-0.090, -0.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.915 (0.914, 0.915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-483.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1970\u0026thinsp;~\u0026thinsp;1979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.272 (-0.273, -0.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.762 (0.761, 0.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1350.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980\u0026thinsp;~\u0026thinsp;1989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.463 (-0.464, -0.463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.629 (0.629, 0.629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2232.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990\u0026thinsp;~\u0026thinsp;1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.684 (-0.684, -0.683)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.505 (0.505, 0.505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3285.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026thinsp;~\u0026thinsp;2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.909 (-0.909, -0.908)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.403 (0.403, 0.403)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3487.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010\u0026thinsp;~\u0026thinsp;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.145 (-1.146, -1.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318 (0.318, 0.319)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2410.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138.0355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3516.6970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAIC: Akaike information criterion; BIC: Bayesian information criterion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs for the SDI stratification, ASPR of BVI decreases in a graded manner as SDI levels increase. The specific ASDR numbers are 13125.5 (10975.8-15371.3), 12496.1 (10452.2-14727), 9822.8 (8220.1-11612.6), 8314.6 (6897.6-9882.5), and 3080.6 (2717.3-3478.5) per 100,000 for Low SDI, Low-middle SDI, Middle SDI, High-Middle SDI, and High SDI region, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among all six WHO regions, ASDR of BVI among South-East Asia Region (13616.9, 95%UI: 11511.9-15908.5) was the highest in 2019, followed by the African Region (11752.2, 95%UI: 9627.4-14052.0), Eastern Mediterranean region (8668.0, 95%UI: 7621.7\u0026ndash;9762.0), Western Pacific Region (8213.1, 95%UI: 6713.6-9886.2), European Region (5555.7, 95%UI: 4722.0-6501.2), and Region of the Americas (5104.5, 95%UI: 4427.7-5852.3). EAPC was the highest in Middle SDI region and European Region, which is 4.6% (-2.16 to 11.83) and 5.21 (-2.69 to 13.75), respectively. But no significant upward or downward trend was found in ASDR of any SDI or WHO region in terms of 95% confidence interval of EAPC.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndependent effect of age, period, and cohort on the prevalence of BVI\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the impact of age, period, and cohort effects on the global prevalence rate of BVI. The prevalence rate of BVI increased with age throughout the periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB); the prevalence rate decreased as the cohorts declined, but the downward trend varied in different periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). For those born between 1910 and 1999, the same cohort had a higher prevalence in the later periods than in the earlier ones.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe independent effect of each variable on the prevalence rate of global BVI was further investigated by the APC model with intrinsic estimators. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the results of the APC model based on the exponential value, and \u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e shows the detailed value of the independent effect trends of age, period, and cohort. In general, the age and period effect showed an upward trend while the cohort effect showed a downward trend from the perspective of coefficients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which indicated that the advancing age experienced higher vision loss morbidity, while later cohorts experienced lower morbidity. RR reflects the deviation of relative risk of a particular group from the average level (1.0 for age, period, and cohort). After adjusting for covariates, the RR of BVI increased slightly from 0.267 in the 1\u0026ndash;9 age group to the 0.278 in the 20\u0026ndash;29 age group, and then increased sharply to the peak of 2.864 in the 70\u0026ndash;79 age group. However, from 70\u0026ndash;79 to 90\u0026ndash;99 years, the RR stopped increasing and exhibited a slight decrease to 2.203 in the 90\u0026ndash;99 age group. As for the period and cohort effects, the RR of BVI increased from 0.820 in the 1990\u0026ndash;1999 period to 1.215 in the 2010\u0026ndash;2019 period, while it decreased from 2.981 in the 1900\u0026ndash;1909 cohort group to 0.318 in the 2010\u0026ndash;2019 cohort group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eImpact of age, period, cohort on the global prevalence rates of Cause-Specific BVI\u003c/h3\u003e\n\u003cp\u003eThe age, period, and cohort effects of the prevalence rates of AMD, cataract, glaucoma, near-vision loss, refractive disorders, and other vision loss are shown in \u003cb\u003eAdditional\u003c/b\u003e f\u003cb\u003eile 1\u0026ndash;3\u003c/b\u003e. Although these pathogeneses differ in global prevalence, changes in each cause are consistent in the overall upward or downward trend of the age, period, and cohort effects. General upward trend of coefficients of age and period of all six vision impairment causes was observed, while general downward trend of coefficients of cohort was seen, with all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.010. More detailed number of coefficients, RR of the three variables were available in \u003cb\u003eAdditional\u003c/b\u003e f\u003cb\u003eile 4\u0026ndash;9\u003c/b\u003e. As cataract for example, the relative prevalence risk in age group 20\u0026ndash;29 is 0.057 (95% CI: 0.057\u0026ndash;0.057), and steadily improves to 4.044 (95% CI: 4.039\u0026ndash;4.048) in age group 90\u0026ndash;99. The relative prevalence risk of cataract increases from 0.698 (95% CI: 0.698\u0026ndash;0.698) of period 1990\u0026ndash;1999 to 1.41 (95% CI: 1.409\u0026ndash;1.410) of period 2010\u0026ndash;2019, while relative prevalence risk decreases from 4.627 (95% CI: 4.618\u0026ndash;4.637) of cohort 1900\u0026ndash;1909 to 0.168 (95% CI: 0.167\u0026ndash;0.169) of cohort 1990\u0026ndash;1999.\u003c/p\u003e\n\u003ch3\u003eModel accuracy analysis\u003c/h3\u003e\n\u003cp\u003eThe Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select a suitable APC model within the data collection in the present study, which aims to minimize bias and evaluate the goodness of the established model [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. When selecting the optimal model from a group of candidate models, the model with the least AIC and BIC is selected. The AIC has a value of 138.04 and the BIC has a value of 3516.70 in the current study (the smallest of all model built), suggesting that our APC model is quite suitable.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study is the first investigation to estimate the secular trends of prevalence in BVI based on a specific model using age, period, and cohort effects independently. We found that cause-specific and region-specific prevalence data of BVI are continuously increasing from 1990 to 2019. Concerning the age effect, we found that the global prevalence rate of BVI rapidly increased in people older than 20 years but gradually decreased after 80 years old. Regarding the period effect, the whole visual impairment rates rose at a constant speed between 1990 and 2019. As for the birth effect, we demonstrated that the prevalence rate of BVI continually declined across cohorts.\u003c/p\u003e \u003cp\u003ePrior studies have investigated the overall trends in vision impairment, revealing that the prevalence of BVI is continuously increasing worldwide, with an estimation of 43.3\u0026nbsp;million people suffering from blindness and 295\u0026nbsp;million people suffering from moderate or severe vision loss in 2020 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Cataract, refractive disorders and near vision loss are the most common eye diseases leading to vision loss and blindness over the past three decades [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Besides, AMD and glaucoma are the other vision-threatening conditions. Consistent with recent research [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], an increasing trend in prevalence rates due to BVI was observed in the current study. Fortunately, during the period from 1990 to 2019, the EAPC of all causes for vision loss is negative, except the AMD and cataract, which may be attributed to the effective health policies and healthcare resources investments in recent years [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding the age effects independently, children and adolescents have much lower visually impaired rates within the study period, suggesting that the BVI is mainly due to age-related eye diseases, such as AMD, cataract, glaucoma, and near vision loss. Prevalence rates rise sharply in people over 30 years of age, peaking between 70 to 79 years, and then decline slowly. Paying more attention to these populations to detect and treat them at an early stage may prevent and delay severe visual impairment, and then reduce the burden on society. In addition, the age effects simply indicate that in each study period, older adults are more likely to be diagnosed with visually impaired eye diseases than the younger population. Due to the age, the effect is a comprehensive estimation within the study period and will not change much in the short term, the results still hold true for all age groups over the next decade, 2020\u0026ndash;2029.\u003c/p\u003e \u003cp\u003eWhile controlling for age and cohort effects, we have found that during the time period 1990 to 2019, the average prevalence rate for BVI is about zero, but the rate numbers keep increasing. The increased number of visually impaired cases might be due to the improvements in awareness and diagnosis of eye health in recent years. The high-resolution optical biometry techniques used in clinical practice, such as the optical coherence tomography (OCT) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], Zeiss IOL Master 700, and OA 2000 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], can improve the detection rate of some eye diseases, including cataract, glaucoma or AMD. Besides, as the global population grows and ages, it is not surprising that the cases of age-related eye diseases are on the rise [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Aiming to eliminate avoidable blindness and provide eye healthcare services, the program World Vision Day initiated in 2000. All of these can raise people\u0026rsquo;s awareness of eye care, and improve the rate of eye disease-related visits as well as detection.\u003c/p\u003e \u003cp\u003eConcerning the cohort effects, the prevalence rates have a continuous decreased trend as the cohort year increases starting with those born in 1990. Those born in earlier years have a higher prevalence of vision impairment than others. Such changes that occur over time may result from changes in diet and increases in physical activity and time spent outdoors. Besides, global economic growth improved living conditions, and investment in healthcare resources in recent years may also explain the decreased trend [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This study successfully identified age, period, and cohort effects of prevalence rates due to BVI, suggesting that healthcare service plays a vital role in the process of eliminating avoidable blindness and improving life quality for the future, especially for the older population.\u003c/p\u003e \u003cp\u003eIn addition, the continuously increased prevalence rate of uncorrected refractive errors cannot be negligible. Similar to the rapidly increasing myopia prevalence reported in previous studies [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the present study showed that the prevalence number due to refractive disorders nearly doubled from 97.6\u0026nbsp;million in 1990 to 157.4\u0026nbsp;million in 2019. Recently, refractive errors have become a major social public health concern and have brought a great economic burden to society [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Both lifestyle and genetic factors play an important role in the etiology of refractive errors. Myopia-associated genetic variants have facilitated the identification of individuals with a higher risk of developing myopia in population screening [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Other than the genetic factors, maybe we should pay more attention to the environmental factors, such as lacking outdoor activity or excessive near work for long periods. Because those factors play an important role in the progression of myopia and can be adjusted in daily life. Timely visual screening and correction of refractive errors can effectively reduce the occurrence or development of avoidable vision loss.\u003c/p\u003e \u003cp\u003eThere are two main limitations need to be noticed for the current study. Firstly, the data used were entirely from the GBD database, and therefore, some possible biases such as data collection bias and assessment bias cannot be avoided. However, we tried to standardize the data before analysis to reduce the bias or errors. Secondly, this is a descriptive study and the trend results are inferred using an updated statistical model based on large-scale population information. The APC model did not adjust some confounding factors, such as lifestyle or education levels, from the raw datasets. However, the effects of some systematic patterns over the regression model have been adjusted separately in the APC method.\u003c/p\u003e \u003cp\u003eIn summary, this study revealed significant age, period, and cohort effects on prevalence rates for BVI over the past three decades. Age is a risk factor for vision impairment, and more attention should be paid to eye diseases in the elderly. The cohort effect on BVI prevalence reflects the influence of environmental factors (e.g. changes in diet and time spent outdoors) occurring during the early life. The period effect reflects social factors such as the improvement of awareness and detection rate of eye diseases. Assuming that the prevalence rate remains constant, we expect that in the coming decades, as the aging population increases, visually impaired individuals will have greater demand for services.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by Human Medical Ethics Committee of the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong (approval number:\u0026nbsp;JSIEC20220501), which is in accordance with the tenets of the Declaration of Helsinki. Participants gave informed consent to participant in the study before taking part.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read this manuscript and consent to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no competing interests in this study. No financial or proprietary interest in any material or method is mentioned.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding supports this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuo CY was responsible for writing the manuscript, original draft preparation, and\u0026nbsp;conducting the search, and investigation.\u0026nbsp;Li YC was responsible for conceptualization, conducting the search, methodology, visualization, data curation, and writing the manuscript. Qiu KL was responsible for reviewing the manuscript. Huang YZ and Jing L were responsible for conducting the search, and investigation. Zhang MZ was responsible for the supervision, project administration, and reviewing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their deepest gratitude to the Global Burden of Disease Study 2019 (GBD 2019) database.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal regional. and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990\u0026ndash;2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1545\u0026ndash;602.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. International classification of impairments, disabilities, and handicaps: a manual of classification relating to the consequences of disease. 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Psychol Methods. 2012;17(2):228\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrends 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\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown DM, Kaiser PK, Michels M, Soubrane G, Heier JS, Kim RY, et al. Ranibizumab versus verteporfin for neovascular age-related macular degeneration. N Engl J Med. 2006;355(14):1432\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBourne R, Dineen B, Jadoon Z, Lee PS, Khan A, Johnson GJ, et al. Outcomes of cataract surgery in Pakistan: results from The Pakistan National Blindness and Visual Impairment Survey. Br J Ophthalmol. 2007;91(4):420\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinakaran N, de Carvalho ER, Petzold A, Wong SH. 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Myopia prevalence in Denmark - a review of 140 years of myopia research. Acta Ophthalmol. 2021;99(2):118\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Z, Jin G, Li Z, Liao Y, Gao X, Zhang Y, et al. Global disease burden of uncorrected refractive error among adolescents from 1990 to 2019. BMC Public Health. 2021;21(1):1975.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Q, Verhoeven VJ, Wojciechowski R, Barathi VA, Hysi PG, Guggenheim JA, et al. Meta-analysis of gene-environment-wide association scans accounting for education level identifies additional loci for refractive error. Nat Commun. 2016;7:11008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTedja MS, Wojciechowski R, Hysi PG, Eriksson N, Furlotte NA, Verhoeven V, et al. Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error. Nat Genet. 2018;50(6):834\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHysi PG, Choquet H, Khawaja AP, Wojciechowski R, Tedja MS, Yin J, et al. Meta-analysis of 542,934 subjects of European ancestry identifies new genes and mechanisms predisposing to refractive error and myopia. Nat Genet. 2020;52(4):401\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blindness and vision loss, Age-Period-Cohort Model, Global Burden of Disease 2019 study (GBD 2019)","lastPublishedDoi":"10.21203/rs.3.rs-2378216/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2378216/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo quantify age, period, and cohort effect on the global secular trend of prevalence of blindness and vision impairment (BVI) based on the age-period-cohort (APC) model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData on global BVI were extracted from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 database. Annual percentage change of age-standardized prevalence rate (ASPR) of BVI was estimated by assuming a linear relationship between natural logarithm of ASPR of disease with time. The prevalence of BVI was evaluated from age, period, and cohort effects based on the APC model with intrinsic estimator.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGlobal prevalence number of BVI was 353.2\u0026nbsp;million in 1990 and increased to 713.9\u0026nbsp;million in 2019, but with an ASPR declined at a speed of -0.14% (95% CI: -7.49\u0026ndash;7.8%) per year from 1990 to 2019. The APC model showed that the prevalence of BVI increased with age and period but decreased with cohorts. Changes in each cause (age-related macular degeneration, cataract, glaucoma, refractive disorders, near-vision loss, and other vision loss) are consistent in the overall upward or downward trend of the age, period, and cohort effects.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGlobal prevalence of BVI has significant age, period and cohort effects. The risk of vision impairment increases with age and period, however, it decreases with the cohort. Cost-effective prevention and control should be implemented more in the older population at high risk.\u003c/p\u003e","manuscriptTitle":"Age-Period-Cohort Analysis of Global Prevalence of Blindness and Vision Loss: Findings From The Global Burden of Disease Study 2019","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-29 22:31:20","doi":"10.21203/rs.3.rs-2378216/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62bfd473-6b7d-447b-aa07-e228cfb96eef","owner":[],"postedDate":"December 29th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-11T09:14:22+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-29 22:31:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2378216","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2378216","identity":"rs-2378216","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}