August 2017
Volume 58, Issue 10
Open Access
Clinical and Epidemiologic Research  |   August 2017
Variations and Trends in Health Burden of Visual Impairment Due to Cataract: A Global Analysis
Author Affiliations & Notes
  • Miao He
    Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-sen University, Guangzhou, People's Republic of China
  • Wei Wang
    Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-sen University, Guangzhou, People's Republic of China
  • Wenyong Huang
    Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-sen University, Guangzhou, People's Republic of China
  • Correspondence: Wenyong Huang, Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-sen University, 54S. Xianlie Road, Guangzhou, China 510060; andyhwyz@aliyun.com
  • Footnotes
     MH and WW are joint first authors.
Investigative Ophthalmology & Visual Science August 2017, Vol.58, 4299-4306. doi:10.1167/iovs.17-21459
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Miao He, Wei Wang, Wenyong Huang; Variations and Trends in Health Burden of Visual Impairment Due to Cataract: A Global Analysis. Invest. Ophthalmol. Vis. Sci. 2017;58(10):4299-4306. doi: 10.1167/iovs.17-21459.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose: To evaluate the global trends in health burden of people visually impaired from cataract in terms of disability-adjusted life years (DALY) and its correlations with national levels of socioeconomic development.

Methods: Global, regional, and national DALY numbers, crude rate, and age-standardized rate of cataract vision loss by age and sex were obtained from the database of the Global Burden of Disease Study 2015. The human development index, per capita gross domestic product, and other country-level data were derived from international open databases. Regression analysis was used to assess the correlations between age-standardized DALY rate and socioeconomic variables.

Results: The global DALY numbers of cataract vision loss increased by 89.42%, from 2048.18 (95%CI [confidence interval]: 1457.60–2761.80) thousands in 1990 to 3879.74 (95% CI: 2766.07–5232.43) thousands in 2015 (P < 0.001). Females had higher DALY number 315.83 (95%CI: 237.17–394.4) and crude rate 38.29 (95%CI: 35.35–41.23) after adjusting for age and country (all P < 0.001). The age-standardized DALY rate was higher in countries with low human development index (HDI), with 91.03 (95%CI: 73.04–108.75) for low HDI, 81.67 (95%CI: 53.24–108.82) for medium HDI, 55.89 (95%CI: 36.87–69.63) for high HDI, and 17.10 (95%CI: 13.91–26.84) for very high HDI countries (P < 0.01), respectively. The national age-standardized DALY rates in 2015 were negatively associated with both HDI (R2 = 0.489, P < 0.001) and per capita gross domestic product (R2 = 0.331, P < 0.001). Stepwise multiple regression showed that HDI was significantly correlated with national age-standardized DALY rates in 2015 after adjusting for other confounding factors (P < 0.001).

Conclusions: The global health burden of vision loss due to cataract increased between 1990 and 2015 despite considerable efforts from the World Health Organization and VISION 2020 initiatives.

Cataract has caused worldwide >50% vision loss, including 33.4% blind people and 18.4% people with moderate-to-severe visual impairment.1 Globally, 10.8 million were blind and 35.1 million people were visually impaired from cataract in 2010.1,2 Along with the aging population and extended life expectancy, the number of people with cataract is expected to increase continuously. Cataract can be cured by surgery, which ranks as the most cost-effective intervention with 4500% financial return on investment.3,4 Cataract surgery leads to huge socioeconomic benefits and improvement in well-being and quality of life. Cataract remains a concern for public health, especially in low- and middle-income countries. A total of $5733 million investment was estimated to be required for eliminating blindness due to cataract between 2010 and 2020.5 
Apart from the prevalence data, the health burden of a disease is also informative for policymakers. The health burden of disease can be quantified by the disability-adjusted life years (DALY), which is defined as the sum of years of healthy life loss caused by premature death or disability. DALY reflects the gap between the actual health status and the normative situation. One DALY means 1 lost year of healthy life owing to the disease. DALY combines both the prevalence of a disease and its impact on mortality and morbidity. The advantage of DALY lies in its aggregative nature, which enables comparisons of different diseases and injuries across time and regions. The most recent estimation demonstrated that vision loss was the third largest impairment in DALY after anemia and hearing loss.6,7 Of all vision-threatening disorders, cataract was the second leading cause of disability and accounted for 3.9 million DALYs, after refraction and accommodation disorders.6 Thus, cataract remains a public health concern. 
The World Health Assembly endorsed disease burden, cataract surgical rate/coverage, and human resources as the national indicator for monitoring eye services.8 In our previous study, we evaluated the changes of cataract surgical rate in each country in the last decade and revealed a close association between cataract surgical rate and country per capita gross domestic product.9 Although it is also valuable for policy making and program planning, knowledge of the health burden of cataract was not adequate. The aim of this study was to evaluate the time trends of the global health burden of cataract vision loss and its distribution across age, sex, and national levels of socioeconomic development. 
Methods
Global Burden of Visual Impairment Caused by Cataract
The data on DALY owing to cataract vision loss were obtained from the open database of the Global Burden of Disease 2015 Study (http://ghdx.healthdata.org/gbd-results-tool; in the public domain), which provided a particular means to estimate the disease burden of 315 diseases and injuries in 196 countries/territories from 1990 to 2015.6,7,10 The DALY is defined as the sum of years of life lost and years lost due to disability resulting from cataract vision loss. The methodology of the Global Burden of Disease 2015 Study has been detailed in previous publications.6,7 In brief, the DALYs of cataract vision loss in a country were obtained in four steps. (1) The overall prevalence of presenting vision loss was modeled; visual impairment was defined in accordance with the guidance of the World Health Organization (WHO), and the cases were constrained to vision loss caused by any type of cataract with the minimum age of 20 years. (2) The prevalence of presenting vision loss caused by uncorrected refractory errors was estimated. (3) The prevalence of cataract vision loss was calculated. (4) The DALYs were obtained by the following algorithm: DALY number = (Number of deaths × Standard life expectancy at age of death in years) + (Number of prevalent cases × Disability weight).6,7 The crude DALY rate was calculated by adjusting for population size (per 100,000 population), and the age-standardized DALY rate was obtained by further adjusting for population size and structure (per 100,000 population). 
The following data were used for statistical analysis: (1) the global DALY number, crude rate, age-standardized rate in 1990, 1995, 2000, 2005, 2010, and 2015; (2) the age- and sex- specific DALY numbers and crude rate in individual countries/territories in 2015; (3) the WHO regional DALY numbers, crude rate, and age-standardized DALY rate in 2015; (4) national age-standardized DALY rate in 2015. 
Country-Level Indicators
The exposure factors were the country-level demographic, socioeconomic, and environmental indicators derived from the following well-known open databases. The open database of the World Bank (http://data.worldbank.org/; in the public domain) was used to obtain the per capita gross domestic product of individual countries in 2015 by the purchasing power parity method in constant 2011 international dollars. The United Nations Development Programme (http://hdr.undp.org/en/data; in the public domain) was used to obtain the national levels of human development index (HDI), which is a composite measure of social and economic achievement. HDI has four components: life expectancy index, mean years of schooling index, expected years of schooling index, and income index. HDI ranges from 0 to 1, with higher value indicating higher socioeconomic level, and countries were classified into four socioeconomic groups: low (HD < 0.550), medium (0.550–0.699), high (0.700–0.799), and very high (0.800 or greater) HDI. The average daily level of ambient ultraviolet radiation, average annual concentration of fine particulate matter (PM2.5), cellular phone subscribers per 100 people, age-standardized mean systolic blood pressure, mean body mass index, and percent urbanization population in a country were obtained from the WHO Global Health Observatory data repository (http://apps.who.int/gho/data/node.imr; in the public domain). The annual PM2.5 concentration is a common measure of air pollution, which is defined as the mean annual concentration of fine suspended particles < 2.5 μm in diameter in a country. 
Statistical Analysis
All statistical analyses were performed by using STATA 12.0SE (Stata Corp, College Station, TX, USA). The outcomes included the geographic, sex, and time distribution of DALY of cataract vision loss, as well as the influence of socioeconomic indicators on DALY of cataract vision loss. The Wilcoxon signed rank test was used to compare the sex differences in DALY number and crude rate for each age group. The multilevel mixed-effects linear model was used to evaluate the influence of sex on overall DALY number and crude rate. Comparisons of age-standardized DALY rate among four HDI-based countries groups were assessed by Kruskal-Wallis test, followed by Mann-Whitney–Wilcoxon test for two-group comparisons. Scatter plots were constructed to explore the relationship between national age-standardized DALY rate and country-level variables. Linear regression analysis was used to explore the influence of country-level indicators on the national age-standardized DALY rate in 2015. Multiple linear regression modeling was fitted using the stepwise backward approach with significance level set at 0.05. A P < 0.05 was considered statistically significant. 
Results
Changes of Health Burden Over the Last 25 Years
The DALY number of cataract vision loss increased by 89.42%, from 2048.18 (95%CI [confidence interval]: 1457.60–2761.80) thousands in 1990 to 3879.74 (95% CI: 2766.07–5232.43) thousands in 2015 (Fig. 1A). During the same period, the crude DALY rate increased by 36.28%, from 38.62 (95%CI: 27.49–52.08) to 52.63 (95%CI: 37.53–70.99) (Fig. 1B). After removing the influence of population size and structure, cataract burden in terms of age-standardized DALY rate increased before 2005, and then decreased to the 1990s level. The global age-standardized DALY rate in 1990 and 2015 was 60.28 (95%CI: 43.01–81.01) and 60.33 (95%CI: 43.14–81.21), respectively (Fig. 1C). 
Figure 1
 
Time trends of global health burden of cataract vision loss in the last 25 years. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates. Red dashed lines represent 95% uncertainty intervals.
Figure 1
 
Time trends of global health burden of cataract vision loss in the last 25 years. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates. Red dashed lines represent 95% uncertainty intervals.
Global Health Burden of Cataract Vision Loss by Age and Sex
Figure 2 shows the sex differences of global DALY numbers and crude rate for each age group in 2015. The two sexes showed a similar trend of global DALY by age, slowly increasing before 50 years of age and sharply increasing after 50 years of age (Fig. 2). Wilcoxon signed rank tests showed significant sex disparities in national DALY numbers and crude rate for each age group (all P < 0.01). Multilevel mixed modeling confirmed that females had higher DALY numbers, 315.83 (95%CI: 237.17–394.4), and crude rate, 38.29 (95%CI: 35.35–41.23), after adjusting for age and country. 
Figure 2
 
Global health burden of cataract vision loss by age and sex in 2015. (A) DALY numbers, (B) crude DALY rates.
Figure 2
 
Global health burden of cataract vision loss by age and sex in 2015. (A) DALY numbers, (B) crude DALY rates.
Global Health Burden of Cataract Vision Loss by WHO Regions
Figure 3 maps the distribution of health burden of cataract vision loss in 2015. As expected, the countries with largest populations had the highest DALY numbers (Fig. 3A). After controlling for population size, the DALY rate was highest in India and lowest in high-income countries (Fig. 3B). After controlling for population size and structure, India, Africa, and South American countries had the heaviest burden of cataract (Fig. 3C). Looking at the health burden in each of the WHO Regions, DALY numbers were highest in Southeast Asia, followed by the Western Pacific Region (Fig. 4A). Southeast Asia also has the highest DALY crude rate, followed by the Eastern Mediterranean Region (Fig. 4B). The age-standardized DALY rate was highest in Southeast Asia, followed by Eastern Mediterranean, Africa, Western Pacific, and America, with the lowest rate in Europe. The age-standardized DALY rates in Southeast Asia, the Eastern Mediterranean, and Africa were higher than that at the global average level (Fig. 4C). 
Figure 3
 
Global map of health burden of people visually impaired from cataract. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates.
Figure 3
 
Global map of health burden of people visually impaired from cataract. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates.
Figure 4
 
Distributions of health burden of cataract vision loss in 2015 by WHO Regions. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates. Dashed line represents global average level.
Figure 4
 
Distributions of health burden of cataract vision loss in 2015 by WHO Regions. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates. Dashed line represents global average level.
Health Burden of Cataract Vision Loss and Socioeconomic Factors
Age-standardized DALY rate differed significantly among countries with different HDI (P < 0.001) (Fig. 5A). The mean value of age-standardized DALY rate was 91.03 (95%CI: 73.04–108.75) for low HDI, 81.67 (95%CI: 53.24–108.82) for medium HDI, 55.89 (95%CI: 36.87–69.63) for high HDI, and 17.10 (95%CI: 13.91–26.84) for very high HDI countries, respectively. Linear regression analysis revealed that HDI was negatively correlated with age-standardized DALY rate (R2 = 0.489, P < 0.001; Fig. 5B). In further analysis of the four components of HDI, expected years of schooling was identified as the most influential indicator, which accounted for 55.6% of global variations in age-standardized DALY rates (Table 1). Similarly, the age-standardized DALY rate was negatively associated with per capita gross domestic product (R2 = 0.331, P < 0.001). 
Figure 5
 
Relationship between health burden of cataract vision loss and national levels of socioeconomic development. (A) Health burden differed among different HDI categories. (B) Health burden was inversely associated with HDI. The blue line represents fitted line.
Figure 5
 
Relationship between health burden of cataract vision loss and national levels of socioeconomic development. (A) Health burden differed among different HDI categories. (B) Health burden was inversely associated with HDI. The blue line represents fitted line.
Table 1
 
Linear Regression Analysis of the Relationship Between National Health Burden of Cataract and Socioeconomic Variables
Table 1
 
Linear Regression Analysis of the Relationship Between National Health Burden of Cataract and Socioeconomic Variables
The daily ultraviolet radiation, annual PM2.5 concentration, urbanization rate, percent cellular subscribers, mean body mass index, and systolic blood pressure also were found to be significantly associated with the age-standardized DALY rate (Fig. 6). Stepwise multiple regression analyses were conducted to eliminate colinearity (Table 2). When the composite HDI was used, three factors (HDI, daily ultraviolet radiation, and average annual PM2.5) were included in the model, which accounted for 62.7% of global variations in age-standardized DALY rate. When the four components of HDI were used, three indicators (mean years of schooling index, daily ultraviolet radiation, and average annual PM2.5) were significant, and the model could explain 63.4% of global variations in age-standardized DALY rate. 
Figure 6
 
Relationship between health burden of cataract vision loss and country-level demographic and environmental factors. (A) Average daily ultraviolet radiation, (B) average annual concentration of PM2.5 in a country, (C) urbanization rate, (D) cellular subscribers per 100 people, (E) mean body mass index, (F) mean systolic blood pressure.
Figure 6
 
Relationship between health burden of cataract vision loss and country-level demographic and environmental factors. (A) Average daily ultraviolet radiation, (B) average annual concentration of PM2.5 in a country, (C) urbanization rate, (D) cellular subscribers per 100 people, (E) mean body mass index, (F) mean systolic blood pressure.
Table 2
 
Stepwise Multiple Regression Analysis of the Relationship Between National Health Burden of Cataract and Country-Level Variables
Table 2
 
Stepwise Multiple Regression Analysis of the Relationship Between National Health Burden of Cataract and Country-Level Variables
Discussion
This study provides an overview of the global pattern of the health burden of cataract vision loss by year, age, sex, region, and socioeconomic levels. From 1990 to 2015, the DALY number of cataract vision loss increased by 89.42%. The age-standardized DALY rate increased before 2005 and then decreased slowly to the 1990s level. The health burden of cataract increased rapidly in people over 50 years of age. Females had a higher health burden compared to males at the same age. The health burden of cataract vision loss is closely associated with socioeconomic factors, even after adjusting for demographic and environmental factors. 
Although prevalence data and DALY could not be compared directly, both types of information have implications for understanding the situation of cataract vision loss. It was estimated that the prevalence of cataract vision loss was decreasing during the period between 1990 and 2010 in all countries, with the exception of eastern Sub-Saharan Africa.1,11 From 1990 to 2010, the number of people with cataract blindness was reduced by 11.4%, and the percentage of blindness due to cataract decreased from 38.6% to 33.4%. This may be associated with global action such as VISION 2020, which set cataract as a priority, and national programs such as the China Million Cataract Surgeries Program.12 However, the improvement in prevalence did not mean a lesser health burden of this condition, as our study demonstrates. The DALY number increased continuously, which may be due to the population growth and elongation of life expectancy. As shown earlier, the DALY was closely associated with population number and life expectancy. In the past 25 years, population and life expectancy increased by approximately 30% and 16%.13 The life expectancy factor contributed to 38.9% of the variation in age-standardized DALY rate across countries (Table 1). However, the educational factor was more influential and accounted for 55.6% of global variations in age-standardized DALY rate. Therefore, regarding the observation that the health burden of cataract blindness increased during the past decade, it is unlikely that it changed if one considers the changes in life expectancy. 
The outcomes and potential complications of cataract surgery were also key components of blind prevention projects. The outcomes in low-income countries are very low compared to those in high-income countries.14 There are several possible causes of poor outcomes of cataract surgery in low-income countries: (1) problems of biometry and availability of intraocular lenses fitted for each patient; (2) postoperative opacification of the capsular bag without YAG laser access; (3) poor surgical techniques; (4) endophthalmitis related to the lack of sterile conditions; and (5) other classic complications (retinal detachment, cystoid macular edema, and so on). For example, it was reported that 0.39% of patients experienced retinal detachment after cataract surgery in Denmark, while the risk was 0.47% for patients in China.15,16 These findings reinforced sustained demands for allocating resources to cataract services and monitoring the quality of cataract surgery. 
We observed a disproportionate distribution of the health burden of cataract. A previous study demonstrated that cataract ranked as the most unevenly distributed eye disorder in 2004, with higher DALYs in low- and middle-income countries.17 The World Health Survey in 70 countries showed that the prevalence of vision difficulty in low-income countries was two times higher than that in high-income countries.18 Among the 21 Super Regions, the prevalence of cataract blindness was highest in Oceania, followed by South and Southeast Asia, and the lowest in high-income countries.1 Consistent with previous studies, the present study observed an unevenly distributed health burden of cataract. The levels of health burden in Southeast Asia, the Eastern Mediterranean, and Africa were higher than the global level; addressing this should be a priority in future programs. 
The national HDI level was independently correlated with the health burden of cataract vision loss, with higher age-standardized DALY rates in lower HDI countries. HDI reflects the quality of wealth, which has become a standard indicator for comparisons of socioeconomic development across countries. The economy may be implicated as one possible determinant of output and quality of cataract surgery.9 The number of ophthalmologists per million people varied with socioeconomic development, with higher concentration in regions with higher HDI and per capita gross domestic product.1921 Globally, the preoperative visual acuity with regard to cataract surgery was associated with increasing HDI and per capita income.22,23 The cost was the main barrier to utilization of cataract surgery in some developing countries.2426 Reduction of the cost effectively increased the cataract surgical rate in southern China by 160%.27 More free or low-price surgeries are needed to reduce the health burden of cataract vision loss.28 
The educational factor was found to be the most prominent HDI component. It was previously reported that increased knowledge was associated with accepting cataract surgery, whereas cost and transportation were not.29 This may be explained by the following reasons. First, high education usually means better knowledge of cataract. Willingness to pay for cataract surgery has been reported to increase with knowledge of cataract.30,31 Secondly, higher education leads to a higher possibility of a steady occupation, as well as higher income and coverage by medical insurance, enabling the cost of cataract surgery to be more affordable. Even in the United States, annual use of eye services was significantly associated with levels of educational attainment after adjusting for other factors.3234 A randomized controlled trial demonstrated that educational interventions can successfully increase the uptake of cataract surgery.35 
Sex differences in the health burden of cataract vision loss were significant for each age group. One possible explanation is that females have a higher incidence of cataract and longer life expectancy. Another explanation may be related to sex inequality in relation to utilization of cataract surgery. Evidence suggests that females are less likely to access eye health, especially in developing countries. Though 60% of cataract blindness was in females, males had 1.39 times odds of uptake of cataract surgery compared to women.36,37 Being female usually was related to a higher rate of illiteracy, especially among the elderly, and less control of finances compared to men, which prevented opportunities for operations.3639 For children with bilateral cataract, access to surgery for girls was also lower than for boys in low-income countries.40 After adjusting for other factors, being female was found to be associated with poor uncorrected visual acuity (P = 0.04) and corrected visual acuity (P = 0.03) post cataract surgery.12 Closing sex disparities in cataract surgical services would be beneficial with regard to improving the health burden of cataract. 
This study has limitations. First, the accuracy of health burden information suffered from the limitations of the data sources. For example, statistical assumptions may introduce bias.6,7 Second, the analytic unit was at the national level rather than the district level, which may introduce the ecologic fallacy. Significant variations in development levels and disease burden may exist within a country.41,42 Finally, subgroup analyses by culture and health system factors were not performed, as corresponding data for these fields were unavailable. Notwithstanding the above limitations, the findings of this study could serve as an impetus for continued efforts toward eliminating cataract blindness. 
To conclude and in summary, the global health burden of vision loss related to cataract increased between 1990 and 2015 despite considerable efforts in the form of the WHO and VISION 2020 initiatives. Older age, being female, less education, and poor socioeconomic status were associated with a higher burden of cataract vision loss. These findings can raise awareness of the disease burden of cataract and could serve as impetus for continued efforts toward eliminating cataract blindness. 
Acknowledgments
Supported in part by a grant from the National Natural Science Foundation of China (81570843). 
Disclosure: M. He, None; W. Wang, None; W. Huang, None 
References
Khairallah M, Kahloun R, Bourne R, et al. Number of people blind or visually impaired by cataract worldwide and in world regions, 1990 to 2010. Invest Ophthalmol Vis Sci. 2015; 56: 6762–6769.
Bourne RR, Stevens GA, White RA, et al. Causes of vision loss worldwide, 1990-2010: a systematic analysis. Lancet Glob Health. 2013; 1: e339–e349.
Brown MM, Brown GC, Lieske HB, Lieske PA. Financial return-on-investment of ophthalmic interventions: a new paradigm. Curr Opin Ophthalmol. 2014; 25: 171–176.
Lansingh VC, Carter MJ, Martens M. Global cost-effectiveness of cataract surgery. Ophthalmology. 2007; 114: 1670–1678.
Armstrong KL, Jovic M, Vo-Phuoc JL, Thorpe JG, Doolan BL. The global cost of eliminating avoidable blindness. Indian J Ophthalmol. 2012; 60: 475–480.
GBD 2015 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016; 388: 1603–1658.
GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016; 388: 1545–1602.
World Health Organization. Universal Eye Health: A Global Action Plan 2014-2019. Geneva, Switzerland: WHO, 2013. Available at: www.who.int/blindness/actionplan/en/. Accessed August 10, 2017.
Wang W, Yan W, Fotis K, et al. Cataract surgical rate and socioeconomics: a global study. Invest Ophthalmol Vis Sci. 2016; 57: 5872–5881.
Global Burden of Disease Study 2015 . Global Burden of Disease Study 2015 (GBD 2015) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2016. Available at: http://ghdx.healthdata.org/gbd-results-tool. Accessed January 1, 2017.
Stevens GA, White RA, Flaxman SR, et al. Global prevalence of vision impairment and blindness: magnitude and temporal trends, 1990-2010. Ophthalmology. 2013; 120: 2377–2384.
Yan X, Guan C, Mueller A, et al. Outcomes and projected impact on vision restoration of the China Million Cataract Surgeries Program. Ophthalmic Epidemiol. 2013; 20: 294–300.
United Nations. Department of Economic and Social Affairs. Population Division. World Population Prospects, the 2015 Revision. Available at: http://esa.un.org/unpd/wpp/. Accessed January 1, 2017.
Wang W, Yan W, Muller A, He M. A global view on output and outcomes of cataract surgery with national indices of socioeconomic development. Invest Ophthalmol Vis Sci. 2017; 58: 3669–3676.
Miao PJ, Li WS, Zheng JW, Wu RH, Xu M. Relative factors of retinal detachment after phacoemulsification cataract extraction and intraocular lens implantation. Zhonghua Yi Xue Za Zhi. 2009; 89: 2462–2467.
Olsen T, Jeppesen P. The incidence of retinal detachment after cataract surgery. Open Ophthalmol J. 2012; 6: 79–82.
Ono K, Hiratsuka Y, Murakami A. Global inequality in eye health: country-level analysis from the Global Burden of Disease Study. Am J Public Health. 2010; 100: 1784–1788.
Freeman EE, Roy-Gagnon MH, Samson E, et al. The global burden of visual difficulty in low, middle, and high income countries. PLoS One. 2013; 8: e63315.
Carvalho RS, Diniz AS, Lacerda FM, Mello PA. Gross domestic product (GDP) per capita and geographical distribution of ophthalmologists in Brazil. Arq Bras Oftalmol. 2012; 75: 407–411.
Hong H, Mujica OJ, Anaya J, Lansingh VC, Lopez E, Silva JC. The challenge of universal eye health in Latin America: distributive inequality of ophthalmologists in 14 countries. BMJ Open. 2016; 6: e12819.
Resnikoff S, Felch W, Gauthier TM, Spivey B. The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200,000 practitioners. Br J Ophthalmol. 2012; 96: 783–787.
Lansingh VC, Carter MJ. Use of global visual acuity data in a time trade-off approach to calculate the cost utility of cataract surgery. Arch Ophthalmol. 2009; 127: 1183–1193.
Shah SP, Gilbert CE, Razavi H, Turner EL, Lindfield RJ. Preoperative visual acuity among cataract surgery patients and countries' state of development: a global study. Bull World Health Organ. 2011; 89: 749–756.
Batlle JF, Lansingh VC, Silva JC, Eckert KA, Resnikoff S. The cataract situation in Latin America: barriers to cataract surgery. Am J Ophthalmol. 2014; 158: 242–250.
Li Z, Song Z, Wu S, et al. Outcomes and barriers to uptake of cataract surgery in rural northern China: the Heilongjiang Eye Study. Ophthalmic Epidemiol. 2014; 21: 161–168.
Jadoon Z, Shah SP, Bourne R, et al. Cataract prevalence, cataract surgical coverage and barriers to uptake of cataract surgical services in Pakistan: the Pakistan National Blindness and Visual Impairment Survey. Br J Ophthalmol. 2007; 91: 1269–1273.
He M, Chan V, Baruwa E, Gilbert D, Frick KD, Congdon N. Willingness to pay for cataract surgery in rural Southern China. Ophthalmology. 2007; 114: 411–416.
Zhang XJ, Liang YB, Liu YP, et al. Implementation of a free cataract surgery program in rural China: a community-based randomized interventional study. Ophthalmology. 2013; 120: 260–265.
Yin Q, Hu A, Liang Y, et al. A two-site, population-based study of barriers to cataract surgery in rural china. Invest Ophthalmol Vis Sci. 2009; 50: 1069–1075.
Ko F, Frick KD, Tzu J, He M, Congdon N. Willingness to pay for potential enhancements to a low-cost cataract surgical package in rural southern China. Acta Ophthalmol. 2012; 90: e54–e60.
Wang M, Zuo Y, Lin X, et al. Willingness to pay for cataract surgery provided by a senior surgeon in urban Southern China. PLoS One. 2015; 10: e142858.
Wagner LD, Rein DB. Attributes associated with eye care use in the United States: a meta-analysis. Ophthalmology. 2013; 120: 1497–1501.
Zhang X, Beckles GL, Chou CF, et al. Socioeconomic disparity in use of eye care services among US adults with age-related eye diseases: National Health Interview Survey, 2002 and 2008. JAMA Ophthalmol. 2013; 131: 1198–1206.
Zhang X, Cotch MF, Ryskulova A, et al. Vision health disparities in the United States by race/ethnicity, education, and economic status: findings from two nationally representative surveys. Am J Ophthalmol. 2012; 154: S53–S62.
Liu T, Congdon N, Yan X, et al. A randomized, controlled trial of an intervention promoting cataract surgery acceptance in rural China: the Guangzhou Uptake of Surgery Trial (GUSTO). Invest Ophthalmol Vis Sci. 2012; 53: 5271–5278.
Lewallen S, Courtright P. Gender and use of cataract surgical services in developing countries. Bull World Health Organ. 2002; 80: 300–303.
Lewallen S, Mousa A, Bassett K, Courtright P. Cataract surgical coverage remains lower in women. Br J Ophthalmol. 2009; 93: 295–298.
Aboobaker S, Courtright P. Barriers to cataract surgery in Africa: a systematic review. Middle East Afr J Ophthalmol. 2016; 23: 145–149.
Finger RP. Cataracts in India: current situation, access, and barriers to services over time. Ophthalmic Epidemiol. 2007; 14: 112–118.
Gilbert CE, Lepvrier-Chomette N. Gender inequalities in surgery for bilateral cataract among children in low-income countries: a systematic review. Ophthalmology. 2016; 123: 1245–1251.
Giebel S, Labopin M, Ehninger G, et al. Association of Human Development Index with rates and outcomes of hematopoietic stem cell transplantation for patients with acute leukemia. Blood. 2010; 116: 122–128.
Bray F, Jemal A, Grey N, Ferlay J, Forman D. Global cancer transitions according to the Human Development Index (2008-2030): a population-based study. Lancet Oncol. 2012; 13: 790–801.
Figure 1
 
Time trends of global health burden of cataract vision loss in the last 25 years. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates. Red dashed lines represent 95% uncertainty intervals.
Figure 1
 
Time trends of global health burden of cataract vision loss in the last 25 years. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates. Red dashed lines represent 95% uncertainty intervals.
Figure 2
 
Global health burden of cataract vision loss by age and sex in 2015. (A) DALY numbers, (B) crude DALY rates.
Figure 2
 
Global health burden of cataract vision loss by age and sex in 2015. (A) DALY numbers, (B) crude DALY rates.
Figure 3
 
Global map of health burden of people visually impaired from cataract. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates.
Figure 3
 
Global map of health burden of people visually impaired from cataract. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates.
Figure 4
 
Distributions of health burden of cataract vision loss in 2015 by WHO Regions. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates. Dashed line represents global average level.
Figure 4
 
Distributions of health burden of cataract vision loss in 2015 by WHO Regions. (A) DALY numbers, (B) crude DALY rates, and (C) age-standardized DALY rates. Dashed line represents global average level.
Figure 5
 
Relationship between health burden of cataract vision loss and national levels of socioeconomic development. (A) Health burden differed among different HDI categories. (B) Health burden was inversely associated with HDI. The blue line represents fitted line.
Figure 5
 
Relationship between health burden of cataract vision loss and national levels of socioeconomic development. (A) Health burden differed among different HDI categories. (B) Health burden was inversely associated with HDI. The blue line represents fitted line.
Figure 6
 
Relationship between health burden of cataract vision loss and country-level demographic and environmental factors. (A) Average daily ultraviolet radiation, (B) average annual concentration of PM2.5 in a country, (C) urbanization rate, (D) cellular subscribers per 100 people, (E) mean body mass index, (F) mean systolic blood pressure.
Figure 6
 
Relationship between health burden of cataract vision loss and country-level demographic and environmental factors. (A) Average daily ultraviolet radiation, (B) average annual concentration of PM2.5 in a country, (C) urbanization rate, (D) cellular subscribers per 100 people, (E) mean body mass index, (F) mean systolic blood pressure.
Table 1
 
Linear Regression Analysis of the Relationship Between National Health Burden of Cataract and Socioeconomic Variables
Table 1
 
Linear Regression Analysis of the Relationship Between National Health Burden of Cataract and Socioeconomic Variables
Table 2
 
Stepwise Multiple Regression Analysis of the Relationship Between National Health Burden of Cataract and Country-Level Variables
Table 2
 
Stepwise Multiple Regression Analysis of the Relationship Between National Health Burden of Cataract and Country-Level Variables
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×