Volume 34 Issue 2
Feb.  2021
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DENG Yan, YANG Dan, YU Jia Ming, XU Jing Xian, HUA Hui, CHEN Ren Tong, WANG Nan, OU Feng Rong, LIU Ru Xi, WU Bo, LIU Yang. The Association of Socioeconomic Status with the Burden of Cataract-related Blindness and the Effect of Ultraviolet Radiation Exposure: An Ecological Study[J]. Biomedical and Environmental Sciences, 2021, 34(2): 101-109. doi: 10.3967/bes2021.015
Citation: DENG Yan, YANG Dan, YU Jia Ming, XU Jing Xian, HUA Hui, CHEN Ren Tong, WANG Nan, OU Feng Rong, LIU Ru Xi, WU Bo, LIU Yang. The Association of Socioeconomic Status with the Burden of Cataract-related Blindness and the Effect of Ultraviolet Radiation Exposure: An Ecological Study[J]. Biomedical and Environmental Sciences, 2021, 34(2): 101-109. doi: 10.3967/bes2021.015

The Association of Socioeconomic Status with the Burden of Cataract-related Blindness and the Effect of Ultraviolet Radiation Exposure: An Ecological Study

doi: 10.3967/bes2021.015
Funds:  This research was supported by a grant from the National Natural Science Foundation of China [No. 81673133 and No. 81273034]
More Information
  • Author Bio:

    DENG Yan, female, born in 1986, PhD, Candidate, majoring in environmental health

  • Corresponding author: LIU Yang, Professor, PhD, E-mail: yangliu@cmu.edu.cn, Tel/Fax: 86-13386885612.
  • Received Date: 2019-12-16
  • Accepted Date: 2020-11-04
  •   Objective  To assess the association of socioeconomic status with the burden of cataract blindness in terms of year lived with disability (YLD) rates and to determine whether ultraviolet radiation (UVR) levels modify the effect of socioeconomic status on this health burden.  Methods  National and subnational age-standardized YLD rates associated with cataract-related blindness were derived from the Global Burden of Disease (GBD) study 2017. The human development index (HDI) from the Human Development Report was used as a measure of socioeconomic status. Estimated ground-level UVR exposure was obtained from the Ozone Monitoring Instrument (OMI) dataset of the National Aeronautics and Space Administration (NASA).   Results  Across 185 countries, socioeconomic status was inversely associated with the burden of cataract blindness. Countries with a very high HDI had an 84% lower age-standardized YLD rate [95% confidence interval (CI): 60%–93%, P < 0.001] than countries with a low HDI; for high-HDI countries, the proportion was 76% (95% CI: 53%–88%, P < 0.001), and for medium-HDI countries, the proportion was 48% (95% CI: 15%–68%, P = 0.010; P for trend < 0.001). The interaction analysis showed that UVR exposure played an interactive role in the association between socioeconomic status and cataract blindness burden (P value for interaction = 0.047).   Conclusion  Long-term high-UVR exposure amplifies the association of poor socioeconomic status with the burden of cataract-related blindness. The findings emphasize the need for strengthening UVR exposure protection interventions in developing countries with high-UVR exposure.
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    [14] GBD 2017 Mortality Collaborators. Global, regional, and national age-sex-specific mortality and life expectancy, 1950-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (London, England), 2018; 392, 1684−1735. doi:  10.1016/S0140-6736(18)31891-9
    [15] Programme UND. Human development indices and indicators. http://www.hdr.undp.org/sites/default/files/2018_human_development_statistical_update.pdf. [2019-12-22].
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    [18] Desai N, Copeland RA. Socioeconomic disparities in cataract surgery. Curr Opin Ophthalmol, 2013; 24, 74−8. doi:  10.1097/ICU.0b013e32835a93da
    [19] Richter GM, Chung J, Azen SP, et al. Prevalence of visually significant cataract and factors associated with unmet need for cataract surgery: Los Angeles Latino Eye Study. Ophthalmology, 2009; 116, 2327−35. doi:  10.1016/j.ophtha.2009.05.040
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    [22] Chang MA, Congdon NG, Baker SK, et al. The surgical management of cataract: barriers, best practices and outcomes. Int Ophthalmol, 2008; 28, 247−60. doi:  10.1007/s10792-007-9121-2
    [23] Ibrahim N, Ramke J, Pozo-Martin F, et al. Willingness to pay for cataract surgery is much lower than actual costs in Zamfara state, northern Nigeria. Ophthalmic Epidemiol, 2018; 25, 227−33. doi:  10.1080/09286586.2017.1408845
    [24] Wang W, Yan W, Müller A, et al. A Global View on Output and Outcomes of Cataract Surgery With National Indices of Socioeconomic Development. Invest Ophthalmol Vis Sci, 2017; 58, 3669−76.
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    [26] Ji Y, Cai L, Zheng T, et al. The mechanism of UVB irradiation induced-apoptosis in cataract. Mol Cell Biochem, 2015; 401, 87−95. doi:  10.1007/s11010-014-2294-x
    [27] Rosmini F, Stazi MA, Milton RC, et al. A dose-response effect between a sunlight index and age-related cataracts. Italian-American Cataract Study Group. Ann Epidemiol, 1994; 4, 266−70.
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    [30] Grant WB, Garland CF, Holick MF. Comparisons of estimated economic burdens due to insufficient solar ultraviolet irradiance and vitamin D and excess solar UV irradiance for the United States. Photochem Photobiol, 2005; 81, 1276−86.
    [31] West SK, Longstreth JD, Munoz BE, et al. Model of risk of cortical cataract in the US population with exposure to increased ultraviolet radiation due to stratospheric ozone depletion. Am J Epidemiol, 2005; 162, 1080−8. doi:  10.1093/aje/kwi329
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The Association of Socioeconomic Status with the Burden of Cataract-related Blindness and the Effect of Ultraviolet Radiation Exposure: An Ecological Study

doi: 10.3967/bes2021.015
Funds:  This research was supported by a grant from the National Natural Science Foundation of China [No. 81673133 and No. 81273034]
  • Author Bio:

  • Corresponding author: LIU Yang, Professor, PhD, E-mail: yangliu@cmu.edu.cn, Tel/Fax: 86-13386885612.

Abstract:   Objective  To assess the association of socioeconomic status with the burden of cataract blindness in terms of year lived with disability (YLD) rates and to determine whether ultraviolet radiation (UVR) levels modify the effect of socioeconomic status on this health burden.  Methods  National and subnational age-standardized YLD rates associated with cataract-related blindness were derived from the Global Burden of Disease (GBD) study 2017. The human development index (HDI) from the Human Development Report was used as a measure of socioeconomic status. Estimated ground-level UVR exposure was obtained from the Ozone Monitoring Instrument (OMI) dataset of the National Aeronautics and Space Administration (NASA).   Results  Across 185 countries, socioeconomic status was inversely associated with the burden of cataract blindness. Countries with a very high HDI had an 84% lower age-standardized YLD rate [95% confidence interval (CI): 60%–93%, P < 0.001] than countries with a low HDI; for high-HDI countries, the proportion was 76% (95% CI: 53%–88%, P < 0.001), and for medium-HDI countries, the proportion was 48% (95% CI: 15%–68%, P = 0.010; P for trend < 0.001). The interaction analysis showed that UVR exposure played an interactive role in the association between socioeconomic status and cataract blindness burden (P value for interaction = 0.047).   Conclusion  Long-term high-UVR exposure amplifies the association of poor socioeconomic status with the burden of cataract-related blindness. The findings emphasize the need for strengthening UVR exposure protection interventions in developing countries with high-UVR exposure.

DENG Yan, YANG Dan, YU Jia Ming, XU Jing Xian, HUA Hui, CHEN Ren Tong, WANG Nan, OU Feng Rong, LIU Ru Xi, WU Bo, LIU Yang. The Association of Socioeconomic Status with the Burden of Cataract-related Blindness and the Effect of Ultraviolet Radiation Exposure: An Ecological Study[J]. Biomedical and Environmental Sciences, 2021, 34(2): 101-109. doi: 10.3967/bes2021.015
Citation: DENG Yan, YANG Dan, YU Jia Ming, XU Jing Xian, HUA Hui, CHEN Ren Tong, WANG Nan, OU Feng Rong, LIU Ru Xi, WU Bo, LIU Yang. The Association of Socioeconomic Status with the Burden of Cataract-related Blindness and the Effect of Ultraviolet Radiation Exposure: An Ecological Study[J]. Biomedical and Environmental Sciences, 2021, 34(2): 101-109. doi: 10.3967/bes2021.015
    • Aseries of efforts, such as including cataracts in most national plans for the prevention of visual impairment[1], have been made to improve cataract-related health services. However, cataracts remain a major public health problem[2]. In 2010, cataracts were responsible for 33.4% of global blindness and 18.4% of global moderate to severe vision impairment[2]. With the increasing life expectancy and rapidly aging population, the number of people with vision impairment due to cataracts is expected to increase continuously. The burden of cataracts can be quantified by disability adjusted life year (DALY) as the sum of year of life lost (YLL) and year lived with disability (YLD) for each location estimated by the Global Burden of Diseases (GBD), Injuries, and Risk Factors Study 2017[3]. The GBD study 2017 was the result of a global collaboration to examine data on 359 diseases and injuries in 195 countries and territories, and it revealed that the global DALYs for cataracts increased by 29.6% from 2007 to 2017, reaching 8,010 thousands[3].

      Cataracts are the most unevenly distributed noncommunicable eye disease in the world, placing the greatest burden on middle- and low-income countries[4], which result from the combined effects of socioeconomic and environmental factors. From a socioeconomic perspective, previous studies have found that the health burden of cataract vision loss is correlated with socioeconomic status[5, 6]. Cataracts can not only cause vision loss but also cause more serious blindness[1, 2], which leading to an increased risk of death[7] and impaired quality of life[8]. We speculate that burden of cataract-related blindness is also correlated with socioeconomic status. From an environmental perspective, as it is well known, exposure to ultraviolet radiation (UVR) is one of the risk factors for cataract blindness, and the association of a higher cataract blindness burden with UVR has been documented in numerous reports[9-11]. A study concluded that the prevalence of cataracts increased at a rate of 3% for each degree of latitude to the south[12]. Therefore, UVR is a major factor leading to an uneven worldwide distribution of the cataract-related blindness burden, and it cannot be ignored. However, little evidence of evaluation of the effect of co-exposure to high-UVR and poor socioeconomic status on burden of cataract-related blindness.

      We hypothesized that co-exposure on high-UVR and poor socioeconomic development jointly leads to the inequality in global cataract-related burden, with a stronger association of socioeconomic status with cataract-related burden in high-UVR countries than in countries of low UVR exposure. Therefore, in addition to evaluating the association of socioeconomic status and the burden of cataract-related blindness, we further explored the potential interaction effect between socioeconomic status and UVR exposure on the burden of cataract blindness in 185 countries and territories worldwide. Our study may be informative and helpful in achieving the aim proposed by the World Health Organization (WHO) Global Action Plan (GAP)[1] of eliminating blindness caused by cataracts.

    • This study involved all countries in the world, using both country-level and subnational-level data. In the country-level analysis, countries with data in the GBD study 2017 but not in the Human Development Report were excluded (Supplementary Table S1 available in www.besjournal.com). We also analyzed subnational-level data if subnational regions were included in the GBD study 2017 (Supplementary Table S2 available in www.besjournal.com). All variables used the latest values, except UVR exposure and gross domestic product (GDP) per capita (Supplementary Table S3 available in www.besjournal.com). Satellite-derived UVR exposure data were incomplete from June 1–14, 2016 and from May 12–16, 2017, so we used 2015 solar UVR data. The GDP per capita of Japan was available only in 2014.

      Country_idaCountry_nameGBD Study 2017Human development report
      6China
      7North Korea
      8Taiwan
      10Cambodia
      11Indonesia
      12Laos
      13Malaysia
      14Maldives
      15Myanmar
      16Philippines
      17Sri Lanka
      18Thailand
      19Timor-Leste
      20Vietnam
      22Fiji
      23Kiribati
      24Marshall Islands
      25Federated States of Micronesia
      26Papua New Guinea
      27Samoa
      28Solomon Islands
      29Tonga
      30Vanuatu
      33Armenia
      34Azerbaijan
      35Georgia
      36Kazakhstan
      37Kyrgyzstan
      38Mongolia
      39Tajikistan
      40Turkmenistan
      41Uzbekistan
      43Albania
      44Bosnia and Herzegovina
      45Bulgaria
      46Croatia
      47Czech Republic
      48Hungary
      49Macedonia
      50Montenegro
      51Poland
      52Romania
      53Serbia
      54Slovakia
      55Slovenia
      57Belarus
      58Estonia
      59Latvia
      60Lithuania
      61Moldova
      62Russian Federation
      63Ukraine
      66Brunei
      67Japan
      68South Korea
      69Singapore
      71Australia
      72New Zealand
      74Andorra
      75Austria
      76Belgium
      77Cyprus
      78Denmark
      79Finland
      80France
      81Germany
      82Greece
      83Iceland
      84Ireland
      85Israel
      86Italy
      87Luxembourg
      88Malta
      89Netherlands
      90Norway
      91Portugal
      92Spain
      93Sweden
      94Switzerland
      95United Kingdom
      97Argentina
      98Chile
      99Uruguay
      101Canada
      102United States
      105Antigua and Barbuda
      106The Bahamas
      107Barbados
      108Belize
      109Cuba
      110Dominica
      111Dominican Republic
      112Grenada
      113Guyana
      114Haiti
      115Jamaica
      116Saint Lucia
      117Saint Vincent and the Grenadines
      118Suriname
      119Trinidad and Tobago
      121Bolivia
      122Ecuador
      123Peru
      125Colombia
      126Costa Rica
      127El Salvador
      128Guatemala
      129Honduras
      130Mexico
      131Nicaragua
      132Panama
      133Venezuela
      135Brazil
      136Paraguay
      139Algeria
      140Bahrain
      141Egypt
      142Iran
      143Iraq
      144Jordan
      145Kuwait
      146Lebanon
      147Libya
      148Morocco
      149Palestine
      150Oman
      151Qatar
      152Saudi Arabia
      153Syria
      154Tunisia
      155Turkey
      156United Arab Emirates
      157Yemen
      160Afghanistan
      161Bangladesh
      162Bhutan
      163India
      164Nepal
      165Pakistan
      168Angola
      169Central African Republic
      170Congo
      171Democratic Republic of the Congo
      172Equatorial Guinea
      173Gabon
      175Burundi
      176Comoros
      177Djibouti
      178Eritrea
      179Ethiopia
      180Kenya
      181Madagascar
      182Malawi
      183Mauritius
      184Mozambique
      185Rwanda
      186Seychelles
      187Somalia
      189Tanzania
      190Uganda
      191Zambia
      193Botswana
      194Lesotho
      195Namibia
      196South Africa
      197Swaziland
      198Zimbabwe
      200Benin
      201Burkina Faso
      202Cameroon
      203Cape Verde
      204Chad
      205Cote d'Ivoire
      206The Gambia
      207Ghana
      208Guinea
      209Guinea-Bissau
      210Liberia
      211Mali
      212Mauritania
      213Niger
      214Nigeria
      215Sao Tome and Principe
      216Senegal
      217Sierra Leone
      218Togo
      298American Samoa
      305Bermuda
      349Greenland
      351Guam
      376Northern Mariana Islands
      385Puerto Rico
      422Virgin Islands, U.S.
      435South Sudan
      522Sudan
        Note. aCountry_id for geographic variables comes from the GBD Study 2017 database that creates and stores unique numeric identifiers.

      Table S1.  Countries included in the GBD study 2017 and human development report

      Location_idaLocation_nameLevelb
      67Japan0
      35446Aichi1
      35428Akita1
      35425Aomori1
      35435Chiba1
      35461Ehime1
      35441Fukui1
      35463Fukuoka1
      35430Fukushima1
      35444Gifu1
      35433Gunma1
      35457Hiroshima1
      35424Hokkaidō1
      35451Hyōgo1
      35431Ibaraki1
      35440Ishikawa1
      35426Iwate1
      35460Kagawa1
      35469Kagoshima1
      35437Kanagawa1
      35462Kōchi1
      35466Kumamoto1
      35449Kyōto1
      35447Mie1
      35427Miyagi1
      35468Miyazaki1
      35443Nagano1
      35465Nagasaki1
      35452Nara1
      35438Niigata1
      35467Ōita1
      35456Okayama1
      35470Okinawa1
      35450Ōsaka1
      35464Saga1
      35434Saitama1
      35448Shiga1
      35455Shimane1
      35445Shizuoka1
      35432Tochigi1
      35459Tokushima1
      35436Tōkyō1
      35454Tottori1
      35439Toyama1
      35453Wakayama1
      35429Yamagata1
      35458Yamaguchi1
      35442Yamanashi1
      102United States0
      523Alabama1
      524Alaska1
      525Arizona1
      526Arkansas1
      527California1
      528Colorado1
      529Connecticut1
      530Delaware1
      531District of Columbia1
      532Florida1
      533Georgia1
      534Hawaii1
      535Idaho1
      536Illinois1
      537Indiana1
      538Iowa1
      539Kansas1
      540Kentucky1
      541Louisiana1
      542Maine1
      543Maryland1
      544Massachusetts1
      545Michigan1
      546Minnesota1
      547Mississippi1
      548Missouri1
      549Montana1
      550Nebraska1
      551Nevada1
      552New Hampshire1
      553New Jersey1
      554New Mexico1
      555New York1
      556North Carolina1
      557North Dakota1
      558Ohio1
      559Oklahoma1
      560Oregon1
      561Pennsylvania1
      562Rhode Island1
      563South Carolina1
      564South Dakota1
      565Tennessee1
      566Texas1
      567Utah1
      568Vermont1
      569Virginia1
      570Washington1
      571West Virginia1
      572Wisconsin1
      573Wyoming1
      93Sweden0
      4944Stockholm1
      4940Sweden except Stockholm1
      95United Kingdom0
      4749England1
      4621East Midlands2
      4623East of England2
      4624Greater London2
      4618North East England2
      4619North West England2
      4625South East England2
      4626South West England2
      4622West Midlands2
      4620Yorkshire and the Humber2
      433Northern Ireland1
      434Scotland1
      4636Wales1
      130Mexico0
      4643Aguascalientes1
      4644Baja California1
      4645Baja California Sur1
      4646Campeche1
      4649Chiapas1
      4650Chihuahua1
      4647Coahuila1
      4648Colima1
      4652Durango1
      4653Guanajuato1
      4654Guerrero1
      4655Hidalgo1
      4656Jalisco1
      4657México1
      4651Mexico City1
      4658Michoacán de Ocampo1
      4659Morelos1
      4660Nayarit1
      4661Nuevo León1
      4662Oaxaca1
      4663Puebla1
      4664Querétaro1
      4665Quintana Roo1
      4666San Luis Potosí1
      4667Sinaloa1
      4668Sonora1
      4669Tabasco1
      4670Tamaulipas1
      4671Tlaxcala1
      4672Veracruz de Ignacio de la Llave1
      4673Yucatán1
      4674Zacatecas1
      135Brazil0
      4750Acre1
      4751Alagoas1
      4753Amapá1
      4752Amazonas1
      4754Bahia1
      4755Ceará1
      4756Distrito Federal1
      4757Espírito Santo1
      4758Goiás1
      4759Maranhão1
      4762Mato Grosso1
      4761Mato Grosso do Sul1
      4760Minas Gerais1
      4763Pará1
      4764Paraíba1
      4765Paraná1
      4766Pernambuco1
      4767Piaui1
      4768Rio de Janeiro1
      4769Rio Grande do Norte1
      4772Rio Grande do Sul1
      4770Rondônia1
      4771Roraima1
      4773Santa Catarina1
      4775São Paulo1
      4774Sergipe1
      4776Tocantins1
      11Indonesia0
      4709Aceh1
      4726Bali1
      4717Bangka-Belitung Islands1
      4725Banten1
      4715Bengkulu1
      4737Gorontalo1
      4720Jakarta1
      4713Jambi1
      4721West Java1
      4722Central Java1
      4724East Java1
      4729West Kalimantan1
      4731South Kalimantan1
      4730Central Kalimantan1
      4732East Kalimantan1
      4719North Kalimantan1
      4718Riau Islands1
      4716Lampung1
      4739Maluku1
      4740North Maluku1
      4727West Nusa Tenggara1
      4728East Nusa Tenggara1
      4742Papua1
      4741West Papua1
      4712Riau1
      4738West Sulawesi1
      4735South Sulawesi1
      4734Central Sulawesi1
      4736Southeast Sulawesi1
      4733North Sulawesi1
      4711West Sumatra1
      4714South Sumatra1
      4710North Sumatra1
      4723Yogyakarta1
        Note. aLocation_id for geographic variables comes from the GBD Study 2017 database that creates and stores unique numeric identifiers. bLevel: Level 0 = country. Levels 1 and 2 = subnational regions.

      Table S2.  Subnational regions included in the GBD study 2017

      CovariateDefinitionSourcePeriodRisk factors
      in the GBD
      Male to female sex ratioSex ratio of the total population (males per 100 females)United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition.2015
      Population using solid fuels (%)The percentage of the population that relies on solid fuels as the primary source of domestic energy for cooking and heatingWorld Health Organization. Available from http://apps.who.int/gho/data/view.main.1701?lang=en2013Yes
      Age-standardized prevalence of current tobacco smoking (%)The percentage of the population aged 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or nondaily basis. Note that most countries collect data about smoking but not smokeless tobacco use, leaving gaps in tobacco use data and preventing global and regional summaries of tobacco use rates. Until data improve, the estimates will reflect the percentage of the population aged 15 years and over who currently smoke.World Health Organization. Available from http://apps.who.int/gho/data/node.imr.SDGTOBACCO?lang=en2016Yes
      Age-standardized diabetes mellitus prevalence (%)Age-standardized diabetes mellitus prevalence (%) both sexesGlobal Burden of Disease Collaborative Network.Global Burden of Disease Study 2017 (GBD 2017) Results.Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.Available from http://ghdx.healthdata.org/gbd-results-tool.2017Yes
      Population living in urban areas (%)The percentage of the de facto population living in areas classified as urban according to the criteria used by each area or country as of 1 July of the year indicated.World Health Organization. Available from http://apps.who.int/gho/data/node.imr.WHS9_96?lang=en2010
      BMI mean (kg·m−2)BMI mean trends among adults, age-standardized (kg/m2)World Health Organization. Available from http://apps.who.int/gho/data/node.main.BMIANTHROPOMETRY?lang=en2016Yes
      GDP per capita (USD)GDP per capita (current US$)World Bank national accounts data and OECD National Accounts data files.2014

      Table S3.  Additional information on the covariates used in the multivariate linear regression analysis at the country level

    • Global Burden of Cataract Blindness The GBD study 2017 provides YLD to quantify the burden of cataract blindness. Age-standardized YLD rates associated with blindness due to cataracts were analyzed. The data were derived from the open-access database of the Global Burden of Disease study 2017 (http://ghdx.healthdata.org/gbd-results-tool), which contains quantitative data on nonfatal health outcomes in terms of YLDs for a list of 354 GBD causes according to different severity splits in 195 countries[13]. The YLDs were estimated as the product of a prevalence estimate and a disability weight for the health states of each mutually exclusive sequela, adjusted for comorbidity[13]. Disability weights employed numbers on a scale from 0 to 1 that represented the severity of health loss associated with a single given health state. Regarding cataracts (International Classification of Diseases 10th Revision (ICD-10) codes H25-H26 and H28-H28.8), the disability weight was 0.187 (0.124–0.260) for blindness[13]. YLD rates were calculated by dividing the number of YLDs by the relevant population. The GBD study 2017 reference population was used to calculate the age-standardized YLD rate[14]. The following GBD study 2017 data concerning cataract blindness were collected as the outcome variables: (1) national age-standardized YLD rates owing to cataract blindness in 2017 and (2) subnational age-standardized YLD rates owing to cataract blindness in 2017. Because the GBD study data can be downloaded from an open access database, ethics approval and informed consent were not required for this study.

      Human Development Index The human development index (HDI), as a regional socioeconomic indicator, is a composite measure of health, education, and income, measured by life expectancy at birth, mean years of schooling and gross national income per capita, respectively[15]. A higher HDI value indicates a higher level of socioeconomic development, ranging from 0 to 1. Country-level HDI data were obtained from the Human Development Report 2018 released by the United Nations Development Programme (UNDP) (http://hdr.undp.org/en/data). Subnational-level HDI data were obtained from The Global Data Lab (https://hdi.globaldatalab.org/areadata/shdi/) of the Institute for Management Research, Radboud University. Using the UNDP categorization[15], countries and subnational regions were divided into four socioeconomic groups: the low-HDI group (less than 0.550), medium-HDI group (0.550–0.699), high-HDI group (0.700–0.799) and very-high-HDI group (0.800 or greater).

      Solar Ultraviolet Radiation Exposure The estimated daily cloud-adjusted ambient solar UVR data were obtained from NASA Goddard Earth Sciences Data and Information Services Center readings from the Ozone Monitoring Instrument (OMI) mounted on the NASA Earth Observing System Auraspacecraft[16]. The OMI is a nadir viewing spectrometer that measures solar reflected and backscattered radiation in the 270–500 nm wavelength range with a spectral resolution of approximately 0.5 nm in the UVR range. The OMI ultraviolet data consider the impact of altitude, ozone, surface albedo, aerosols and cloud coverage to accurately measure the amount of solar UVR that reaches the Earth’s surface[17]. We estimated the UVR for analysis by averaging the daily estimates (in J/m2) in 2015 with an OMI Level 3 surface UV irradiance product, which was provided on a 1° × 1° (longitude × latitude) grid, with each cell covering an area of 110 km (north–south) × 66 km (east–west) according to the World Geodetic System 84 coordinate system. An average daily UVR level was obtained for each country using ArcGIS version 10.2 software (http://www.esri.com/software/arcgis/index.html). First, we built a raster layer in the ArcGIS program with UVR data. Second, we built a superimposed polygon vector layer on the basis of the raster layer with a world map of national and subnational borders. Third, we used the zonal statistics tool to quantify the UVR level per country. Subnational UVR data were calculated for limited countries with the corresponding value of the subnational burden of cataract blindness provided by the GBD study 2017 following similar procedures.

    • The covariates were selected based on the GBD study 2017 (Supplementary Table S4 available in www.besjournal.com) and previous literature[4, 5, 6]. We further selected covariates if they showed a significant association (P < 0.05) with the burden of cataract blindness in the univariate analysis or if one of the regression coefficients changed by at least 10% after covariates were added to the multivariable-adjusted model. Overall, we controlled for the following potential confounding variables: country-specific male to female sex ratio, proportion of population using solid fuels, age-standardized prevalence of current tobacco smoking, age-standardized diabetes mellitus prevalence, proportion of population living in urban areas, population-mean body mass index (BMI), and GDP per capita at nominal values. Supplementary Table S3 in lists additional information for each confounder. Subnational-level covariates including male to female ratio, age-standardized diabetes mellitus prevalence and GDP per capita at nominal values were measured by region (Supplementary Table S5 available in www.besjournal.com). Other subnational variables were supposed to be homogeneous within each country.

      Risk factorsExposure definition
      Household air pollution from solid fuelsIndividual exposure to PM2.5 due to the use of solid cooking fuel.
      SmokingThe prevalence of the current use of any smoked tobacco product and the prevalence of former use of any smoked tobacco product. Among current smokers, cigarette equivalents smoked per smoker per day and cumulative pack-years of exposure. Among former smokers, number of years since quitting.
      High fasting plasma glucoseSerum fasting plasma glucose measured in mmol/L.
      High BMIBMI, measured in kg/m2.

      Table S4.  GBD study 2017 risk factors and accompanying exposure definitions for cataracts

      CountryInformation source
      Japan
      Sex male to female ratioStatistics Bureau. JAPAN STATISTICAL YEARBOOK 2018. Geography and Population. Population by Prefecture (1920 to 2016). Available from http://www.stat.go.jp/english/data/nenkan/67nenkan/index.html
      Age-standardized diabetes mellitus prevalence (%)Global Burden of Disease Collaborative Network.Global Burden of Disease Study 2017 (GBD 2017) Results.Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.Available from http://ghdx.healthdata.org/gbd-results-tool.
      GDP per capita (USD)The Organisation for Economic Co-operation and Development (OECD). Stats. Regional Statistics. Regional Economy. Regional GDP per Capita. Available from https://stats.oecd.org/
      United States
      Sex male to female ratioUnited States Census Bureau. American Fact Finder. Annual Estimates of the Resident Population for Selected Age Groups by Sex for the United States, States, Counties, and Puerto Rico Commonwealth and Municipios: April 1, 2010 to July 1, 2017. Available from https://factfinder.census.gov/faces/nav/jsf/pages/download_center.xhtml#none
      Age-standardized diabetes mellitus prevalence (%)Global Burden of Disease Collaborative Network.Global Burden of Disease Study 2017 (GBD 2017) Results.Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.Available from http://ghdx.healthdata.org/gbd-results-tool.
      GDP per capita (USD)The Organisation for Economic Co-operation and Development (OECD). Stats. Regional Statistics. Regional Economy. Regional GDP per Capita. Available from https://stats.oecd.org/.
      Sweden
      Sex male to female ratioEuropean statistics. Population on 1 January by age, sex and NUTS 2 region (demo_r_d2jan). Eurostat Data Explorer. Available from https://ec.europa.eu/eurostat/web/regions/data/database
      Age-standardized diabetes mellitus prevalence (%)Global Burden of Disease Collaborative Network.Global Burden of Disease Study 2017 (GBD 2017) Results.Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.Available from http://ghdx.healthdata.org/gbd-results-tool.
      GDP per capita (USD)2014 GDP per capita: Twenty-one regions below half of the EU average. European statistics. News releases 2016. Archived from https://ec.europa.eu/eurostat/documents/2995521/7192292/1-26022016-AP-EN.pdf/602b34e8-abba-439e-b555-4c3cb1dbbe6e
      United Kingdom
      Sex male to female ratioEuropean statistics. Population on 1 January by age, sex and NUTS 2 region (demo_r_d2jan). Eurostat Data Explorer. Available from https://ec.europa.eu/eurostat/web/regions/data/database.
      Age-standardized diabetes mellitus prevalence (%)Global Burden of Disease Collaborative Network.Global Burden of Disease Study 2017 (GBD 2017) Results.Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.Available from http://ghdx.healthdata.org/gbd-results-tool.
      GDP per capita (USD)2014 GDP per capita: Twenty-one regions below half of the EU average. European statistics. News releases 2016. Archived from https://ec.europa.eu/eurostat/documents/2995521/7192292/1-26022016-AP-EN.pdf/602b34e8-abba-439e-b555-4c3cb1dbbe6e.
      Mexico
      Sex male to female ratioThe Organisation for Economic Co-operation and Development (OECD). Stats. Regional Statistics. Regional Demography. Sex Ratio, Total Population (% population males over females). Available from https://stats.oecd.org/.
      Age-standardized diabetes mellitus prevalence (%)Global Burden of Disease Collaborative Network.Global Burden of Disease Study 2017 (GBD 2017) Results.Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.Available from http://ghdx.healthdata.org/gbd-results-tool.
      GDP per capita (USD)Instituto Nacional De Estadística y Geografía (INEGI). Estadística - Producto interno bruto por entidad federativa, base 2013 Información. Available from https://www.inegi.org.mx/sistemas/bie/?idserPadre=10200070#D10200070.
      Brazil
      Sex male to female ratioThe Organisation for Economic Co-operation and Development (OECD). Stats. Regional Statistics. Regional Demography. Sex Ratio, Total Population (% population males over females). Available from https://stats.oecd.org/.
      Age-standardized diabetes mellitus prevalence (%)Global Burden of Disease Collaborative Network.Global Burden of Disease Study 2017 (GBD 2017) Results.Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.Available from http://ghdx.healthdata.org/gbd-results-tool.
      GDP per capita (USD)IBGE: Instituto Brasileiro de Geografia e Estatística. Economic Statistics. Regional Accounts 2014: five states account for nearly two thirds of Brazilian GDP. Available from https://agenciadenoticias.ibge.gov.br/en/agencia-press-room/2185-news-agency/releases-en/10156-regional-accounts-2014-five-states-account-for-nearly-two-thirds-of-brazilian-gdp.
      Indonesia
      Sex male to female ratio2010 Population Census Data - Statistics Indonesia. Population by Age Group, Urban/Rural, and Sex in Provinces of Indonesia. Available from https://sp2010.bps.go.id/
      Age-standardized diabetes mellitus prevalence (%)Global Burden of Disease Collaborative Network.Global Burden of Disease Study 2017 (GBD 2017) Results.Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.Available from http://ghdx.healthdata.org/gbd-results-tool.
      GDP per capita (USD)Statistics Indonesia. Statistical Yearbook of Indonesia 2015. Available from https://www.bps.go.id/publication/2015/08/12/5933145e1d037f5148a67bac/statistik-indonesia-2015.html.

      Table S5.  Information sources for subnational-level data per country

    • Data are presented as the mean ± standard deviation (SD) or median (interquartile) for continuous variables and as frequency or percentage for categorical variables. Linear regression models were used to evaluate the associations between HDI, UVR exposure, and cataract age-standardized YLD rate owing to blindness using country-level data and subnational-level data in three steps. First, we examined conditions of normality and log-transformed the outcome measures if the normality assumption was violated. Second, we built an adjusted model depending on the inclusion of covariates. Third, interaction and stratified analyses were conducted according to UVR exposure (high UVR and low UVR), HDI status (low HDI, medium HDI, high HDI, and very high HDI), and the burden of cataract-related blindness. All analyses were performed with the statistical software packages R (http://www.R-project.org, The R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA). A two-sided significance level of 0.05 was used to evaluate statistical significance.

    • The GBD study 2017 was based on a geographical hierarchy that included 195 countries and territories, of which 185 countries were also included in the Human Development Report. Both HDI and age-standardized YLD rates in 2017 were available for 185 countries (Table 1, Supplementary Table S1), covering 94.87% of all countries and territories worldwide (Supplementary Figure S1 available in www.besjournal.com), including 38 very-high-HDI, 39 high-HDI, 51 medium-HDI and 57 low-HDI countries. The global distribution of country-specific age-standardized YLD rates owing to blindness in 2017 was unequal (Supplementary Figure S2 available in www.besjournal.com). The geometric means of age-standardized YLD rates owing to blindness in each HDI group ranked from low to very high HDI, were as follows: 77.98, 45.47, 19.51, and 10.48. The subnational estimation of age-standardized YLD rates owing to blindness in the GBD study 2017 included 206 subnational regions belonging to seven countries: Japan, the United States, Sweden, the United Kingdom, Mexico, Brazil, and Indonesia (Supplementary Table S5). Of these subnational regions, 22 were in the medium-HDI group, 63 were in the high-HDI group, and 121 were in the very-high-HDI group, and the corresponding geometric mean of age-standardized YLD rates owing to blindness were 122.28, 51.28, and 4.64, respectively. Additional characteristics of the covariates in this study, stratified by the HDI of included countries and subnational regions, are shown in Table 1 and Supplementary Table S6 (available in www.besjournal.com). The mean country-specific daily UVR levels ranged from 732.19 to 4876.66 J·m−2·day−1 (Supplementary Figure S3 available in www.besjournal.com). The mean daily UVR exposure doses declined from 3,696.64 (low HDI), 3,529.58 (medium HDI), 3,133.49 (high HDI), to 1,858.61 J·m−2·day−1 (very high HDI) with the increase in HDI at the country level, and the trend was also observed at the subnational level, with a range from 4,430.78 (medium HDI) and 3,835.03 (high HDI) to 1,731.55 J·m−2·day−1 (very high HDI).

      CharacteristicsTotalHDI categoriesd
      Low HDIMedium HDIHigh HDIVery high HDI
      Countries na (%)18538 (20.54)39 (21.08)51 (27.57)57 (30.81)
      Blindness age-standardized YLD rate per 100,000 populationb25.59 ± 3.39 77.98 ± 1.80 45.47 ± 2.65 19.51 ± 3.07 10.48 ± 2.64
      Mean UVR exposure (J·m−2·day−1)c2939.86 ± 1143.853696.64 ± 453.50 3529.58 ± 657.50 3133.49 ± 1097.471858.61 ± 969.23
      Male to female sex ratio102.00 ± 22.09 99.56 ± 2.64 100.25 ± 5.64 99.45 ± 5.98 107.11 ± 38.81
      Population using solid fuels (%)34.91 ± 34.7381.26 ± 22.6452.72 ± 27.8218.82 ± 19.046.23 ± 4.93
      Age-standardized prevalence of current tobacco smoking (%)21.40 ± 8.77 13.76 ± 5.84 22.44 ± 8.72 22.18 ± 7.45 25.09 ± 8.60
      Age-standardized diabetes mellitus prevalence (%)7.99 ± 3.086.99 ± 2.418.82 ± 3.748.81 ± 3.647.37 ± 1.96
      Population living in urban areas (%)55.35 ± 22.9933.67 ± 14.7342.89 ± 14.6656.77 ± 19.6177.06 ± 14.01
      BMI mean (kg·m−2)25.69 ± 2.19 23.41 ± 1.55 24.94 ± 2.39 26.93 ± 1.69 26.61 ± 1.27
      GDP per capita (USD)e5466.43839.172842.946492.0528671.35
        Note. aFor which data are available. bGeometric mean ± SD. cMean ± SD (all such values). dCategorized as follows: < 0.550 (low HDI); 0.550–0.699 (medium HDI); 0.700–0.799 (high HDI); > 0.800 (very high HDI). eMedian.

      Table 1.  Characteristics of included countries

      Figure S1.  Global map of the HDI in countries included in the GBD study 2017. HDI, human development index; GBD, Global Burden of Disease

      Figure S2.  Global map of health burden of cataract-related blindness with age-standardized YLD rates in 2017. YLD, year lived with disability

      CharacteristicsTotalHDId
      Medium HDIHigh HDIVery high HDI
      Subnational regions na (%)2062263121
      Blindness age-standardized YLD rate per 100,000 population b13.71 ± 4.33 122.28 ± 1.26 51.28 ± 1.57 4.64 ± 2.13
      Mean UVR exposure (J·m−2·day−1) c2,660.06 ± 1,231.884,430.78 ± 282.59 3,825.03 ± 511.14 1,731.55 ± 535.56
      Sex male to female ratio97.42 ± 4.71 102.73 ± 5.16 98.42 ± 4.25 95.94 ± 4.01
      Population using solid fuels (%)12.17 ± 12.4837.91 ± 5.12 15.83 ± 12.735.58 ± 2.34
      Age-standardized prevalence of current tobacco smoking (%)22.71 ± 8.27 38.35 ± 5.39 19.34 ± 10.3621.63 ± 2.05
      Age-standardized diabetes mellitus prevalence (%)7.00 ± 2.709.96 ± 1.257.98 ± 3.265.96 ± 1.87
      Population living in urban areas (%)77.98 ± 13.2351.17 ± 5.95 74.73 ± 13.0984.55 ± 4.86
      BMI mean (kg·m−2)24.29 ± 4.77 23.32 ± 1.04 21.21 ± 6.36 26.08 ± 3.07
      GDP per capita (USD) e33,960.502713.2611,731.0040,380.00
        Note. aFor which data are available. bGeometric mean ± SD. cMean ± SD (all such values). dCategorized as follows: 0.550–0.699 (medium HDI); 0.700–0.799 (high HDI); > 0.800 (very high HDI). eMedian.

      Table S6.  Characteristics of included subnational regions

      Figure S3.  Global map of UVR levels in 2015. UVR, ultraviolet radiation

      National and subnational age-standardized YLD rates were log-transformed because of violations of the assumption of normality. In univariate analyses, population using solid fuels, current tobacco smoking prevalence, urbanization, BMI and GDP were significantly associated with the age-standardized YLD rate (P < 0.001) at the country level, and all covariates were significantly associated with the age-standardized YLD rate (P < 0.001) at the subnational level (Supplementary Table S7 available in www.besjournal.com). Urbanization and population using solid fuels were excluded due to collinear relationships in the subnational analysis.

      VariablesCountriesSubnational regions
      Regression coefficient a
      (95% CI)
      P valueRegression coefficient a
      (95% CI)
      P value
      Male to female sex ratio1.01 (1.00, 1.01)0.1031.15 (1.10, 1.19)< 0.001
      Population using solid fuels (%)1.02 (1.01, 1.02)< 0.0011.09 (1.08, 1.11)< 0.001
      Age-standardized prevalence of current tobacco smoking (%)0.95 (0.93, 0.97)< 0.0011.06 (1.04, 1.08)< 0.001
      Age-standardized diabetes mellitus prevalence (%)0.98 (0.93, 1.04)0.4931.33 (1.25, 1.42)< 0.001
      Population living in urban areas (%)0.98 (0.98, 0.99)< 0.0010.92 (0.91, 0.93)< 0.001
      BMI mean (kg·m−2)0.80 (0.75, 0.87)< 0.0010.88 (0.85, 0.92)< 0.001
      GDP per capita per 1000 (USD)0.98 (0.97, 0.99)< 0.0010.97 (0.96, 0.97)< 0.001
        Note. aAntilog values. Outcome measures were log-transformed in the analysis.

      Table S7.  Univariate association between covariates and cataract blindness age-standardized YLD rate in countries

      Linear regression analysis showed that HDI was negatively associated with cataract age-standardized YLD rate owing to blindness in the crude model. In multivariable analyses, a consistent reverse association between HDI and the burden of cataract-related blindness among countries was retained in the adjusted model (Table 2). The adjusted model was adjusted for male to female sex ratio, GDP, population using solid fuels, age-standardized prevalence of current tobacco smoking, age-standardized diabetes mellitus prevalence, population living in urban areas and BMI mean. Very-high-HDI countries had an 84% lower age-standardized YLD rate [95% confidence interval (CI): 60%–93%, P < 0.001] compared to low-HDI countries. For high-HDI countries, the proportion was 76% (95% CI: 53%–88%, P < 0.001), and for medium-HDI countries, the proportion was 48% (95% CI: 15%–68%, P = 0.010; P for trend < 0.001) (Table 2). Although there was a lack of low-HDI subnational regions, the association between HDI and the burden of cataract-related blindness in subnational-level analyses had a similar trend, but a lower magnitude than that in the country-level analyses.

      VariablesCrude modelAdjusted modelc
      Regression coefficienta (95% CI)P valueRegression coefficienta (95% CI)P value
      Countries
       HDI per 0.010.95 (0.94, 0.96)< 0.0010.93 (0.91, 0.96)< 0.001
       HDI categoriesb
        Low HDI1.00 (reference)1.00 (reference)
        Medium HDI0.58 (0.38, 0.89)0.0140.52 (0.32, 0.85)0.010
        High HDI0.25 (0.17, 0.37)< 0.0010.24 (0.12, 0.47)< 0.001
        Very high HDI0.13 (0.09, 0.20)< 0.0010.16 (0.07, 0.40)< 0.001
         P value for trend< 0.001< 0.001
      Subnational regions
       HDI per 0.010.87 (0.86, 0.87)< 0.0010.96 (0.94, 0.98)< 0.001
       HDI categoriesb
        Medium HDI1.00 (reference)1.00 (reference)
        High HDI0.42 (0.31, 0.57)< 0.0010.93 (0.70, 1.25)0.637
        Very high HDI0.04 (0.03, 0.05)< 0.0010.62 (0.40, 0.97)0.039
        P value for trend< 0.0010.021
        Note. HDI, human development index. aAntilog values. Outcome measures were log-transformed in the analysis. bCategorized as follows: < 0.550 (low HDI); 0.550–0.699 (medium HDI); 0.700–0.799 (high HDI); > 0.800 (very high HDI). cAdjusted for male to female sex ratio, GDP, population using solid fuels, age-standardized prevalence of current tobacco smoking, age-standardized diabetes mellitus prevalence, population living in urban areas and BMI mean.

      Table 2.  Multivariate linear regression model analysis of the relationship between HDI and cataract age-standardized YLD rates owing to blindness

      To assess potential effect modification by UVR exposure, we stratified the analysis by the median value of countries’ UVR exposure (high UVR > 3251.68 and low UVR ≤ 3251.68). The age-standardized YLD rate declined with increasing HDI levels in both UVR groups, and only countries with high HDIs in different UVR categories had significant differences (P < 0.05) (Figure 1). The mean UVR exposure of the low-UVR group decreased by the order of 2947.84 (low HDI), 2796.09 (medium HDI), 2073.29 (high HDI) and 1621.24 (very high HDI). Equivalent figures for the high-UVR group were 3810.10, 3940.34, 4004.37, and 3876.17, respectively. As shown in Figure 2 and Supplementary Figure S4 (available in www.besjournal.com), adjusted linear regression analysis indicated that the age-standardized YLD rate owing to cataract blindness was negatively correlated with HDI in both UVR categories in countries and subnational regions (P < 0.001). Table 3 presents the association of HDI with the cataract age-standardized YLD rate owing to blindness modified by UVR exposure in countries. UVR exposure was an effect modifier of HDI and the burden of cataract-related blindness in the adjusted model (P value for interaction = 0.047).

      Figure 1.  Geometric mean of age-standardized YLD rate owing to cataract blindness across categories of socioeconomic status expressed as HDI at the country level. *P < 0.05

      Figure 2.  Relationship between HDI and log age-standardized YLD rate owing to cataract blindness for both UVR categories at the country level after adjusting for all covariates. The lines represent fitted lines. HDI, human development index. YLD, year lived with disability. UVR, ultraviolet radiation.

      Figure S4.  Relationship between HDI and log age-standardized YLD rate owing to blindness for both UVR categories at the subnational level after adjusting for all covariates. HDI, human development index; YLD, year lived with disability; UVR, ultraviolet radiation

      VariablesCountries na (%) Mean UVR exposure
      J·m−2·day−1
      Crude modelAdjusted modeld
      Regression coefficientb
      (95% CI)
      P valueRegression coefficientb
      (95% CI)
      P value
      High UVR92
       HDI categoriesc
        Low HDI33 (35.87)3810.101.00 (reference)1.00 (reference)
        Medium HDI25 (27.17)3940.340.64 (0.44, 0.94) 0.0200.62 (0.40, 0.96)0.036
        High HDI28 (30.43)4004.370.38 (0.27, 0.56)< 0.0010.39 (0.21, 0.73)0.005
        Very high HDI6 (6.52)3876.170.16 (0.09, 0.31)< 0.0010.12 (0.04, 0.36)< 0.001
         P for trend< 0.0010.001
      Low UVR93
       HDI categoriesc
        Low HDI5 (5.38)2947.841.00 (reference)1.00 (reference)
        Medium HDI14 (15.05)2796.090.49 (0.16, 1.48) 0.2100.48 (0.15, 1.50)0.209
        High HDI23 (24.73)2073.290.15 (0.05, 0.42)< 0.0010.15 (0.04, 0.55)0.006
        Very high HDI51 (54.84)1621.240.13 (0.05, 0.35)< 0.0010.14 (0.03, 0.68)0.016
         P for trend< 0.0010.023
      P value for interactione 0.1361 0.047
        Note. HDI,human development index. aFor which data are available. bAntilog values. Outcome measures were log-transformed in the analysis. cCategorized as follows: < 0.550 (low HDI); 0.550–0.699 (medium HDI); 0.700–0.799 (high HDI); > 0.800 (very high HDI). dAdjusted for male to female sex ratio, GDP, population using solid fuels, age-standardized prevalence of current tobacco smoking, age-standardized diabetes mellitus prevalence, population living in urban areas and BMI mean. eTest for the interaction between HDI (low HDI, medium HDI, high HDI, and very high HDI) and UVR (high UVR and low UVR).

      Table 3.  Effect of UVR exposure on the cataract age-standardized YLD rate owing to blindness in countries with elevated HDI

    • This study showed that socioeconomic status was inversely correlated with the burden of cataract blindness and revealed that UVR exposure modified the association of socioeconomic status with the health burden of cataract blindness, taking into account differences in the male to female sex ratio, GDP, population using solid fuels, age-standardized prevalence of current tobacco smoking, age-standardized diabetes mellitus prevalence, population living in urban areas and BMI. Countries with higher levels of socioeconomic development were found to have lower cataract-related blindness burdens. When countries were replaced by subnational regions, the trend of the negative correlation of socioeconomic status with the burden of cataract blindness was largely retained but was lower in magnitude.

      A previous study estimated the prevalence of and number of people with blindness due to cataracts and found that among 21 GBD study regions, the percentages of blindness caused by cataracts were lower in the high-income regions (< 15%) and higher (> 40%) in South and Southeast Asia and Oceania[2]. Our study showed that the cataract-related blindness burden is more concentrated in countries with lower socioeconomic status. The HDI level was independently associated with the health burden of cataract-related blindness, with lower age-standardized YLD rates in higher HDI countries. A possible explanation is that the HDI level is related to the output and quality of cataract surgery. Cataract surgery is considered one of the most cost-effective health-care interventions, with a cost per DALY saved of US$ 20–40 million, and is performed with increasing frequency in all regions[1]. However, barriers to cataract surgery services still exist in most countries; the most commonly cited barriers are socioeconomic factors, including income, insurance coverage and low government funding[18-20]. A systematic review demonstrated inequalities in cataract surgery rates were found among countries grouped by income and were associated with socioeconomic indicators[21]. Although the global initiative known as ‘VISION 2020: the Right to Sight’ has made many efforts to promote cataract surgery services at a cost that all patients can afford worldwide[1], the cost still represents a significant expenditure, especially for patients of lower socioeconomic levels[20]. In developing countries worldwide, over half of people with cataract blindness do not undergo cataract surgery[22], mainly because of a low willingness to pay for it[23]. In addition, the quality of cataract surgery is still a concern, with poorer outcomes related to low socioeconomic levels[24]; this disparity aggravates between-country disparities in cataract blindness. Using subnational data, our study observed that the HDI was negatively correlated with the burden of cataract-related blindness. When subnational instead of national data were considered, the difference in socioeconomic status between regions within a country decreased, which helped us to observe the impact of socioeconomic status on the burden of cataract blindness at a smaller geographical coverage and at a detailed level. Although limited subnational data were included according to the GBD study, the findings suggest that socioeconomic disparities in the burden of cataract blindness exist not only among countries but also among subnational regions with different development levels.

      Studies have shown that UVR exposure is one of the most important factors of cataractogenesis and is related to cataract development by inducing apoptosis and photooxidation[25, 26]. Several findings have demonstrated that the association of UVR exposure with cataracts is dose dependent[27, 28]. More UVR exposure implies an increasing risk of cataracts, leading to more cases of cataract blindness. The increased risk for cataract extraction in subjects exposed to high lifetime ambient total UVR (42.718 KJ·m−2) was confirmed [odds ratio (OR) = 1.53] by comparison with subjects exposed to moderate ambient total UVR (39.887 KJ·m−2)[29]. High UVR exposure causes an increased cataract blindness burden and leads to added national medical expenditures related to socioeconomic level. The costs of environmental measures are often seen as an impediment to economic development. In regard to cataracts, the economic burden attributed to excess UVR is US$4.5 billion in the United States[30]. The increase in economic costs due to high UVR may be unaffordable and present a concern for low- and middle-income countries. Ambient levels of UVR have been increasing and may persist at elevated levels in the future because of continuous stratospheric ozone depletion, and the increase represents an excess cost to address additional cases of cataracts[31]. This serves as a warning that the countries with high UVR exposure and poor economic development must pay particular attention to ensuring protectionin order to reduce the incidence of cataract blindness. At low latitudes, there are a large number of countries that have lower economic conditions and suffer from high UV exposure. It is unrealistic to change the economic situation of these countries in a short time. Therefore, more economical interventions to protect the eyes from UVR exposure, to make the public realize that UVR exposure is harmful to the eyes, and to raise public recognition of UVR exposure. The WHO provides the UV Index (UVI) to guide crowd behavior, increase the population's attention to UVR exposure, and strengthen self-protection. In many countries the UVI is reported along with the weather forecast in newspapers, on TV, and on the radio. The publicity of UVI should be enhanced, and the public should be reminded to take eye protection measures such as wearing hats and sunglasses to avoid high UVR exposure in countries with lower socioeconomic status. Our findings may have significance for public health, given that cataracts are easily treatable[32], and strengthening UVR protection could be an cost-effective intervention for delaying cataract blindness.

      Several potential limitations need to be considered. First, the UVR exposure dose derived from the OMI surface UVR product might be overestimated compared to ground-based spectral UVR measurements[33]. The estimated cataract-related blindness burden in the GBD study 2017 may be inadequate due to limited data sources and possible selection bias resulting from the reliance on clinical data records[13]. Second, our study might be subject to ecological fallacy and bias, because the use of aggregate country-level data did not provide information on individuals. Third, the linear regression analysis included subnational-level data of age-standardized YLD rates owing to blindness, UVR exposure, HDI, male to female sex ratio, age-standardized diabetes mellitus prevalence and GDP. The remaining variables, such as population using solid fuels, age-standardized prevalence of current tobacco smoking, population living in urban areas and BMI mean, were not available at the subnational level. We assumed that these variables had a homogeneous distribution throughout each country, which covered important subnational differences. Furthermore, the study was restricted to the locations included in the GBD study 2017, therefore, only 206 subnational regions belonging to seven countries were included. For other countries with large geographic latitude coverage, we were forced to use a single national age-standardized YLD rate to represent the burden of cataract-related blindness without detailed subnational data. The insufficient sample size, especially the lack of low-HDI subnational regions, may cause poor representativeness. Despite these limitations, ecological studies are an effective method to explore associations on a worldwide level, especially based upon openly available data[34].

    • In conclusion, long-term high-UVR exposure amplified the association of poor socioeconomic status with the burden of cataract-related blindness. The findings highlight that in addition to existing efforts toward eliminating cataract blindness, UVR exposure protection interventions, such as wearing glasses, wearing a cap and a reduction of outdoor activity time, must be reinforced in developing regions with high-UVR exposure to achieve the global target proposed by the WHO GAP.

    • The authors declare no conflict of interest.

    • DENG Yan calculated the solar ultraviolet radiation exposure, analyzed the data, and wrote the original article. YANG Dan obtained data on the global burden of cataract blindness and other related data. YU Jia Ming and XU Jing Xian determined the covariates related to cataracts. HUA Hui, CHEN Ren Tong, and WANG Nan performed the covariate calculations, statistical analysis and graphing. OU Feng Rong helped revise the manuscript. LIU Ru Xi and WU Bo performed data processing. LIU Yang designed the experiment. All authors contributed to the writing and editing of the final paper.

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