The Global Burden of Glaucoma: Findings from the Global Burden of Disease 2019 Study and Predictions by Bayesian Age–Period–Cohort Analysis
Abstract
1. Introduction
2. Methods
2.1. Overview
2.2. Data Sources
2.3. Statistical Analysis
3. Results
3.1. Global Level
3.2. National Level
3.3. The Association between SDI and Age-Standardized DALY Rate
3.4. Trends of Age-Standardized DALY Rates Predicted by BAPC
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Stein, J.D.; Khawaja, A.P.; Weizer, J.S. Glaucoma in adults-screening, diagnosis, and management: A review. JAMA 2021, 325, 164–174. [Google Scholar] [CrossRef] [PubMed]
- Tham, Y.-C.; Li, X.; Wong, T.Y.; Quigley, H.A.; Aung, T.; Cheng, C.-Y. Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology 2014, 121, 2081–2090. [Google Scholar] [CrossRef] [PubMed]
- Jonas, J.B.; Aung, T.; Bourne, R.R.; Bron, A.M.; Ritch, R.; Panda-Jonas, S. Glaucoma. Lancet 2017, 390, 2183–2193. [Google Scholar] [CrossRef] [PubMed]
- Zhao, P.Y.; Rahmathullah, R.; Stagg, B.C.; Almobarak, F.; Edward, D.P.; Robin, A.L.; Stein, J.D. A worldwide price comparison of glaucoma medications, laser trabeculoplasty, and trabeculectomy surgery. JAMA Ophthalmol. 2018, 136, 1271–1279. [Google Scholar] [CrossRef] [PubMed]
- Santomauro, D.F.; Melen, S.; Mitchison, D.; Vos, T.; Whiteford, H.; Ferrari, A.J. The hidden burden of eating disorders: An extension of estimates from the global burden of disease study 2019. Lancet Psychiatry 2021, 8, 320–328. [Google Scholar] [CrossRef] [PubMed]
- The Lancet GBD 2017: A Fragile World. Lancet 2018, 392, 1683. [CrossRef] [PubMed]
- GBD 2013 DALYs and HALE Collaborators; Murray, C.J.L.; Barber, R.M.; Foreman, K.J.; Abbasoglu Ozgoren, A.; Abd-Allah, F.; Abera, S.F.; Aboyans, V.; Abraham, J.P.; Abubakar, I.; et al. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: Quantifying the epidemiological transition. Lancet 2015, 386, 2145–2191. [Google Scholar] [CrossRef] [PubMed]
- Odnoletkova, I.; Kindle, G.; Quinti, I.; Grimbacher, B.; Knerr, V.; Gathmann, B.; Ehl, S.; Mahlaoui, N.; Van Wilder, P.; Bogaerts, K.; et al. The burden of common variable immunodeficiency disorders: A Retrospective analysis of the European society for immunodeficiency (ESID) registry fata. Orphanet J. Rare Dis. 2018, 13, 201. [Google Scholar] [CrossRef] [PubMed]
- Sun, Y.; Chen, A.; Zou, M.; Liu, Z.; Young, C.A.; Zheng, D.; Jin, G. Disease burden of glaucoma in China: Findings from the global burden of disease 2019 study. Clin. Epidemiol. 2022, 14, 827–834. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Jin, G.; Fan, M.; Lin, Y.; Wen, X.; Li, Z.; Zeng, P.; Zheng, D.; Lan, Y. Time trends and heterogeneity in the disease burden of glaucoma, 1990-2017: A global analysis. J. Glob. Health 2019, 9, 020436. [Google Scholar] [CrossRef] [PubMed]
- Sun, Y.; Chen, A.; Zou, M.; Zhang, Y.; Jin, L.; Li, Y.; Zheng, D.; Jin, G.; Congdon, N. Time trends, associations and prevalence of blindness and vision loss due to glaucoma: An analysis of observational data from the global burden of disease study 2017. BMJ Open 2022, 12, e053805. [Google Scholar] [CrossRef] [PubMed]
- Wang, W.; He, M.; Li, Z.; Huang, W. Epidemiological variations and trends in health burden of glaucoma worldwide. Acta Ophthalmol. 2019, 97, e349–e355. [Google Scholar] [CrossRef] [PubMed]
- GBD 2016 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2016: A systematic analysis for the global burden of disease study 2016. Lancet 2017, 390, 1260–1344. [Google Scholar] [CrossRef] [PubMed]
- Varma, R.; Lee, P.P.; Goldberg, I.; Kotak, S. An assessment of the health and economic burdens of glaucoma. Am. J. Ophthalmol. 2011, 152, 515–522. [Google Scholar] [CrossRef]
- Song, P.; Wang, J.; Bucan, K.; Theodoratou, E.; Rudan, I.; Chan, K.Y. National and subnational prevalence and burden of glaucoma in China: A systematic analysis. J. Glob. Health 2017, 7, 020705. [Google Scholar] [CrossRef]
- Wong, T.Y.; Zheng, Y.; Jonas, J.B.; Flaxman, S.R.; Keeffe, J.; Leasher, J.; Naidoo, K.; Pesudovs, K.; Price, H.; White, R.A.; et al. Prevalence and causes of vision loss in East Asia: 1990-2010. Br. J. Ophthalmol. 2014, 98, 599–604. [Google Scholar] [CrossRef]
- Wu, J.; Yu, X.; Ping, X.; Xu, X.; Cui, Y.; Yang, H.; Zhou, J.; Yin, Q.; Shentu, X. Socioeconomic disparities in the global burden of glaucoma: An analysis of trends from 1990 to 2016. Graefes Arch. Clin. Exp. Ophthalmol. 2020, 258, 587–594. [Google Scholar] [CrossRef]
- Riebler, A.; Held, L. Projecting the future burden of cancer: Bayesian age-period-cohort analysis with integrated nested laplace approximations. Biom. J. 2017, 59, 531–549. [Google Scholar] [CrossRef] [PubMed]
- Du, Z.; Chen, W.; Xia, Q.; Shi, O.; Chen, Q. Trends and Projections of kidney cancer incidence at the global and national levels, 1990-2030: A bayesian age-period-cohort modeling study. Biomark. Res. 2020, 8, 16. [Google Scholar] [CrossRef] [PubMed]
Location | Prevalence | DALYs | ||||
---|---|---|---|---|---|---|
Counts (1990, 95%UI) | Counts (2019, 95%UI) | Percentage Change between 1990 and 2019 (95% CI) | Counts (1990, 95%UI) | Counts (2019, 95%UI) | Percentage Change between 1990 and 2019 (95% CI) | |
Global | 3,881,624 (3,301,963 to 4,535,045) | 7,473,400 (6,347,183 to 8,769,520) | 0.93% (0.86 to 1) | 442,182 (301,827 to 626,486) | 748,308 (515,636 to 1,044,667) | 0.69% (0.64 to 0.76) |
High SDI | 583,642 (491,335 to 684,860) | 1,084,524 (916,780 to 1,273,892) | 0.86% (0.81 to 0.92) | 65,476 (45,542 to 91,255) | 112,534 (78,105 to 154,478) | 0.72% (0.66 to 0.79) |
High-middle SDI | 923,190 (781,067 to 1,076,231) | 1,641,067 (1,387,922 to 1,922,562) | 0.78% (0.7 to 0.86) | 108,268 (73,461 to 152,050) | 164,967 (113,869 to 227,607) | 0.52% (0.46 to 0.59) |
Middle SDI | 1,196,364 (1,004,698 to 1,407,525) | 2,525,628 (2,125,515 to 2,988,280) | 1.11% (1.01 to 1.22) | 138,295 (93,244 to 197,281) | 246,219 (169,633 to 347,587) | 0.78% (0.7 to 0.87) |
Low-middle SDI | 774,596 (648,008 to 911,241) | 1,449,917 (1,210,186 to 1,717,083) | 0.87% (0.8 to 0.95) | 82,230 (55,679 to 117,365) | 138,559 (95,425 to 194,861) | 0.69% (0.61 to 0.76) |
Low SDI | 401,260 (338,417 to 472,395) | 767,566 (646,730 to 904,910) | 0.91% (0.85 to 0.98) | 47,603 (32,365 to 67,819) | 85,509 (58,216 to 121,784) | 0.8% (0.73 to 0.87) |
Andean Latin America | 33,900 (27,740 to 40,839) | 75,131 (62,646 to 90,013) | 1.22% (1.08 to 1.37) | 4052 (2670 to 5816) | 7851 (5270 to 11,067) | 0.94% (0.78 to 1.1) |
Australasia | 12,963 (10,877 to 15,290) | 28,496 (23,616 to 34,370) | 1.2% (1.08 to 1.33) | 1390 (940 to 1952) | 2863 (1937 to 4008) | 1.06% (0.88 to 1.28) |
Caribbean | 40,064 (32,819 to 48,306) | 66,986 (54,973 to 80,149) | 0.67% (0.6 to 0.75) | 4746 (3117 to 6884) | 7205 (4812 to 10,375) | 0.52% (0.45 to 0.61) |
Central Asia | 53,215 (43,951 to 63,930) | 59,804 (49,043 to 71,960) | 0.12% (0.07 to 0.18) | 6349 (4134 to 9072) | 6539 (4342 to 9371) | 0.03% (−0.03 to 0.1) |
Central Europe | 63,099 (52,093 to 75,447) | 88,149 (72,719 to 105,966) | 0.4% (0.34 to 0.46) | 6344 (4257 to 8991) | 8053 (5448 to 11,209) | 0.27% (0.2 to 0.34) |
Central Latin America | 114,319 (95,652 to 135,883) | 281,748 (234,405 to 334,567) | 1.46% (1.36 to 1.58) | 11,780 (7940 to 16,634) | 25,307 (17,472 to 35,120) | 1.15% (1.05 to 1.27) |
Central Sub-Saharan Africa | 23,669 (19,438 to 28,487) | 53,424 (44,112 to 63,895) | 1.26% (1.15 to 1.37) | 2337 (1559 to 3339) | 4969 (3345 to 7013) | 1.13% (0.98 to 1.29) |
East Asia | 590,332 (495,480 to 693,596) | 1,354,016 (1,124,434 to 1,610,382) | 1.29% (1.11 to 1.5) | 67,661 (44,439 to 97,443) | 113,256 (77,966 to 161,812) | 0.67% (0.54 to 0.85) |
Eastern Europe | 201,339 (169,564 to 235,975) | 232,086 (195,560 to 272,596) | 0.15% (0.11 to 0.2) | 21,251 (14,391 to 29,752) | 22,277 (15,263 to 30,698) | 0.05% (0 to 0.1) |
Eastern Sub-Saharan Africa | 172,107 (144,040 to 202,487) | 307,589 (259,594 to 361,278) | 0.79% (0.72 to 0.85) | 22,263 (14,991 to 32,087) | 37,596 (25,496 to 53,573) | 0.69% (0.61 to 0.76) |
High-income Asia Pacific | 113,450 (96,834 to 131,550) | 292,514 (249,497 to 340,022) | 1.58% (1.44 to 1.74) | 13,621 (9270 to 18,995) | 31,811 (21,984 to 43,721) | 1.34% (1.18 to 1.51) |
High-income North America | 162,452 (137,066 to 189,578) | 294,458 (249,498 to 343,827) | 0.81% (0.77 to 0.85) | 17,258 (11,990 to 23,680) | 30,561 (21,274 to 41,693) | 0.77% (0.71 to 0.83) |
North Africa and Middle East | 412,550 (341,236 to 493,642) | 733,620 (610,514 to 872,051) | 0.78% (0.7 to 0.87) | 52,478 (34,717 to 75,092) | 83,758 (56,200 to 119,571) | 0.6% (0.53 to 0.67) |
Oceania | 2257 (1794 to 2774) | 4728 (3838 to 5779) | 1.09% (1.01 to 1.2) | 282 (183 to 415) | 555 (362 to 813) | 0.97% (0.8 to 1.15) |
South Asia | 720,664 (597,199 to 856,070) | 1,521,185 (1,257,875 to 1,812,713) | 1.11% (1.02 to 1.2) | 74,014 (50,166 to 104,261) | 141,813 (97,099 to 197,855) | 0.92% (0.81 to 1.02) |
Southeast Asia | 234,342 (195,458 to 278,737) | 456,660 (381,637 to 543,467) | 0.95% (0.88 to 1.02) | 27,074 (18,312 to 38,895) | 47,666 (31,744 to 68,337) | 0.76% (0.69 to 0.83) |
Southern Latin America | 33,190 (27,123 to 39,678) | 55,975 (45,715 to 67,459) | 0.69% (0.6 to 0.78) | 3553 (2400 to 5104) | 5465 (3693 to 7549) | 0.54% (0.41 to 0.67) |
Southern Sub-Saharan Africa | 54,805 (45,210 to 64,814) | 83,834 (70,365 to 98,761) | 0.53% (0.47 to 0.6) | 7045 (4630 to 9998) | 9691 (6484 to 13,680) | 0.38% (0.32 to 0.44) |
Tropical Latin America | 145,854 (123,600 to 171,567) | 370,683 (313,202 to 436,338) | 1.54% (1.47 to 1.62) | 14,081 (9597 to 19,893) | 33,436 (23,076 to 46,432) | 1.37% (1.28 to 1.47) |
Western Europe | 462,486 (382,041 to 548,825) | 690,822 (570,690 to 822,355) | 0.49% (0.43 to 0.56) | 58,015 (39,495 to 82,218) | 81,583 (55,393 to 113,969) | 0.41% (0.34 to 0.49) |
Western Sub-Saharan Africa | 234,567 (199,432 to 274,436) | 421,489 (356,968 to 493,611) | 0.8% (0.75 to 0.84) | 26,588 (18,247 to 37,741) | 46,053 (31,544 to 64,751) | 0.73% (0.69 to 0.78) |
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Lin, Y.; Jiang, B.; Cai, Y.; Luo, W.; Zhu, X.; Lin, Q.; Tang, M.; Li, X.; Xie, L. The Global Burden of Glaucoma: Findings from the Global Burden of Disease 2019 Study and Predictions by Bayesian Age–Period–Cohort Analysis. J. Clin. Med. 2023, 12, 1828. https://doi.org/10.3390/jcm12051828
Lin Y, Jiang B, Cai Y, Luo W, Zhu X, Lin Q, Tang M, Li X, Xie L. The Global Burden of Glaucoma: Findings from the Global Burden of Disease 2019 Study and Predictions by Bayesian Age–Period–Cohort Analysis. Journal of Clinical Medicine. 2023; 12(5):1828. https://doi.org/10.3390/jcm12051828
Chicago/Turabian StyleLin, Yi, Bingcai Jiang, Yuanqing Cai, Wangdu Luo, Xiaomin Zhu, Qianyi Lin, Min Tang, Xiangji Li, and Lin Xie. 2023. "The Global Burden of Glaucoma: Findings from the Global Burden of Disease 2019 Study and Predictions by Bayesian Age–Period–Cohort Analysis" Journal of Clinical Medicine 12, no. 5: 1828. https://doi.org/10.3390/jcm12051828
APA StyleLin, Y., Jiang, B., Cai, Y., Luo, W., Zhu, X., Lin, Q., Tang, M., Li, X., & Xie, L. (2023). The Global Burden of Glaucoma: Findings from the Global Burden of Disease 2019 Study and Predictions by Bayesian Age–Period–Cohort Analysis. Journal of Clinical Medicine, 12(5), 1828. https://doi.org/10.3390/jcm12051828