Prevalence, temporal trend, burden, and prediction of refractive disorders among children and adolescents
Bibliographic record
Abstract
To the Editor: Refractive disorder (RD) is an initiative undertaken so as to eliminate avoidable blindness by 2020 (VISION 2020: the Right to Sight).[1] It is one of the most common causes of visual impairments worldwide. Uncorrectable RD is the main cause of visual impairment in children and adolescents.[2] Thus, RD, especially myopia, has a high impact on economic costs and adds to the burden of visually disabling age-related complications in adulthood.[3] In China, RDs are also a public health issue. Current epidemic studies provide the characteristics of refractive errors among Chinese children and adolescents,[4,5] while national-level data on the epidemic characteristics of RDs and the disease burden are still unknown. This study assessed the burden of refractive errors in all the provinces across China among Chinese children and adolescents younger than 20 years over the past three decades. This study is a part of the GBD 2019, generated by the Institute for Health Metrics and Evaluation (IHME), which is currently the main source of population-based studies in global health. Detailed information on the methodology has been extensively reported elsewhere.[6] A summary of the data sources used in the GBD 2019 Chinese blindness and vision loss analysis is provided elsewhere.[7] The China Statistical Yearbook reported the gross domestic product (GDP) by the province in 2019. Ethics approval and informed consent were not required for this study because of the public accessibility to the data. Moderate vision impairment (MVI) indicated a visual acuity (VA) ≥6/60 and <6/18 server vision impairment (SVI) was defined as VA ≥3/60 and <6/60 based on the Snellen chart, while the blindness was VA <3/60 or the visual field around central fixation <10%. All vision loss equaled to the sum of different stages of vision loss. We estimated the prevalence and burden of RDs in China and compared them with the Group of 20 (G20) countries during the study period. Prevalence and the years lived with disabilities (YLDs) rates of RDs were expressed as the number per 100,000 population with 95% uncertainty intervals (UIs). The average annual percentage change (AAPC) analysis was performed on the changes in the prevalence and the YLDs rates using the Joinpoint 4.8.0.1 software (Surveillance Research Program at the National Cancer Institute,United States). The Bayesian age–period-cohort (BAPC) model was used to project the change in disease burden from 2020 to 2050. More information on the methods is shown in the Supplementary Methods, https://links.lww.com/CM9/C216. The global estimation of RDs reported decreasing trends in the prevalence (AAPC: −0.2%, 95% confidential interval [CI]: −0.2% to −0.2%) and the YLDs rates (AAPC: −0.2%, 95% CI: −0.3% to −0.2%) among populations younger than 20 years from 1990 to 2019 [Table 1]. Additionally, in China the YLDs rates per 100,000 rose slightly from 24.9 (95% UI: 15.2 to 38.1) in 1990 to 28.8 (95% UI: 17.7 to 44.2) in 2019. Among G20 countries, only China, Turkey, and the United States of America have an upward trend in both the prevalence and the YLDs rates [Table 1]. Specifically, MVI prevalence and the YLDs rates due to RDs were significantly increased [Supplementary Tables 1 and 2, https://links.lww.com/CM9/C216]. Table 1 - Prevalence, YLDs, and changes of RDs among populations aged younger than 20 years in global, China, and the G20 countries. Country/region Prevalence (per 100,000) YLDs rate (per 100,000) 1990 2019 AAPC (%), 1990–2019 P-value 1990 2019 AAPC (%), 1990–2019 P-value Global 991.9 (801.1–1207.0) 954.5 (771.9–1162.6) –0.2 (–0.2 to –0.2) <0.001 35.8 (22.1–54.5) 34.1 (20.9–52.5) –0.2 (–0.3 to –0.2) <0.001 G20 countries China 669.9 (540.6–815.8) 791.2 (634.1–968.2) 0.5 (0.4–0.7) <0.001 24.9 (15.2–38.1) 28.8 (17.7–44.2) 0.4 (0.3–0.6) <0.001 Argentina 1496.6 (1220.9–1826.6) 1469.0 (1190.3–1791.3) –0.1 (–0.1 to –0.1) <0.001 53.6 (32.9–82.0) 51.9 (32.1–80.2) –0.1 (–0.1 to –0.1) <0.001 Australia 1329.0 (1078.7–1627.9) 1370.0 (1105.6–1680.8) 0.10 (–0.01 to 0.20) 0.067 45.3 (27.0–71.9) 46.4 (28.2–71.5) 0.1 (0–0.2) 0.050 Brazil 1784.8 (1456.8–2168.8) 1717.5 (1396.9–2095.8) 0.20 (0.03–0.30) 0.018 64.5 (39.9–99.3) 61.3 (37.4–94.2) 0.10 (–0.05 to 0.20) 0.202 Canada 844.3 (686.2–1030.5) 841.8 (680.8–1039.8) –0.01 (–0.04 to 0.03) 0.698 29.6 (18.1–46.6) 29.4 (18.2–46.2) –0.03 (–0.10 to 0) 0.053 European Union 1174.1 (956.7–1432.9) 1177.5 (955.6–1449.1) 0.04 (0.02–0.10) <0.001 41.3 (25.5–64.0) 41.1 (24.9–64.4) 0.02 (0–0.05) 0.047 France 1127.6 (896.7–1416.7) 1106.9 (873.4–1397.1) –0.1 (–0.1 to –0.1) <0.001 38.9 (23.6–60.3) 38 (23.1–59.4) –0.1 (–0.1 to –0.1) <0.001 Germany 1106.9 (894.6–1359.3) 1100.4 (892.3–1358.0) –0.040 (–0.100 to 0.003) 0.069 38.4 (23.3–58.9) 38.1 (23.1–60.2) –0.04 (–0.10 to –0.01) 0.021 India 1072.8 (857.7–1311.2) 1007.6 (810.0–1229.1) –0.5 (–0.6 to –0.4) <0.001 39.1 (24.3–59.5) 36.5 (22.4–56.2) –0.5 (–0.7 to –0.4) <0.001 Indonesia 1179.4 (943.9–1451.3) 1125.3 (896.0–1389.0) –0.2 (–0.3 to –0.2) <0.001 40.5 (24.3–62.9) 38.1 (22.8–59.5) –0.3 (–0.3 to –0.2) <0.001 Italy 1698.1 (1384.2–2089.6) 1628.4 (1335.1–2001.7) –0.1 (–0.2 to –0.1) <0.001 60.4 (37.6–92.9) 57.2 (34.8–88.4) –0.2 (–0.2 to –0.1) <0.001 Japan 985.7 (802.5–1204.3) 954.4 (773.0–1162.2) –0.1 (–0.1 to –0.1) <0.001 35.9 (22.1–55.1) 34.6 (21.2–53.3) –0.1 (–0.2 to –0.1) <0.001 Mexico 1057.6 (833.4–1312.0) 1025.1 (807.9–1270.7) –0.4 (–0.5 to –0.2) <0.001 39.2 (23.8–60.4) 37.5 (22.6–58.4) –0.4 (–0.5 to –0.3) <0.001 Republic of Korea 1149.6 (926.4–1407.2) 1113.2 (916.1–1361.3) –0.02 (–0.10 to 0.03) 0.476 41.4 (25.3–64.5) 39.5 (24.3–61.3) –0.10 (–0.10 to –0.01) 0.011 Russian Federation 1149.2 (926.7–1406.6) 1120.3 (900–1377.3) –0.2 (–0.3 to –0.2) <0.001 40.4 (24.4–62.3) 39.1 (23.5–60.9) –0.3 (–0.3 to –0.2) <0.001 Saudi Arabia 2098.5 (1716.1–2592.5) 1916.2 (1546.6–2331.7) –0.3 (–0.4 to –0.3) <0.001 83.1 (51.4–124.7) 70.7 (44.3–108.9) –0.6 (–0.6 to –0.5) <0.001 South Africa 590.4 (463.8–733.4) 551.3 (422.7–697.0) –0.3 (–0.3 to –0.2) <0.001 21.4 (12.8–32.8) 19.8 (11.8–31.4) –0.2 (–0.3 to –0.2) <0.001 Turkey 1015.6 (843.9–1217.2) 1031.1 (833.1–1277.0) 0.1 (0.1–0.2) <0.001 37.4 (23.9–56.6) 37.2 (22.8–56.9) 0.10 (0.02–0.10) 0.003 United Kingdom 1367.8 (1121.1–1679.4) 1353.9 (1105–1660.5) –0.03 (–0.1 to 0.002) 0.068 47.1 (28.6–73.6) 46.6 (28.1–72.8) –0.03 (–0.1 to –0.01) 0.008 United States of America 881.6 (713.4–1077.8) 935.5 (754.6–1151.2) 0.4 (0.2–0.6) <0.001 30.7 (18.8–48.1) 32.5 (19.9–50.6) 0.4 (0.2–0.5) <0.001 AAPC: Average annual percentage change; G20: Group of 20; RDs: Refractive disorders; YLDs: Years lived with disabilities. Date shown as rates (95% Uncertainty interval) in prevalence and YLDs, and values (95% Confidence interval) for AAPC. For children and adolescents aged younger than 20 years in China, the overall estimated prevalence of RDs per 100,000 was 840.1 (95% UI: 672.5 to 1030.4) in females and 749.4 (95% UI: 599.7 to 913.8) in males in each age group in 2019, respectively [Supplementary Table 3, https://links.lww.com/CM9/C216]. Females have higher upward trends (AAPC: 0.6%, 95% CI: 0.4% to 0.8%) than that in males (AAPC: 0.5%, 95% CI: 0.4% to 0.6%) from 1990 to 2019 [Supplementary Table 1, https://links.lww.com/CM9/C216]. The blindness prevalence attributable to RDs in Chinese females decreased significantly (AAPC: −0.6%, 95% CI: −0.9% to −0.3%). The prevalence in Chinese males and females showed a slight increase and then there was a rapid rising since 2015, while rapidly declining since 2017 [Supplementary Figure 1A, https://links.lww.com/CM9/C216]. Similar results were also found in the YLDs rates of RDs in Chinese children and adolescents [Supplementary Figure 1B, https://links.lww.com/CM9/C216]. Also in 2019, the highest prevalence (989.7 per 100,000, 95% UI: 746.4 to 1272.8 per 100,000) and the YLDs rates (36.4 per 100,000, 95% UI: 22.1 to 57.5, per 100,000) were found in the 10–14 age group for both sexes [Supplementary Table 4, https://links.lww.com/CM9/C216]. In terms of SVI and blindness due to RDs, all age groups showed stability in the burden [Supplementary Table 5, https://links.lww.com/CM9/C216]. Furthermore, both the prevalence and the YLDs rates were the lowest with smooth trends among children less than 5-years-old during the past three decades [Supplementary Figure 1C,D, https://links.lww.com/CM9/C216]. The RD burden in the 33 provinces in 2019 are shown in Supplementary Figure 2, https://links.lww.com/CM9/C216. Totally, Hong Kong (1553.9 per 100,000, 95% UI: 1232.7–1930.2 per 100,000) has the highest prevalence of RDs in 2019 [Supplementary Table 6, https://links.lww.com/CM9/C216]. Moreover, the proportions of the prevalence and the YLDs of MVI due to RDs in 2019 increased across the country and in the 33 provinces relative to those in 1990 [Supplementary Table 7, https://links.lww.com/CM9/C216]. The highest prevalence and the YLDs rates were observed in Hong Kong in 2019, while Gansu has the largest AAPC of prevalence and the YLDs rates from 1990 to 2019. Chongqing and Xizang have the highest disease burden of SVI and blindness due to RDs in 2019, respectively [Supplementary Tables 8 and 9, https://links.lww.com/CM9/C216]. Associations between GDP and RD prevalence and its burden estimates over time for each province are shown in Supplementary Figure 2, https://links.lww.com/CM9/C216. The prevalence and the YLDs rates across the country and in the 33 provinces generally decreased as their GDP increased. From 2020 to 2050, the prevalence and the YLDs rates of RD will continue to increase [Supplementary Table 10, https://links.lww.com/CM9/C216]. The current study highlights the fact that China faces a severe burden of RDs among children and adolescents that varies across its provinces. During the past three decades, China witnessed a significant upward trend in the RD disease burden. During the study period, females and the population aged 10–14 years accounted for larger prevalence and the YLDs rates of RDs. The spatial–temporal characteristics of long-term trends in MVI due to RDs across provinces have also increased, while SVI and blindness have varied in different regions since 1990. Our findings support the fact that progress has been made in reducing Chinese RDs prevalence since 1990, but geographical, age, and sex disparities persist. Facing the rising trend in the future, examining the causes of variation in the burden of disease, equitable investment in the health system, as well as the prioritization of programs and policies are needed to reduce disease burden and its disparities across the country. Funding This work was supported by grants from the Guangdong Provincial People’s Hospital Supporting Fund for Talent Program (No.KY0120220263) and the China Postdoctoral Science Foundation (No.2021MD703910). Conflicts of interest None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".