Prevalence and associated factors of epiretinal membrane using spectralis OCT in a Chinese population: The Fujian Eye Study

Prevalence and associated factors of epiretinal membrane using spectralis OCT in a Chinese population: The Fujian Eye Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prevalence and associated factors of epiretinal membrane using spectralis OCT in a Chinese population: The Fujian Eye Study Yang Li, Xiaoxin Li, Yonghua Hu, Bin Wang, Qinrui Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4810546/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Purpose: We aimed to determine the prevalence and risk factors of epiretinal membrane in a population-based study of residents aged 50 years and older in Fujian Province, Southeast China. Methods: The Fujian Eye Study is a population-based cross-sectional eye study in Fujian province, Southeast China. Residents aged 50 years and older were enrolled and did the questionnaire (educational background, income, blood type, disease history, medication history, smoking, drinking and tea consumption, et al), physical and ophthalmological examinations with height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), refraction, intraocular pressure (IOP), slit lamp, nonmydriatic fundus photograph and spectralis optical coherence tomography (OCT) imaging. Nonmydriatic fundus photograph and Spectralis OCT were used to assess ERM according to a standardised protocol. Results: A total of 8173 residents were included in this study. Among them, 8.42% (95%CI: 0.0782 to 0.0902) had ERM in at least one eye. Multiple logistic regression showed the presence of ERM was only associated with urbanization and geographic location, but not with age, sex, refractive error, IOP, SBP, DBP, HR, BMI, hypertension, diabetic mellitus (DM), hyperlipidemia, education, income, smoking, alcohol and tea consumption. Conclusions: ERM is common among Chinese with 8.42% in at least one eye. Urbanization and geographic location are the only associated factors for ERM in Fujian Eye Study. Health sciences/Health care/Public health/Epidemiology Health sciences/Risk factors cross-sectional eye study epiretinal membrane prevalence related factor urbanization geographic location Introduction With the rapid development of ophthalmic diagnosis and treatment technology, Optical Coherence Tomography (OCT) has become one of the most important tools for diagnosing ophthalmic diseases.[ 1 ] Among them, Spectralis OCT, as a new type of OCT technology, provides new possibilities for accurate diagnosis of retinal diseases by providing higher image resolution and deeper tissue penetration.[ 2 ] Epiretinal Membrane (ERM) is a fundus disease that affects vision and mainly occurs in middle-aged and elderly populations.[ 3 ] Due to the fact that the macular membrane may not have obvious symptoms in the early stages of onset, and traditional ophthalmic examination methods are difficult to accurately diagnose, it is crucial to find an efficient and accurate screening tool for the early diagnosis and intervention of this disease. Spectralis OCT can clearly display the structure and morphology of each layer of the retina in the macular area, thereby accurately identifying the presence and degree of the macular membrane.[ 4 ] However, there is still relatively less research on the application of Spectralis OCT in macular membrane screening, and there is a lack of large-scale and systematic epidemiological investigations.[ 5 – 7 ] As one of the regions with a relatively developed economy and a high degree of aging population in China, the health issues of middle-aged and elderly people in Fujian are increasingly receiving attention. In order to better understand the prevalence of ERM in the middle-aged and elderly population in Fujian province and provide scientific basis for its prevention and treatment, this study planed to use Spectralis OCT technology to conduct a large-scale screening of the middle-aged and elderly population in this region. Through this study, we hope to provide strong support for the early detection and intervention of ERM in the middle-aged and elderly population in Fujian region, and also provide useful reference and guidance for the prevention and treatment of eye diseases in other middle-aged and elderly populations. In addition, the results of this study will also contribute to promoting the application and development of Spectralis OCT technology in the field of ophthalmology, and contribute new strength to the diagnosis and treatment of ophthalmic diseases. Materials and methods Study design A population based cross-sectional study, Fujian Eye Study (FJES) was performed on residents aged 50 years and older in Fujian Province, Southeast China from May 2018 to October 2019. Random cluster sampling was used in this investigation, and the calculation formula and sample size have been reported[ 5 ]. A clinical study registry was obtained for the 2018–2019 FJES study (register number: ChiCTR2100043349, registration date: 2021-02-21) and the study protocol was approved by the Ethics Committee of Xiamen Eye Center afliated with Xiamen University (Acceptance number: XMYKZX-KY-2018-001). This study follows the Helsinki Declaration and written informed consent was obtained from all participants. Participants Participants underwent a comprehensive physical examination in a mobile clinic. Those who were unable to participate in on-site examination in the screening were asked for the consent of home visits and simple ophthalmic examinations. On-site examination The main contents of this study include the following: questionnaire (age, sex, and race; educational background, income level, occupation, etc.); visual acuity, refraction, intraocular pressure (IOP), fundus photograph and Spectralis OCT. The flowchart of field screening has been reported.[ 8 ] A fundus photograph of each eye was taken using a scanning laser device (Digital Fundus Camera, VISUCAM 524, Goeschwitzer Strasse 51–52, 07745 Jena, Germany) and Spectralis OCT (Spectralis OCT, Heidelberg Engineering GmbH 69,121, Heidelberg, Germany) was used for high-resolution imaging of the optic disc and central retina in both eyes. The protocols have been reported[ 5 ]. Statistical analysis Data analysis was carried out using Stata/SE statistical software (Stata for Windows, version 15.1, StataCorp LLC, Lakeway Drive, College Station, TX, USA). A ᵡ 2 tests was used for categorical variables. Linear regression was used to determine whether ERM occurrence and potential factors were related. Logistic regression was used for correlation degree of each group. Multiple logistic regression was used to assess the association of factors with ERM. The odds ratios (ORs) or r values with 95% confidential intervals (CIs) were presented. A p -value less than 0.05 was considered statistically significant for all the estimates. Results Characteristics of the participants Finally, 8211 participants were included in our whole study. A total of 8173 residents underwent the Spectralis OCT examination, of which 4 had unclear photos, so ultimately 8169 residents were included in this study. of 8173, 688 (8.42%, 95% CI: 0.0782 to 0.0902) had ERM in at least one eye, and 413 (60.03%) were female, 418 (60.76%) were from urban area, 579 (84.16%) were from coastal region, 605 (87.9%) had any degree of education, and 423 (61.5%) had any level of income. (Table 1 ) Table 1 The characteristics of the study participants and the prevalence of epiretinal membrane (n = 8173) Characteristics Total population Epiretinal membrane Prevalence (%) p-value Age (years) group 50 to 54 1245 94 7.55 0.354 55 to 59 1379 121 8.77 60 to 64 1592 120 7.54 65 to 69 1664 148 8.89 70 to 74 1187 116 9.77 75 to 79 603 46 7.63 80 and above 503 43 8.55 Total 8173 688 8.42 Sex Male 3354 275 8.20 0.552 Female 4819 413 8.57 Total 8173 688 8.42 Urbanization Urban 4661 418 8.97 < 0.001 Rural 3512 270 7.69 Total 8173 688 8.42 Geographic location Coastal 6402 579 9.04 0.039 Inland 1771 109 6.15 Total 8173 688 8.42 Education level Illiteracy 1268 97 7.65 0.079 Primary school 1511 119 7.88 Middle school 3089 281 9.10 College and above 1189 108 9.08 Total 7057 605 8.57 Income level 5000 587 55 9.37 Total 4934 423 8.57 Smoking Yes 1257 104 8.27 0.692 No 5406 466 8.62 Total 6663 570 8.55 Drinking Yes 1151 100 8.69 0.678 No 5292 440 8.31 Total 6443 540 8.38 Tea consumption Yes 2959 262 8.85 0.718 No 3373 290 8.60 Total 6332 552 8.72 Duration of phone use (hours, h) 0 2206 183 8.30 0.838 2 1016 78 7.68 Total 6912 586 8.48 ERM and associations with sociodemographic characteristics In this study population, ERM (OR = 1.183, P = 0.039) was significantly correlated with urbanization. The percentage of ERM was significantly lower in the rural group than that in the urban group. ERM (OR = 1.516, P < 0.001) was also significantly associated with geographic location (coastal and inland) (OR = 1.516, P < 0.001). (Table 2 ) In general, ERM was not significantly correlated with age (OR = 1.004, p = 0.354), sex (OR = 1.050, p = 0.552), educational background (OR = 1.081, p = 0.079), income (OR = 1.125, p = 0.104), smoking (OR = 0.956, p = 0.692), alcohol consumption (OR = 1.049, p = 0.678) and tea consumption (OR = 1.033, p = 0.718). (Table 2 ) Correlation of ERM with refractive error and IOP In the whole study population, ERM was not significantly correlated with spherical equivalent (SE) (OR = 0.993, p = 0.644) and IOP (OR = 1.006, p = 0.579). (Table 2 ) Table 2 The logistic regression in epiretinal membrane with several research factors in Fujian Eye Study Research factors Epiretinal membrane Odds Ratio 95% Confidential Interval (CI) P -value Geographic location 1.183 1.008 to 1.388 0.039 Urbanization 1.516 1.227 to 1.874 < 0.001 Age 1.004 0.995 to 1.013 0.354 Sex 1.050 0.895 to 1.231 0.552 Height 0.998 0.988 to 1.008 0.744 Weight 0.995 0.987 to 1.003 0.24 BMI 0.984 0.960 to 1.009 0.201 SBP 0.999 0.005 to 1.003 0.58 DBP 0.997 0.991 to 1.003 0.344 IOP 1.006 0.984 to 1.029 0.579 SE 0.993 0.966 to 1.022 0.644 Hypertension 1.020 0.872 to 1.194 0.800 DM 0.912 0.766 to 1.086 0.299 Education 1.08 0.991 to 1.179 0.079 Income 1.125 0.976 to 1.298 0.104 Smoking 0.956 0.766 to 1.194 0.692 Drinking 1.049 0.836 to 1.317 0.678 Tea consumption 1.033 0.8767 to 1.230 0.718 Phone use in the dark 1.035 0.863 to 1.243 0.709 Duration of phone use 0.991 0.912 to 1.077 0.838 BMI: body mass index; IOP: intraocular pressure; SE: spherical equivalent; DM: diebetes millus. Multiple logistic regression The multiple logistic regression demonstrated that the prevalence of ERM was correlated with coastal geographic location (OR = 1.540, p < 0.001) and the degree of urbanization (OR = 1.210, p = 0.020). Discussion The epidemiology of ERM initially primarily originated from population-based studies using non dilated retina photography and subsequent research combined the use of OCT. Our study used spectralis OCT to increase the sensitivity and reliability of detection and was the first time that multicolor OCT has been used for epidemiological investigations in China. At present, the research baseline on the prevalence and related factors of ERM was not uniform, and great variability in the reported prevalence of ERM among different races and countries made comparison difficult. A review showed the prevalence of ERM was 7–11.8%, with increasing age being the most important risk factor, and gender did not appear to be a major risk factor.[ 9 ] While another review and meta analysis showed that only greater age and female significantly conferred a higher risk of ERMs.[ 10 ] A study showed a very high prevalence of 34% for epiretinal membranes in a cohort of the elderly (63–102 years) in the United States.[ 11 ] A French population-based follow-up study of 3 times (every two years) showed that the incidence rate of ERM was 9.42%.[ 12 ] While in China, the Jiangning Eye Study reported the prevalence of epiretinal membrane was 8.4% in residents aged 50 years and older,[ 13 ] which was similar with our study result (8.42%). A Population-Based Cohort Study of Older Adults in UK found that the prevalence of ERM was 7.6% (CI, 7.0%-8.3%). ERM was present more often in more myopic eyes, associated with an increase in levels of high-density lipoprotein (HDL) cholesterol and triglycerides.[ 14 ] Our study showed ERM was only associated with urbanization and geographic location. The level of urbanization is one of the important factors affecting the incidence of ERM. Our study found that in areas with higher levels of urbanization, the incidence of ERM is also correspondingly higher. This may be related to factors such as lifestyle changes and increased work pressure brought about by urbanization. With the acceleration of urbanization, people's lifestyles, dietary habits, and work environments have undergone significant changes. For example, a study conducted in Beixinjing area of Shanghai found that the prevalence rate of ERM was 1.02%, and it was significantly related to diabetes and higher education level.[ 15 ] This indicates that people in the highly urbanized areas receive better education and medical services, but may also increase the risk of some chronic diseases (such as diabetes) due to changes in lifestyle (such as reduced eating habits and physical activity), thus affecting the prevalence of ERM. On the one hand, adverse factors such as environmental pollution brought about by urbanization may cause damage to the eyes. A review on the effects of air pollution on the eye showed that air pollution not only affects the surface of the eye, but may also lead to more serious eye diseases such as glaucoma, cataracts, and age-related macular degeneration, and indoor pollution (such as environmental tobacco smoke, smoke from heating and cooking) is also associated with various eye diseases;[ 16 ] On the other hand, urbanization has also brought more medical resources and popularization of health knowledge, making it easier for people to access eye disease diagnosis and treatment.[ 17 – 21 ] In addition to the level of urbanization, residential geographic location is also an important factor affecting the incidence of ERM. Fujian Province has a complex terrain and diverse climate, 80% are mountainous areas and 5 out of 9 cities are coastal, and environmental factors in different regions may have different impacts on the eyes.[ 22 – 26 ] Our study found that in some specific areas of Fujian, such as coastal areas, the incidence of ERM is significantly higher than that in other areas. This may be related to factors such as climate, environment, and dietary habits in these regions. For example, in coastal areas, ultraviolet radiation is strong, the climate is humid and the salt content in the air is high, which may cause certain irritation to the eyes. Recently, the impact of environmental factors on eye health has received increasing attention. And several reviews showed that the dietary patterns may affect on the incidence and progression of age-related eye diseases, namely age-related macular degeneration (AMD), cataracts, diabetic retinopathy (DR), and glaucoma.[ 27 – 32 ] Consumption of fruits, vegetables, fish, and olive oil, named the Mediterranean diet may be correlated with a lower risk of DR.[ 32 ] More than half of the cities in Fujian are coastal areas, where people tend to consume seafood in their diet, while our study found that the prevalence of ERM among coastal residents was higher than that among inland residents. This may indicate that the influence of social environmental factors is greater than that of geographical location. And the impact of these social environmental factors needs to be validated through basic experiments in the future. Above all, the FJES team has discovered that the prevalence of ERM in Fujian province stands at 8.42%, significantly higher than the national average. Moreover, the risk factors for this condition are closely related to the degree of urbanization and coastal geographical location of residence. This finding provides crucial insights into the pathogenesis of ERM and the development of effective prevention and treatment strategies. In the future, we will continue to delve deeper into the mechanisms and associated risk factors of ERM, aiming to discover more effective prevention and treatment measures. We also call for greater attention and emphasis from all sectors of society towards eye diseases, contributing jointly to the advancement of human eye health. Abbreviations OCT Optical Coherence Tomography ERM epiretinal membrane FJES Fujian Eye Study OR odd ratio CI confidential interval IOP intraocular pressure SE spherical equivalent HDL high-density lipoprotein AMD age-related macular degeneration DR diabetic retinopathy. Declarations Ethics approval and consent to participate A clinical study registry was obtained for the 2018–2019 FJES study (register number: ChiCTR2100043349, registration date: 2021-02-21) and the study protocol was approved by the Ethics Committee of Xiamen Eye Center afliated with Xiamen University (Acceptance No. XMYKZX-KY-2018-001). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Eye Institute and Affiliated Xiamen Eye Center of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. 2 Fujian Provincial Key Laboratory of Corneal & Ocular Surface Diseases, Xiamen, Fujian, China. 3 Xiamen Municipal Key Laboratory of Corneal & Ocular Surface Diseases, Xiamen, Fujian, China. 4 Xiamen Research Center for Eye Diseases and Key Laboratory of Ophthalmology, Xiamen, Fujian, China. 5 Department of Ophthalmology, Peking University People’s Hospital, Beijing100044, China. 6 Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing 100191, China. Funding This study was supported by the National Natural Science Foundation of China (NSFC, No.81870672). The funding organization had no role in the study design, collection, analysis and interpretation of data. Author Contribution Yang Li and Qinrui Hu took part in all parts of the study, including the study design, data collection, data analysis, the preparation of related data, writing and revision. Yonghua Hu helped in guiding the statistic analysis. Bin Wang assisted in article revision and plotting. Xiaoxin Li provided this project and oversaw the whole research. Acknowledgement We thank all FJES group members (Zhenglingling Yao, Liting Wang, Yi Liu, Wufu Qiu, Menging Lin, Yanhong Zhang) who made tremendous efforts to make the study successful, especially in the field examinations and data collection. Data Availability Data of this article can be obtained from the leader of this project for researchers ( [email protected] ) after this manuscript is accepted for publication. And the applicants need to write a guarantee letter confirming that our data will not be used for commercial purposes. References Soomro T, Shah N, Niestrata-Ortiz M, Yap T, Normando EM, Cordeiro MF. Recent advances in imaging technologies for assessment of retinal diseases. Expert Rev Med Devices. 2020;17(10):1095–1108. doi: 10.1080/17434440.2020.1816167. Epub 2020 Sep 15. PMID: 32885710. Vaz-Pereira S, Morais-Sarmento T, De Salvo G. Sensitivity and specificity of MultiColor imaging in detecting proliferative diabetic retinopathy. Int Ophthalmol. 2022;42(2):455–467. doi: 10.1007/s10792-021-02062-y . Epub 2021 Oct 26. PMID: 34698967; PMCID: PMC8545774. Fung AT, Galvin J, Tran T. Epiretinal membrane: A review. Clin Exp Ophthalmol. 2021;49(3):289–308. doi: 10.1111/ceo.13914. Epub 2021 Mar 24. PMID: 33656784. Tan AC, Fleckenstein M, Schmitz-Valckenberg S, Holz FG. Clinical Application of Multicolor Imaging Technology. Ophthalmologica. 2016;236(1):8–18. doi: 10.1159/000446857. Epub 2016 Jul 13. PMID: 27404384. Apostolopoulos S, Salas J, Ordóñez JLP, Tan SS, Ciller C, Ebneter A, Zinkernagel M, Sznitman R, Wolf S, De Zanet S, Munk MR. Automatically Enhanced OCT Scans of the Retina: A proof of concept study. Sci Rep. 2020;10(1):7819. doi: 10.1038/s41598-020-64724-8 . PMID: 32385371; PMCID: PMC7210925. Lee YJ, Park KH, Jeoung JW. False-positive classification and associated factors in segmented macular layers and retinal nerve fiber layer analysis: Spectralis OCT deviation map study. Sci Rep. 2023;13(1):6782. doi: 10.1038/s41598-023-33944-z . PMID: 37185807; PMCID: PMC10130102. Fung AT, Galvin J, Tran T. Epiretinal membrane: A review. Clin Exp Ophthalmol. 2021;49(3):289–308. doi: 10.1111/ceo.13914. Epub 2021 Mar 24. PMID: 33656784. Li Y, Hu Q, Li X, Hu Y, Wang B, Qin X, Ren T. The Fujian eye cross sectional study: objectives, design, and general characteristics. BMC Ophthalmol. 2022;22(1):112. doi: 10.1186/s12886-022-02346-6 . PMID: 35277140; PMCID: PMC8915769. Fung AT, Galvin J, Tran T. Epiretinal membrane: A review. Clin Exp Ophthalmol. 2021;49(3):289–308. doi: 10.1111/ceo.13914. Epub 2021 Mar 24. PMID: 33656784. Xiao W, Chen X, Yan W, Zhu Z, He M. Prevalence and risk factors of epiretinal membranes: a systematic review and meta-analysis of population-based studies. BMJ Open. 2017;7(9):e014644. doi: 10.1136/bmjopen-2016-014644 . PMID: 28951399; PMCID: PMC5623383. Meuer SM, Myers CE, Klein BE, Swift MK, Huang Y, Gangaputra S, Pak JW, Danis RP, Klein R. The epidemiology of vitreoretinal interface abnormalities as detected by spectral-domain optical coherence tomography: the beaver dam eye study. Ophthalmology. 2015;122(4):787–95. doi: 10.1016/j.ophtha.2014.10.014 . Epub 2014 Dec 31. PMID: 25556116; PMCID: PMC4372472. Morillon C, Le Goff M, Gattoussi S, Korobelnik JF, Rougier MB, Schweitzer C, Delcourt C, Delyfer MN. INCIDENCE, PROGRESSION, AND RISK FACTORS OF EPIRETINAL MEMBRANES IN THE ELDERLY. Retina. 2021;41(3):495–504. doi: 10.1097/IAE.0000000000002871 . PMID: 32568986. Ye H, Zhang Q, Liu X, Cai X, Yu W, Yu S, Wang T, Lu W, Li X, Hu Y, Yang B, Zhao P. Prevalence and associations of epiretinal membrane in an elderly urban Chinese population in China: the Jiangning Eye Study. Br J Ophthalmol. 2015;99(12):1594-7. doi: 10.1136/bjophthalmol-2015-307050 . Epub 2015 Jun 11. PMID: 26069300. Quinn NB, Steel DH, Chakravarthy U, Peto T, Hamill B, Muldrew A, Graham K, Elliott D, Hennessy R, Cruise S, McGuinness B, Young IS, Kee F, Hogg RE. Assessment of the Vitreomacular Interface Using High-Resolution OCT in a Population-Based Cohort Study of Older Adults. Ophthalmol Retina. 2020;4(8):801–813. doi: 10.1016/j.oret.2020.02.013. Epub 2020 Feb 29. PMID: 32335034. Zhu XF, Peng JJ, Zou HD, Fu J, Wang WW, Xu X, Zhang X. Prevalence and risk factors of idiopathic epiretinal membranes in Beixinjing blocks, Shanghai, China. PLoS One. 2012;7(12):e51445. doi: 10.1371/journal.pone.0051445 . Epub 2012 Dec 10. PMID: 23251536; PMCID: PMC3519558. Lin, C., Chiu, C., Lee, P., Chen, K., He, C., Hsu, S., & Cheng, K. (2022). The Adverse Effects of Air Pollution on the Eye: A Review. International Journal of Environmental Research and Public Health, 19. https://doi.org/10.3390/ijerph19031186 . Shao Q, Tao R, Luca MM. The Effect of Urbanization on Health Care Expenditure: Evidence From China. Front Public Health. 2022;10:850872. doi: 10.3389/fpubh.2022.850872 . PMID: 35242736; PMCID: PMC8885621. Zhang Z, Zhao M, Zhang Y, Feng Y. How does urbanization affect public health? New evidence from 175 countries worldwide. Front Public Health. 2023;10:1096964. doi: 10.3389/fpubh.2022.1096964 . PMID: 36684862; PMCID: PMC9852986. Robertson LJ. The impact of urbanization on mental health service provision: a Brazil, Russia, India, China, South Africa and Africa focus. Curr Opin Psychiatry. 2019;32(3):224–231. doi: 10.1097/YCO.0000000000000495 . PMID: 30724752. Sørensen, K. (2018). Health Literacy: A Key Attribute for Urban Settings. In Urban Health Literacy. Huang D, Yang LH, Pescosolido BA. Understanding the public's profile of mental health literacy in China: a nationwide study. BMC Psychiatry. 2019;19(1):20. doi: 10.1186/s12888-018-1980-8 . PMID: 30642305; PMCID: PMC6332702. Ghosha, R., Ghosh, D., Ghosh, S., & Mukherjee, G. (2023). Impact of climate change and related environmental factors on eye health in India - A narrative review. Ecology, Environment and Conservation. https://doi.org/10.53550/eec.2023.v29i02.051 . Cao F, Liu ZR, Ni QY, Zha CK, Zhang SJ, Lu JM, Xu YY, Tao LM, Jiang ZX, Pan HF. Emerging roles of air pollution and meteorological factors in autoimmune eye diseases. Environ Res. 2023;231(Pt 1):116116. doi: 10.1016/j.envres.2023.116116 . Epub 2023 May 12. PMID: 37182831. Wang W, Zhang W, Ge H, Chen B, Zhao J, Wu J, Kang Z, Guo X, Deng F, Ma Q. Association between air pollution and emergency room visits for eye diseases and effect modification by temperature in Beijing, China. Environ Sci Pollut Res Int. 2022;29(15):22613–22622. doi: 10.1007/s11356-021-17304-w . Epub 2021 Nov 18. PMID: 34792769. Zhong JY, Lee YC, Hsieh CJ, Tseng CC, Yiin LM. Association between Dry Eye Disease, Air Pollution and Weather Changes in Taiwan. Int J Environ Res Public Health. 2018;15(10):2269. doi: 10.3390/ijerph15102269 . PMID: 30332806; PMCID: PMC6210160. He J, Liu Y, Zhang A, Liu Q, Yang X, Sun N, Yao B, Liang F, Yan X, Liu Y, Mao H, Chen X, Tang NJ, Yan H. Joint effects of meteorological factors and PM2.5 on age-related macular degeneration: a national cross-sectional study in China. Environ Health Prev Med. 2023;28:3. doi: 10.1265/ehpm.22-00237 . PMID: 36631073; PMCID: PMC9845061. Francisco SG, Smith KM, Aragonès G, Whitcomb EA, Weinberg J, Wang X, Bejarano E, Taylor A, Rowan S. Dietary Patterns, Carbohydrates, and Age-Related Eye Diseases. Nutrients. 2020;12(9):2862. doi: 10.3390/nu12092862 . PMID: 32962100; PMCID: PMC7551870. Medori MC, Naureen Z, Dhuli K, Placidi G, Falsini B, Bertelli M. Dietary supplements in retinal diseases, glaucoma, and other ocular conditions. J Prev Med Hyg. 2022;63(2 Suppl 3):E189-E199. doi: 10.15167/2421-4248/jpmh2022.63.2S3.2760 . PMID: 36479474; PMCID: PMC9710404. Bryl A, Mrugacz M, Falkowski M, Zorena K. The Effect of Diet and Lifestyle on the Course of Diabetic Retinopathy-A Review of the Literature. Nutrients. 2022;14(6):1252. doi: 10.3390/nu14061252 . PMID: 35334909; PMCID: PMC8955064. Šalková Kráľová J, Kolář P, Kapounová Z, Veselý P, Derflerová Brázdová Z. Dietary habits and dietary nutrient intake in patients with age-related macular degeneration: A case-control study. Cent Eur J Public Health. 2023;31(2):140–143. doi: 10.21101/cejph.a7617 . PMID: 37451248. Fahmideh F, Marchesi N, Barbieri A, Govoni S, Pascale A. Non-drug interventions in glaucoma: Putative roles for lifestyle, diet and nutritional supplements. Surv Ophthalmol. 2022 May-Jun;67(3):675–696. doi: 10.1016/j.survophthal.2021.09.002. Epub 2021 Sep 23. PMID: 34563531. Bryl A, Mrugacz M, Falkowski M, Zorena K. A Mediterranean Diet May Be Protective in the Development of Diabetic Retinopathy. Int J Mol Sci. 2023;24(13):11145. doi: 10.3390/ijms241311145 . PMID: 37446322; PMCID: PMC10342183. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Oct, 2024 Reviews received at journal 08 Sep, 2024 Reviewers agreed at journal 31 Aug, 2024 Reviewers invited by journal 24 Aug, 2024 Editor assigned by journal 19 Aug, 2024 Editor invited by journal 06 Aug, 2024 Submission checks completed at journal 06 Aug, 2024 First submitted to journal 26 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4810546","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":348061371,"identity":"f6dd4e49-1713-49fd-b1fa-05e10b0ec468","order_by":0,"name":"Yang Li","email":"","orcid":"","institution":"Eye Institute and Affiliated Xiamen Eye Center of Xiamen University, Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":348061372,"identity":"71d703db-f3e6-4033-b67e-57c17d4d6db9","order_by":1,"name":"Xiaoxin Li","email":"","orcid":"","institution":"Eye Institute and Affiliated Xiamen Eye Center of Xiamen University, Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxin","middleName":"","lastName":"Li","suffix":""},{"id":348061373,"identity":"b01b7143-0540-4362-9c7f-2aaf3983bffc","order_by":2,"name":"Yonghua Hu","email":"","orcid":"","institution":"Peking University Health Science Centre","correspondingAuthor":false,"prefix":"","firstName":"Yonghua","middleName":"","lastName":"Hu","suffix":""},{"id":348061374,"identity":"8a3057d8-b60c-4a4d-84f5-d2e9f703ea3c","order_by":3,"name":"Bin Wang","email":"","orcid":"","institution":"Eye Institute and Affiliated Xiamen Eye Center of Xiamen University, Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":348061375,"identity":"34db3cfd-ad4d-4622-b8e9-79f00c4c0790","order_by":4,"name":"Qinrui Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACAyA+AMQ8bAznHz4Ac4nWwsd4htmAaC1gIMd8hk2CKIeZSyRvPFy457AMG9vZY5U/Cu7IM7AfProBnxbLGWkFh2c8O8zDxnMu7TaPwTPDBp60tBt4HXYjx+AwzwGgFokDZrcZDA4zNkjwmBGpRf6BWeEPg8P2JGhhOGPGwGNwOJGwljPPCoBa0oFajiVLA7UktxH0y/HkzZ95DljbyzccPvjxx5/Dtv3sh4/h1cLAgB59bASUY9EyCkbBKBgFowAdAAAQj02dWfJC+gAAAABJRU5ErkJggg==","orcid":"","institution":"Eye Institute and Affiliated Xiamen Eye Center of Xiamen University, Xiamen University","correspondingAuthor":true,"prefix":"","firstName":"Qinrui","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-07-27 01:08:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4810546/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4810546/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-88234-7","type":"published","date":"2025-02-04T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75930497,"identity":"12dd9627-408a-4322-b105-bc8895a11daf","added_by":"auto","created_at":"2025-02-10 16:12:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":784955,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4810546/v1/116fbf14-d53c-4935-921f-c1571aaf0ee7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and associated factors of epiretinal membrane using spectralis OCT in a Chinese population: The Fujian Eye Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the rapid development of ophthalmic diagnosis and treatment technology, Optical Coherence Tomography (OCT) has become one of the most important tools for diagnosing ophthalmic diseases.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Among them, Spectralis OCT, as a new type of OCT technology, provides new possibilities for accurate diagnosis of retinal diseases by providing higher image resolution and deeper tissue penetration.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eEpiretinal Membrane (ERM) is a fundus disease that affects vision and mainly occurs in middle-aged and elderly populations.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Due to the fact that the macular membrane may not have obvious symptoms in the early stages of onset, and traditional ophthalmic examination methods are difficult to accurately diagnose, it is crucial to find an efficient and accurate screening tool for the early diagnosis and intervention of this disease.\u003c/p\u003e \u003cp\u003eSpectralis OCT can clearly display the structure and morphology of each layer of the retina in the macular area, thereby accurately identifying the presence and degree of the macular membrane.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] However, there is still relatively less research on the application of Spectralis OCT in macular membrane screening, and there is a lack of large-scale and systematic epidemiological investigations.[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAs one of the regions with a relatively developed economy and a high degree of aging population in China, the health issues of middle-aged and elderly people in Fujian are increasingly receiving attention. In order to better understand the prevalence of ERM in the middle-aged and elderly population in Fujian province and provide scientific basis for its prevention and treatment, this study planed to use Spectralis OCT technology to conduct a large-scale screening of the middle-aged and elderly population in this region.\u003c/p\u003e \u003cp\u003eThrough this study, we hope to provide strong support for the early detection and intervention of ERM in the middle-aged and elderly population in Fujian region, and also provide useful reference and guidance for the prevention and treatment of eye diseases in other middle-aged and elderly populations. In addition, the results of this study will also contribute to promoting the application and development of Spectralis OCT technology in the field of ophthalmology, and contribute new strength to the diagnosis and treatment of ophthalmic diseases.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA population based cross-sectional study, Fujian Eye Study (FJES) was performed on residents aged 50 years and older in Fujian Province, Southeast China from May 2018 to October 2019. Random cluster sampling was used in this investigation, and the calculation formula and sample size have been reported[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A clinical study registry was obtained for the 2018\u0026ndash;2019 FJES study (register number: ChiCTR2100043349, registration date: 2021-02-21) and the study protocol was approved by the Ethics Committee of Xiamen Eye Center afliated with Xiamen University (Acceptance number: XMYKZX-KY-2018-001). This study follows the Helsinki Declaration and written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eParticipants underwent a comprehensive physical examination in a mobile clinic. Those who were unable to participate in on-site examination in the screening were asked for the consent of home visits and simple ophthalmic examinations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOn-site examination\u003c/h2\u003e \u003cp\u003eThe main contents of this study include the following: questionnaire (age, sex, and race; educational background, income level, occupation, etc.); visual acuity, refraction, intraocular pressure (IOP), fundus photograph and Spectralis OCT. The flowchart of field screening has been reported.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eA fundus photograph of each eye was taken using a scanning laser device (Digital Fundus Camera, VISUCAM 524, Goeschwitzer Strasse 51\u0026ndash;52, 07745 Jena, Germany) and Spectralis OCT (Spectralis OCT, Heidelberg Engineering GmbH 69,121, Heidelberg, Germany) was used for high-resolution imaging of the optic disc and central retina in both eyes. The protocols have been reported[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was carried out using Stata/SE statistical software (Stata for Windows, version 15.1, StataCorp LLC, Lakeway Drive, College Station, TX, USA). A ᵡ\u003csup\u003e2\u003c/sup\u003e tests was used for categorical variables. Linear regression was used to determine whether ERM occurrence and potential factors were related. Logistic regression was used for correlation degree of each group. Multiple logistic regression was used to assess the association of factors with ERM. The odds ratios (ORs) or \u003cem\u003er\u003c/em\u003e values with 95% confidential intervals (CIs) were presented. A \u003cem\u003ep\u003c/em\u003e-value less than 0.05 was considered statistically significant for all the estimates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the participants\u003c/h2\u003e \u003cp\u003eFinally, 8211 participants were included in our whole study. A total of 8173 residents underwent the Spectralis OCT examination, of which 4 had unclear photos, so ultimately 8169 residents were included in this study. of 8173, 688 (8.42%, 95% CI: 0.0782 to 0.0902) had ERM in at least one eye, and 413 (60.03%) were female, 418 (60.76%) were from urban area, 579 (84.16%) were from coastal region, 605 (87.9%) had any degree of education, and 423 (61.5%) had any level of income. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe characteristics of the study participants and the prevalence of epiretinal membrane (n\u0026thinsp;=\u0026thinsp;8173)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEpiretinal membrane\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrevalence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eAge (years) group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 to 54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 to 59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 to 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 to 69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 to 74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 to 79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUrbanization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGeographic location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliteracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIncome level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000\u0026ndash;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTea consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDuration of phone use (hours, h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 to 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eERM and associations with sociodemographic characteristics\u003c/h2\u003e \u003cp\u003eIn this study population, ERM (OR\u0026thinsp;=\u0026thinsp;1.183, P\u0026thinsp;=\u0026thinsp;0.039) was significantly correlated with urbanization. The percentage of ERM was significantly lower in the rural group than that in the urban group. ERM (OR\u0026thinsp;=\u0026thinsp;1.516, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was also significantly associated with geographic location (coastal and inland) (OR\u0026thinsp;=\u0026thinsp;1.516, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn general, ERM was not significantly correlated with age (OR\u0026thinsp;=\u0026thinsp;1.004, p\u0026thinsp;=\u0026thinsp;0.354), sex (OR\u0026thinsp;=\u0026thinsp;1.050, p\u0026thinsp;=\u0026thinsp;0.552), educational background (OR\u0026thinsp;=\u0026thinsp;1.081, p\u0026thinsp;=\u0026thinsp;0.079), income (OR\u0026thinsp;=\u0026thinsp;1.125, p\u0026thinsp;=\u0026thinsp;0.104), smoking (OR\u0026thinsp;=\u0026thinsp;0.956, p\u0026thinsp;=\u0026thinsp;0.692), alcohol consumption (OR\u0026thinsp;=\u0026thinsp;1.049, p\u0026thinsp;=\u0026thinsp;0.678) and tea consumption (OR\u0026thinsp;=\u0026thinsp;1.033, p\u0026thinsp;=\u0026thinsp;0.718). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of ERM with refractive error and IOP\u003c/h2\u003e \u003cp\u003eIn the whole study population, ERM was not significantly correlated with spherical equivalent (SE) (OR\u0026thinsp;=\u0026thinsp;0.993, p\u0026thinsp;=\u0026thinsp;0.644) and IOP (OR\u0026thinsp;=\u0026thinsp;1.006, p\u0026thinsp;=\u0026thinsp;0.579). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe logistic regression in epiretinal membrane with several research factors in Fujian Eye Study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResearch factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eEpiretinal membrane\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidential Interval (CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographic location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.008 to 1.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrbanization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.227 to 1.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.995 to 1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.895 to 1.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.988 to 1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.987 to 1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.960 to 1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005 to 1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.991 to 1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.984 to 1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.966 to 1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.872 to 1.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.766 to 1.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.991 to 1.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.976 to 1.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.766 to 1.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.836 to 1.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8767 to 1.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhone use in the dark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.863 to 1.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of phone use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.912 to 1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBMI: body mass index; IOP: intraocular pressure; SE: spherical equivalent; DM: diebetes millus.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultiple logistic regression\u003c/h2\u003e \u003cp\u003eThe multiple logistic regression demonstrated that the prevalence of ERM was correlated with coastal geographic location (OR\u0026thinsp;=\u0026thinsp;1.540, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the degree of urbanization (OR\u0026thinsp;=\u0026thinsp;1.210, p\u0026thinsp;=\u0026thinsp;0.020).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe epidemiology of ERM initially primarily originated from population-based studies using non dilated retina photography and subsequent research combined the use of OCT. Our study used spectralis OCT to increase the sensitivity and reliability of detection and was the first time that multicolor OCT has been used for epidemiological investigations in China.\u003c/p\u003e \u003cp\u003eAt present, the research baseline on the prevalence and related factors of ERM was not uniform, and great variability in the reported prevalence of ERM among different races and countries made comparison difficult. A review showed the prevalence of ERM was 7\u0026ndash;11.8%, with increasing age being the most important risk factor, and gender did not appear to be a major risk factor.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] While another review and meta analysis showed that only greater age and female significantly conferred a higher risk of ERMs.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] A study showed a very high prevalence of 34% for epiretinal membranes in a cohort of the elderly (63\u0026ndash;102 years) in the United States.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] A French population-based follow-up study of 3 times (every two years) showed that the incidence rate of ERM was 9.42%.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] While in China, the Jiangning Eye Study reported the prevalence of epiretinal membrane was 8.4% in residents aged 50 years and older,[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] which was similar with our study result (8.42%). A Population-Based Cohort Study of Older Adults in UK found that the prevalence of ERM was 7.6% (CI, 7.0%-8.3%). ERM was present more often in more myopic eyes, associated with an increase in levels of high-density lipoprotein (HDL) cholesterol and triglycerides.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Our study showed ERM was only associated with urbanization and geographic location.\u003c/p\u003e \u003cp\u003eThe level of urbanization is one of the important factors affecting the incidence of ERM. Our study found that in areas with higher levels of urbanization, the incidence of ERM is also correspondingly higher. This may be related to factors such as lifestyle changes and increased work pressure brought about by urbanization. With the acceleration of urbanization, people's lifestyles, dietary habits, and work environments have undergone significant changes. For example, a study conducted in Beixinjing area of Shanghai found that the prevalence rate of ERM was 1.02%, and it was significantly related to diabetes and higher education level.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] This indicates that people in the highly urbanized areas receive better education and medical services, but may also increase the risk of some chronic diseases (such as diabetes) due to changes in lifestyle (such as reduced eating habits and physical activity), thus affecting the prevalence of ERM. On the one hand, adverse factors such as environmental pollution brought about by urbanization may cause damage to the eyes. A review on the effects of air pollution on the eye showed that air pollution not only affects the surface of the eye, but may also lead to more serious eye diseases such as glaucoma, cataracts, and age-related macular degeneration, and indoor pollution (such as environmental tobacco smoke, smoke from heating and cooking) is also associated with various eye diseases;[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] On the other hand, urbanization has also brought more medical resources and popularization of health knowledge, making it easier for people to access eye disease diagnosis and treatment.[\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn addition to the level of urbanization, residential geographic location is also an important factor affecting the incidence of ERM. Fujian Province has a complex terrain and diverse climate, 80% are mountainous areas and 5 out of 9 cities are coastal, and environmental factors in different regions may have different impacts on the eyes.[\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Our study found that in some specific areas of Fujian, such as coastal areas, the incidence of ERM is significantly higher than that in other areas. This may be related to factors such as climate, environment, and dietary habits in these regions. For example, in coastal areas, ultraviolet radiation is strong, the climate is humid and the salt content in the air is high, which may cause certain irritation to the eyes. Recently, the impact of environmental factors on eye health has received increasing attention. And several reviews showed that the dietary patterns may affect on the incidence and progression of age-related eye diseases, namely age-related macular degeneration (AMD), cataracts, diabetic retinopathy (DR), and glaucoma.[\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] Consumption of fruits, vegetables, fish, and olive oil, named the Mediterranean diet may be correlated with a lower risk of DR.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] More than half of the cities in Fujian are coastal areas, where people tend to consume seafood in their diet, while our study found that the prevalence of ERM among coastal residents was higher than that among inland residents. This may indicate that the influence of social environmental factors is greater than that of geographical location. And the impact of these social environmental factors needs to be validated through basic experiments in the future.\u003c/p\u003e \u003cp\u003eAbove all, the FJES team has discovered that the prevalence of ERM in Fujian province stands at 8.42%, significantly higher than the national average. Moreover, the risk factors for this condition are closely related to the degree of urbanization and coastal geographical location of residence. This finding provides crucial insights into the pathogenesis of ERM and the development of effective prevention and treatment strategies. In the future, we will continue to delve deeper into the mechanisms and associated risk factors of ERM, aiming to discover more effective prevention and treatment measures. We also call for greater attention and emphasis from all sectors of society towards eye diseases, contributing jointly to the advancement of human eye health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOptical Coherence Tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eERM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eepiretinal membrane\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFJES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFujian Eye Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodd ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidential interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIOP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintraocular pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003espherical equivalent\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eage-related macular degeneration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediabetic retinopathy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eA clinical study registry was obtained for the 2018\u0026ndash;2019 FJES study (register number: ChiCTR2100043349, registration date: 2021-02-21) and the study protocol was approved by the Ethics Committee of Xiamen Eye Center afliated with Xiamen University (Acceptance No. XMYKZX-KY-2018-001).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor details\u003c/h2\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eEye Institute and Affiliated Xiamen Eye Center of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.\u003c/p\u003e \u003cp\u003e \u003csup\u003e2\u003c/sup\u003eFujian Provincial Key Laboratory of Corneal \u0026amp; Ocular Surface Diseases, Xiamen, Fujian, China.\u003c/p\u003e \u003cp\u003e \u003csup\u003e3\u003c/sup\u003eXiamen Municipal Key Laboratory of Corneal \u0026amp; Ocular Surface Diseases, Xiamen, Fujian, China.\u003c/p\u003e \u003cp\u003e \u003csup\u003e4\u003c/sup\u003eXiamen Research Center for Eye Diseases and Key Laboratory of Ophthalmology, Xiamen, Fujian, China.\u003c/p\u003e \u003cp\u003e \u003csup\u003e5\u003c/sup\u003eDepartment of Ophthalmology, Peking University People\u0026rsquo;s Hospital, Beijing100044, China.\u003c/p\u003e \u003cp\u003e \u003csup\u003e6\u003c/sup\u003eDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing 100191, China.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Natural Science Foundation of China (NSFC, No.81870672). The funding organization had no role in the study design, collection, analysis and interpretation of data.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYang Li and Qinrui Hu took part in all parts of the study, including the study design, data collection, data analysis, the preparation of related data, writing and revision. Yonghua Hu helped in guiding the statistic analysis. Bin Wang assisted in article revision and plotting. Xiaoxin Li provided this project and oversaw the whole research.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all FJES group members (Zhenglingling Yao, Liting Wang, Yi Liu, Wufu Qiu, Menging Lin, Yanhong Zhang) who made tremendous efforts to make the study successful, especially in the field examinations and data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData of this article can be obtained from the leader of this project for researchers ([email protected]) after this manuscript is accepted for publication. And the applicants need to write a guarantee letter confirming that our data will not be used for commercial purposes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSoomro T, Shah N, Niestrata-Ortiz M, Yap T, Normando EM, Cordeiro MF. Recent advances in imaging technologies for assessment of retinal diseases. Expert Rev Med Devices. 2020;17(10):1095\u0026ndash;1108. doi: 10.1080/17434440.2020.1816167. Epub 2020 Sep 15. PMID: 32885710.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaz-Pereira S, Morais-Sarmento T, De Salvo G. Sensitivity and specificity of MultiColor imaging in detecting proliferative diabetic retinopathy. Int Ophthalmol. 2022;42(2):455\u0026ndash;467. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10792-021-02062-y\u003c/span\u003e\u003cspan address=\"10.1007/s10792-021-02062-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021 Oct 26. PMID: 34698967; PMCID: PMC8545774.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFung AT, Galvin J, Tran T. Epiretinal membrane: A review. Clin Exp Ophthalmol. 2021;49(3):289\u0026ndash;308. doi: 10.1111/ceo.13914. Epub 2021 Mar 24. PMID: 33656784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan AC, Fleckenstein M, Schmitz-Valckenberg S, Holz FG. Clinical Application of Multicolor Imaging Technology. Ophthalmologica. 2016;236(1):8\u0026ndash;18. doi: 10.1159/000446857. Epub 2016 Jul 13. PMID: 27404384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApostolopoulos S, Salas J, Ord\u0026oacute;\u0026ntilde;ez JLP, Tan SS, Ciller C, Ebneter A, Zinkernagel M, Sznitman R, Wolf S, De Zanet S, Munk MR. Automatically Enhanced OCT Scans of the Retina: A proof of concept study. Sci Rep. 2020;10(1):7819. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-64724-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-64724-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 32385371; PMCID: PMC7210925.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee YJ, Park KH, Jeoung JW. False-positive classification and associated factors in segmented macular layers and retinal nerve fiber layer analysis: Spectralis OCT deviation map study. Sci Rep. 2023;13(1):6782. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-33944-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-33944-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37185807; PMCID: PMC10130102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFung AT, Galvin J, Tran T. Epiretinal membrane: A review. Clin Exp Ophthalmol. 2021;49(3):289\u0026ndash;308. doi: 10.1111/ceo.13914. Epub 2021 Mar 24. PMID: 33656784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Hu Q, Li X, Hu Y, Wang B, Qin X, Ren T. The Fujian eye cross sectional study: objectives, design, and general characteristics. BMC Ophthalmol. 2022;22(1):112. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12886-022-02346-6\u003c/span\u003e\u003cspan address=\"10.1186/s12886-022-02346-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 35277140; PMCID: PMC8915769.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFung AT, Galvin J, Tran T. Epiretinal membrane: A review. Clin Exp Ophthalmol. 2021;49(3):289\u0026ndash;308. doi: 10.1111/ceo.13914. Epub 2021 Mar 24. PMID: 33656784.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao W, Chen X, Yan W, Zhu Z, He M. Prevalence and risk factors of epiretinal membranes: a systematic review and meta-analysis of population-based studies. BMJ Open. 2017;7(9):e014644. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2016-014644\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2016-014644\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 28951399; PMCID: PMC5623383.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeuer SM, Myers CE, Klein BE, Swift MK, Huang Y, Gangaputra S, Pak JW, Danis RP, Klein R. The epidemiology of vitreoretinal interface abnormalities as detected by spectral-domain optical coherence tomography: the beaver dam eye study. Ophthalmology. 2015;122(4):787\u0026ndash;95. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ophtha.2014.10.014\u003c/span\u003e\u003cspan address=\"10.1016/j.ophtha.2014.10.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2014 Dec 31. PMID: 25556116; PMCID: PMC4372472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorillon C, Le Goff M, Gattoussi S, Korobelnik JF, Rougier MB, Schweitzer C, Delcourt C, Delyfer MN. INCIDENCE, PROGRESSION, AND RISK FACTORS OF EPIRETINAL MEMBRANES IN THE ELDERLY. Retina. 2021;41(3):495\u0026ndash;504. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/IAE.0000000000002871\u003c/span\u003e\u003cspan address=\"10.1097/IAE.0000000000002871\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 32568986.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe H, Zhang Q, Liu X, Cai X, Yu W, Yu S, Wang T, Lu W, Li X, Hu Y, Yang B, Zhao P. Prevalence and associations of epiretinal membrane in an elderly urban Chinese population in China: the Jiangning Eye Study. Br J Ophthalmol. 2015;99(12):1594-7. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bjophthalmol-2015-307050\u003c/span\u003e\u003cspan address=\"10.1136/bjophthalmol-2015-307050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2015 Jun 11. PMID: 26069300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuinn NB, Steel DH, Chakravarthy U, Peto T, Hamill B, Muldrew A, Graham K, Elliott D, Hennessy R, Cruise S, McGuinness B, Young IS, Kee F, Hogg RE. Assessment of the Vitreomacular Interface Using High-Resolution OCT in a Population-Based Cohort Study of Older Adults. Ophthalmol Retina. 2020;4(8):801\u0026ndash;813. doi: 10.1016/j.oret.2020.02.013. Epub 2020 Feb 29. PMID: 32335034.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu XF, Peng JJ, Zou HD, Fu J, Wang WW, Xu X, Zhang X. Prevalence and risk factors of idiopathic epiretinal membranes in Beixinjing blocks, Shanghai, China. PLoS One. 2012;7(12):e51445. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0051445\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0051445\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2012 Dec 10. PMID: 23251536; PMCID: PMC3519558.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, C., Chiu, C., Lee, P., Chen, K., He, C., Hsu, S., \u0026amp; Cheng, K. (2022). The Adverse Effects of Air Pollution on the Eye: A Review. International Journal of Environmental Research and Public Health, 19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph19031186\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19031186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao Q, Tao R, Luca MM. The Effect of Urbanization on Health Care Expenditure: Evidence From China. Front Public Health. 2022;10:850872. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2022.850872\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.850872\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 35242736; PMCID: PMC8885621.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Zhao M, Zhang Y, Feng Y. How does urbanization affect public health? New evidence from 175 countries worldwide. Front Public Health. 2023;10:1096964. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2022.1096964\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2022.1096964\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36684862; PMCID: PMC9852986.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobertson LJ. The impact of urbanization on mental health service provision: a Brazil, Russia, India, China, South Africa and Africa focus. Curr Opin Psychiatry. 2019;32(3):224\u0026ndash;231. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/YCO.0000000000000495\u003c/span\u003e\u003cspan address=\"10.1097/YCO.0000000000000495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 30724752.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;rensen, K. (2018). Health Literacy: A Key Attribute for Urban Settings. In Urban Health Literacy.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang D, Yang LH, Pescosolido BA. Understanding the public's profile of mental health literacy in China: a nationwide study. BMC Psychiatry. 2019;19(1):20. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12888-018-1980-8\u003c/span\u003e\u003cspan address=\"10.1186/s12888-018-1980-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 30642305; PMCID: PMC6332702.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosha, R., Ghosh, D., Ghosh, S., \u0026amp; Mukherjee, G. (2023). Impact of climate change and related environmental factors on eye health in India - A narrative review. Ecology, Environment and Conservation. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.53550/eec.2023.v29i02.051\u003c/span\u003e\u003cspan address=\"10.53550/eec.2023.v29i02.051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao F, Liu ZR, Ni QY, Zha CK, Zhang SJ, Lu JM, Xu YY, Tao LM, Jiang ZX, Pan HF. Emerging roles of air pollution and meteorological factors in autoimmune eye diseases. Environ Res. 2023;231(Pt 1):116116. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.envres.2023.116116\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2023.116116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2023 May 12. PMID: 37182831.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, Zhang W, Ge H, Chen B, Zhao J, Wu J, Kang Z, Guo X, Deng F, Ma Q. Association between air pollution and emergency room visits for eye diseases and effect modification by temperature in Beijing, China. Environ Sci Pollut Res Int. 2022;29(15):22613\u0026ndash;22622. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11356-021-17304-w\u003c/span\u003e\u003cspan address=\"10.1007/s11356-021-17304-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2021 Nov 18. PMID: 34792769.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong JY, Lee YC, Hsieh CJ, Tseng CC, Yiin LM. Association between Dry Eye Disease, Air Pollution and Weather Changes in Taiwan. Int J Environ Res Public Health. 2018;15(10):2269. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph15102269\u003c/span\u003e\u003cspan address=\"10.3390/ijerph15102269\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 30332806; PMCID: PMC6210160.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe J, Liu Y, Zhang A, Liu Q, Yang X, Sun N, Yao B, Liang F, Yan X, Liu Y, Mao H, Chen X, Tang NJ, Yan H. Joint effects of meteorological factors and PM2.5 on age-related macular degeneration: a national cross-sectional study in China. Environ Health Prev Med. 2023;28:3. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1265/ehpm.22-00237\u003c/span\u003e\u003cspan address=\"10.1265/ehpm.22-00237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36631073; PMCID: PMC9845061.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrancisco SG, Smith KM, Aragon\u0026egrave;s G, Whitcomb EA, Weinberg J, Wang X, Bejarano E, Taylor A, Rowan S. Dietary Patterns, Carbohydrates, and Age-Related Eye Diseases. Nutrients. 2020;12(9):2862. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu12092862\u003c/span\u003e\u003cspan address=\"10.3390/nu12092862\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 32962100; PMCID: PMC7551870.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedori MC, Naureen Z, Dhuli K, Placidi G, Falsini B, Bertelli M. Dietary supplements in retinal diseases, glaucoma, and other ocular conditions. J Prev Med Hyg. 2022;63(2 Suppl 3):E189-E199. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15167/2421-4248/jpmh2022.63.2S3.2760\u003c/span\u003e\u003cspan address=\"10.15167/2421-4248/jpmh2022.63.2S3.2760\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36479474; PMCID: PMC9710404.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBryl A, Mrugacz M, Falkowski M, Zorena K. The Effect of Diet and Lifestyle on the Course of Diabetic Retinopathy-A Review of the Literature. Nutrients. 2022;14(6):1252. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu14061252\u003c/span\u003e\u003cspan address=\"10.3390/nu14061252\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 35334909; PMCID: PMC8955064.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠalkov\u0026aacute; Kr\u0026aacute;ľov\u0026aacute; J, Kol\u0026aacute;ř P, Kapounov\u0026aacute; Z, Vesel\u0026yacute; P, Derflerov\u0026aacute; Br\u0026aacute;zdov\u0026aacute; Z. Dietary habits and dietary nutrient intake in patients with age-related macular degeneration: A case-control study. Cent Eur J Public Health. 2023;31(2):140\u0026ndash;143. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21101/cejph.a7617\u003c/span\u003e\u003cspan address=\"10.21101/cejph.a7617\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37451248.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFahmideh F, Marchesi N, Barbieri A, Govoni S, Pascale A. Non-drug interventions in glaucoma: Putative roles for lifestyle, diet and nutritional supplements. Surv Ophthalmol. 2022 May-Jun;67(3):675\u0026ndash;696. doi: 10.1016/j.survophthal.2021.09.002. Epub 2021 Sep 23. PMID: 34563531.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBryl A, Mrugacz M, Falkowski M, Zorena K. A Mediterranean Diet May Be Protective in the Development of Diabetic Retinopathy. Int J Mol Sci. 2023;24(13):11145. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms241311145\u003c/span\u003e\u003cspan address=\"10.3390/ijms241311145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37446322; PMCID: PMC10342183.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cross-sectional eye study, epiretinal membrane, prevalence, related factor, urbanization, geographic location","lastPublishedDoi":"10.21203/rs.3.rs-4810546/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4810546/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e \u003cp\u003eWe aimed to determine the prevalence and risk factors of epiretinal membrane in a population-based study of residents aged 50 years and older in Fujian Province, Southeast China.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThe Fujian Eye Study is a population-based cross-sectional eye study in Fujian province, Southeast China. Residents aged 50 years and older were enrolled and did the questionnaire (educational background, income, blood type, disease history, medication history, smoking, drinking and tea consumption, et al), physical and ophthalmological examinations with height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), refraction, intraocular pressure (IOP), slit lamp, nonmydriatic fundus photograph and spectralis optical coherence tomography (OCT) imaging. Nonmydriatic fundus photograph and Spectralis OCT were used to assess ERM according to a standardised protocol.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eA total of 8173 residents were included in this study. Among them, 8.42% (95%CI: 0.0782 to 0.0902) had ERM in at least one eye. Multiple logistic regression showed the presence of ERM was only associated with urbanization and geographic location, but not with age, sex, refractive error, IOP, SBP, DBP, HR, BMI, hypertension, diabetic mellitus (DM), hyperlipidemia, education, income, smoking, alcohol and tea consumption.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eERM is common among Chinese with 8.42% in at least one eye. Urbanization and geographic location are the only associated factors for ERM in Fujian Eye Study.\u003c/p\u003e","manuscriptTitle":"Prevalence and associated factors of epiretinal membrane using spectralis OCT in a Chinese population: The Fujian Eye Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-02 07:52:50","doi":"10.21203/rs.3.rs-4810546/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-03T07:10:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-09T03:48:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170872572611734008974420769041367561439","date":"2024-08-31T15:49:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-24T06:19:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-19T06:22:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-06T14:25:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-06T14:21:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-27T01:07:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c196b821-b667-4e80-8baf-e6efa2585eb3","owner":[],"postedDate":"September 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36924067,"name":"Health sciences/Health care/Public health/Epidemiology"},{"id":36924068,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-02-10T16:04:55+00:00","versionOfRecord":{"articleIdentity":"rs-4810546","link":"https://doi.org/10.1038/s41598-025-88234-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-04 15:57:05","publishedOnDateReadable":"February 4th, 2025"},"versionCreatedAt":"2024-09-02 07:52:50","video":"","vorDoi":"10.1038/s41598-025-88234-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-88234-7","workflowStages":[]},"version":"v1","identity":"rs-4810546","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4810546","identity":"rs-4810546","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}