Purpose: This study aimed to investigate the prevalence, causes, and influencing factors of vision impairment in the elderly population aged 60 years and above in Mangxin Town, Kashgar region, Xinjiang, China. Located in a region characterized by intense ultraviolet radiation and arid climatic conditions, Mangxin Town presents unique environmental challenges that may exacerbate ocular health issues. Despite the global emphasis on addressing vision impairment among aging populations, there remains a paucity of updated and region-specific data in Xinjiang, necessitating this comprehensive assessment to inform targeted interventions. Methods: A cross-sectional study was conducted from May to June 2024, involving 1,311 elderly participants (76.76% participation rate) out of a total eligible population of 1,708 individuals aged ≥60 years. Participants underwent detailed ocular examinations, including assessments of uncorrected visual acuity (UVA) and best-corrected visual acuity (BCVA) using standard logarithmic charts, slit-lamp biomicroscopy, optical coherence tomography (OCT, Topcon DRI OCT Triton), fundus photography, and intraocular pressure measurement (Canon TX-20 Tonometer). A multidisciplinary team of 10 ophthalmologists and 2 local village doctors, trained rigorously in standardized protocols, ensured consistent data collection. Demographic, lifestyle, and medical history data were collected via questionnaires. Statistical analyses, performed using Stata 16, included multivariate logistic regression to identify risk factors, with significance defined as P < 0.05. Results: The overall prevalence of vision impairment was 13.21% (95% CI: 11.37–15.04), with low vision at 11.76% (95% CI: 10.01–13.50) and blindness at 1.45% (95% CI: 0.80–2.10). Cataract emerged as the leading cause, responsible for 68.20% of cases, followed by glaucoma (5.80%), optic atrophy (5.20%), and age-related macular degeneration (2.90%). Vision impairment prevalence escalated significantly with age: 7.74% in the 60–69 age group, 17.79% in 70–79, and 33.72% in those ≥80. Males exhibited higher prevalence than females (15.84% vs. 10.45%, P = 0.004). Multivariate analysis revealed age ≥80 years (OR = 6.43, 95% CI: 3.79–10.90), male sex (OR = 0.53, 95% CI: 0.34–0.83), and daily exercise (OR = 0.44, 95% CI: 0.20–0.95) as significant factors. History of eye disease showed a non-significant trend toward increased risk (OR = 1.49, P = 0.107). Education level, income, and smoking status showed no significant associations. Conclusion: This study underscores cataract as the predominant cause of vision impairment in Mangxin Town’s elderly population, with age and sex as critical determinants. The findings align with global patterns but highlight region-specific challenges, such as environmental factors contributing to cataract prevalence. Public health strategies should prioritize improving access to cataract surgery, enhancing grassroots ophthalmic infrastructure, and integrating portable screening technologies for early detection of fundus diseases. Additionally, promoting health education on UV protection and lifestyle modifications, such as regular exercise, may mitigate risks. Future research should expand to broader regions in Xinjiang, employ advanced diagnostic tools for complex conditions like glaucoma, and explore longitudinal trends to refine intervention strategies. These efforts are vital to reducing preventable blindness and improving quality of life for aging populations in underserved areas.

Purpose: This study aimed to investigate the prevalence, causes, and influencing factors of vision impairment in the elderly population aged 60 years and above in Mangxin Town, Kashgar region, Xinjiang, China. Located in a region characterized by intense ultraviolet radiation and arid climatic conditions, Mangxin Town presents unique environmental challenges that may exacerbate ocular health issues. Despite the global emphasis on addressing vision impairment among aging populations, there remains a paucity of updated and region-specific data in Xinjiang, necessitating this comprehensive assessment to inform targeted interventions. Methods: A cross-sectional study was conducted from May to June 2024, involving 1,311 elderly participants (76.76% participation rate) out of a total eligible population of 1,708 individuals aged ≥60 years. Participants underwent detailed ocular examinations, including assessments of uncorrected visual acuity (UVA) and best-corrected visual acuity (BCVA) using standard logarithmic charts, slit-lamp biomicroscopy, optical coherence tomography (OCT, Topcon DRI OCT Triton), fundus photography, and intraocular pressure measurement (Canon TX-20 Tonometer). A multidisciplinary team of 10 ophthalmologists and 2 local village doctors, trained rigorously in standardized protocols, ensured consistent data collection. Demographic, lifestyle, and medical history data were collected via questionnaires. Statistical analyses, performed using Stata 16, included multivariate logistic regression to identify risk factors, with significance defined as P < 0.05. Results: The overall prevalence of vision impairment was 13.21% (95% CI: 11.37–15.04), with low vision at 11.76% (95% CI: 10.01–13.50) and blindness at 1.45% (95% CI: 0.80–2.10). Cataract emerged as the leading cause, responsible for 68.20% of cases, followed by glaucoma (5.80%), optic atrophy (5.20%), and age-related macular degeneration (2.90%). Vision impairment prevalence escalated significantly with age: 7.74% in the 60–69 age group, 17.79% in 70–79, and 33.72% in those ≥80. Males exhibited higher prevalence than females (15.84% vs. 10.45%, P = 0.004). Multivariate analysis revealed age ≥80 years (OR = 6.43, 95% CI: 3.79–10.90), male sex (OR = 0.53, 95% CI: 0.34–0.83), and daily exercise (OR = 0.44, 95% CI: 0.20–0.95) as significant factors. History of eye disease showed a non-significant trend toward increased risk (OR = 1.49, P = 0.107). Education level, income, and smoking status showed no significant associations. Conclusion: This study underscores cataract as the predominant cause of vision impairment in Mangxin Town’s elderly population, with age and sex as critical determinants. The findings align with global patterns but highlight region-specific challenges, such as environmental factors contributing to cataract prevalence. Public health strategies should prioritize improving access to cataract surgery, enhancing grassroots ophthalmic infrastructure, and integrating portable screening technologies for early detection of fundus diseases. Additionally, promoting health education on UV protection and lifestyle modifications, such as regular exercise, may mitigate risks. Future research should expand to broader regions in Xinjiang, employ advanced diagnostic tools for complex conditions like glaucoma, and explore longitudinal trends to refine intervention strategies. These efforts are vital to reducing preventable blindness and improving quality of life for aging populations in underserved areas.

目的：建立并评估儿童Ⅱ期人工晶状体(intraocular lens,IOL)植入术后青光眼相关不良事件(glaucoma-related adverse events,GRAEs)的预测模型。方法：选取于中山大学中山眼科中心行Ⅱ期IOL植入术的无晶状体眼患儿205例(356眼)，并在术后对其随访3年。采用Cox比例风险模型确定GRAEs的预测因子，并建立列线图预测模型。采用随时间变化的受试者工作特征(receiver operating characteristic,ROC)曲线、决策曲线分析、Kaplan-Meier曲线评估模型性能，并通过Bootstrapping的C指数和校准图进行内部验证。结果：行Ⅱ期IOL植入术时年龄较大(HR=1.50, 95% CI: 1.03 ~2.19)、术后一过性高眼压(HR=9.06, 95% CI: 2.97~27.67)和IOL睫状沟植入术(HR=14.55, 95% CI: 2.11~100.57)是GRAEs的危险因素(均P<0.05)，并据此建立了两个列线图预测模型。在术后1、2、3年，模型1的ROC曲线下面积(area under curve,AUC)分别为0.747(95% CI: 0.776 ~0.935)、0.765 (95% CI: 0.804 ~0.936)和0.748 (95% CI: 0.736~0.918)，模型2的AUC分别为0.881 (95% CI: 0.836 ~0.926)、0.895 (95% CI: 0.852 ~0.938)和0.848 (95% CI: 0.752~0.945)。在内部验证和评价中，两种模型均表现出良好的性能和临床净效益。Kaplan-Meier曲线显示两个不同的风险组在两个模型中都能被显著且稳健地区分。此外，本研究也构建了在线风险计算器。结论：两种列线图均能灵敏、准确地识别Ⅱ期IOL植入术后GRAEs的高危患儿，有助对其进行早期识别和及时干预。

Aims: To establish and evaluate predictive models for glaucoma-related adverse events (GRAEs) following secondary intraocular lens (IOL) implantation in paediatric eyes. Methods: 205 children (356 aphakic eyes) receiving secondary IOL implantation at Zhongshan Ophthalmic Center with a 3-year follow-up were enrolled. Cox proportional hazard model was used to identify predictors of GRAEs and developed nomograms. Model performance was evaluated with time-dependent receiver operating characteristic (ROC) curves, decision curve analysis, Kaplan-Meier curves and validated internally through C-statistics and calibration plot of the bootstrap samples. Results: Older age at secondary IOL implantation (HR=1.5, 95% CI: 1.03 to 2.19), transient intraocular hypertension (HR=9.06, 95% CI: 2.97 to  27.67) and ciliary sulcus implantation (HR=14.55, 95% CI: 2.11 to 100.57) were identified as risk factors for GRAEs (all p<0.05). Two nomograms were established. At postoperatively 1, 2 and 3 years, model 1 achieved area under the ROC curves (AUCs) of 0.747 (95% CI: 0.776 to 0.935), 0.765 (95% CI: 0.804 to 0.936) and 0.748 (95% CI: 0.736 to 0.918), and the AUCs of model 2 were 0.881 (95% CI: 0.836 to 0.926), 0.895 (95% CI: 0.852 to 0.938) and 0.848 (95% CI: 0.752 to 0.945). Both models demonstrated fine clinical net benefit and performance in the interval validation. The Kaplan-Meier curves showing two distinct risk groups were well discriminated and robust in both models. An online risk calculator was constructed. Conclusions: Two nomograms could sensitively and accurately identify children at high risk of GRAEs after secondary IOL implantation to help early identification and timely intervention.