目的：探讨2023年1月至2025年3月青海玉树地区年龄相关性白内障的时空分布及环境因素的关联。

方法：收集2023年1月至2025年3月在我院收治的年龄相关性白内障患者1 121例为研究对象，根据纳排标准排除121例患者，最终纳入1 000例居住于青海省玉树藏族自治州的患者，平均年龄71.45±8.25岁。采用ArcGIS10.2绘制青海玉树地区各区县年龄相关性白内障时空分布地图，利用OpenGeoda进行全局空间自相关分析。通过二元Logistic回归分析环境因素与年龄相关性白内障发病的相关性。通过多因素Logistic回归分析年龄相关性白内障发病的影响因素。LCA(Mplus 7.4软件)用于比较因素分布特征的差异。

结果：最终纳入的1 000例年龄相关性白内障患者中，2023年438例，2024年472例，2025年90例。青海玉树地区各区县年龄相关性白内障时空分布地图显示，曲麻莱县和治多县发病率最高，称多县和玉树市发病率较高，囊谦县、杂多县发病率最低。空间分布呈“西北高、东南低”特征。2023年7-9月和2024年7-9月确诊量最高，占研究人数的31.90%，可能与高原夏季紫外线强度峰值相关。全局自相关Moran's I系数显示，年龄相关性白内障在区县尺度上的空间分布特征呈现为双峰分布，Moran's I系数均为正值，历年年龄相关性白内障发病率都具有空间正相关性(P<0.05)。其中，2023年7-9月和2024年7-9月为波峰，峰值分别为0.423和0.451。紫外线辐射年均值、海拔高度、PM2.5年均浓度与年龄相关性白内障均呈正相关(均P<0.05)，年平均气温、年降水量与年龄相关性白内障均呈负相关，但只有年平均气温与年龄相关性白内障关系显著(P<0.05)，调整混杂因素后，相关性仍稳定存在(P<0.05)。多因素Logistic回归分析显示，紫外线辐射年均值、海拔高度、年平均气温、PM2.5年均浓度为年龄相关性白内障发生的影响因素(均P<0.05)。LCA显示紫外线辐射年均值、海拔高度、年平均气温、PM2.5年均浓度对年龄相关性白内障的影响均具有异质性。

结论：2023年1月至2025年3月，青海玉树地区年龄相关性白内障发病存在明显时空特征与分布规律。时间上，每年7-9月确诊量达到峰值。空间上，各区县发病率差异显著，呈现“西北高、东南低”的分布特征。此外，紫外线辐射、海拔高度、气温及PM2.5浓度是影响青海玉树地区年龄相关性白内障发病的重要环境因素。

METHODS:A total of 1 121 patients with age-related cataract admitted to our hospital from January 2023 to March 2025 were collected, 121 patients were excluded according to the exclusion criteria, and finally 1 000 patients living in Yushu Tibetan Autonomous Prefecture, Qinghai Province were included, with an average age of 71.45±8.25 years. ArcGIS10.2 was used to draw the spatiotemporal distribution map of age-related cataract in each county of Yushu, Qinghai Province, and OpenGeoda was used for global spatial autocorrelation analysis. Binary Logistic regression was used to analyze the correlation between environmental factors and the incidence of age-related cataract. Multivariate Logistic regression was used to analyze the influencing factors of age-related cataract. LCA(Mplus 7.4 software)was used to compare the differences in factor distribution characteristics.

RESULTS:There were 438 cases in 2023, 472 cases in 2024, and 90 cases in 2025 among the 1 000 cases of age-related cataract. The spatiotemporal distribution map of age-related cataract in each county of Yushu, Qinghai Province showed that the incidence rate was the highest in Qumalai County and Zhiduo County, followed by Chengduo County and Yushu City, and the lowest in Nangqian County and Zaduo County. The spatial distribution presented a incidence feature of high in the northwest and low in the southeast. The highest number of confirmed cases was from July to September 2023 and 2024, accounting for 31.90% of the study population, which might be related to the peak of ultraviolet radiation intensity in the summer on the plateau. The global autocorrelation Moran's I coefficient showed that the spatial distribution characteristics of age-related cataract at the county level presented a bimodal distribution, with all Moran's I coefficients being positive. The incidence of age-related cataract in each year had a spatial positive correlation(P<0.05). The peaks were in July to September 2023 and 2024, with values of 0.423 and 0.451, respectively. The annual average ultraviolet radiation, altitude, and annual average PM2.5 concentration positively correlated with age-related cataract(all P<0.05), while the annual average temperature and annual precipitation negatively correlated with age-related cataract. However, only the annual average temperature significantly correlated with age-related cataract(P<0.05). After adjusting for confounding factors, the correlations remained stable(P<0.05). Multivariate Logistic regression analysis showed that the annual average ultraviolet radiation, altitude, annual average temperature, and annual average PM2.5 concentration were independent influencing factors for age-related cataract(all P<0.05). LCA showed that the effects of annual average ultraviolet radiation, altitude, annual average temperature, and annual average PM2.5 concentration on age-related cataract were heterogeneous.

CONCLUSION:From January 2023 to March 2025, there were obvious spatiotemporal characteristics and distribution patterns of age-related cataract in Yushu, Qinghai Province. Temporally, the number of confirmed cases reached the peak in July to September each year. Spatially, the incidence rates in each county were significantly different, presenting a distribution feature of high in the northwest and low in the southeast. In addition, ultraviolet radiation, altitude, temperature, and PM2.5 concentration are important environmental factors affecting the incidence of age-related cataract in Yushu, Qinghai Province.