Ding Zheyuan, Wu Haocheng, Wu Chen, Liu Kui, Lu Qinbao, Wang Xinyi, Fu Tianying, Li Junjie, Yang Ke, Song Queping, Lin Junfen
Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.
Hangzhou Medical College, Hangzhou, Zhejiang, China.
Front Public Health. 2025 Jul 2;13:1604018. doi: 10.3389/fpubh.2025.1604018. eCollection 2025.
The emergence of advanced diagnostic techniques and improved disease surveillance have led to increased recognition of psittacosis cases in recent years. This study aimed to characterize the epidemiological patterns and spatiotemporal distribution of psittacosis in Zhejiang Province, China, and to identify high-risk clusters through predictive modeling.
We conducted a comprehensive analysis of reported psittacosis cases in Zhejiang Province from January 2019 to June 2024. Demographic characteristics and seasonal trends were systematically analyzed. Spatial epidemiological methods, including spatiotemporal distribution mapping, spatial autocorrelation analysis, and Kriging interpolation, were employed to identify disease hotspots and predict risk areas.
During the study period, 315 psittacosis cases were reported, with an annual average incidence rate of 0.0914 per 100,000 population, showing a significant increasing trend. The geographic distribution of cases expanded over time. More cases were reported in winter. Cases demonstrated a male predominance (sex ratio 1.1:1) with a median age of 64 years. Occupational analysis revealed farmers as the most affected group (52.4%). Spatial analysis identified significant clustering ( = 0.5428, < 0.001), with high-incidence areas concentrated in western and central regions. Kriging interpolation predicted the highest disease risk in western Zhejiang, followed by central, southwestern and parts of northern regions. Western and southwestern regions had high risks of cluster.
Our findings demonstrate a concerning upward trend in psittacosis incidence with expanding geographic distribution in Zhejiang Province. The identification of high-risk clusters in western, central, and northern regions provides critical evidence for targeted public health interventions, including enhanced surveillance in agricultural communities and seasonal prevention campaigns during winter months.
近年来，先进诊断技术的出现和疾病监测的改善使得鹦鹉热病例的识别有所增加。本研究旨在描述中国浙江省鹦鹉热的流行病学模式和时空分布，并通过预测模型识别高危聚集区。
我们对2019年1月至2024年6月浙江省报告的鹦鹉热病例进行了全面分析。系统分析了人口统计学特征和季节性趋势。采用时空分布绘图、空间自相关分析和克里金插值等空间流行病学方法来识别疾病热点并预测风险区域。
在研究期间，共报告了315例鹦鹉热病例，年平均发病率为每10万人口0.0914例，呈显著上升趋势。病例的地理分布随时间扩大。冬季报告的病例更多。病例显示男性占主导（性别比1.1:1），中位年龄为64岁。职业分析显示农民是受影响最严重的群体（52.4%）。空间分析发现存在显著聚集（ = 0.5428， < 0.001），高发病区集中在西部和中部地区。克里金插值预测浙江省西部疾病风险最高，其次是中部、西南部和北部部分地区。西部和西南部地区存在高聚集风险。
我们的研究结果表明，浙江省鹦鹉热发病率呈令人担忧的上升趋势，地理分布不断扩大。在西部、中部和北部地区识别出高危聚集区为有针对性的公共卫生干预提供了关键证据，包括加强对农业社区的监测以及在冬季开展季节性预防活动。
