Spatio-temporal clustering of Vibrio parahaemolyticus detection rate among foodborne disease cases in Zhejiang province, 2016 – 2017: a monitoring data analysis
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摘要:
目的 了解浙江省食源性疾病监测病例副溶血性弧菌检出率的时空聚集性特征，为浙江省食源性疾病的监测与防治提供建议。 方法 在描述性统计学的分析基础上，利用空间分析法和时空扫描统计法，对2016 — 2017年浙江省101家哨点医院上报的食源性疾病病例副溶血性弧菌检测数据进行分析。 结果 副溶血性弧菌在8月有检出最高峰，检出主要集中在成年人群，尤其26～35岁年龄段。副溶血性弧菌检出率在时间和空间上存在明显的聚集性，其中空间聚集区域2016年3个，2017年3个，时间聚类情况为2016年8 — 10月，2017年7 — 10月。 结论 浙江省食源性疾病监测病例副溶血性弧菌检出率具有年龄和季节性分布特点，并存在明显的时空聚集性。 Abstract:Objective To explore spatio-temporal clustering of Vibrio parahaemolyticus detection rate among reported foodborne disease cases in Zhejiang province and to provide evidence to surveillance and prevention of foodborne disease in the region. Methods The data on Vibrio parahaemolyticus detection among foodborne disease cases reported by 101 sentinel hospitals in Zhejiang province during 2016 – 2017 were collected from provincial reporting system for foodborne disease monitoring. Descriptive statistics analysis, spatial analysis and spatio-temporal scanning statistics were performed on the data obtained. Results Among 43386 and 33407 specimens from foodborne diarrhoea cases in 2016 and 2017, 1978 and 1346 were positive for Vibrio parahaemolyticus, with the detection rate of 4.56 % and 4.03 %. The Vibrio parahaemolyticus detection rate was the highest in August during a year and the detection rate was higher in adult cases, especially in those aged 26 – 35 years. Obvious temporal and spatial clustering of Vibrio parahaemolyticus detection rate were identified, with spatial clustering in 3 regions in 2016 and another 3 regions in 2017 and temporal clustering during August – October of 2016 and July – October of 2017, respectively． Conclusion Among reported foodborne disease cases in Zhejiang province, the Vibrio parahaemolyticus detection rate is of uneven age and seasonal distribution and the detection rate is also of obvious spatial and temporal clustering. -
表 1 2016 — 2017年浙江省区县水平副溶血性弧菌检出率时空聚集性分析
年份（年） 聚集区域（类） 聚集时间 区县个数 RR 值 LLR 值 P 值 2016 1 2016.8.1 — 2016.9.30 21 4.54 286.67 < 0.001 2 2016.8.1 — 2016.10.31 13 4.01 209.97 < 0.001 3 2016.8.1 — 2016.8.31 25 1.82 17.88 < 0.001 2017 1 2017.8.1 — 2017.9.30 28 4.76 209.57 < 0.001 2 2017.7.1 — 2017.9.30 23 2.93 107.28 < 0.001 3 2017.8.1 — 2017.10.31 1 3.90 17.11 < 0.001 
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