摘要
目的概率评估上海市非伤寒沙门菌(以下简称"沙门菌")感染的流行病学负担,为上海市食源性疾病的防控提供决策依据。方法随机抽取上海市60个街道/乡镇,通过入户问卷调查获得居民急性胃肠炎的人年发病及就诊情况。随机抽取上海市23家监测医院,获得患者采样率、检测率、阳性率等数据。从国家"传染病报告信息管理系统"中获得沙门菌感染病例的报告率。根据疾病发病与报告金字塔建立模型,采用蒙特卡洛模拟,从主动监测和被动报告两种方法估计上海市沙门菌流行病学负担。结果上海市居民急性胃肠炎人群调查获得年发生率为23 261例/10万,患者就诊率为20.47%(26/127)。监测医院腹泻病例的粪便采样率为21.66%(5 974/27 587),沙门菌阳性率为3.53%(211/5 974)。沙门菌阳性的报告率为81.04%(171/211)。根据被动报告病例直接计算的年发病率仅为5例/10万。被动报告概率估计的上海市沙门菌年发病率为270例/10万[95%可信区间(95%CI):185~399],估计每年发病人数6.21万(95%CI:4.25~9.18),总低估系数为60∶1。主动监测估计沙门菌感染年发病率为686例/10万(95%CI:549~843),估计每年发病人数为15.78万(95%CI:12.64~19.42),总低估系数为154∶1。经过比较,主动监测估计结果为被动报告估计结果的2.54倍。沙门菌感染具有较明显季节性,每年5~8月是沙门菌感染的高发期。结论食源性疾病主动监测估算沙门菌感染年发病率远高于被动报告估算的年发病率,说明主动监测在掌握疾病发病、就诊和流行病学负担估计方面具有较好的优势,同时发现患者就诊率是影响沙门菌感染总病例数估计的最大因素。本研究进一步证明疾病负担金字塔模型在我国食源性疾病单病种疾病负担领域的可行性。
Objective To probabilistically estimate the epidemiological burden of nontyphoidal Salmonella infection in Shanghai and to provide decision-making basis for enacting appropriate foodborne diseases prevention policy. Methods Sixty streets/towns in Shanghai were randomly sampled,and annual incidence and hospital-visiting rate of people with acute gastroenteritis were obtained by questionnaire survey. Multi-stage stratified random sampling was used to extract 23 hospitals,and patient stool sampling rate,laboratory testing rate,and positive rate of Salmonella were obtain. Positive case reporting rate of Salmonella was obtained from National Notifiable Disease Reporting System（ NNDRS）. Annual incidence and epidemiological burden of nontyphoidal Salmonella were calculated by Monte-Carlo simulation according to the epidemiological burden pyramid model,and the active surveillance and passive report system were compared. Results The annual incidence of acute gastroenteritis among residents in Shanghai was 23 261 cases per 100 000,and hospitalvisiting rate was 20. 47%（ 26/127）. Patient stool sampling rate from surveillance hospital was 21. 66%（ 5 974/27 587）,and positive rate of Salmonella was 3. 53%（ 211/5 974）. Positive cases reporting rate of Salmonella in active surveillance was about 81. 04%（ 171/211）. The annual incidence of passive reporting system was estimated only 5 cases per 100 000. It was probabilistically estimated that the annual incidence of nontyphoidal Salmonella from passive report system was 270 cases（ 95% CI： 185-399） per 100 000,with 0. 062 1 million cases（ 95% CI： 0. 042 5-0. 091 8）and the total underestimation coefficient was 60 ∶ 1. While the estimated annual incidence of nontyphoidal Salmonella from active surveillance was 686 cases（ 95% CI： 549-843） per 100 000, with 0. 157 8 million cases（ 95% CI：0. 126 4-0. 194 2） and the total underestimation coefficient was 154 ∶ 1. After comparison,the estimates from active surveillance were 2. 54 times to passive report system. The incidence of Salmonella infection had obvious seasonal character. The speak season was from May to August. Conclusion The estimated annual incidence of nontyphoidal Salmonella infection from foodborne diseases active surveillance was much higher than that from passive report system. It showed that active surveillance had a good advantage for better understanding of the morbidity,hospital-visiting and epidemiological burden estimation for foodborne diseases. At the same time,the hospital-visiting rate was the biggest factor affecting the total cases of Salmonella infection. This study further demonstrated that epidemiological burden pyramid model was feasible for foodborne diseases in china,especially single category diseases.
作者
罗宝章
段胜钢
蔡华
宋夏
陆冬磊
吴春峰
陶芳芳
陈敏
刘弘
LUO Bao-zhang;DUAN Sheng-gang;CAI Hua;SONG Xia;LU Dong-lei;WU Chun-feng;TAO Fang-fang;CHEN Min;LIU Hong(Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, Chin)
出处
《中国食品卫生杂志》
北大核心
2018年第1期12-17,共6页
Chinese Journal of Food Hygiene
基金
上海市卫生与计划生育委员会青年课题(20144Y0156)
关键词
沙门菌感染
流行病学负担
金字塔模型
食源性疾病
概率评估
Salmonella infection
epidemiological burden
pyramid model
foodborne disease
probabilistic estimate
作者简介
罗宝章 男 主管医师 研究方向为食品安全风险监测与评估E-mail：luobaozhang@scdc．sh．cn;通信作者：刘弘 男 主任医师 研究方向为食品安全风险监测与评估E-mail：liuhong@scdc．sh．cn
