云南省血防专业人员钉螺与拟钉螺鉴别影响因素分析

中国血吸虫病防治杂志 ›› 2024, Vol. 36 ›› Issue (5): 514-520.

云南省血防专业人员钉螺与拟钉螺鉴别影响因素分析

崔晓1△，宋静 2△，李春英3，王洪琼2，杜春红2，沈美芬2，杨早改3，史欣平3，李石柱4*，董毅2*

1 大理大学公共卫生学院（云南大理 671000）；2 云南省地方病防治所、云南省自然疫源性疾病防控技术重点实验室（云南大理 671000）；3 昆明医科大学；4 中国疾病预防控制中心寄生虫病预防控制所（上海 200027）

出版日期:2024-10-25 发布日期:2024-11-18
通讯作者: 董毅dydali@sina.com；李石柱lisz@chinacdc.cn
作者简介:崔晓，女，硕士研究生。研究方向：血吸虫病流行病学与控制技术宋静，女，硕士研究生，副主任医师。研究方向：血吸虫病防治
基金资助:
国家卫生健康委员会寄生虫病原与媒介生物学重点实验室开放课题（NHCKFKT2023⁃08）、大理州科技计划项目（D2022ZA0115）

Factors affecting differentiation between Oncomelania hupensis and Tricula snails among schistosomiasis control professionals in Yunnan Province

CUI Xiao1△, SONG Jing2△, LI Chunying3, WANG Hongqiong2, DU Chunhong2, SHEN Meifen2, YANG Zaogai3, SHI Xinping3, LI Shizhu4*, DONG Yi2*

1 School of Public Health, Dali University, Dali, Yunnan 671000, China; 2 Yunnan Provincial Institute of Endemic Disease Prevention and Control, Yunnan Provincial Key Laboratory of Natural Epidemic Disease Prevention and Control Technology, Dali, Yunnan 671000, China; 3 Kunming Medical University, Kunming, Yunnan Province, China; 4 National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai 200027, China

Online:2024-10-25 Published:2024-11-18

摘要/Abstract

摘要： 目的　评价云南省血吸虫病防治（血防）专业人员对钉螺与拟钉螺形态差异的鉴别能力，并分析其影响因素，评估拟钉螺对钉螺调查工作的干扰程度。方法　在云南省9个血吸虫病流行县（市、区）采集钉螺和拟钉螺，利用线上问卷调查和实物盲测两种方法调查血防专业人员对两种螺的鉴别能力，统计判断正确率、误判率和漏判率。采用统计软件SPSS 25.0建立单因素及多因素logistic回归分析模型，分析影响血防专业人员钉螺判断正确率的因素。结果　问卷调查和实物盲测结果显示，云南省血防专业人员钉螺判断总正确率分别为56.77%（2 305/4 060）和68.28%（1 556/2 279）。线上问卷调查单因素logistic回归分析结果显示，性别［比值比（odds ratio，OR） = 1.244，95%可信区间（confidence interval，CI）：（1.073，1.441），P < 0.05］、职称［OR = 0.628、1.741，95% CI：（0.453，0.871）、（1.109，2.734），P均< 0.05］、工作年限［OR = 0.979，95% CI：（0.971，0.987），P < 0.05］和血吸虫病流行地区达标分类［OR = 1.410、0.293、0.523，95% CI：（1.103，1.804）、（0.237，0.361）、（0.416，0.657），P均< 0.05］是云南省血防专业人员钉螺鉴别正确率的影响因素。校正后经多因素logistic回归分析发现，男性血防专业人员发生误判的概率是女性的1.179倍［OR = 1.179，95% CI：（1.006，1.382），P < 0.05］，消除有螺地区血防专业人员发生误判的概率是消除无螺地区的1.474倍［OR = 1.474，95% CI：（1.145，1.898），P < 0.05］，传播阻断有螺地区和消除有螺地区血防专业人员发生漏判的概率分别是消除无螺地区的0.284倍［OR = 0.284，95% CI：（0.225，0.359），P < 0.05］和0.523倍［OR = 0.523，95% CI：（0.412，0.664），P < 0.05］。现场实物盲测单因素回归分析结果显示，年龄［OR = 2.381，95% CI：（1.677，3.381），P < 0.05］、职称［OR = 1.688，95% CI：（1.103，2.582），P < 0.05］、工作年限［OR = 0.970，95% CI：（0.956，0.984），P < 0.05］和血吸虫病流行地区达标分类［OR = 0.262、0.593，95% CI：（0.188，0.364）、（0.420，0.837），P均< 0.05］是云南省血防专业人员钉螺鉴别正确率的影响因素。校正后经多因素logistic回归分析发现，传播阻断有螺地区和消除有螺地区血防专业人员发生漏判的概率分别是消除无螺地区的0.263倍［OR = 0.263，95% CI：（0.176，0.394），P < 0.05］和0.604倍［OR = 0.604，95% CI：（0.416，0.875），P < 0.05］。结论　云南省血防专业人员对钉螺和拟钉螺鉴别正确率不高，性别和血吸虫病流行地区达标类型是影响鉴别能力的因素。云南省有螺环境拟钉螺对钉螺调查工作造成的干扰程度较高，需加强该省血防专业人员对钉螺与拟钉螺的鉴别培训。

关键词: 钉螺, 拟钉螺, 鉴别, 误判, 漏判, 云南省

Abstract: Objective　To investigate the capability for distinguishing between the morphology of Oncomelania hupensis and Tricula snails and its influencing factors among schistosomiasis control professionals in Yunnan Province, so as to evaluate the interference of Tricula snails with O. hupensis surveys. Methods　O. hupensis and Tricula snails were sampled from 9 schistosomiasis⁃endemic counties (districts) in Yunnan Province. The capability for distinguishing between O. hupensis and Tricula snails was evaluated using online questionnaire surveys and field blind tests among schistosomiasis control professionals, and the proportions of correct judgment, misjudgment and missed judgment were calculated. Univariate and multivariate logistic regression models were created using the software SPSS 25.0, and factors affecting the proportion of correct judgments of O. hupensis snails were identified among schistosomiasis control professionals. Results　Questionnaire surveys and field blind tests showed that the overall proportions of correct judgments of O. hupensis snails were 56.77% (2 305/4 060) and 68.28% (1 556/2 279) among schistosomiasis control professionals in Yunnan Province, respectively. Univariate logistic regression analysis of online questionnaire surveys identified gender [odds ratio (OR) = 1.244, 95% confidential interval (CI): (1.073, 1.441), P < 0.05], professional title [OR = 0.628, 1.741, 95% CI: (0.453, 0.871), (1.109, 2.734), both P < 0.05], working duration [OR = 0.979, 95% CI: (0.971, 0.987), P < 0.05] and classification of schistosomiasis epidemics in endemic foci [OR = 1.410, 0.293, 0.523, 95% CI: (1.103, 1.804), (0.237, 0.361), (0.416, 0.657), both P < 0.05] as factors affecting the proportion of correct judgments of O. hupensis snails among schistosomiasis control professionals in Yunnan Province, and multivariate logistic regression analysis after adjustments showed that the proportion of O. hupensis snail misjudgments was 1.179 times higher among male schistosomiasis control professionals than among females [OR = 1.179, 95% CI: (1.006, 1.382), P < 0.05], and 1.474 times higher among schistosomiasis control professionals in schistosomiasis⁃elimination areas with snails than in areas without snails [OR = 1.474, 95% CI: (1.145, 1.898), P < 0.05], and the proportions of missed judgments of O. hupensis snails were 0.284 [OR = 0.284, 95% CI: (0.225, 0.359), P < 0.05] and 0.523 times [OR = 0.523, 95% CI: (0.412, 0.664), P < 0.05] higher among schistosomiasis control professionals in transmission⁃interruption areas with snails and schistosomiasis⁃elimination areas with snails than in schistosomiasis⁃elimination areas without snails. Univariate logistic regression analysis of field blind tests showed identified age [OR = 2.381, 95% CI: (1.677, 3.381), P < 0.05], professional title [OR = 1.688, 95% CI: (1.103, 2.582), P < 0.05］, working duration [OR = 0.970, 95% CI: (0.956, 0.984), P < 0.05] and classification of schistosomiasis epidemics in endemic foci [OR = 0.262, 0.593, 95% CI: (0.188, 0.364), (0.420, 0.837), both P < 0.05] as factors affecting the proportion of correct judgments of O. hupensis snails among schistosomiasis control professionals in Yunnan Province, and multivariate logistic regression analysis after adjustments showed the proportions of missed judgments of O. hupensis snails were 0.263 [OR = 0.263, 95% CI: (0.176, 0.394), P < 0.05] and 0.604 times [OR = 0.604, 95% CI: (0.416, 0.875), P < 0.05] higher among schistosomiasis control professionals in transmission⁃interruption areas with snails and schistosomiasis⁃elimination areas with snails than in schistosomiasis⁃elimination areas without snails. Conclusions　Schistosomiasis control professionals in Yunnan Province have a low accuracy rate for distinguishing between the morphology of O. hupensis and Tricula snails, and gender and classification of schistosomiasis epidemics in endemic foci are factors that affect their ability to distinguish. The presence of Tricula snails causes a high degree of interference with O. hupensis surveys in O. hupensis snail⁃infested areas of Yunnan Province. Reinforced training for distinguishing between O. hupensis and Tricula snails is required among schistosomiasis control professionals in Yunnan Province.

Key words: Oncomelania hupensis, Tricula, Differentiation, Misjudgment, Missed judgement, Yunnan Province

中图分类号:

R383.24

崔晓, 宋静 , 李春英, 王洪琼, 杜春红, 沈美芬, 杨早改, 史欣平, 李石柱, 董毅. 云南省血防专业人员钉螺与拟钉螺鉴别影响因素分析[J]. 中国血吸虫病防治杂志, 2024, 36(5): 514-520.

CUI Xiao, SONG Jing, LI Chunying, WANG Hongqiong, DU Chunhong, SHEN Meifen, YANG Zaogai, SHI Xinping, LI Shizhu, DONG Yi. Factors affecting differentiation between Oncomelania hupensis and Tricula snails among schistosomiasis control professionals in Yunnan Province[J]. Chinese Journal of Schistosomiasis Control, 2024, 36(5): 514-520.

[1]	白少文, 周济华, 董毅, 张键锋, 施亮, 杨坤. 基于EfficientNet⁃B4模型的云南省湖北钉螺滇川亚种视觉智能识别模型的建立[J]. 中国血吸虫病防治杂志, 2024, 36(6): 555-561.
[2]	高风华. 基于5种机器学习模型的安徽省钉螺扩散面积预测研究[J]. 中国血吸虫病防治杂志, 2024, 36(6): 572-576.
[3]	张宗亚, 杜春红, 张云, 王洪琼, 宋静, 周济华, 王丽芳, 孙佳昱, 沈美芬, 陈春琼, 江华, 颜嘉琦, 冯锡光, 王文雅, 钱沛君, 薛靖波, 李石柱, 董毅. 基于随机森林和最大熵模型的云南省钉螺潜在地理分布预测[J]. 中国血吸虫病防治杂志, 2024, 36(6): 562-571,613.
[4]	施倩雯, 沈玲娥, 周靖, 吴敬之. 2016—2023年江苏省苏州市钉螺扩散时空分布特征[J]. 中国血吸虫病防治杂志, 2024, 36(6): 577-583.
[5]	储琼, 查明, 姚金付. 2016—2022年安庆市不同水系钉螺空间分布特征[J]. 中国血吸虫病防治杂志, 2024, 36(6): 614-619.
[6]	黄轶昕. 无人机在药物灭螺中的应用[J]. 中国血吸虫病防治杂志, 2024, 36(5): 439-440,449.
[7]	高风华, 张世清, 丁宋军, 许晓娟, 汪天平, 代波. 基于Mann⁃Kendall检验的2004—2023年安徽省有螺面积变化趋势分析[J]. 中国血吸虫病防治杂志, 2024, 36(5): 460-465.
[8]	陈勇, 许晓娟, 闻道龙, 代波, 高岚, 张戎, 黄青青, 李琳琳, 查帆, 方亮, 张萍, 张世清, 曹淳力. 无人机施药技术在湖沼型环境药物灭螺成本⁃效果评价[J]. 中国血吸虫病防治杂志, 2024, 36(5): 502-506.
[9]	周瑜, 杨淑娟, 杨媛, 和雁, 袁淑莲, 陈朝闻, 任天广. 2023年云南省龙陵县居民恙虫病防治知信行现状及影响因素[J]. 中国血吸虫病防治杂志, 2024, 36(5): 507-513.
[10]	朱泽林, 张瑕, 何君逸, 陈盈, 王味思, 胡合华, 曹淳力, 鲍子平, 郭苏影, 段李平, 元艺, 许静, 李石柱, 周晓农. 沟渠环境无人机施用不同剂型杀螺胺乙醇胺盐灭螺效果评价[J]. 中国血吸虫病防治杂志, 2024, 36(5): 527-530,541.
[11]	曹淳力, 张键锋, 李叶芳, 神学慧, 何君逸, 鲍子平, 郭苏影, 杨坤, 许静, 李石柱, 周晓农. 江滩环境无人机喷撒5%杀螺胺乙醇胺盐颗粒剂灭螺效果评价[J]. 中国血吸虫病防治杂志, 2024, 36(5): 531-534.
[12]	胡飞, 吕尚标, 李宜锋, 李宗光, 何婷婷, 谢婧姿, 袁敏, 林丹丹. 干旱对鄱阳湖区钉螺孳生的影响[J]. 中国血吸虫病防治杂志, 2024, 36(4): 370-375.
[13]	宋静, 张宗亚, 沈美芬, 周济华, 李春英, 杨早改, 董毅, 杜春红. 日本血吸虫和中华血吸虫尾蚴外部形态及运动方式比较[J]. 中国血吸虫病防治杂志, 2024, 36(4): 384-387.
[14]	张云, 王丽芳, 冯锡光, 吴明寿, 沈美芬, 江华, 宋静, 孙佳昱, 陈春琼, 颜嘉琦, 张宗亚, 周济华, 董毅, 杜春红. 云南省血吸虫病传播阻断历程与展望[J]. 中国血吸虫病防治杂志, 2024, 36(4): 422-427.
[15]	何欢, 周金星, 高梓欣, 陆子淳, 代仁龙, 胡兴宜, 庞宏东. 长江滩地不同土地利用类型土壤微生物与理化性质及其对钉螺分布的影响[J]. 中国血吸虫病防治杂志, 2024, 36(2): 148-153,200.