中小学校水痘暴发疫情风险预测模型构建

郑永韬; 叶春梅; 倪作为; 张佳妮; 来芬华; 高艳敏; 杨东波; 王岩梅

doi:10.16835/j.cnki.1000-9817.2024191

中小学校水痘暴发疫情风险预测模型构建

doi: 10.16835/j.cnki.1000-9817.2024191

1.
浙江省杭州市萧山区疾病预防控制中心，311203
2.
杭州市临平区疾病预防控制中心
3.
浙江大学医学院附属第一医院
4.
上海市杨浦区控江社区卫生服务中心
5.
杭州市萧山区瓜沥镇社区卫生服务中心
6.
上海中医药大学附属市中医医院

基金项目:

上海中医药大学科技发展项目 23YGZX06

杭州市卫生科技计划项目 B20230317

萧山区农业和发展重大科技计划项目 20231070

详细信息

作者简介:
郑永韬(1993-)，男，浙江绍兴人，硕士，主管医师，主要研究方向为学校疫情防控

通讯作者:
王岩梅，E-mail：wym1135@163.com

利益冲突声明 所有作者声明无利益冲突。
中图分类号: G478 R181.3⁺² R511.5
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- 被引次数: 0
出版历程
- 收稿日期: 2024-03-29
- 修回日期: 2024-04-23
- 网络出版日期: 2024-06-27
- 刊出日期: 2024-06-25

Establish of the risk predictive model for varicella outbreaks in primary and middle schools

1.
Xiaoshan District Center for Disease Control and Prevention, Hangzhou (311203), Zhejiang Province, China

摘要

摘要: 目的探究中小学校水痘暴发疫情的流行特征并构建风险预测模型，为学校水痘疫情防控提供科学指导。方法采用巢式病例对照研究，将2023年1—12月上海市(杨浦区、静安区)及杭州市(萧山区、临平区)4个区县的中小学校作为研究对象，观察学校水痘疫情发生情况。分析学校水痘暴发疫情发生的影响因素，构建学校水痘疫情发生风险预测模型，并采用H-L拟合优度检验、受试者工作特征曲线(ROC)、Calibration校准曲线、决策曲线(DCA)等对预测模型进行评价。结果水痘暴发疫情中小学校有98所，同期无水痘暴发疫情的195所学校作为对照，厕所温水洗手台、厕所洗手台洗手液、班级平均学生数、学生每日在校时长、专职校医、校长水痘疫苗犹豫、水痘疫苗1剂次接种率、水痘疫苗2剂次接种率均是学校水痘暴发疫情发生的影响因素，组间差异均有统计学意义(χ²/Z值分别为10.01，20.49，17.43，9.74，32.17，6.60，2.20，3.39，P值均 < 0.05)。采用上述8个变量构建风险预测模型，H-L拟合优度检验χ²值为5.863(P>0.05)；ROC曲线下面积(AUC)为0.846(95%CI=0.799~0.893)；Calibration校准曲线显示模型的预测值和实际值一致性较好，DCA曲线显示在较大范围内模型的预测效果较好。结论学校水痘暴发疫情风险预测模型具有较好的准确性和效果。可利用好学校水痘暴发疫情风险预测模型并采取相应措施，降低水痘疫情在学校的传播风险。
- 水痘 /
- 疾病暴发流行 /
- 列线图 /
- 学生保健服务
Abstract: Objective To investigate the epidemiological characteristics of varicella outbreaks in primary and middle schools, and to establish a risk predictive model, so as to provide scientific guidance for the prevention of varicella outbreaks in schools. Methods Based on a nested case-control study, primary and middle schools in 4 districts of Shanghai (Yangpu District and Jingan District) and Hangzhou (Xiaoshan District and Linping District) from January to December 2023 were selected to observe the status of varicella outbreaks. Associated factors of varicella outbreaks were investigated and used for establishing the predictive model, which was evaluated by the Hosmer-Lemeshow(H-L) goodness of fit test, receiver operating characteristic (ROC) curve, Calibration curve, decision curve analysis (DCA). Results A total of 98 varicella outbreaks were included, with 195 schools without varicella outbreaks during the same period as controls. Eight factors, including the availability of warm water in restroom, availability of hand soap in restroom, average class size, duration of student attendance at school per day, presence of a full-time school doctor, hesitancy of the school principal towards varicella vaccination, and rates of first and second doses of varicella vaccination, were identified as potential factors for school varicella outbreaks, with statistically significant differences (χ²/Z=10.01, 20.49, 17.43, 9.74, 32.17, 6.60, 2.20, 3.39, P < 0.05). The 8 variables above were employed to construct a risk predictive model, and Hosmer-Lemeshow goodness of fit test yielded a χ² value of 5.863 (P>0.05); the area under the ROC curve (AUC) was 0.846 (95%CI=0.799-0.893); Calibration curve analysis indicated good consistency between predicted and actual values of the model. DCA demonstrated favorable predictive performance of the model over a wide range. Conclusions The predictive model for school varicella outbreaks demonstrates satisfactory accuracy and efficacy. It suggested to make good use of this prediction model and take relevant measures to reduce the risk of varicella transmission in schools.
- Chickenpox /
- Disease outbreaks /
- Nomograms /
- Student health services
注释:

1) 利益冲突声明 所有作者声明无利益冲突。

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图 1 2023年上海和杭州4区县不同月份中小学校水痘疫情暴发情况

Figure 1. Varicella outbreaks status of primary and middle schools from 4 districts of Shanghai and Hangzhou in different months of 2023

下载: 全尺寸图片幻灯片

图 2 学校水痘暴发疫情发生风险预测模型

Figure 2. The predictive model for risk of school varicella outbreaks

下载: 全尺寸图片幻灯片

图 3 中小学校水痘暴发疫情发生风险预测模型Calibration校准曲线

Figure 3. Calibration curve of the predictive model for risk of varicella outbreaks among primary and middle schools

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图 4 中小学校水痘暴发疫情发生风险预测模型DCA曲线

Figure 4. DCA curve of the predictive model for risk of varicella outbreaks among primary and middle schools

下载: 全尺寸图片幻灯片

表 1 上海和杭州中小学校水痘暴发疫情的单因素分析

Table 1. Univariate analysis of school varicella outbreaks among primary and middle schools in Shanghai and Hangzhou

影响因素	选项	学校数	疫情学校数	χ²值	P值
办学层次	托幼机构	26	10(38.46)	2.05	0.56
	小学	136	46(33.82)
	初中	108	32(29.63)
	高中	23	10(43.48)
所在地	农村	205	72(35.12)	0.86	0.35
	城镇	88	26(29.55)
办学性质	公办	247	78(31.58)	2.47	0.12
	民办	46	20(43.48)
厕所温水洗手台	无或损坏	42	23(54.76)	10.01	< 0.01
	有	251	75(29.88)
厕所洗手台洗手液	无或过期	70	39(55.71)	20.49	< 0.01
	有	223	59(26.46)
学校学生总数	< 500	78	24(30.77)	4.71	0.10
	500~1 000	146	57(39.04)
	>1 000	69	17(24.64)
班级平均学生数	≤40	137	29(21.17)	17.43	< 0.01
	>40	156	69(44.23)
寄宿制	否	255	86(33.73)	0.07	0.79
	是	38	12(31.58)
学生每日在校时长/h	≤8	163	42(25.77)	9.74	< 0.01
	>8	130	56(43.08)
专职校医	无	159	76(47.80)	32.17	< 0.01
	有	134	22(16.42)
校长水痘疫苗犹豫	无	191	54(28.27)	6.60	0.01
	有	102	44(43.14)
注： ()内数字为报告率/%。

下载: 导出CSV

表 2 上海和杭州中小学校水痘暴发疫情的多因素Logistic回归分析(n=98)

Table 2. Multivariable Logistic regression analysis of school varicella outbreaks among primary and middle schools in Shanghai and Hangzhou(n=98)

自变量	β值	标准误	Wald χ²值	OR值(95%CI)	P值
厕所温水洗手台
无或损坏				1.00
有	-1.10	0.42	-2.62	0.33(0.14~0.75)	0.01
厕所洗手台洗手液
无或损坏				1.00
有	-1.41	0.36	-3.91	0.24(0.12~0.49)	< 0.01
班级平均学生数/人
≤40				1.00
>40	1.24	0.33	3.82	3.46(1.85~6.66)	< 0.01
学生每日在校时长/h
≤8				1.00
>8	0.82	0.32	2.59	2.27(1.23~4.25)	0.01
专职校医
无				1.00
有	-1.77	0.34	-5.21	0.17(0.09~0.33)	< 0.01
校长水痘疫苗犹豫
无				1.00
有	0.65	0.33	1.95	1.91(1.00~3.68)	0.05
水痘疫苗1剂次接种率
< P₂₅				1.00
P₂₅~P₅₀	-0.27	0.46	-0.57	0.77(0.31~1.91)	0.57
>P₅₀~P₇₅	-0.76	0.45	-1.68	0.47(0.19~1.13)	0.09
>P₇₅	-1.15	0.47	-2.44	0.32(0.12~0.79)	0.02
水痘疫苗2剂次接种率
< P₂₅				1.00
P₂₅~P₅₀	-1.17	0.45	-2.58	0.31(0.13~0.74)	0.01
>P₅₀~P₇₅	-1.37	0.44	-3.10	0.26(0.11~0.60)	< 0.01
>P₇₅	-1.41	0.47	-2.99	0.25(0.10~0.60)	< 0.01

下载: 导出CSV

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