Serfling回归模型在浙江省金华市结核病流行特征解析中的应用研究

doi:10.19982/j.issn.1000-6621.20250523

摘要/Abstract

摘要：

目的: 构建调整Serfling回归模型分析金华市结核病流行期和超额病例数,为防控措施提供依据。方法: 从“中国疾病预防控制信息系统”子系统“结核病信息管理系统”,收集发病时间为2015年1月1日至2025年10月31日,经临床诊断和实验室诊断的现住址为金华市的所有结核病报告病例数据,采用X13-ARIMA-SEATS、HP滤波法以及Joinpoint模型进行结核病流行特征描述性分析,并拟合调整Serfling回归模型结合自回归积分移动平均(auto regressive integrated moving average, ARIMA)模型误差调整,判断结核病的流行期和估计超额发病数,预测2025年1—10月的发病数。结果: 2015—2024年共报告35659例结核病患者,平均年发病率为51.73/10万(35659/6893.6万),2015—2024年金华市结核病发病率从2015年的63.44/10万(4075/642.3万)下降至2024年的43.72/10万(3132/716.3万),总体呈现下降趋势(AAPC=-5.05,P<0.001)。调整Serfling回归模型拟合效果较好(AIC=521.19,BIC=533.66),一共出现46个流行月,3月为固定流行月。超额发病病例总数为2580(95%CI: 2337~2822)例,2017年超额病例数最多,为509(95%CI: 408~610)例;其次为2018年,为369(95%CI: 289~449)例。2025年1—10月结核病发病预测显示,2025年2月和9月为流行月,超额病例数为49(95%CI: 13~85)例和44(95%CI: 8~80)例,预测值高峰与实际发病数高峰一致。结论: 2015—2024年金华市结核病疫情呈下降趋势,需关注春季结核病的防控,调整Serfling回归模型拟合效果较好,可用于结核病的早期预警和估计疾病负担。

关键词: 结核, 流行病学研究特征, 回归分析, 预测

Abstract:

Objective: To establish an adjusted Serfling regression model for analyzing TB epidemic periods and excess cases, thereby providing evidence for control measures. Methods: Data of all clinically and laboratory-confirmed TB cases with disease onset between January 1, 2015, and October 31, 2025 and address in Jinhua City, was extracted from the Tuberculosis Information Management System, a subsystem of the China Disease Prevention and Control Information System. Descriptive analysis of TB epidemic characteristics was performed using X-13-ARIMA-SEATS, the Hodrick-Prescott (HP) filter, and the Joinpoint regression model. An adjusted Serfling regression model incorporating ARIMA error correction was established to identify the epidemic periods and estimate the number of excess cases, and predict the incidences from January to October 2025. Results: From 2015 to 2024, a total of 35659 TB cases were reported, with an average annual incidence rate of 51.73 per 100000 population (35659/68.936 million). The TB incidence rates in Jinhua City decreased from 63.44 per 100000 (4075/6.423 million) in 2015 to 43.72 per 100000 (3132/7.163 million) in 2024, showing a decline (AAPC=-5.05, P<0.001) trend. The adjusted Serfling regression model had a good fitting effect (AIC=521.19,BIC=533.66), a total of 46 epidemic months were identified, with March being a fixed epidemic month. The total number of excess cases was 2580 (95%CI: 2337-2822). The highest number of excess cases was recorded in 2017 (509 cases, 95%CI: 408-610), followed by 2018 (369 cases, 95%CI: 289-449). The prediction of TB incidence from January to October 2025 indicated that February and September 2025 would be epidemic months, with 49 (95%CI: 13-85) and 44 (95%CI: 8-80) excess cases, respectively. The peak of the predicted values was consistent with that of the actual incidence. Conclusion: From 2015 to 2024, the TB epidemic in Jinhua City showed a downward trend. Attention should be paid to TB prevention and control in spring. The adjusted Serfling regression model has a good fitting effect and can be used for early warning of TB and estimation of disease burden.

Key words: Tuberculosis, Epidemiologic study characteristics, Regression analysis, Forecasting

中图分类号:

R521

陈依娜, 李卫丹, 朱凯强, 唐慧玲. Serfling回归模型在浙江省金华市结核病流行特征解析中的应用研究[J]. 中国防痨杂志, 2026, 48(7): 941-947. doi: 10.19982/j.issn.1000-6621.20250523

Chen Yina, Li Weidan, Zhu Kaiqiang, Tang Huiling. Serfling regression model for analyzing tuberculosis epidemiological characteristics in Jinhua City,Zhejiang Province[J]. Chinese Journal of Antituberculosis, 2026, 48(7): 941-947. doi: 10.19982/j.issn.1000-6621.20250523

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统计值	第1次回归	第2次回归	第3次回归
纳入月份数	120	59	59
R²值	0.634	0.820	0.820
F值	69.573	89.050	89.050
P值	<0.001	<0.001	<0.001
截距项	354.658	324.614	324.614
t值	-0.950	-0.865	-0.865
cos(2πt/12)	-33.057	-35.771	-35.771
sin(4πt/12)	-11.346	-14.576	-14.576
剔除月份数	61	0	0

统计值	估计值	标准误	Z值	P值
AR	0.283	0.131	2.163	0.031
截距项	323.635	7.021	46.095	<0.001
t值	-0.852	0.101	-8.447	<0.001
cos(2πt/12)	-35.395	3.784	-9.354	<0.001
sin(4πt/12)	-13.329	3.071	-4.340	<0.001

年份	流行月数	流行期(月)	超额发病数(例,95%CI)
2015	6	1,3,5,7,8,12	274(186~362)
2016	6	1,3,8,10,11,12	302(214~390)
2017	8	1,3,5,7,8,9,10,12	509(408~610)
2018	5	1,3,5,10,12	369(289~449)
2019	5	1,2,3,5,6	295(215~375)
2020	1	9	82(46~118)
2021	3	3,6,8	119(39~199)
2022	3	3,7,9	148(68~228)
2023	3	3,8,12	153(73~233)
2024	6	3,8,9,10,11,12	329(249~409)
合计	46		2580(2337~2822)