非药物性干预措施对湖北省荆州市法定传染病发病的影响

Impact of non-pharmaceutical interventions on incidence of notifiable infectious disease in Jingzhou,Hubei

目的评价非药物性干预措施(NPIs)对湖北省荆州市法定传染病(NIDs)发病的影响。方法收集2014年12月29日(2015年第1周)至2021年1月3日(2020年第53周)荆州市NIDs周发病数。2015年1周至2020年5周作为历史数据拟合贝叶斯结构时间序列(BSTS)模型并预测2020年6—53周NIDs发病数。2020年6—53周实际值分别与2015—2019年同期NIDs发病数、模型预测值比较,采用相对下降(RR)评价NPIs对NIDs的影响。分病种、分性别、分年龄、分类(甲乙类、丙类;呼吸道、肠道、血源及性传播、自然疫源性)、分时期(6—18周、19—24周、25—53周)进行亚组分析。结果2020年6—53周累计报告NIDs 20种18952例,较2015—2019年同期下降45.26%。基于BSTS模型,2020年6—53周较预期下降60.42%(95%CI:50.98%~69.66%),其中甲乙类NIDs、丙类NIDs分别下降36.63%(95%CI:30.60%~43.19%)、60.72%(95%CI:33.97%~85.32%)。6—18周、19—24周、25—53周分别下降70.29%(95%CI:56.88%~83.29%)、61.71%(95%CI:45.34%~79.13%)、55.68%(95%CI:45.60%~67.29%)。从传播途径来看,呼吸道传染病下降幅度最大(RR=61.53%,95%CI:22.60%~100.87%);其次为肠道传染病(RR=19.72%,95%CI:2.98%~35.29%)。分年龄来看,15~64岁年龄组下降幅度最大(RR=72.74%,95%CI:65.68%~80.10%),其次为0~14岁年龄组(RR=54.51%,95%CI:19.78%~87.15%)。分地区来看,2020年不同地区新型冠状病毒肺炎(新冠肺炎)发病率与降幅存在正相关性(r_(s)=0.714,P=0.058)。RR最大的前5位病种分别为风疹(999.77%,95%CI:−2326.82%~4510.97%)、流行性感冒(68.93%,95%CI:91.76~117.10%)、猩红热(88.59%,95%CI:64.78%~112.12%)、手足口病(86.09%,95%CI:77.46%~94.55%)、急性出血性结膜炎(78.54%,95%CI:28.11%~127.26%)。结论NPIs在控制新冠肺炎疫情同时,大幅降低湖北省荆州市NIDs发病数,其效果在人群、疾病、时间和空间方面具有异质性。

Objective To evaluate the impact of non-pharmaceutical interventions(NPIs)on the incidence of notifiable infectious disease in Jingzhou of Hubei province.Methods The weekly incidence of notifiable infectious disease in Jingzhou from December 29,2014(1^(st) week in 2015)to January 3,2021(53^(rd) week in 2020)was collected.The 1st week of 2015 to the 5th week of 2020 was used to fit a Bayesian structural time series(BSTS)model and predict the incidence of notifiable infectious disease from 6^(th) to 53^(rd) weeks in 2020.The incidence of notifiable infectious disease from 6^(th) week to 53^(rd) week in 2020 was compared with those during the same periods of 2015−2019 and the predicted value of the BSTS model in 2020,and the relative reduction(RR)was used to evaluate the impact of NPIs on the incidence of notifiable infectious disease.Disease types,gender,age groups,disease classification(class A,B and C;respiratory infectious diseases,intestinal infectious diseases,blood-borne and sexually transmitted infections,natural focal diseases),and periods(6^(th)−18^(th) week,19^(th)−24^(th) week,25^(th)−53^(rd) week)were used for subgroup analysis.Results A total of 18952 cases of 20 types of notifiable infectious diseases were reported in Jingzhou from 6^(th) week to 53^(rd) week in 2020,a decrease of 45.26%compared with the same periods during 2015−2019.Based on the BSTS model,a decrease of 60.42%was than expected during 6^(th) week−53^(rd) week of 2020(95%CI:50.98%–69.66%).The notifiable infectious diseases in class A and B and in class C decreased by 36.63%(95%CI:30.60%–43.19%)and 60.72%(95%CI:33.97%–85.32%),respectively.The incidence of notifiable infectious disease during 6^(th) week−18^(th) week,19^(th) week−24^(th) week,and 25^(th) week−53^(rd) week in 2020 decreased by 70.29%(95%CI:56.88%–83.29%),61.71%(95%CI:45.34%–79.13%),55.68%(95%CI:45.60%–67.29%)respectively.In terms of transmission routes,the incidence of respiratory infectious disease had the greatest decrease(RR=61.53%,95%CI:22.60%–100.87%),followed by intestinal infectious disease(RR=19.72%,95%CI:2.98%–35.29%).In terms of age,the largest decrease was found for people aged 15–64 years(RR=72.74%,95%CI:65.68%–80.10%),followed by children aged 0–14 years(RR=54.51%,95%CI:19.78%–87.15%).There was a positive correlation between the incidence of COVID-19 and the incidence decrease in different areas in 2020(r_(s)=0.714,P=0.058).The top 5 diseases with the largest incidence decrease were rubella(999.77%,95%CI:−2326.82%–4510.97%),influenza(68.93%,95%CI:91.76%–117.10%),scarlet fever(88.59%,95%CI:64.78%–112.12%),hand foot and mouth disease(86.09%,95%CI:77.46%–94.55%)and acute hemorrhagic conjunctivitis(78.54%,95%CI:28.11%–127.26%).Conclusion The NPIs against COVID-19 could significantly reduce the incidence of notifiable infectious disease in Jingzhou,and its effect had heterogeneity in terms of population,disease,time and space.