| 黄硕,林胜红,张翠红,耿梦杰,林帆,郭玉清,邓源,郑建东,王丽萍.2019-2021年京津冀地区季节性流感流行强度评估[J].中华流行病学杂志,2023,44(3):438-444 |
| 2019-2021年京津冀地区季节性流感流行强度评估 |
| Assessment of intensity of seasonal influenza activity in Beijing-Tianjin-Hebei region, 2019-2021 |
| 收稿日期:2022-09-09 出版日期:2023-03-14 |
| DOI：10.3760/cma.j.cn112338-20220909-00773 |
| 中文关键词: 季节性流感 移动流行区间法 流行强度 |
| 英文关键词: Seasonal influenza Moving epidemic method Activity intensity |
| 基金项目:国家自然科学基金（91846302）；国家科技重大专项（2018ZX10713001-001） |
| 作者 | 单位 | E-mail | | 黄硕 | 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206 | | | 林胜红 | 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206
军事医学研究院微生物流行病研究所, 北京 100071 | | | 张翠红 | 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206 | | | 耿梦杰 | 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206 | | | 林帆 | 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206 | | | 郭玉清 | 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206 | | | 邓源 | 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206 | | | 郑建东 | 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206 | | | 王丽萍 | 中国疾病预防控制中心传染病管理处/传染病监测预警重点实验室, 北京 102206 | wanglp@chinacdc.cn |
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| 中文摘要: |
| 目的 从城市群视角探讨移动流行区间法（MEM）评估季节性流感（流感）流行强度的可行性，评估2019-2021年京津冀地区流感流行强度，并对监测指标可靠性进行评价。方法 收集2011-2021年京津冀地区每周的流感报告发病率（IR）和流感样病例百分比（ILI%）数据分别建立MEM模型，评价模型拟合效果和两种数据可靠性，建立最优模型评估2019-2021年京津冀地区流感流行强度。模型筛选使用交叉验证法，模型评价指标为约登指数、灵敏度和特异度。结果 无论在京津冀地区层面还是省（市）层面，基于ILI%的拟合模型的约登指数均高于基于IR的拟合模型。基于ILI%建立的MEM模型显示，京津冀地区2019-2020年流行开始阈值为4.42%，流行结束阈值为4.66%，中、高和极高流行强度阈值分别为5.38%、7.22%和7.84%，全年流行期共10周（2019年第50周至2020年第7周），其中，自2019年第50周进入流行期后，连续6周在中流行强度水平上下波动，并于2020年第4周进入高流行强度，第5周突破极高流行强度阈值，此后流行强度逐渐减弱，直至第8周跌破流行阈值（流行期结束）；2020-2021年流行开始阈值为4.29%，流行结束阈值为4.35%，全年流感活动始终未突破流行阈值，无流行期出现。结论 MEM模型可在京津冀地区流感流行强度评估中应用，且使用ILI%评估流行强度较IR数据更为可靠。京津冀地区流感流行强度在2019-2020年较高，2020-2021年显著降低。 |
| 英文摘要: |
| Objective To explore the feasibility of moving epidemic method (MEM) in the assessment of seasonal influenza (influenza) activity intensity from the perspective of urban agglomeration, assess influenza activity intensity in the Beijing-Tianjin-Hebei region from 2019 to 2021 and evaluate the reliability of surveillance data and the effectiveness of the MEM model application.Methods The weekly reported incidence rate (IR) of influenza and the percentage of influenza-like illness (ILI%) from 2011-2021 in Beijing-Tianjin-Hebei region were collected to establish MEM models respectively. The model fitting effect and the reliability of the two data were evaluated for the purpose of establishing an optimal model to assess the influenza activity intensity in Beijing-Tianjin-Hebei region from 2019-2021. A cross-validation procedure was used to evaluate the performance of the models by calculating the Youden's index, sensitivity and specificity. Results The MEM model fitted with weekly ILI% had a higher Youden's index compared with the model fitted with weekly IR at both Beijing-Tianjin-Hebei region level and provincial level. The MEM model based on ILI% showed that the epidemic threshold in Beijing-Tianjin-Hebei region during 2019-2020 was 4.42%, the post-epidemic threshold was 4.66%, with medium, high and very high intensity thresholds as 5.38%, 7.22% and 7.84%, respectively. The influenza season during 2019-2020 had 10 weeks (week 50 of 2019 to week 7 of 2020). The influenza season started in week 50 of 2019, and the intensity fluctuated above and below medium epidemic level for six consecutive weeks. The high intensity was observed in week 4 of 2020, the threshold of very high intensity was excessed in week 5, and the intensity gradually declined and became lower than the threshold at the end of the influenza season in week 8. The epidemic threshold was 4.29% and the post-epidemic threshold was 4.35% during 2020-2021. Influenza activity level never excessed the epidemic threshold throughout the year, and no epidemic period emerged. Conclusions The MEM model could be applied in the assessment of influenza activity intensity in Beijing-Tianjin-Hebei region, and the use of ILI% to assess influenza activity intensity in this region was more reliable than IR data. Influenza activity intensity in Beijing-Tianjin-Hebei region was higher during 2019-2020 but significantly lower in 2020-2021. |
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