关键词: 结核,肺, 时空聚类分析, 流行病学研究, 数据说明,统计, 天津

Abstract:

Objective: To analyze the spatial-temporal distribution and clustering characteristic of pulmonary tuberculosis (PTB) in Tianjin from 2011 to 2022, and provide a theoretical basis for improving the level of tuberculosis control and development of tuberculosis control strategies. Methods: Through the “China Disease Prevention and Control Information System” subsystem “Tuberculosis Management Information System” and Tianjin Statistical Yearbook and China Infectious Disease Surveillance Information Report Management System, collected the notification rate of PTB from 2011 to 2022, patient datas (patient registration time and current address), administrative areas, and the relevant demographic information in Tianjin. To analyze the spatial-temporal distribution and clustering characteristic of PTB using SPSS 26.0, ArcGIS 10.7 and SaTScan 10.1 software. Results: Form 2011 to 2022, the notification rate of PTB in Tianjin ranged from 20.40/100000 (2801/13730000) to 26.42/100000 (3543/13410000), with no statistically significant difference ($x^{2}_{trend}=1.144$, P=0.233). The annual average notification rate of PTB were 23.82/100000 (39907/167506000) and the average annual decline rate was 2.32% $\left[\left(\sqrt[11]{\frac{20.40}{26.42}}-1\right) \times 100 \%\right]$. Form 2012 to 2016, the notification rate of PTB in Tianjin ranged from 23.15/100000 (3190/13780000) to 25.16/100000 (3548/14100000), with spatial aggregation (Moran’s I=0.138, 0.228, 0.130, 0.238, 0.288; Z=2.474, 3.625, 2.417, 3.732, 4.368; P=0.013, <0.001, 0.016, <0.001, <0.001). The local spatial autocorrelation analysis showed that there were two kinds of aggregation models (high-high and low-high models) in Tianjin except for 2021, in which clustering area were not formed. High-high clusters are mainly concentrated in the central urban area (Heping District, Hexi District, Hedong District, Nankai District, Hebei District, Hongqiao District) and the ring urban area (Beichen District, Dongli District, Xiqing District, and Jinnan District). The spatio-temporal scanning results showed that two spatio-temporal clustering areas existed. The first-level clustering area was mainly in six districts of Tianjin City from 2011-2016 (LLR=383.11, RR=1.42, P<0.001), and the second-level clustering area was in Binhai New Area from 2014-2019 (LLR=27.12, RR=1.32, P<0.001). Conclusion: The notification rate of PTB in Tianjin tends to be stable from 2011 to 2022 with spatial clustering from 2012 to 2016. The high-high clustering was mostly found in the central urban area and the ring urban area. And there were spatio-temporal clustering in the central urban areas and the Binhai New Area. As a consequence, we should pay attention to the changes of PTB epidemic in areas with large population mobility, dense population and economic development, to adjusted tuberculosis control strategy according to local conditions by improving the level of differential diagnosis and explored the strategy of active cases finding in the areas.

Key words: Tuberculosis, pulmonary, Space-time clustering, Epidemiologic studies, Data interpretation, statistical, Tianjin City