Application of the flexible spatial scan statistic to analyzing the clustering of pulmonary tuberculosis based on the street level in Guangzhou
Objective To explore the distribution of pulmonary tuberculosis aggregation at the street level in Guangzhou from 2015 to 2022,to identify the high-risk areas and find out the changing trends,and to provide a basis for the deployment of disease prevention and control.Methods A geographic information system database was established based on the epidemic data of pulmonary tuberculosis reported in Guangzhou City during 2015-2022 from the China Information System for Disease Control and Prevention and the population data from the Guangzhou Statistical Yearbook,and the pulmonary tuberculosis aggregation areas were detected.Results The overall epidemic of pulmonary tuberculosis in Guangzhou City from 2015 to 2022 showed a decreasing trend(APC=-7.65,95％CI:-8.90--6.39,t=-14.299,P＜0.001).There was a local aggregated distribution of pulmonary tuberculosis across streets in the city,with 8,5,7,5,5,6,4 and 5 spatial clusters of pulmonary tuberculosis incidence detected in 2015-2022,involving a total of 38,30,31,33,36,39,33 and 34 streets respectively.The most likely clusters in 2015-2018,2020 and 2022 were concentrated in Yuexiu District,with the log likelihood ratio(LLR)being 439.36,377.15,275.06,33.92,21.89 and 21.91 respectively,those in 2019 were concentrated in Fengyang Street and adjacent Nanzhou and Jianghai Streets in Haizhu District,with the LLR of 30.22,and those in 2021 were concentrated in Luogang Street and some adjacent streets in Huangpu District,with the LLR of 29.92.Conclusion There was an obvious local spatial aggregation of pulmonary tuberculosis in Guangzhou City from 2015 to 2022,and the aggregation situation had not shown a year-on-year trend of improvement.Targeted measures should be taken in clustered streets with different characteristics so as to control the pulmonary tuberculosis epidemic at the street level.
pulmonary tuberculosisflexible spatial scanaggregation analysisstreet levelGuangzhou City
NETL
NSTL
万方数据
