Found programs: National Natural Science Foundation of China(Nos.72163033,72064036,72174175)
Authors:Li Feifei; Zhou Peiyao; Lu Yaoqin; Zheng Yanling; Zhang Liping
Keywords:tuberculosis;Kriging interpolation;center of gravity transfer model;spatiotemporal distribution;spatio-temporal scan statistics;standard deviational ellipse analysis
DOI:10.19405/j.cnki.issn1000-1492.2024.12.016
〔Abstract〕 Objective To analyze the spatio-temporal distribution characteristics of pulmonary tuberculosis incidence in Xinjiang, and to provide reference for the prevention and control of pulmonary tuberculosis. Methods The reported incidence numbers of tuberculosis and population data from various counties, cities(prefectures), and districts in Xinjiang from 2010 to 2022 were collected. Based on spatial-temporal scan statistics, standard deviational ellipse analysis, and centroid migration models, the clustering and migration trends of tuberculosis incidence were evaluated. Utilizing Kriging interpolation techniques, an interpolation analysis of the 2022 incidence rate was conducted on an annual scale, with the reported incidence rate in 2018 serving as a temporal control, to identify hotspots of the spatio-temporal distribution. ArcGIS software was employed to visualize the continuous spatial trends of incidence rate changes. Results The annual reported incidence rate of tuberculosis in Xinjiang from 2010 to 2022 varied year by year, with fluctuations and increases prior to 2018, peaking in 2018, and then declining annually thereafter. The spatial distribution of the incidence rate exhibited a trend of initial clustering followed by diffusion, with the centroid of incidence shifting towards the northeast, yet the epicenter of the epidemic remained in Aksu Prefecture. The results of spatiotemporal scan statistics analysis revealed that the three-level aggregated areas of the epidemic encompassed a total of 41 prefectures, counties, and cities, with the tuberculosis incidence risk in the primary and secondary aggregated areas being significantly higher than that in other regions(P<0.01). The Kriging interpolation prediction map suggested that the four prefectures in southern Xinjiang continued to be high-risk regions for tuberculosis(incidence rate>300/100 000). The overall incidence rate in the northern region was relatively low, with the lowest rate observed in Urumqi, radiating outwards. Conclusion The incidence rate of tuberculosis in Xinjiang shows an upward trend before 2018, followed by a year-on-year decrease. The centroid of the incidence rate shifts towards the northeast. From 2010 to 2022, the tuberculosis epidemic in Xinjiang exhibits a notable spatiotemporal clustering, particularly prominent in the southwestern region, where the four prefectures constitute high-risk areas for tuberculosis. The prevention and control efforts of tuberculosis in Xinjiang should prioritize the regions with high tuberculosis incidence, intensifying prevention and control measures as well as policy support.