Spatial and temporal clustering analysis of pulmonary tuberculosis and its associated risk factors in southwest China

• Published in: Geospatial health Vol. 18; no. 1
• Main Authors: Wang, Jianjiao, Liu, Xiaoning, Jing, Zhengchao, Yang, Jiawai
• Format: Journal Article
• Language: English
• Published: Italy PAGEPress Publications 25.05.2023
• Subjects: China; China - epidemiology; Cluster Analysis; Humans; Incidence; pulmonary tuberculosis; regression analysis; Risk Factors; Spacetime; Spatial Analysis; spatial-temporal analysis; Spatio-Temporal Analysis; Tuberculosis; Tuberculosis, Pulmonary - epidemiology; China
• ISSN: 1827-1987; 1970-7096; 1970-7096
• DOI: 10.4081/gh.2023.1169

Pulmonary tuberculosis (PTB) remains a serious public health problem, especially in areas of developing countries. This study aimed to explore the spatial-temporal clusters and associated risk factors of PTB in south-western China. Space-time scan statistics were used to explore the spatial and temporal distribution characteristics of PTB. We collected data on PTB, population, geographic information and possible influencing factors (average temperature, average rainfall, average altitude, planting area of crops and population density) from 11 towns in Mengzi, a prefecture-level city in China, between 1 January 2015 and 31 December 2019. A total of 901 reported PTB cases were collected in the study area and a spatial lag model was conducted to analyse the association between these variables and the PTB incidence. Kulldorff’s scan results identified two significant space-time clusters, with the most likely cluster (RR = 2.24, p < 0.001) mainly located in northeastern Mengzi involving five towns in the time frame June 2017 - November 2019. A secondary cluster (RR = 2.09, p < 0.05) was located in southern Mengzi, covering two towns and persisting from July 2017 to December 2019. The results of the spatial lag model showed that average rainfall was associated with PTB incidence. Precautions and protective measures should be strengthened in high-risk areas to avoid spread of the disease.