Abstract
OBJECTIVE: To explore the spatial and temporal variations in the long-term risk of developing tuberculosis (TB) and the factors influencing it in order to contribute to the goal of eradicating TB. DESIGN: Observational longitudinal study. SETTING: Xinjiang, China, 2005-2019. PRIMARY AND SECONDARY OUTCOME MEASURES: Comparison of TB incidence across age, period, cohort and space using socioeconomic (including gross domestic product per capita, population density, public budget revenue and total retail sales of consumer goods), public health (including the number of hospital beds, health technicians and basic medical insurance for urban residents) and environmental variables (PM2.5, mean air temperature, mean wind speed, mean relative humidity and precipitation). The relative importance of these variables to pulmonary TB (PTB) is revealed by the Q-value (0, 1), with larger values indicating that the spatial heterogeneity of the explanatory variables to PTB is more pronounced. PARTICIPANTS: All clinically diagnosed and confirmed cases in Xinjiang, China, were collected. The descriptive analysis included confirmed cases from 2005 to 2019, while cases from various districts and counties between 2011 and 2019 were subjected to further analysis. RESULTS: From 2005 to 2019, a total of 642 332 cases of PTB were reported in Xinjiang, with an average annual incidence rate of 172/100 000. The age risk of PTB presented a bimodal distribution, namely 20-24 years and the elderly (>60 years). The high prevalence of PTB was distributed in the southern part of Xinjiang. Among the influencing factors that had a greater effect on the incidence of PTB, the lower GDP per capita (Q-value=0.65) had a largest effect on PTB in Xinjiang compared with others factors (higher PM2.5: Q-value=0.56, lower health personnel: Q-value=0.49, higher average temperature: Q-value=0.47 and higher urban residents' health insurance: Q-value=0.46). The main influencing factors were heterogeneous in different regions. Furthermore, the interactions among these factors enhanced the explanatory power regarding the incidence of the disease. CONCLUSIONS: Identifying the high-risk groups, regions, influencing factors and interactions of PTB in Xinjiang, China, will expand the epidemiological knowledge of PTB in high-risk areas and potentially aid in designing targeted interventions.