基于地理探测器的中国肺结核发病率影响因子研究

Study on Influencing Factors of Tuberculosis Incidence in China Based on Geographical Detector

摘要: 基于2010~2020年中国31个省级行政区的肺结核发病率数据，综合运用Joinpoint回归模型、空间自相关分析和地理探测器方法，探究全国肺结核发病率的时空演变特征及其驱动因子。结果显示，全国肺结核发病率整体呈“西高东低”的空间分布格局，西部地区年均发病率为89.93/10万，高于东部、中部及东北部地区。时间趋势上，多数省份的AAPC为负值，肺结核发病率呈显著下降趋势，但西藏、青海等地区呈上升趋势。全局空间自相关分析表明，Moran’s I指数由2010年0.2834升至2020年0.4306，发病率存在显著空间正相关性，高发区域聚集效应逐年增强。地理探测器分析揭示，医疗因素中医疗卫生机构数的q值最大，为0.5011，是解释发病率空间分异性的首要因子；交互作用探测表明，多因子协同效应导致非线性增强效应或双因子增强效应，其中医疗卫生机构数∩O₃组合解释力最高，q值为0.81。

Abstract: Based on the incidence rates of tuberculosis in 31 provincial-level administrative regions in China from 2010 to 2020, we investigated the spatial and temporal evolution of the incidence rates of tuberculosis and its driving factors by using the Joinpoint regression model, spatial autocorrelation analysis, and geodetic detector methods. The results showed that the overall spatial distribution pattern of TB incidence was “high in the west and low in the east”, and the average annual incidence rate in the western region was 89.93/100,000, which was higher than that in the eastern, central and northeastern regions. In terms of temporal trend, the AAPC was negative in most provinces, and the incidence rate of TB showed a significant downward trend, but Tibet, Qinghai and other regions showed an upward trend. Global spatial autocorrelation analysis showed that Moran’s I index increased from 0.2834 in 2010 to 0.4306 in 2020, with a significant positive spatial correlation in incidence rates and the clustering effect of high prevalence regions increasing year by year. Geographic detector analysis revealed that the q-value of the number of healthcare institutions among the medical factors was the largest, 0.5011, which was the primary factor explaining the spatial heterogeneity of the incidence rate; interaction detection showed that the synergistic effect of multiple factors led to a nonlinear enhancement effect or a two-factor enhancement effect, in which the number of healthcare institutions ∩O₃ combination had the highest explanatory power, with a q-value of 0.81.