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目的探讨山东省结核病及其影响因素间的局域关系,为制定适宜的结核病防控策略提供依据。方法收集山东省2005—2008年各县区结核病登记报告资料和相关影响因素资料;采用全局空间自相关系数Moran’I检验区域结核病发病的空间自相关性;构建地理加权回归(GWR)模型定量分析结核病登记率与各影响因素间的局域关系,并应用ArcGIS9.0绘制地图。山东省2005—2008年各县(区)活动性结核病登记率分别为12.79/10万～107.35/10万、16.01/10万～86.52/10万、17.36/10万～92.10/10万和17.86/10万～114.86/10万。结果空间自相关分析表明2005—2008年各县区结核病登记率在空间分布上具有明显的空间正相关关系(Moran’s I分别为0.3517、0.3505、0.3337和0.3116,P值均<0.05)。GWR模型分析显示其拟合效果优于全局OLS模型[赤池信息准则(akaike information criterion,AIC)下降均大于3,R2均增大],如2008年GWR模型与OLS模型的AIC和R2分别为1168.8380和1173.5410,0.3537和0.1350);各模型的R2均具有明显的空间变异性,如2008年R2为0.1162～0.1798。结论GWR模型能够揭示影响因素对结核病登记率影响的空间异质性;应根据各因素的空间分布特征及其与结核病登记率间的局域关系制定区域化的结核病防控规划和策略。
Objective To explore the local relationship between tuberculosis and its influencing factors in Shandong Province, and to provide the basis for making appropriate strategies for prevention and control of tuberculosis. Methods Data of tuberculosis registrations and related influencing factors in all counties of Shandong Province from 2005 to 2008 were collected. Spatial autocorrelation of tuberculosis incidence in the region was tested by global spatial autocorrelation coefficient Moran’I. Quantitative analysis of GWR models was constructed Tuberculosis registration rate and the impact of various factors between the local and ArcGIS9.0 mapping. The registration rates of active tuberculosis in counties (districts) in Shandong Province from 2005 to 2008 were 12.79 / 100000 ~ 107.35 / 100000, 16.01 / 100000 ~ 86.52 / 100000, 17.36 / 100000 ~ 92.10 / 100000 and 17.86 / 100,000 to 114.86 / 100,000. Results Spatial autocorrelation analysis showed that the spatial distribution of tuberculosis registration rates in all counties in 2005-2008 had a significant spatial correlation (Moran’s I were 0.3517,0.3505,0.3337 and 0.3116, P <0.05 respectively). GWR model shows that the fitting effect is better than the global OLS model [Akaike information criterion (AIC) decreases by more than 3 and R2 increase). For example, the AIC and R2 of the 2008 GWR and OLS models are 1168.8380 And 1173.5410,0.3537 and 0.1350 respectively). R2 of each model has obvious spatial variability, for example R2 of 2008 is 0.1162 ~ 0.1798. Conclusion The GWR model can reveal the spatial heterogeneity of the influencing factors on the TB registration rate. The regional tuberculosis control programs and strategies should be formulated according to the spatial distribution of each factor and its relationship with the TB registration rate.