Analysis of brucellosis incidence and influencing factors in Yunnan Province based on multi-scale geographically weighted poisson regression model
ZHANG Lele and LI Ke contribute equally to this article
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摘要:
目的 分析云南省布鲁氏菌病(简称布病)发病的影响因素，揭示各影响因素作用效应的空间异质性，为不同地区制定科学的防控措施提供参考依据。 方法 收集2022年云南省129个县区布病的发病和相关影响因素数据，分析布病发病率的空间自相关性。比较广义线性泊松模型、地理加权泊松回归模型和多尺度地理加权泊松回归模型的拟合优度，基于最优模型分析布病发病影响因素。 结果 2022年云南省布病发病数为1 015例，呈现东高西低的空间分布格局，布病发病存在空间相关性。多尺度地理加权泊松回归模型的拟合优度最高[百分比偏差解释(percent deviance explained, D2)=0.77, 修正赤池信息准则(Akaike′s information criterion corrected, AICc)=718.27, 均方根误差(root mean square error, RMSE)=9.79]，模型结果显示羊存栏量(β=0.13~1.49)、牛存栏量(β=-0.28~0.72)、猪存栏量(β=0.23~0.31)、人均国内生产总值(β=-0.66~1.29)、第一产业生产总值占比(β=-0.83~0.47)、草地面积(β=-0.69~1.66)和年降水量(β=-1.31~0.40)在全局或局部地区对布病发病有影响。不同因素的作用效应在云南省不同地区存在异质性。 结论 多尺度地理加权泊松回归模型在处理回归关系的空间异质性上表现更好，也更适用于云南省布病发病影响因素的探讨。社会经济、自然环境等多种因素对布病发病有影响，且作用效应存在空间差异，提示云南省不同地区在制定或改进防控措施时应因地制宜。 -
关键词:
- 布鲁氏菌病 /
- 多尺度地理加权泊松回归模型 /
- 空间异质性
Abstract:Objective To analyze the influencing factors of brucellosis incidence in various districts of Yunnan Province in 2022, and to reveal the spatial heterogeneity of their effects, so as to provide a reference for formulating scientific prevention and control measures in different regions. Methods Data on brucellosis incidence and related influencing factors were collected from 129 districts in Yunnan Province in 2022. The spatial autocorrelation of the brucellosis incidence was analyzed. The goodness of fit of the generalized linear Poisson model (GLM-Poisson), geographically weighted poisson regression (GWPR) model and multi-scale geographically weighted poisson regression (MGWPR) model was compared. Based on the optimal model, influencing factors of brucellosis incidence were analyzed. Results In 2022, 1 015 brucellosis cases were reported in Yunnan Province, showing an east-high-west-low spatial distribution pattern with spatial autocorrelation. The MGWPR model had the highest goodness of fit [percent deviance explained (D2)=0.77, Akaike′s information criterion corrected(AICc)=718.27, root mean square error (RMSE)=9.79]. Model results showed that sheep stock (β=0.13-1.49), cattle stock (β=-0.28-0.72), pig stock (β=0.23-0.31), GDP per capita (β=-0.66-1.29), proportion of primary industry GDP (β=-0.83-0.47), grassland area (β=-0.69-1.66), and annual precipitation (β=-1.31-0.40) had significant influences on the incidence of brucellosis at local or global scales. The effects of different factors were heterogeneous across different regions of Yunnan. Conclusions The MGWPR model performs better in addressing the spatial heterogeneity of regression relationships and is more suitable for exploring influencing factors of brucellosis in Yunnan. Various factors such as socioeconomic and natural factors significantly influence brucellosis incidence with spatial variations, suggesting that region-specific prevention measures should be formulated. -
表 1 2022年云南省布鲁氏菌病发病空间扫描分析结果
Table 1. Spatial scan analysis of brucellosis incidence in Yunnan Province, 2022
聚集区类型
Cluster type聚集县区
Cluster region扫描半径
Scanning radius/km实际发病数
Actual number of cases期望发病数
Expected casesLLR值
valueRR值
valueP值
value一类
Category 1石林彝族自治县、陆良县、宜良县、泸西县
Shilin Yi Autonomous County, Luliang County, Yiliang County, Luxi County44.55 400 34.42 694.21 18.53 < 0.001 二类
Category 2鲁甸县、昭阳区Ludian County, Zhaoyang District 30.55 61 27.54 15.62 2.29 < 0.001 注：LLR，对数似然比。
Note: LLR, log likelihood ratio.
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表 2 模型的拟合优度比较结果
Table 2. Comparison of different models' goodness of fit
评价指标
Evaluation metricsGLM-Poisson GWPR MGWPR D2 0.35 0.75 0.77 AICc 1 822.71 776.88 718.27 RMSE 19.10 11.26 9.79 注：D2，百分比偏差解释；AICc，修正赤池信息准则；RMSE，均方根误差；GLM-Poisson，广义线性泊松模型；GWPR，地理加权泊松回归模型；MGWPR，多尺度地理加权泊松回归模型。
Note: D2, percent deviance explained; AICc, Akaike′s information criterion corrected; RMSE, root mean square error; GLM-Poisson, generalized linear poisson regression model; GWPR, geographically weighted poisson regression model; MGWPR, multi-scale geographically weighted poisson regression model.
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表 3 布鲁氏菌病发病影响因素的多尺度地理加权泊松回归参数估计结果
Table 3. Estimation results of multi-scale geographically weighted poisson regression for factors influencing brucellosis incidence
变量
Variable参数统计
Parameter estimation系数及比例
Coefficient and proportion最大值
Max最小值
Min平均值
Mean标准差
s显著系数
Significance
coefficient+/%① -/%② 羊存栏量Sheep stock 1.49 0.13 0.69 0.35 47.29 100.00 0 牛存栏量Cattle stock 0.72 -0.28 0.11 0.37 79.84 42.72 57.28 猪存栏量Pig stock 0.31 0.23 0.26 0.02 100.00 100.00 0 人均GDP/元GDP per capita/yuan 1.29 -0.66 0.07 0.52 64.34 39.76 60.24 第一产业生产总值占比Proportion of primary industry GDP/% 0.47 -0.83 0.09 0.35 57.36 75.68 24.32 草地面积Grassland area/km2 1.66 -0.69 -0.10 0.59 71.32 25.00 75.00 年降水量Annual precipitation/mm 0.40 -1.31 -0.54 0.47 83.72 14.81 85.19 注：GDP, 人均国内生产总值。
① +/%，显著系数中正值占比；② -/%，显著系数中负值占比。
Note: GDP, gross domestic product.
① +/%, proportion of positive values among significant coefficients; ② -/%, proportion of negative values among significant coefficients.
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