Temporal-spatial distribution characteristics and its influencing factors of tuberculosis epidemic in the Chinese mainland from 2014 to 2018
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摘要:
目的 分析2014-2018年中国大陆31个省份结核病发病率受每千人床位数、人均国内生产总值(gross domestic product, GDP)、人口密度与全年日照时数等因素影响的时空变化特征，为今后结核病的防治提供理论依据。 方法 通过空间自相关分析，探究各年份中国大陆结核病发病的时空集聚性；建立时空加权Logistic回归分析模型，揭示结核病发病的时空非平稳性，同时与经典Logistic回归分析模型拟合结果相比较。 结果 2014-2018年中国大陆结核病发病率全局空间Moran's I指数分别为0.43、0.44、0.47、0.49、0.37，呈现一定的时空相关性；时空加权Logistic回归分析模型系数函数取值在(-1，1)区间波动，能很好反映结核病发病的时空异质性，各系数函数可信区间均不含0，通过时空非平稳性检验。多项拟合优度检验结果均优于经典Logistic回归分析模型。 结论 中国大陆31个省份结核病发病率与四个宏观因素密切相关，各地区需因时因地制宜制定区域防控策略。 -
关键词:
- 结核病 /
- 时空自相关 /
- 时空加权Logistic回归分析模型 /
- 时空非平稳性
Abstract:Objective The number of beds per thousand, gross domestic product (GDP) per capita, population density and annual sunshine hours were investigated to analyze the spatio-temporal characteristics of tuberculosis incidence of 31 provinces in the Chinese mainland from 2014 to 2018, so as to provide a theoretical basis for the prevention and control of the spread of tuberculosis in the future. Methods The spatial autocorrelation analysis was performed to explore the spatio-temporal agglomeration of the incidence of tuberculosis in the Chinese mainland in each year. A geographically and temporally weighted Logistic regression analysis model was established to reveal the spatio-temporal non-stationarity of tuberculosis incidence, and to compare the fitting results with the classic Logistic regression analysis model. Results The global spatial Moran's I index of tuberculosis incidence in the Chinese mainland from 2014 to 2018 were 0.43, 0.44, 0.47, 0.49, 0.37, showing a certain spatiotemporal correlation. The coefficient function of the geographically and temporally weighted Logistic regression analysis model fluctuated in the interval (-1, 1), and could reflect the spatio-temporal heterogeneity of tuberculosis well. The confidence interval of each coefficient function did not contain 0, which had spatio-temporal non-stationarity. The results of the goodness of fit test were better than the classic Logistic regression analysis model. Conclusions The incidence of tuberculosis in 31 provinces in the Chinese mainland was closely related to four macro factors, and appropriate strategies should be formulated for tuberculosis prevention and control according to the difference of regional social macro factors. -
图 1 2014-2018年中国大陆结核病发病率分布图
Figure 1. Distribution of tuberculosis incidenc in the Chinese mainland from 2014 to 2018
表 1 2014-2018年中国大陆结核病发病率时空相关性分析结果
Table 1. The spatio-temporal correlation of tuberculosis incidence in the Chinese mainland from 2014 to 2018
年份(年) 全局Moran's I指数 P值 2014 0.43 0.001 2015 0.44 0.001 2016 0.47 0.001 2017 0.49 0.001 2018 0.37 0.002 
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表 2 2014-2018年中国大陆结核病发病率宏观指标分布表(/10万)
Table 2. Distribution of macro-indicators of tuberculosis incidence in the Chinese mainland from 2014 to 2018 (/100 000)
年份(年) 最小值 P25 P50 P75 最大值 四分位间距 中国大陆发病率 2014 21.27 48.15 61.84 85.88 176.00 37.73 65.63 2015 19.52 44.82 56.66 83.00 184.53 38.18 63.42 2016 21.14 42.74 56.77 75.50 185.66 32.76 61.00 2017 20.91 42.79 57.53 76.07 202.59 33.28 60.53 2018 21.39 38.71 55.81 73.37 304.94 34.66 59.27
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表 3 GTWLR模型与LR模型系数函数估计值范围
Table 3. Range of coefficient function estimates of GTWLR and LR
系数 宏观因素 最小值 P25 P50 P75 最大值 LR模型系数值 β1 每千人床位数 -0.507 -0.102 0.122 0.220 0.744 0.08 β2 人均GDP -0.883 -0.127 -0.081 -0.002 0.447 -0.10 β3 人口密度 -1.297 -0.047 -0.007 0.043 1.120 -0.01 β4 全年日照时数 -1.225 -0.482 -0.310 -0.057 1.009 -0.06
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表 4 时空非平稳性检验
Table 4. Spatio-temporal non-stationary test
系数 β1 β2 β3 β4 GTWLR (0.070，0.140) (-0.120，-0.050) (-0.150，-0.010) (-0.360，-0.230) LR (0.010，0.160) (-0.140，-0.070) (-0.020，0.001) (-0.170，0.050)
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表 5 拟合优度检验
Table 5. Goodness of fit test
回归模型 AICc R2值 RSS GTWLR 584.71 0.97 103.18 LR 855.35 0.45 2 120.57
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表 6 时空加权Logistic回归分析模型系数函数分布
Table 6. Distribution of coefficient function of geographically and temporally weighted Logistic regression analysis model
β变化区间 区间内地区分布 β1 (-0.35, 0.00) 吉林、黑龙江、广西、陕西、甘肃、青海 (0.00, 0.23) 内蒙古、辽宁、江苏、浙江、安徽、福建、江西、湖北、湖南、广东、海南、贵州、云南、西藏、宁夏、新疆 (0.23, 0.60) 北京、天津、河北、山西、山东、河南、重庆、四川、上海 β2 (-0.63, -0.32) 重庆、四川、云南、贵州 (-0.32, 0.00) 北京、天津、河北、山西、辽宁、吉林、黑龙江、上海、江苏、浙江、安徽、福建、江西、山东、河南、湖北、湖南、广东、广西、海南 (0.00, 0.36) 内蒙古、西藏、陕西、甘肃、青海、宁夏、新疆 β3 (-1.18, -0.66) 西藏、甘肃、青海、宁夏 (-0.66, 0.00) 北京、山西、内蒙古、辽宁、吉林、黑龙江、上海、江苏、浙江、安徽、福建、江西、湖北、新疆 (0.00, 0.58) 天津、河北、山东、河南、湖南、广东、广西、海南、重庆、四川、贵州、云南、陕西 β4 (-0.97, -0.64) 甘肃、青海、宁夏 (-0.64, 0.00) 北京、天津、河北、山西、内蒙古、辽宁、吉林、黑龙江、上海、江苏、浙江、安徽、福建、山东、河南、湖北、湖南、广西、重庆、四川、贵州、云南、西藏、陕西 (0.00, 0.97) 江西、广东、海南、新疆
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