基于空间点模式分析的青岛市结核病时空流行病学研究
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摘要
结核病(tuberculosis, TB)作为一种古老的慢性传染病已有几千年的历史,素有“白色瘟疫”之称。我国是全球22个结核病高负担国家之一,活动性肺结核病人数居世界第二位。结核病不仅是重要的公共卫生问题,而且是一个复杂的社会经济问题,给患者带来了严重的经济和精神负担。然而,结核病作为一种传染病,其分布具有空间异质性和空间尺度依赖性,空间尺度不同其空间异质性特征不同。因而搞清结核病例在不同空间尺度上的分布状况及聚集程度对于该病的防治具有重要意义。以往相关研究多集中在空间大尺度或中等尺度上,这在一定程度上没能充分利用病例的空间信息。由于其所采用的空间尺度不够细化,不能搞清结核病例在点模式下的空间动态分布特征及其影响因素,这在一定程度上影响了结核病有效预防措施的制定。为此,本研究根据2005年-2009年青岛市各县、市(区)新登记结核病患病资料,以地理信息系统(Geographic Information System,GIS)为数据管理与分析平台,应用CrimestatⅢ空间分析软件,从空间描述和空间推断两个方面揭示了青岛市结核病的空间流行特征,旨在对青岛市结核病卫生资源的配置、结核病预防控制策略的制定及其效果评价等诸多方面提出指导性的建议。
主要研究结果如下：
(1)山东省青岛市2005年-2009年的结核病例空间分布的分布中心分别为(156.87,158)(154.26,155)和(155.35,145)、(157.62,138.95)、(158.41,145)。其空间四分位数间距距离分别为27.69km、29.39km、27.74km、28.35km和26.89km。5年来新发结核病例的空间分布重心基本稳定,略呈南移趋势。
(2)各县市区各年间的病例平均密度基本一致,位居前几位的是市北区、市南区、李沧区与四方区；平均密度最低的为平度市,其次为莱西市、胶州市与即墨市。5年间新登记结核病例的分布相当广泛,几乎覆盖了全市的整个区域。
(3) Ripley'sK函数分析表明,五年间青岛市新登记结核病的空间分布均呈现聚集性。其第一聚集高峰约16km；而各年间的最强聚集尺度相差较大,2007年、2008年的最强聚集尺度在16km左右,而2005年、2006年与2009年的最强聚集尺度则在25km左右。5年的最大聚集尺度均大于48km。从最大聚集高峰来看,2005年的聚集强度最大,而以2007年的聚集强度最小。在消除人口密度的影响后,仍呈聚集状态,且聚集强度较校正前均所有提高。
(4)在村级空间尺度上,青岛市5年间新发结核病例一阶空间聚集“热点”均在50个以上,其中2007年聚集“热点”最多,达到170个；2005年聚集“热点”最少,为55个。在α=0.05的检验水准上经蒙特卡罗模拟检验,各年间一阶空间聚集“热点”均具有统计学意义。各年间的空间热点分布基本一致且较为集中,主要分布在市区及各县市的城区中心,市区的“热点”密度明显高于农村。
与以村级水平为空间尺度相比,乡镇水平空间尺度上的空间聚集“热点”的分布发生了较大的变化,其空间聚集热点主要分布于胶南-黄岛-市南、市北、李沧、四方、城阳5区-即墨市呈西南-东北方向的沿海空间“热点”分布密集区和平度市的中部和北部。空间热点分布区域较为广泛,但其空间异质性较大,市区的“热点”密度仍然明显高于农村。由于各年度热点均无统计学意义,所以在乡镇水平上,不是结核病空间聚集的最佳尺度,而村级水平却是最佳聚集尺度,结核病空间聚集的尺度较小。
当Ripley'K函数计算的最强聚集尺度空间尺度上,5年间新发结核病例一阶空间聚集“热点”相差很大,其中2005年热点数最少,2007年最多；在α=0.05的检验水准上经蒙特卡罗模拟检验,各年间的一阶空间聚集“热点”均无统计学意义。各年间空间热点分布的空间异质性较大。其中2005年、2008年和2009年聚集“热点”主要分布于胶南-黄岛-市南、市北、李沧、四方、城阳5区-即墨市呈西南-东北方向的沿海空间“热点”分布密集区；而2006年、2007两年间的空间热点分布区域非常广泛,很多农村地区出现了聚集“热点”,基本覆盖了青岛市整个区域。
(5)在消除人口密度的影响后,青岛市新发结核病例一阶空间聚集“热点”分布更加广泛、数量更多,特别是在很多农村地区出现了众多聚集热点,特别是平度市和胶南市的南部出现了“热点”聚集区。但从总体上来看,空间分布模式仍是市区“热点”密度高于农村“热点”密度。
主要研究结论：
(1)青岛市2005年-2009年间的新登记结核病例的空间分布中心位置相近,5年间病例的分布范围相当广泛,基本覆盖了青岛市的整个区域。随着时间的推移分布中心呈现略向南移动的趋势。
(2)最近邻空间系统聚类结果表明,青岛市结核病空间聚集的最佳尺度是自然村大小；在调整人口因素前,空间聚集“热点”主要聚集在市区和市县的中心；而调整人口因素后,空间分布“热点”明显增多,特别在很多农村地区出现聚集“热点”。
(3)在不同空间尺度上的聚类分析结果均显示聚集热点主要分布于胶南-黄岛-市南、市北、李沧、四方、城阳5区-即墨市呈西南-东北方向的沿海空间“热点”分布密集区。
(4)青岛市结核病例分布的空间异质性对于结核病的防治具有重要意义。以空间分析为基础,对结核病相关卫生资源进行重新分配,在聚集地区和“热点”自然村实行重点监测或进行重点人群的筛检工作,或许为目前“寸步难行”的结核防治工作带来新的希望。
TB (tuberculosis, TB), known as "white plague", is an ancient infectious disease which has done harm to human for thousands of years in history. China is one of the 22 TB high-burden countries in the world and the number of active tuberculosis patients ranks second in the world. TB is not only a major public health issue, but is also a complex socio-economic problem. Previous studies ignored the geographic correlation, however, TB, as a communicable disease, is associated with the local environment, population, climate. And there is spatial relationship between the cases. Therefore it is necessary to take into account of geographical information and the relevant attributes to study its factor influencing the incidence of TB. In this study, we explore the spatial features of TB in Qingdao in 2005-2009 combined with Geographic Information System (GIS) with an objective for allocation of health resources, as well as prevention and control of tuberculosis.
Results:
(i) The spatial distribution centers of TB cases was (156.87,158) (154.26,155),(155.35,145), (157.62,138.95), (158.41,145) respectively in 2005-2009 in Qingdao, Shandong Province.Quartile spatial distances of 5 years were 27.69km, 29.39km,27.74km,28.35km and 26.89km. The spatial distribution of New TB cases was stable in five years, slightly southward trend.
(ii) The average densities of TB cases in Qingdao in the five years are nearly the same. The relatively higher districts are North District, South District, Licang District and Sifang District; the lowest density of TB cases is Pingdu city, followed by Laixi City, Jiaozhou and Jimo. The distribution of newly registered TB cases was quite extensive, covering almost the entire city area.
(iii) Ripley's K function analysis showed that, newly registered TB showed aggregation in five years. The peak of first clustering was about 16km; the strongest in large scale were different. the strongest clustering measure in 2007,2008 were about 16km, while the 2005,2006 and 2009, gathered the strongest measure in the about 25km. maximum aggregate size of 5 years were all longer than 48km. the largest peak of clustering in 2005 was largest, and with the smallest in 2007. After eliminating the effects of population density, the clusters of TB cases years have reduced in the scope, mainly concentrated in the urban north and south districts.
(iv) In the spatial scale of villages, the the first-order spatial cluster "hot spots" were all more than 50 in 5 years. They are all statistically significant at a=0.05 level in the test via Monte Carlo simulation on the test. The hot spot spatial distribution was very extensive and more concentrated mainly in the urban areas and counties of the city center, the urban "hot spots" density was significantly higher than in rural areas.
Compared with the village space scale, "hot spots" distribution in township level changed a lot. The "hot spots" mainly distributed in Jiaonan, Huangdao, Shinan, Shibei, Licang,Sifang, Chengyang, Jimo, coastal distribution concentration in southwest-northeastern direction and the north and central of Pingdu. Space "hot spots" distributed extensive, and had spatial heterogeneity. "hot spots"density in urban still significantly higher than rural areas. Because there were not statistically significant, so the township scale was not the best measure of tuberculosis space together, and village level is the best clustering scale. the scale of tuberculosis clustering was small.
In the spatial scale of clustering scope of Ripley's L function, the numbers of "hot spots" in Qingdao City vary greately in 5 years in the first-order spatial cluster. The Monte Carlo simulation test showed that all of them don't have statistical significance at a 0.05 level. The "hot spots" mainly distributed in Jiaonan, Huangdao, Shinan, Shibei, Licang,Sifang, Chengyang, Jimo, coastal distribution concentration in southwest-northeastern direction in 2005,2008 and 2009. and distributed widely in 2006 and 2007, expecially in many rurul areas.
(v) When adjusting the effect of population density, the numbers of "hot spots" in Qingdao City in 2006 is 149 and 106 in 2007 in the first-order scale, based on which the numbers of "hot spots" in Qingdao City in 2006 is 6 and 8 in 2007 in the second-order scale. All of them have statistical significance at a 0.05 level.
Conclusions:
(i) The average distribution of TB cased in Qingdao City in 2005-2009 were nearly the same. Their centers are both close to the urban area; and the distribution of TB cases in the 5 years covers a wide range of entire region of Qingdao. The distribution center showed slightly southward trend over time.
(ii) The nearest neighbor clustering analysis showed the best clustering scale was village level. They were mainly distributed in the urban areas and cities and counties in the center before adjusted. After adjustment of the population density, the "hot spots" show that clusters of TB cases also occurred in rural areas, different from the results before the adjustment that clusters appear mainly in the urban area.
(iii) All clustering analysis at different scales showed the "hot Spots" mainly distributed distributed in Jiaonan, Huangdao, Shinan, Shibei, Licang,Sifang, Chengyang, Jimo, coastal distribution concentration in southwest-northeastern direction
4. The spatial heterogeneity of TB cases in Qingdao City is very important for the prevention and treatment of tuberculosis. This study could provide useful information for allocation of health resources, perhaps brings new hope for the current "step" of TB prevention and treatment.
主要研究结果如下：
(1)山东省青岛市2005年-2009年的结核病例空间分布的分布中心分别为(156.87,158)(154.26,155)和(155.35,145)、(157.62,138.95)、(158.41,145)。其空间四分位数间距距离分别为27.69km、29.39km、27.74km、28.35km和26.89km。5年来新发结核病例的空间分布重心基本稳定,略呈南移趋势。
(2)各县市区各年间的病例平均密度基本一致,位居前几位的是市北区、市南区、李沧区与四方区；平均密度最低的为平度市,其次为莱西市、胶州市与即墨市。5年间新登记结核病例的分布相当广泛,几乎覆盖了全市的整个区域。
(3) Ripley'sK函数分析表明,五年间青岛市新登记结核病的空间分布均呈现聚集性。其第一聚集高峰约16km；而各年间的最强聚集尺度相差较大,2007年、2008年的最强聚集尺度在16km左右,而2005年、2006年与2009年的最强聚集尺度则在25km左右。5年的最大聚集尺度均大于48km。从最大聚集高峰来看,2005年的聚集强度最大,而以2007年的聚集强度最小。在消除人口密度的影响后,仍呈聚集状态,且聚集强度较校正前均所有提高。
(4)在村级空间尺度上,青岛市5年间新发结核病例一阶空间聚集“热点”均在50个以上,其中2007年聚集“热点”最多,达到170个；2005年聚集“热点”最少,为55个。在α=0.05的检验水准上经蒙特卡罗模拟检验,各年间一阶空间聚集“热点”均具有统计学意义。各年间的空间热点分布基本一致且较为集中,主要分布在市区及各县市的城区中心,市区的“热点”密度明显高于农村。
与以村级水平为空间尺度相比,乡镇水平空间尺度上的空间聚集“热点”的分布发生了较大的变化,其空间聚集热点主要分布于胶南-黄岛-市南、市北、李沧、四方、城阳5区-即墨市呈西南-东北方向的沿海空间“热点”分布密集区和平度市的中部和北部。空间热点分布区域较为广泛,但其空间异质性较大,市区的“热点”密度仍然明显高于农村。由于各年度热点均无统计学意义,所以在乡镇水平上,不是结核病空间聚集的最佳尺度,而村级水平却是最佳聚集尺度,结核病空间聚集的尺度较小。
当Ripley'K函数计算的最强聚集尺度空间尺度上,5年间新发结核病例一阶空间聚集“热点”相差很大,其中2005年热点数最少,2007年最多；在α=0.05的检验水准上经蒙特卡罗模拟检验,各年间的一阶空间聚集“热点”均无统计学意义。各年间空间热点分布的空间异质性较大。其中2005年、2008年和2009年聚集“热点”主要分布于胶南-黄岛-市南、市北、李沧、四方、城阳5区-即墨市呈西南-东北方向的沿海空间“热点”分布密集区；而2006年、2007两年间的空间热点分布区域非常广泛,很多农村地区出现了聚集“热点”,基本覆盖了青岛市整个区域。
(5)在消除人口密度的影响后,青岛市新发结核病例一阶空间聚集“热点”分布更加广泛、数量更多,特别是在很多农村地区出现了众多聚集热点,特别是平度市和胶南市的南部出现了“热点”聚集区。但从总体上来看,空间分布模式仍是市区“热点”密度高于农村“热点”密度。
主要研究结论：
(1)青岛市2005年-2009年间的新登记结核病例的空间分布中心位置相近,5年间病例的分布范围相当广泛,基本覆盖了青岛市的整个区域。随着时间的推移分布中心呈现略向南移动的趋势。
(2)最近邻空间系统聚类结果表明,青岛市结核病空间聚集的最佳尺度是自然村大小；在调整人口因素前,空间聚集“热点”主要聚集在市区和市县的中心；而调整人口因素后,空间分布“热点”明显增多,特别在很多农村地区出现聚集“热点”。
(3)在不同空间尺度上的聚类分析结果均显示聚集热点主要分布于胶南-黄岛-市南、市北、李沧、四方、城阳5区-即墨市呈西南-东北方向的沿海空间“热点”分布密集区。
(4)青岛市结核病例分布的空间异质性对于结核病的防治具有重要意义。以空间分析为基础,对结核病相关卫生资源进行重新分配,在聚集地区和“热点”自然村实行重点监测或进行重点人群的筛检工作,或许为目前“寸步难行”的结核防治工作带来新的希望。
TB (tuberculosis, TB), known as "white plague", is an ancient infectious disease which has done harm to human for thousands of years in history. China is one of the 22 TB high-burden countries in the world and the number of active tuberculosis patients ranks second in the world. TB is not only a major public health issue, but is also a complex socio-economic problem. Previous studies ignored the geographic correlation, however, TB, as a communicable disease, is associated with the local environment, population, climate. And there is spatial relationship between the cases. Therefore it is necessary to take into account of geographical information and the relevant attributes to study its factor influencing the incidence of TB. In this study, we explore the spatial features of TB in Qingdao in 2005-2009 combined with Geographic Information System (GIS) with an objective for allocation of health resources, as well as prevention and control of tuberculosis.
Results:
(i) The spatial distribution centers of TB cases was (156.87,158) (154.26,155),(155.35,145), (157.62,138.95), (158.41,145) respectively in 2005-2009 in Qingdao, Shandong Province.Quartile spatial distances of 5 years were 27.69km, 29.39km,27.74km,28.35km and 26.89km. The spatial distribution of New TB cases was stable in five years, slightly southward trend.
(ii) The average densities of TB cases in Qingdao in the five years are nearly the same. The relatively higher districts are North District, South District, Licang District and Sifang District; the lowest density of TB cases is Pingdu city, followed by Laixi City, Jiaozhou and Jimo. The distribution of newly registered TB cases was quite extensive, covering almost the entire city area.
(iii) Ripley's K function analysis showed that, newly registered TB showed aggregation in five years. The peak of first clustering was about 16km; the strongest in large scale were different. the strongest clustering measure in 2007,2008 were about 16km, while the 2005,2006 and 2009, gathered the strongest measure in the about 25km. maximum aggregate size of 5 years were all longer than 48km. the largest peak of clustering in 2005 was largest, and with the smallest in 2007. After eliminating the effects of population density, the clusters of TB cases years have reduced in the scope, mainly concentrated in the urban north and south districts.
(iv) In the spatial scale of villages, the the first-order spatial cluster "hot spots" were all more than 50 in 5 years. They are all statistically significant at a=0.05 level in the test via Monte Carlo simulation on the test. The hot spot spatial distribution was very extensive and more concentrated mainly in the urban areas and counties of the city center, the urban "hot spots" density was significantly higher than in rural areas.
Compared with the village space scale, "hot spots" distribution in township level changed a lot. The "hot spots" mainly distributed in Jiaonan, Huangdao, Shinan, Shibei, Licang,Sifang, Chengyang, Jimo, coastal distribution concentration in southwest-northeastern direction and the north and central of Pingdu. Space "hot spots" distributed extensive, and had spatial heterogeneity. "hot spots"density in urban still significantly higher than rural areas. Because there were not statistically significant, so the township scale was not the best measure of tuberculosis space together, and village level is the best clustering scale. the scale of tuberculosis clustering was small.
In the spatial scale of clustering scope of Ripley's L function, the numbers of "hot spots" in Qingdao City vary greately in 5 years in the first-order spatial cluster. The Monte Carlo simulation test showed that all of them don't have statistical significance at a 0.05 level. The "hot spots" mainly distributed in Jiaonan, Huangdao, Shinan, Shibei, Licang,Sifang, Chengyang, Jimo, coastal distribution concentration in southwest-northeastern direction in 2005,2008 and 2009. and distributed widely in 2006 and 2007, expecially in many rurul areas.
(v) When adjusting the effect of population density, the numbers of "hot spots" in Qingdao City in 2006 is 149 and 106 in 2007 in the first-order scale, based on which the numbers of "hot spots" in Qingdao City in 2006 is 6 and 8 in 2007 in the second-order scale. All of them have statistical significance at a 0.05 level.
Conclusions:
(i) The average distribution of TB cased in Qingdao City in 2005-2009 were nearly the same. Their centers are both close to the urban area; and the distribution of TB cases in the 5 years covers a wide range of entire region of Qingdao. The distribution center showed slightly southward trend over time.
(ii) The nearest neighbor clustering analysis showed the best clustering scale was village level. They were mainly distributed in the urban areas and cities and counties in the center before adjusted. After adjustment of the population density, the "hot spots" show that clusters of TB cases also occurred in rural areas, different from the results before the adjustment that clusters appear mainly in the urban area.
(iii) All clustering analysis at different scales showed the "hot Spots" mainly distributed distributed in Jiaonan, Huangdao, Shinan, Shibei, Licang,Sifang, Chengyang, Jimo, coastal distribution concentration in southwest-northeastern direction
4. The spatial heterogeneity of TB cases in Qingdao City is very important for the prevention and treatment of tuberculosis. This study could provide useful information for allocation of health resources, perhaps brings new hope for the current "step" of TB prevention and treatment.
引文
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[14]温亮,杨祖虎,徐德忠,等.基于GIS的海南省疟疾监测预警系统.解放军预防医学杂志,2006,24(6)：458.
[15]杨进,董柏青,张杰,等.地理信息系统技术在伤寒发热监测中应用.中国公共卫生,2007,23(9)：1086-1088.
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[20]岳永杰,余新晓,武军,等.北京山区天然次生林种群空间分布的点格局分析.中国水土保持科学,2008,6(3)：59-64
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[27]邵琦.基于GIS的小地域生态环境内HFRS病毒与宿主鼠类之间时空关系的研究[D].山东大学硕士论文,2008.
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[30]刘巧兰、李晓松、冯子键等.Knox指数在传染病时空聚集性时空探测中的应用.中华流行病学杂志,2007,28(8)：802-805.
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[9]杜娟,关泽群.GIS在流行病学研究中的应用[J].现代预防医学,2007,34(19)：3691-3698.
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[12]白廷军,水黎明,孙灵英等.GIS在突发公共卫生事件应急处理中的应用[J].海峡预防医学杂志,2007,13(2)：75-76.
[13]肖雪,肖建国,修良昌.地理信息系统在传染病中的应用[J].现代预防医学,2005,32(10)：1308-1310.
[14]温亮,杨祖虎,徐德忠,等.基于GIS的海南省疟疾监测预警系统.解放军预防医学杂志,2006,24(6)：458.
[15]杨进,董柏青,张杰,等.地理信息系统技术在伤寒发热监测中应用.中国公共卫生,2007,23(9)：1086-1088.
[16]陈玉敏,文松,杨长虹,等.血吸虫病地理信息系统(GIS)软件的开发和应用.实用寄生虫病杂志,2001,9(4)：185-186.
[17]薛亚婷,刘勇,孟蕾,等.应用地理信息系统研究甘肃省麻疹流行的空间分 布规律.兰州大学学报(医学版),2007,33(1)：51-54.
[18]Krishna Prasad Vadrevu, K. V. S. Badarinath, Eaturu Anuradha. Spatial patterns in vegetation fires in the Indian region. Environ Monit Assess,2008,147:1-13.
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[20]岳永杰,余新晓,武军,等.北京山区天然次生林种群空间分布的点格局分析.中国水土保持科学,2008,6(3)：59-64
[21]张金屯,孟东平.芦芽山华北落叶松林不同龄级立木的点格局分析.生态学报,2004,24(1)：35-39.
[22]Ned Levine. CrimeStat:A Spatial Statistics Program for the Analysis of Crime Incident Locations (v 3.0). Ned Levine & Associates, Houston, TX, and the National Institute of Justice, Washington, DC.2004.
[23]王远飞,何洪林.空间数据分析方法.北京：科学出版社,2007.李月辉,冯秀,周锐,等.基于道路廊道的辽宁省旅游景区(点)空间格局分析.生态学杂志,2006,8.
[24]Nisha V, Gad SS, Selvapandian D, et al. Geographical information system (GIS) in investigation of an outbreak [J] J Commun Dis,2005,37 (1):39-43.
[25]Z. Munch, S.W.P. Van Lill, C.N. Booysen, et al. Tuberculosis transmission patterns in a high-incidence area:a spatial analysis. INT J TUBERC LUNG DIS, 2003,7(3):271-277.
[26]曹志冬,王劲峰,高一鸽,等.广州SARS流行过程的空间模式与分异特征.地理研究,2008,27(5)：1139-1149.
[27]邵琦.基于GIS的小地域生态环境内HFRS病毒与宿主鼠类之间时空关系的研究[D].山东大学硕士论文,2008.
[28]迟文学,王劲峰,李新虎,等.出生缺陷的空间点格局分析.环境与健康杂志,2007,4
[29]张志杰,彭文祥,周艺彪,基于空间点模式分析的疾病分布状态的量化指标研究.中华预防医学杂志,2008,42(6)：422-426.
[30]刘巧兰、李晓松、冯子键等.Knox指数在传染病时空聚集性时空探测中的应用.中华流行病学杂志,2007,28(8)：802-805.
[31]高杰.流行病学标点地图统计分析方法体系的研究,山东大学硕士论文.2009
[32]Lee J, Wong DWS. Statistical analysis with Arcview GIS. New York:John Wiley & Sons Inc,2001,33-57.
[33]Ned Levine. CrimeStat:A Spatial Statistics Program for the Analysis of Crime Incident Locations (v 3.0). Ned Levine & Associates, Houston, TX, and the National Institute of Justice, Washington, DC.2004.
[34]World Health Organizationl Global Tuberculosis Control:Surveillance, Planning, Financing[R].WHO Report,2002.
[35]Stein L. Glasgow tuberculosis and housing [J].Tubercle,1954,35:195-203.
[36]Drucker E, Alcabes P, Boswort h W, et al 1 Childhood tuberculosis in the Bronx, New York [J].Lancet 1994,343:1482-1485.
[37]Coker R,McKee M,Atun R, et al. Risk factors for pulmonary tuberculosis in Russia:case-control study [J] 1BMJ,2006,332 (7533):85-87.
[38]Hill PC, J ackson2Sillah D, Donkor SA, et al. Risk factors for pulmonary tuberculosis:a clinic-based case control study in The Gambia[J].BMC Public Health,2006,6:156.
[39]董碧蓉,葛宁,刘关键,等.社会经济地位、环境因素及个人行为对肺结核发病危险的研究[J].中华流行病学杂志,2001,22(2)：102-104.
[40]Hawker J I,Bakhshi SS,Ali S, Farrington CP1 Ecological analysis of ethnic differences in relation between tuberculosis and poverty[J].BMJ,1999,319 (7216):1 031-1034.
[41]Barr RG, Diez-Roux AV, Knirsch CA, et al. Neighborhood Poverty and the Resurgence of Tuberculosis in New York City,1984-1992[J].Am J Public Health,2001,91 (9):1487-1493.
[42]Grange J, Story A, Zumla Al Tuberculosis in Disadvantaged Groups [J]. Curr Opin Pulm Med,2001,7 (3):160-164.
[43]Nishiura H1 Socioeconomic Factors for Tuberculosis in Tokyo, Japan unemployment, Overcrowding, Poverty and Migrant s [J]. Kekkaku,2003,78 (6):419-426.
[44]Jackson S, Sleigh AC, Wang GJ, et al. Poverty and t he economic effects of TB in rural China [J]. Int J Tuberc Lung Dis,2006,10(10):1 10421 1101
[45]吕华坤,许卫国,王蓓,等.江苏省农村居民结核病危险因素病例对照研究[J].中国公共卫生,2004,20(12)：1450-1451.
[46]Tekkel M, Rahu M,Loit HM, et al. Risk factors for pulmonary tuberculosis in Estonia [J]. Int J Tuberc Lung Dis,2002,6:887-894.
[47]Ili'c M.What is t he knowledge of tubercular patients about risk factors contributing to development of t heir disease [J].Srp ArhCelok Lekl 2006,134 Suppl 2:122-127.
[48]Pang PT, Leung CC, Lee SS. Neighbourhood risk factors for tuberculosis in Hong Kong. Int J Tuberc Lung Dis.2010 May;14(5):585-92.
[49]Randremanana RV, Sabatier P, Rakotomanana F, Randriamanantena A, Richard V. Spatial clustering of pulmonary tuberculosis and impact of the care factors in Antananarivo City. Trop Med Int Health.2009 Apr;14(4):429-37. Epub 2009 Feb 17.
[50]Maryam B. Haddad; Todd W. Wilson; Kashef Ijaz; et al. Tuberculosis and Homelessness in the United States,1994-2003. JAMA.2005; 293(22):2762-2766.
[51]Curtis AB, Ridzon R, Novick LF, et al. Analysis of Mycobacterium tuberculosis transmission patterns in a homeless shelter outbreak. Int J Tuberc Lung Dis.2000; 4: 308-313.
[52]Maryam B. Haddad; Todd W. Wilson; Kashef Ijaz; et al. Tuberculosis and Homelessness in the United States,1994-2003. JAMA.2005; 293(22):2762-2766
[53]Jia ZW, Jia XW, Liu YX, Dye C. Spatial analysis of tuberculosis cases in migrants and permanent residents, Beijing,2000-2006. Emerg Infect Dis.2008; Sep; 14(9):1413-1419.