| 彭小冬,彭 琳,吴库生.影响乳腺癌发生的空间危险因素分析[J].中国肿瘤,2015,24(10):805-810. |
| 影响乳腺癌发生的空间危险因素分析 |
| An Analysis of Spatial Risk Factors on Breast Cancer by Using Geographic Information System |
| 投稿时间：2015-03-06 |
| DOI：10.11735/j.issn.1004-0242.2015.10.A001 |
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| 中文关键词: 地理信息系统 乳腺肿瘤 气候 地理 |
| 英文关键词:geographic information system breast cancer climate geography |
| 基金项目:汕头大学医学院临床科研提升计划（201413）； |
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| 摘要点击次数: 2245 |
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| 中文摘要: |
| 摘 要：［目的］ 在地理信息系统支持下探讨乳腺癌死亡率与空间环境因素之间的关系。［方法］ 在Excel中建立1/10抽样地区乳腺癌死亡率数据库(1990~1992年)，在Arc/Info 9.0中连接已制作的抽样地区电子地图。利用空间分析的方法提取抽样地区分县的相应空间环境数据(1961~1990年/月平均数据)。对乳腺癌死亡率与相应地区空间环境因素进行Pearson或Spearman相关分析；对环境数据进行因子/主成分分析后与死亡率数据进行多元逐步回归分析。［结果］ 空间分布地图显示中国乳腺癌死亡率分布呈现一定的分布规律，具有东北部向中部、西南部递减的趋势。相关分析显示乳腺癌标化死亡率与1~12月月平均风速呈正相关(相关系数按月份顺序分别为0.164，0.137，0.135，0.141，0.153，0.136，0.134，0.143，0.193，0.186，0.183，0.156， P<0.05)，与海拔呈负相关(rs=-0.159，P<0.05)。经因子/主成分分析后多元逐步回归建模，最终1~12月月平均风速进入模型。［结论］ 中国乳腺癌在空间分布上有明显的趋势，乳腺癌死亡率与风速、海拔相关。 |
| 英文摘要: |
| Abstract：［Purpose］ To explore the relationship between breast cancer mortality and ambient climate，geographic factors using geographic information system(GIS). ［Methods］ Database of breast cancer mortality of 1/10 sampling areas surveyed in 1990~1992 was established in Excel and linked with the digital polygon maps of study areas in Arc/Info 9.0. Geographic and climate data of sampling areas were extracted from the raster dataset(1961~1990，annual/monthly average data). Pearson correlation analysis or Spearman correlation analysis and multiple regression analysis after factor analysis were carried out to analyze the relationship between breast cancer mortality and the spatial environment factors.［Results］ Spatial distribution maps showed that China breast cancer mortality distribution had regularities of distribution，which decreased from northeast，middle to southwest. Correlation analysis showed that breast cancer standardization mortality was positively correlated with average wind speed from January to December(According to the month order，correlation coefficient was 0.164，0.137，0.135，0.141，0.153，0.136，0.134，0.143，0.193，0.186， 0.183，0.156，respectively. P<0.05)，but negatively correlated with altitude (rs=-0.159，P<0.05). After factor/principal component analysis and multiple stepwise regression analysis，the variable average wind speed from January to December finally entered the regression model. ［Conclusions］ Breast cancer had significant spatial aggregation in China，and are correlated with average wind speed and altitude. |
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