摘要
目的为制定中国老年前期女性用力肺活量正常参考值的统一标准提供科学依据。方法收集了中国38个单位测定的8604例老年前期女性用力肺活量正常参考值,应用SPSS统计软件,运用相关分析和回归分析的方法,研究了其与8项地理因素指标的关系。结果发现老年前期女性用力肺活量正常参考值与中国地理因素之间有很显著的相关关系(F=5.070,P=0.002)。用逐步回归分析的方法推导出了一个回归方程:Y^=13.286-0.0003454X1-0.08413X3-0.03991X4-0.09713X6+0.4798X8±0.265。在以上的回归方程中,Y^是老年前期女性用力肺活量正常参考值(L),X1是海拔高度(m),X3是年日照百分率(%),X4是年平均气温(℃),X6是年平均相对湿度(%),X8是年平均风速(m/s);0.265是剩余标准差的值。结论如果知道了中国某地的地理因素,就可以用回归方程计算这个地区的老年前期女性用力肺活量正常参考值。依据老年前期女性用力肺活量正常参考值与地理因素的依赖关系,把中国分为8个区。
Objective To supply a scientific basis for uniting the normal reference value standard of forced vital capacity (FVC) of Chinese presenile women. Methods A research was made about the relationship between the normal reference value of 8 604 examples of FVC of Chinese healthy presenile women and eight geographical factors in 38 areas in China. Results The correlation of geographical factors and the normal reference value of FVC of Chinese presenile women were quite significant ( F = 5.070, P = 0. 002). The method of mathemat- ical regression analysis was used to infer one regression equation:Y = 13. 286 -0. 000 345 4X1 -0. 084 13X3 -0. 039 91X4 -0. 097 13X6 + 0. 479 8X8±0. 265. In the above equation, Y was the normal reference value of FVC of presenile women (L) ; X1 was the altitude (m) ; X3 was the annual percentage of sunshine duration ( % ) ; X4 was the annual mean air temperature ( ℃ ) ; X6 was the annual mean relative humidity ( % ) ; Xs was the annual mean wind speed (m/s) ; 0. 265 was the value of the residual standard deviations. Conclusions If geographical values are obtained in some areas, the normal reference value of FVC of Chinese presenile women of this area can be reckoned by using the regression equations. Furthermore, according to the similarity of the normal reference value of FVC of Chinese presenile women, China can be divided into eight regions.
出处
《中国老年学杂志》
CAS
CSCD
北大核心
2009年第14期1729-1733,共5页
Chinese Journal of Gerontology
基金
国家自然科学基金资助项目(40671005)
关键词
用力肺活量
正常参考值
地理要素
回归分析
Forced vital capacity
Normal reference value
Geographical elements
Regression analysis