TROPICAL GEOGRAPHY ›› 2017, Vol. 37 ›› Issue (3): 409-416.doi: 10.13284/j.cnki.rddl.002948

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Relationship between Geographical Factors and the Reference Value of High Sensitive C-reactive Protein

LIN Qianyi1,GE Miao1,WANG Congxia2,CEN Minyi1,JIANG Jilin1,HE Jinwei3,LI Mengjiao1,LIU Xin1   

  1. (1.Institute of Health Geography,College of Tourism and Environment,Shaanxi Normal University,Xi'an 710062,China;2.Department of Cardiovasology,the Second Affiliated Hospital,Medical School of Xi'an Jiao Tong University,Xi'an 710004,China; 3.Department of Public Health,Medical School of Yan’an University,Yan’an 716000,China)
  • Online:2017-05-05 Published:2017-05-05

Abstract: High sensitive C-reactive protein (hs-CRP) is an important predictor of cardiovascular and cerebrovascular diseases. In the dynamic balance human-nature system, the residents’ physical functions differ from each other in different regions. However, the geographical factors are neglected while establishing the reference value of hs-CRP. It will lead to inaccurate clinical diagnosis while using the same standard system of reference values of hs-CRP on the residents of different areas. Therefore, in this paper the relationship between reference value of hs-CRP and geographical factors was analyzed in order to establish a more comprehensive standard of reference values of hs-CRP. Firstly, by collecting the observed hs-CRP values of 8 350 Chinese healthy adults from 84 cities in China, the correlation analysis method was adopted to investigate the relationship between the reference value and 23 geographical factors with SPSS 21.0. Then the five geographical factors that were significantly correlated with the reference values of hs-CRP were extracted to perform the Support Vector Machine Regression model (SVR) with the reference values of hs-CRP. Good fitting of the model was obtained in this case, and reference values of hs-CRP of 2 322 cities in China were predicted by using the model. Finally, ArcGIS 10.0 was used to make Kriging interpolation with the predicted data and form the SVR model to produce the geographic distribution map of the reference values of hs-CRP of healthy Chinese. The results show that the geographical environment has an important effect on the hs-CRP reference value, and the reference value of hs-CRP is significantly correlated with 5 indexes, namely, the altitude, the average relative humidity, the annual average precipitation, the annual temperature range and the annual average wind speed. The Support Vector Machine Regression model got good fitting effect in this case with a small prediction error. And the extraction Kriging interpolation model also obtained good prediction accuracy. From the spatial distribution of the map, it can be seen that the overall reference values of hs-CRP of China is decreasing from northwest to southeast. At the end of this paper, the specific influence mechanism of geographical factors on hs-CRP reference value was discussed.

Key words: health geography, hs-CRP, natural environment, support vector machine regression, disjunctive Kriging