Spatio-temporal process and the influencing factors on influenza A (H1N1) pandemic in Changsha / 中华流行病学杂志

ABSTRACT

Objective To analyze the spatio-temporal process on 2009 influenza A (HlNl) pandemic in Changsha and the influencing factors during the diffusion process. Methods Data were from the following 5 sources, influenza A (HlNl) pandemic gathered in 2009, Geographic Information System (GIS) of Changsha, the broad range of theorems and techniques of hot spot analysis, spatio-temporal process analysis and Spearman correlation analysis. Results Hot spot areas appeared to be more in the economically developed areas, such as cities and townships. The cluster of spatial-temporal distribution of influenza A (HlNl) pandemic was most likely appearing in Liuyang city (RR=22.70,P<0.01). The secondary cluster would include districts as Yuelu (RR=6A9,P< 0.01) , Yuhua (RR=81.63, P<0.01). Xingsha township appeared as the center in the Changsha county (RR=2.90, P<0.01) while townships as Yutangping (RR=19.31, P<0.01) , Chengjiao (RR=73.14,P<0.01) and Longtian appeared as the center in the west of Ningxiang county (RR= 14.43,P<0.01) and Wushan as the center in the Wangcheng county (RR= 13.84,P<0.01). As time went on, the epidemic moved towards the eastern and more developed regions. Regarding factor analysis, population, the amount of students, geographic relationship and business activities etc. appeared to be the key elements influencing the transmission of influenza A (H1N1) pandemic. At the beginning of the epidemic, population density served as the main factor (r=0.477, P<0.05) but during the initial and fast growing stages, it was replaced by the size of students to serve as the important indicator (r=0.831, P<0.01; r=0.518, P<0.01). However, during the peak of the epidemics, the business activities played an important role (r=-0.676, P<0.01). Conclusion Groups under high risk and districts with high incidence rates were shifting, along with the temporal process of influenza A(H1N1) pandemic, suggesting that the protection measures need to be adjusted, according to the significance of influencing factors at different stages.