Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China

• DOI: 10.1186/1471-2334-11-331
• Publication Date: 2011-12-03T01:54:27Z
• Authors: Xiaodong Liu; Baofa Jiang; Weidong Gu; Qiyong Liu
• Keywords: China; Climate; Health, Toxicology and Mutagenesis; Veterinary medicine; Principal component analysis; Infectious and parasitic diseases; RC109-216; FOS: Health sciences; Impact of Climate Change on Forest Wildfires; 03 medical and health sciences; Engineering; 0302 clinical medicine; Sociology; Risk Factors; Health Sciences; FOS: Mathematics; Animals; Humans; Demography; Climatology; Global and Planetary Change; Models, Statistical; Geography; Statistics; Impact of Climate Change on Human Health; Geology; FOS: Earth and related environmental sciences; Transmission (telecommunications); FOS: Sociology; 3. Good health; Infectious Diseases; Hemorrhagic Fever with Renal Syndrome; Autocorrelation; Electrical engineering; Environmental Science; Physical Sciences; Medicine; Crimean-Congo Hemorrhagic Fever; Seasons; Regression analysis; Viral Hemorrhagic Fevers and Zoonotic Infections; Mathematics; Research Article

Hemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused by different species of hantaviruses. As a rodent-borne seasonal distribution, external environmental factors including climate may play significant role in its transmission. The city Shenyang one the most seriously endemic areas for HFRS. Here, we characterized dynamic temporal trend HFRS, and identified climate-related risk their roles HFRS transmission Shenyang, China. annual monthly cumulative numbers cases from 2004 to 2009 were calculated plotted show fluctuation Shenyang. Cross-correlation autocorrelation analyses performed detect lagged effect on cases. Principal component analysis was constructed using data extract principal components reduce co-linearity. extracted terms added into multiple regression model called (PCR) quantify relationship between factors, PCR compared general conducted only as independent variables. A distinctly declining incidence identified. reported every month, two peak periods occurred spring (March May) winter (November January), during which, nearly 75% reported. Three contribution rate 86.06%. Component 1 represented MinRH0, MT1, RH1, MWV1; 2 RH2, MaxT3, MAP3; 3 MaxT2, MAP2, MWV2. composed three terms. association epidemics better explained (F = 446.452, P < 0.001, adjusted R 0.75) than 223.670, 0.000, 0.51). distribution varied years trend. trends significantly associated local temperature, relative humidity, precipitation, air pressure, wind velocity previous months. this study will make surveillance simpler control more targeted