Air pollution and hemorrhagic fever with renal syndrome in South Korea: an ecological correlation study

BMC Public Health

Yon Su Kim³  Suhnggwon Kim³  Yunhee Choi¹  Sunhee Kim¹  Seung Seok Han² 

[1] Medical Research Collaborating Center, Seoul National University College of Medicine, Seoul, Korea;Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehakro, Jongno-gu, Seoul 110-744, Korea;Kidney Research Institute, Seoul National University, Seoul, Korea

关键词: Infection;    Particulate matter;    Hemorrhagic fever with renal syndrome;    Hantavirus;    Air pollution;   

Others  :  1162333
DOI  :  10.1186/1471-2458-13-347

 received in 2012-11-05, accepted in 2013-04-11,  发布年份 2013

【 摘 要 】

Background

The effects of air pollution on the respiratory and cardiovascular systems, and the resulting impacts on public health, have been widely studied. However, little is known about the effect of air pollution on the occurrence of hemorrhagic fever with renal syndrome (HFRS), a rodent-borne infectious disease. In this study, we evaluated the correlation between air pollution and HFRS incidence from 2001 to 2010, and estimated the significance of the correlation under the effect of climate variables.

Methods

We obtained data regarding HFRS, particulate matter smaller than 10 μm (PM₁₀) as an index of air pollution, and climate variables including temperature, humidity, and precipitation from the national database of South Korea. Poisson regression models were established to predict the number of HFRS cases using air pollution and climate variables with different time lags. We then compared the ability of the climate model and the combined climate and air pollution model to predict the occurrence of HFRS.

Results

The correlations between PM₁₀ and HFRS were significant in univariate analyses, although the direction of the correlations changed according to the time lags. In multivariate analyses of adjusted climate variables, the effects of PM₁₀ with time lags were different. However, PM₁₀ without time lags was selected in the final model for predicting HFRS cases. The model that combined climate and PM₁₀ data was a better predictor of HFRS cases than the model that used only climate data, for both the study period and the year 2011.

Conclusions

This is the first report to document an association between HFRS and PM₁₀ level.

【 授权许可】

   
2013 Han et al.; licensee BioMed Central Ltd.

【 图 表 】

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