Email updates

Keep up to date with the latest news and content from BMC Public Health and BioMed Central.

Open Access Research article

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

Seung Seok Han1, Sunhee Kim2, Yunhee Choi2, Suhnggwon Kim13 and Yon Su Kim13*

Author affiliations

1 Department of Internal Medicine, Seoul National University College of Medicine, 101 Daehakro, Jongno-gu, Seoul 110-744, Korea

2 Medical Research Collaborating Center, Seoul National University College of Medicine, Seoul, Korea

3 Kidney Research Institute, Seoul National University, Seoul, Korea

For all author emails, please log on.

Citation and License

BMC Public Health 2013, 13:347  doi:10.1186/1471-2458-13-347

Published: 15 April 2013

Abstract

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.

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