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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao The Science of The T...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
The Science of The Total Environment
Article . 2018 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Impact of meteorological factors on hemorrhagic fever with renal syndrome in 19 cities in China, 2005–2014

Authors: Jianjun Xiang; Alana Hansen; Qiyong Liu; Michael Xiaoliang Tong; Xiaobo Liu; Yehuan Sun; Scott Cameron; +5 Authors

Impact of meteorological factors on hemorrhagic fever with renal syndrome in 19 cities in China, 2005–2014

Abstract

This study aims to investigate the associations between meteorological factors and hemorrhagic fever with renal syndrome (HFRS) in 19 cities selected from HFRS high risk areas across different climate zones in three Provinces of China. De-identified daily reports of HFRS in Anhui, Heilongjiang, and Liaoning Provinces for 2005-2014 were obtained from the Chinese Center for Disease Control and Prevention. Daily weather data from each study location were obtained from the China meteorological Data Sharing Service System. Generalised estimating equation models (GEE) were used to quantify the city-specific HFRS-weather associations. Multivariate random-effects meta-regression models were used to pool the city-specific HFRS-weather effect estimates. HFRS showed an overall downward trend during the study period with a slight rebound after 2010. Meteorological factors were significantly associated with HFRS incidence. HFRS was relatively more sensitive to weather variability in subtropical regions (Anhui Province) than in temperate regions (Heilongjiang and Liaoning Provinces). The size of effect estimates and the duration of lagged effects varied by locations. Pooled results of the 19 cities showed that a 1 °C increase in maximum temperature (Tmax) resulted in a 1.6% (95% CI: 1.0%-2.2%) increase in HFRS; a 1 mm increase in weekly precipitation was associated with 0.2% (95%CI: 0.1%-0.3%) increase in HFRS; a 1% increase in average relative humidity was associated with a 0.9% (95%CI: 0.5%-1.2%) increase in HFRS. The lags with the largest effects for Tmax, precipitation, and relative humidity occurred in weeks 29, 22, and 16, respectively. Lagged effects of meteorological factors did not end after an epidemic season but waned gradually in the following 3-4 epidemic seasons. Weather variability plays a significant role in HFRS transmission in China. The long duration of lagged effects indicates the necessity of continuous interventions following the epidemics.

Country
Australia
Keywords

China, Meteorological Concepts, Climate Change, Rain, Hemorrhagic fever with renal syndrome, 333, hemorrhagic fever with renal syndrome, Cities, Weather, Incidence, Temperature, Humidity, Environmental Exposure, climate change, weather, Hemorrhagic Fever with Renal Syndrome, Seasons

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    53
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
53
Top 10%
Top 10%
Top 10%