Literature DB >> 31740063

Relationship of meteorological factors and human brucellosis in Hebei province, China.

Long-Ting Cao1, Hong-Hui Liu1, Juan Li1, Xiao-Dong Yin1, Yu Duan2, Jing Wang3.   

Abstract

BACKGROUND: Brucellosis has always been one of the major public health problems in China. Investigating the influencing factors of brucellosis is conducive to its prevention and control. The incidence trend of brucellosis shows an obvious seasonality, suggesting that there may be a correlation between brucellosis and meteorological factors, but related studies were few. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological factors and brucellosis.
METHODS: The data of monthly incidence of brucellosis and meteorological factors in Hebei province from January 2004 to December 2015 were collected from the Chinese Public Health Science Data Center and Chinese meteorological data website. An ARIMA model incorporated with covariables was conducted to estimate the effects of meteorological variables on brucellosis.
RESULTS: There was a highest peak from May to July every year and an upward trend during the study period. Atmospheric pressure, wind speed, mean temperature, and relative humidity had significant effects on brucellosis. The ARIMA(1,0,0)(1,1,0)12 model with the covariates of atmospheric pressure, wind speed and mean temperature was the optimal model. The results showed that the atmospheric pressure with a 2-month lag (β = -0.004, p = 0.037), the wind speed with a 1-month lag (β = 0.030, p = 0.035), and the mean temperature with a 2-month lag (β = -0.003, p = 0.034) were significant predictors.
CONCLUSION: Our study suggests that atmospheric pressure, wind speed, mean temperature, and relative humidity have a significant impact on brucellosis. Further understanding of its mechanism would help facilitate the monitoring and early warning of brucellosis.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  ARIMA model; Brucellosis; Meteorology factors; Time series analysis

Mesh:

Year:  2019        PMID: 31740063     DOI: 10.1016/j.scitotenv.2019.135491

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


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