Literature DB >> 30711589

Effect of meteorological factors on scarlet fever incidence in Guangzhou City, Southern China, 2006-2017.

Jian-Yun Lu1, Zong-Qiu Chen1, Yan-Hui Liu1, Wen-Hui Liu1, Yu Ma1, Tie-Gang Li2, Zhou-Bin Zhang3, Zhi-Cong Yang3.   

Abstract

OBJECTIVE: To explore the relationship between meteorological factors and scarlet fever incidence from 2006 to 2017 in Guangzhou, the largest subtropical city of Southern China, and assist public health prevention and control measures.
METHODS: Data for weekly scarlet fever incidence and meteorological variables from 2006 to 2017 in Guangzhou were collected from the National Notifiable Disease Report System (NNDRS) and the Guangzhou Meteorological Bureau (GZMB). Distributed lag nonlinear models (DLNMs) were conducted to estimate the effect of meteorological factors on weekly scarlet fever incidence in Guangzhou.
RESULTS: We observed nonlinear effects of temperature, relative humidity, and wind velocity. The risk was the highest when the weekly mean temperature was 31 °C during lag week 14, yielding a relative risk (RR) of 1.48 (95% CI: 1.01-2.17). When relative humidity was 43.5% during lag week 0, the RR was 1.49 (95% CI: 1.04-2.12); the highest RR (1.55, 95% CI: 1.20-1.99) was reached when relative humidity was 93.5% during lag week 20. When wind velocity was 4.4 m/s during lag week 13, the RR was highest at 3.41 (95% CI: 1.57-7.44). Positive correlations were observed among weekly temperature ranges and atmospheric pressure with scarlet fever incidence, while a negative correlation was detected with aggregate rainfall. The cumulative extreme effect of meteorological variables on scarlet fever incidence was statistically significant, except for the high effect of wind velocity.
CONCLUSION: Weekly mean temperature, relative humidity, and wind velocity had double-trough effects on scarlet fever incidence; high weekly temperature range, high atmospheric pressure, and low aggregate rainfall were risk factors for scarlet fever morbidity. Our findings provided preliminary, but fundamental, information that may be useful for a better understanding of epidemic trends of scarlet fever and for developing an early warning system. Laboratory surveillance for scarlet fever should be strengthened in the future.
Copyright © 2019. Published by Elsevier B.V.

Entities:  

Keywords:  Distributed lag nonlinear models; Meteorological factors; Scarlet fever

Mesh:

Year:  2019        PMID: 30711589     DOI: 10.1016/j.scitotenv.2019.01.318

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


  4 in total

1.  Bioaerosol sampling optimization for community exposure assessment in cities with poor sanitation: A one health cross-sectional study.

Authors:  Lucas Rocha-Melogno; Olivia Ginn; Emily S Bailey; Freddy Soria; Marcos Andrade; Michael H Bergin; Joe Brown; Gregory C Gray; Marc A Deshusses
Journal:  Sci Total Environ       Date:  2020-05-18       Impact factor: 7.963

2.  Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model.

Authors:  W W Wu; Q Li; D C Tian; H Zhao; Y Xia; Y Xiong; K Su; W G Tang; X Chen; J Wang; L Qi
Journal:  Epidemiol Infect       Date:  2022-04-21       Impact factor: 4.434

3.  A Bayesian Spatiotemporal Analysis of Pediatric Group A Streptococcal Infections.

Authors:  Angela Wang; Andrew M Fine; Erin Buchanan; Mark Janko; Lise E Nigrovic; Paul M Lantos
Journal:  Open Forum Infect Dis       Date:  2019-12-10       Impact factor: 3.835

4.  The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004-2018.

Authors:  Yongbin Wang; Chunjie Xu; Jingchao Ren; Yingzheng Zhao; Yuchun Li; Lei Wang; Sanqiao Yao
Journal:  Sci Rep       Date:  2020-10-14       Impact factor: 4.379

  4 in total

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