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The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (...

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Autores principales: Mahara, Gehendra, Wang, Chao, Yang, Kun, Chen, Sipeng, Guo, Jin, Gao, Qi, Wang, Wei, Wang, Quanyi, Guo, Xiuhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5129293/
https://www.ncbi.nlm.nih.gov/pubmed/27827946
http://dx.doi.org/10.3390/ijerph13111083
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author Mahara, Gehendra
Wang, Chao
Yang, Kun
Chen, Sipeng
Guo, Jin
Gao, Qi
Wang, Wei
Wang, Quanyi
Guo, Xiuhua
author_facet Mahara, Gehendra
Wang, Chao
Yang, Kun
Chen, Sipeng
Guo, Jin
Gao, Qi
Wang, Wei
Wang, Quanyi
Guo, Xiuhua
author_sort Mahara, Gehendra
collection PubMed
description (1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R(2) = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R(2) = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R(2) = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention.
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spelling pubmed-51292932016-12-11 The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models Mahara, Gehendra Wang, Chao Yang, Kun Chen, Sipeng Guo, Jin Gao, Qi Wang, Wei Wang, Quanyi Guo, Xiuhua Int J Environ Res Public Health Article (1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R(2) = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R(2) = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R(2) = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention. MDPI 2016-11-04 2016-11 /pmc/articles/PMC5129293/ /pubmed/27827946 http://dx.doi.org/10.3390/ijerph13111083 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mahara, Gehendra
Wang, Chao
Yang, Kun
Chen, Sipeng
Guo, Jin
Gao, Qi
Wang, Wei
Wang, Quanyi
Guo, Xiuhua
The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models
title The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models
title_full The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models
title_fullStr The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models
title_full_unstemmed The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models
title_short The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models
title_sort association between environmental factors and scarlet fever incidence in beijing region: using gis and spatial regression models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5129293/
https://www.ncbi.nlm.nih.gov/pubmed/27827946
http://dx.doi.org/10.3390/ijerph13111083
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