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Int. J. Environ. Res. Public Health 2016, 13(11), 1083; doi:10.3390/ijerph13111083

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

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Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, China
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Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
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Department of Statistics and Information, Beijing Center for Disease Control and Prevention (CDC), Beijing Center for Preventive Medical Research, Beijing 100013, China
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Systems and Intervention Research Centre for Health, School of Medical Sciences, Edith Cowan University, Perth 6027, Australia
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Institute for Infectious Disease & Endemic Disease Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Center for Preventive Medical Research, Beijing 100013, China
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Authors to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Received: 3 September 2016 / Revised: 18 October 2016 / Accepted: 21 October 2016 / Published: 4 November 2016
View Full-Text   |   Download PDF [954 KB, uploaded 9 November 2016]   |  

Abstract

(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 (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 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. View Full-Text
Keywords: spatial regression analysis; scarlet fever; meteorological factors; air pollutant factors; Beijing spatial regression analysis; scarlet fever; meteorological factors; air pollutant factors; Beijing
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MDPI and ACS Style

Mahara, G.; Wang, C.; Yang, K.; Chen, S.; Guo, J.; Gao, Q.; Wang, W.; Wang, Q.; Guo, X. The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models. Int. J. Environ. Res. Public Health 2016, 13, 1083.

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