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

Author

Listed:
  • Gehendra Mahara

    (Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

  • Chao Wang

    (Department of Statistics and Information, Beijing Center for Disease Control and Prevention (CDC), Beijing Center for Preventive Medical Research, Beijing 100013, China)

  • Kun Yang

    (Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

  • Sipeng Chen

    (Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

  • Jin Guo

    (Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

  • Qi Gao

    (Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

  • Wei Wang

    (Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
    Systems and Intervention Research Centre for Health, School of Medical Sciences, Edith Cowan University, Perth 6027, Australia)

  • Quanyi Wang

    (Institute for Infectious Disease & Endemic Disease Control, Beijing Center for Disease Control and Prevention (CDC), Beijing Center for Preventive Medical Research, Beijing 100013, China)

  • Xiuhua Guo

    (Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

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 (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.

Suggested Citation

  • Gehendra Mahara & Chao Wang & Kun Yang & Sipeng Chen & Jin Guo & Qi Gao & Wei Wang & Quanyi Wang & Xiuhua Guo, 2016. "The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models," IJERPH, MDPI, vol. 13(11), pages 1-15, November.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:11:p:1083-:d:82193
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    2. Maomao Zhang & Weigang Chen & Kui Cai & Xin Gao & Xuesong Zhang & Jinxiang Liu & Zhiyuan Wang & Deshou Li, 2019. "Analysis of the Spatial Distribution Characteristics of Urban Resilience and Its Influencing Factors: A Case Study of 56 Cities in China," IJERPH, MDPI, vol. 16(22), pages 1-22, November.
    3. Amritpal Kaur Khakh & Victoria Fast & Rizwan Shahid, 2019. "Spatial Accessibility to Primary Healthcare Services by Multimodal Means of Travel: Synthesis and Case Study in the City of Calgary," IJERPH, MDPI, vol. 16(2), pages 1-19, January.
    4. Tony G. Reames & Dorothy M. Daley & John C. Pierce, 2021. "Exploring the Nexus of Energy Burden, Social Capital, and Environmental Quality in Shaping Health in US Counties," IJERPH, MDPI, vol. 18(2), pages 1-13, January.

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