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Spatial patterns and effects of air pollution and meteorological factors on hospitalization for chronic lung diseases in Beijing, China

  • Lin Tian
  • Chuan Yang
  • Zijun Zhou
  • Ziting Wu
  • Xiaochuan PanEmail author
  • Archie C. A. Clements
Research Paper
  • 13 Downloads

Abstract

Chronic obstructive pulmonary disease (COPD), lung cancer (LC) and tuberculosis (TB) are common chronic lung diseases that generate a large disease burden and significant health care resource use in China. The aim of this study was to quantify spatial patterns and effects of air pollution and meteorological factors on hospitalization of COPD, LC and TB in Beijing. Daily counts of hospitalization for 2010 were obtained from the Beijing Urban Employees Basic Medical Insurance (UEBMI) system. Bayesian hierarchical Poisson regression models were applied to identify spatial patterns of hospitalization for COPD, LC and TB at the district level and explore associations with inhalable particulate matter (aerodynamic diameter <10 μm, PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), mean temperature and relative humidity. There were 18,882, 14,295 and 2,940 counts of hospitalizations for COPD, LC and TB respectively, in Beijing in 2010. Clusters of high relative risk were in different locations for the three diseases. The effect of relative humidity on COPD hospitalization was most significant with a relative risk (RR) of 1.070 (95%CI: 1.054, 1.086) per one percent increase. For lung cancer hospitalization, exposure to ambient SO2 was associated with a RR of 1.034 (95%CI: 1.011, 1.058) per μg m–3 increase. For tuberculosis, the effect of mean temperature was significant with a RR of 1.107 (95%CI: 1.038, 1.180) per °C increase. Risk factors and spatial patterns were different for hospitalization of non-infectious and infectious chronic lung disease in Beijing. Even over a short time period (one year), associations were apparent with air pollution and meteorological factors.

Keywords

air pollution meteorological factors chronic lung disease spatial analysis Bayesian model 

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Lin Tian
    • 1
    • 2
  • Chuan Yang
    • 3
    • 4
  • Zijun Zhou
    • 4
  • Ziting Wu
    • 1
  • Xiaochuan Pan
    • 1
    Email author
  • Archie C. A. Clements
    • 5
  1. 1.Department of Occupational and Environmental Health, School of Public HealthPeking UniversityBeijingChina
  2. 2.Institute for Packaging Materials and Pharmaceutical Excipients ControlNational Institutes for Food and Drug ControlBeijingChina
  3. 3.Peking University Third HospitalBeijingChina
  4. 4.Department of Health Policy and Management, School of Public HealthPeking UniversityBeijingChina
  5. 5.Research School of Population Health, College of Medicine, Biology and EnvironmentThe Australian National UniversityCanberra, Australian Capital TerritoryAustralia

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