Effects of meteorological factors on daily hospital admissions for asthma in adults: a time-series analysis

PLoS One. 2014 Jul 14;9(7):e102475. doi: 10.1371/journal.pone.0102475. eCollection 2014.


Background: There is limited evidence for the impacts of meteorological changes on asthma hospital admissions in adults in Shanghai, China.

Objectives: To quantitatively evaluate the short-term effects of daily mean temperature on asthma hospital admissions.

Methods: Daily hospital admissions for asthma and daily mean temperatures between January 2005 and December 2012 were analyzed. After controlling for secular and seasonal trends, weather, air pollution and other confounding factors, a Poisson generalized additive model (GAM) combined with a distributed lag non-linear model were used to explore the associations between temperature and hospital admissions for asthma.

Results: During the study periods, there were 15,678 hospital admissions for asthma by residents of Shanghai, an average 5.6 per day. Pearson correlation analysis found a significant negative correlation (r = -0.174, P<0.001) between asthma hospitalizations and daily mean temperature (DMT). The DMT effect on asthma increased below the median DMT, with lower temperatures associated with a higher risk of hospital admission for asthma. Generally, the cold effect appeared to be relatively acute, with duration lasting several weeks, while the hot effect was short-term. The relative risk of asthma hospital admissions associated with cold temperature (the 25th percentile of temperature relative to the median temperature) was 1.20 (95% confidence interval [CI], 1.01∼1.41) at lag0-14. However, warmer temperatures were not associated with asthma hospital admissions.

Conclusions: Cold temperatures may trigger asthmatic attacks. Effective strategies are needed to protect populations at risk from the effects of cold.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Air Pollution / analysis
  • Asthma / epidemiology*
  • China / epidemiology
  • Humans
  • Meteorological Concepts*
  • Models, Theoretical
  • Nonlinear Dynamics
  • Patient Admission / statistics & numerical data*
  • Poisson Distribution
  • Temperature*
  • Time Factors

Grants and funding

This study was funded by the Global Environment Change Research in Fudan University (Grant No. EZH1829007/003), the Chinese Meteorological Administration (Grant No. GYHY201206027), Chinese National Science Foundation (30800937) and the program of Key Discipline Construction of Public Health of Shanghai (Grant No. 12GWZX0101). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.