• Bao, H., J. Dong, X. Liu, E. Tan, J. Shu, and S. Li, 2020a: Association between ambient particulate matter and hospital outpatient visits for chronic obstructive pulmonary disease in Lanzhou, China. Environ. Sci. Pollut. Res. Int., 27, 22 84322 854, https://doi.org/10.1007/s11356-020-08797-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bao, H., X. Liu, L. Tanen, J. Shu, J. Dong, and S. Li, 2020b: Effects of temperature and relative humidity on the number of outpatients with chronic obstructive pulmonary disease and their interaction effect in Lanzhou, China. J. Peking Univ., 52, 308316, https://doi.org/10.19723/j.issn.1671-167X.2020.02.019.

    • Search Google Scholar
    • Export Citation
  • Chai, G., H. He., Y. Su, Y. Sha, and S. Zong, 2020: Lag effect of air temperature on the incidence of respiratory diseases in Lanzhou, China. Int. J. Biometeor., 64, 8393, https://doi.org/10.1007/s00484-019-01795-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gasparrini, A., 2011: Distributed lag linear and non-linear models in R: The package dlnm. J. Stat. Software, 43 (8), 120, https://doi.org/10.18637/jss.v043.i08.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gasparrini, A., 2014: Modeling exposure-lag-response associations with distributed lag non-linear models. Stat. Med., 33, 881899, https://doi.org/10.1002/sim.5963.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gasparrini, A., and B. Armstrong, 2013: Reducing and meta-analyzing estimates from distributed lag non-linear models. BMC Med. Res. Methodol., 13, 1, https://doi.org/10.1186/1471-2288-13-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gasparrini, A., B. Armstrong, and M. G. Kenward, 2010: Distributed lag non-linear models. Stat. Med., 29, 22242234, https://doi.org/10.1002/sim.3940.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geng, D., H. Sun, W. Jiang, S.-G. Wang, K.-Z. Shang, Y. Zhang, and X.-P. Ma, 2015: The relationship between respiratory disease death toll and meteorological factors in Nanjing City. J. Lanzhou Univ., 51, 9397.

    • Search Google Scholar
    • Export Citation
  • Jing, W. C., and Y. X. Ma, 2011: Analysis on the relationship between the respiratory diseases and meteorological factors in Lanzhou. 28th Annual Meeting of the Chinese Meteorological Society, Xiamen, China, China Meteorological Administration, 51–65.

  • Li, L., C. Guoa, P.-Y. Chena, C.-Q. Ou, and Y. Guo, 2015: Particulate matter modifies the magnitude and time course of the non-linear temperature-mortality association. Environ. Pollut., 196, 423430, https://doi.org/10.1016/j.envpol.2014.11.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X. X., and et al. , 2002: Analysis on relationship between respiratory tract disease and meteorological conditions in Lanzhou region during autumn and winter. Gansu Qixiang, 20, 3133.

    • Search Google Scholar
    • Export Citation
  • Lin, S., M. Luo, R. J. Walker, X. Liu, S.-A. Hwang, and R. Chinery, 2009: Extreme high temperatures and hospital admissions for respiratory and cardiovascular diseases. Epidemiology, 20, 738746, https://doi.org/10.1097/EDE.0b013e3181ad5522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, P., S.-G. Wang, K.-Z. Shang, T.-S. Li, and L. Yin, 2018: The impact of meteorological comfort conditions on respiratory disease. Chin. Environ. Sci., 38, 374382.

    • Search Google Scholar
    • Export Citation
  • Ma, Y., B. Xiao, C. Liu, Y. Zhao, and X. Zheng, 2016: Association between ambient air pollution and emergency room visits for respiratory diseases in spring dust storm season in Lanzhou, China. Int. J. Environ. Res. Public Health, 13, 613, https://doi.org/10.3390/ijerph13060613.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, Y., H. Zhang, Y. Zhao, J. Zhou, S. Yang, X. Zheng, and S. Wang, 2017: Short-term effects of air pollution on daily hospital admissions for cardiovascular diseases in western China. Environ. Sci. Pollut. Res., 24, 1407114079, https://doi.org/10.1007/s11356-017-8971-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mäkinen, T. M., and et al. , 2009: Cold temperature and low humidity are associated with increased occurrence of respiratory tract infections. Respir. Med., 103, 456462, https://doi.org/10.1016/j.rmed.2008.09.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, Y.-Z., Y.-A. Zheng, H. Tao, M.-M. Xu, G.-X. Li, F.-M. Dong, J.-H. Liu, and X.-C. Pan, 2012: Relationship between daily mean temperature and emergency department visits for respiratory diseases: A time-series analysis. J. Peking Univ., 44, 416420.

    • Search Google Scholar
    • Export Citation
  • Mukaka, M., 2012: Statistics corner: A guide to appropriate use of Correlation coefficient in medical research. Malawi Med. J., 24, 6971.

    • Search Google Scholar
    • Export Citation
  • National Health Commission of the People’s Republic of China, 2020: Press Conference on the Joint Prevention and Control Mechanism of the State Council (in Chinese). http://www.nhc.gov.cn/xwzb/webcontroller.do?titleSeq=11325&gecstype=1.

  • Wang, H. X., 2013: Analysis of the effects of meteorological factors on public health in Lanzhou. M.S. thesis, Lanzhou University, 133 pp.

  • Wang, M. Z., 2013: Research on the response of respiratory disease to meteorological elements and prediction in three representative cities of China. Ph.D. dissertation, Lanzhou University, 143 pp.

  • Wang, M. Z., and et al. , 2016: Interaction of temperature and relative humidity on emergency room visits for respiratory diseases. Chin. Environ. Sci., 36, 581588.

    • Search Google Scholar
    • Export Citation
  • Wang, Y. C., and Y. K. Lin, 2015: Temperature effects on outpatient visits of respiratory diseases, asthma, and chronic airway obstruction in Taiwan. Int. J. Biometeor., 59, 815825, https://doi.org/10.1007/s00484-014-0899-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y. J., 2018: Correlation between meteorological factors and daily respiratory emergency in Urumqi City. M.S. thesis, Xinjiang Medical University, 65 pp.

  • Yue, M., S. G. Wang, J. J. Xie, P. Ma, and K. Z. Shang, 2018: Study about the impact of environmental conditions on respiratory diseases and prediction in Zunyi City. Chin. Environ. Sci., 38, 43344347.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., Y. L. Jin, C. Guoquan, Y. Chao, L. Li, L. Chengcheng, X. Dongqun, 2014a: Impact of cold wave on respiratory diseases in Harbin in 2009–2011. J. Environ. Hyg., 4, 125127.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y., H. Kan, L. Peng, Y. Liu, and W. Wang, 2014b: Effects of daily mean temperature on respiratory hospital admissions in Shanghai: Time-series analysis. Chin. J. Prev. Med., 48, 795799.

    • Search Google Scholar
    • Export Citation
  • Zhou, X.-F., and et al. , 2015: Effect of meteorological factors on outpatient visits in patients with chronic obstructive pulmonary disease. Environ. Occup. Med., 32, 711716.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 224 224 60
Full Text Views 31 31 6
PDF Downloads 39 39 9

Interactive Effects between Temperature and Humidity on Outpatient Visits of Respiratory Diseases in Lanzhou, China

View More View Less
  • 1 a School of Management, Lanzhou University, Lanzhou, China
  • | 2 b Hospital Management Research Center, Lanzhou University, Lanzhou, China
  • | 3 c College of Economics and Management, Lanzhou Institute of Technology, Lanzhou, China
  • | 4 d Department of Gerontal Respiratory Medicine, the First Hospital of Lanzhou University, Lanzhou, China
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

This study assessing the lag and interactive effects between the daily average temperature and relative humidity on respiratory disease (RD) morbidity in Lanzhou, China, using data from daily outpatient visits for RD between 2014 and 2017 and meteorological and pollutant data during the same period analyzed with Poisson generalized linear model and distributed lag nonlinear models; the effects are further explored by classifying the RD by gender, age, and disease type. The results showed that the effect of temperature and relative humidity on outpatient visits of different populations and types of RD is nonlinear, with a significant lag effect. Relative to 11°C, every 1°C decrease in temperature is associated with 10.98% [95% confidence interval (CI): 9.87%–12.11%] increase for total RD. Chronic obstructive pulmonary disease is affected only by low temperature, upper respiratory tract infection is affected by both low and high temperatures, and asthma is influenced by high temperature. When the relative humidity is less than 32%, every 1% decrease in relative humidity is associated with 6.00% (95% CI: 3.00%–9.11%) increase for total RD; relative humidity has different effects on the outpatient risk of different types of RD. Temperature and relative humidity have an obvious interactive effect on different types and populations of RD: when both temperature and humidity are at low levels, the number of outpatient visits for RD is higher. When the relative humidity is ≤50% and the temperature is ≤11°C, total RD outpatient visits increase by 4.502% for every 1°C drop in temperature; that is, a dry environment with low temperature has the most significant impact on RD.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Guorong Chai, chaigr@lzu.edu.cn

Abstract

This study assessing the lag and interactive effects between the daily average temperature and relative humidity on respiratory disease (RD) morbidity in Lanzhou, China, using data from daily outpatient visits for RD between 2014 and 2017 and meteorological and pollutant data during the same period analyzed with Poisson generalized linear model and distributed lag nonlinear models; the effects are further explored by classifying the RD by gender, age, and disease type. The results showed that the effect of temperature and relative humidity on outpatient visits of different populations and types of RD is nonlinear, with a significant lag effect. Relative to 11°C, every 1°C decrease in temperature is associated with 10.98% [95% confidence interval (CI): 9.87%–12.11%] increase for total RD. Chronic obstructive pulmonary disease is affected only by low temperature, upper respiratory tract infection is affected by both low and high temperatures, and asthma is influenced by high temperature. When the relative humidity is less than 32%, every 1% decrease in relative humidity is associated with 6.00% (95% CI: 3.00%–9.11%) increase for total RD; relative humidity has different effects on the outpatient risk of different types of RD. Temperature and relative humidity have an obvious interactive effect on different types and populations of RD: when both temperature and humidity are at low levels, the number of outpatient visits for RD is higher. When the relative humidity is ≤50% and the temperature is ≤11°C, total RD outpatient visits increase by 4.502% for every 1°C drop in temperature; that is, a dry environment with low temperature has the most significant impact on RD.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Guorong Chai, chaigr@lzu.edu.cn
Save