Analysis of the influence of meteorological factors on the number of patients with pulmonary heart disease in Liangzhou district of Gansu Province

JIA Ru-ge; ZHANG Zhong-lin; FEI Shan-shan; ZHANG Jing

doi:10.16462/j.cnki.zhjbkz.2019.06.012

Volume 23 Issue 6

Jun. 2019

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JIA Ru-ge, ZHANG Zhong-lin, FEI Shan-shan, ZHANG Jing. Analysis of the influence of meteorological factors on the number of patients with pulmonary heart disease in Liangzhou district of Gansu Province[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(6): 679-684. doi: 10.16462/j.cnki.zhjbkz.2019.06.012

Citation:

JIA Ru-ge, ZHANG Zhong-lin, FEI Shan-shan, ZHANG Jing. Analysis of the influence of meteorological factors on the number of patients with pulmonary heart disease in Liangzhou district of Gansu Province[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(6): 679-684. doi: 10.16462/j.cnki.zhjbkz.2019.06.012

Citation:

JIA Ru-ge, ZHANG Zhong-lin, FEI Shan-shan, ZHANG Jing. Analysis of the influence of meteorological factors on the number of patients with pulmonary heart disease in Liangzhou district of Gansu Province[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2019, 23(6): 679-684. doi: 10.16462/j.cnki.zhjbkz.2019.06.012

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Analysis of the influence of meteorological factors on the number of patients with pulmonary heart disease in Liangzhou district of Gansu Province

doi: 10.16462/j.cnki.zhjbkz.2019.06.012

1.
Signal and Information Processing School of Electronic Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2.
Gansu Provincial Center for Disease Control and Prevention, Chronic Non-communicable Disease Prevention and Control Institute, Lanzhou 730000, China, Deputy Chief Physician

Funds:

National Natural Science Foundation of China 61662043

More Information

Corresponding author: JIA Ru-ge, E-mail: 956207885@qq.com; ZHANG Zhong-lin, E-mail: zhangzl@mail.lzjtu.cn
Received Date: 2019-01-04
Rev Recd Date: 2019-04-11
Publish Date: 2019-06-10

Abstract

Abstract

Objective To investigate the effect of meteorological factors on the number of outpatients with pulmonary heart disease in Liangzhou district of Gansu province. Methods We collected the daily meteorological data (temperature, air pressure, precipitation, sunshine hours, etc.) of Liangzhou district of Gansu province and the number of daily outpatients with the pulmonary heart disease from 2014 to 2016, and used the distribution lag model to analyze the impact relationship and hysteresis effect of the meteorological factors on the number of outpatients to pulmonary heart disease clinics. Results The total number of outpatients with pulmonary heart disease was 20 462 in Liangzhou district from 2014 to 2016, and the average number of outpatients per day was 18.67. The number of outpatients with pulmonary heart disease per day was positively correlated with temperature and sunshine hours, and negatively correlated with air pressure, relative humidity and precipitation. Among them, the average daily temperature had the most significant effect on the number of outpatients with pulmonary heart disease (r=0.133, P < 0.001). At the highest daily average temperature, lagging 16 days, the relative risk coefficient (RR value) was the highest (1.26, 95% CI: 1.13-1.40). For every 1℃ increase in temperature, the number of outpatients with pulmonary heart disease increased by 1.26 (95% CI: 1.13-1.40). There was no risk of morbidity at an extreme low temperature (-18℃), and the relative risk of the number of the pulmonary heart disease outpatients was the greatest at lag 0-15 at an extreme high temperatures (29℃). Conclusion Meteorological factor is an important factor affecting the number of outpatients with pulmonary heart disease in Liangzhou district. The risk of pulmonary heart disease will increase due to temperature changes, and the impact will occur immediately on the same day. The high temperature effect is short-lived and the relative risk is high, while the relative risk of low temperature to the number of outpatients is relatively low and the lag time is long.
- Meteorological factors,
- Pulmonary heart disease,
- Temperature,
- Number of outpatients,
- Distributed lag nonlinear model

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References(17)

References

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