JOURNAL ARTICLE

[Influence of air pollution on the development of intensive care unit pneumonia patients: a summary of 2 454 cases from 2014 to 2016 in Nanchang City]

Wenhan Xia, Tingyu Li, Huiwei He, Chunli Yang
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 2018, 30 (8): 760-763
30220277

OBJECTIVE: To analyze the main characteristics of air pollution in Nanchang City, and discuss the correlation between air pollution exposure (especially PM2.5 ) and the development of pneumonia related intensive care unit (ICU) patients and their lag effect.

METHODS: 2 454 patients who lived in Nanchang City admitted to ICU of Jiangxi Provincial People's Hospital from January 1st, 2014 to December 31st, 2016 were enrolled. According to the diagnosis, the patients were divided into pneumonia group (156 cases) and non-pneumonia group (2 298 cases). The general clinical characteristics of patients and air pollution concentration in Nanchang in the year between 2014-2016 were collected. Multiple regression model was used to analyze the influence of meteorological factors on the condition of ICU patients associated with pneumonia. Using odds ratio (OR), the correlation intensity of air pollution exposure and pneumonia related ICU patients' development was reflected, and the confidence level of association intensity was reflected by the 95% confidence interval (95%CI). The distribution lag nonlinear model (DLNM) was established to evaluate the effect of air mass parameters on the time lag effect.

RESULTS: The results of air pollution analysis in Nanchang City in the year between 2014-2016 showed that the annual average concentration of carbon monoxide (CO), sulfur dioxide (SO2 ) and nitrogen dioxide (NO2 ) was low and maintained at the same level in the year between 2014-2016. The annual average concentration of CO and NO2 increased in the year between 2014-2016, but the average annual concentration of SO2 decreased rapidly in the year between 2014-2016, and the average annual concentration of PM2.5 tended to slow down after the year between 2014-2016. The annual average concentration of PM10 decreased in the year between 2014-2016, but continued to rise in the year between 2014-2016. The annual mean concentration of O3 showed a trend of continuous increase from the year between 2014-2016. The age of pneumonia group was generally higher than that of non-pneumonia group, most of them were male, and had higher expected mortality and acute physiology and chronic health evaluation II (APACHE II) score. The average air temperature in the pneumonia group was lower than that in the non-pneumonia group on the day of entering the group, and the air pollutants such as PM2.5 and PM10 were significantly higher than those in the non-pneumonia group. The analysis of multiple regression models showed that PM2.5 and air temperature were significantly related to patients with ICU pneumonia on the day of entry (PM2.5 : OR = 1.02, 95%CI = 1.00-1.03, P < 0.05; air temperature: OR = 0.96, 95%CI = 0.92-0.98, P < 0.05), and the effect of PM2.5 on patients with ICU pneumonia could last for at least 5 days (OR = 1.04, 95%CI = 1.00-1.09, P < 0.05), and the effect disappeared until the 7th day. According to the analysis of the influence of different concentrations of PM2.5 on ICU pneumonia patients, when the PM2.5 concentration reached 200 μg/m3 , its effect on ICU pneumonia patients would last for 5 days (OR = 1.45, 95%CI = 1.07-1.76, P < 0.01).

CONCLUSIONS: PM2.5 and air temperature are significantly related to the condition of ICU patients with pneumonia, and the influence of high concentration of PM2.5 on ICU patients with pneumonia has a lag effect.

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