The effect of consecutive ambient air pollution on the hospital admission from chronic obstructive pulmonary disease in the Chengdu region, China

Abstract

Hospitalisation risks for chronic obstructive pulmonary disease (COPD) have been attributed to ambient air pollution worldwide. However, a rise in COPD hospitalisations may indicate a considerable increase in fatality rate in public health. The current study focuses on the association between consecutive ambient air pollution (CAAP) and COPD hospitalisation to offer predictable early guidance towards estimates of COPD hospital admissions in the event of consecutive exposure to air pollution. Big data analytics were collected from 3-year time series recordings (from 2015 to 2017) of both air data and COPD hospitalisation data in the Chengdu region in China. Based on the combined effects of CAAP and unit increase in air pollutant concentrations, a quasi-Poisson regression model was established, which revealed the association between CAAP and estimated COPD admissions. The results show the dynamics and outbreaks in the variations in COPD admissions in response to CAAP. Cross-validation and mean squared error (MSE) are applied to validate the goodness of fit. In both short-term and long-term air pollution exposures, Z test outcomes show that the COPD hospitalisation risk is greater for men than for women; similarly, the occurrence of COPD hospital admissions in the group of elderly people (> 65 years old) is significantly larger than that in lower age groups. The time lag between the air quality and COPD hospitalisation is also investigated, and a peak of COPD hospitalisation risk is found to lag 2 days for air quality index (AQI) and PM₁₀, and 1 day for PM_2.5. The big data-based predictive paradigm would be a measure for the early detection of a public health event in post-COVID-19. The study findings can also provide guidance for COPD admissions in the event of consecutive exposure to air pollution in the Chengdu region.

Keywords: Air pollutant concentration; Big data analytics; Chronic obstructive pulmonary disease; Consecutive ambient air pollution; Hospital admission.

Fig. 1

The locations and numbers of air pollutant monitoring stations and public hospitals in Chengdu. The red star indicates 8 real-time monitoring stations in meteorological observatories, i.e., Sanwa Kiln, Shilidian, Junping Street, Liangjiaxiang, Shahepu, Lingyan Temple, Caotang Temple, and Jinquan Lianghe. The colour bar represents the number of public hospitals in CBD, suburbs, and townships in Chengdu (Zhang et al. 2020).

Fig. 2

Estimates of changes in COPD admissions in response to CAAP days. The percentage change (%) in daily hospitalisation was calculated by dividing the number of COPD admissions on certain CAAP day by the number of COPD admissions on the first CAAP day. For instance, the percentage change in the 10th CAAP day was a ratio of the number of admissions on the 10th CAAP day over that on the first CAAP day

Fig. 3

The estimates of COPD admissions (in comparison to the real COPD records). The estimated outcomes were based on the quasi-Poisson regression model with CAAP, and the real outcomes were given from the Chengdu 3-year databases for both CAAP and COPD hospitalisations

Fig. 4

Associations between PM_2.5/PM₁₀/AQI concentrations and COPD hospitalisations in short-term (2–9 CAAP days) and long-term (10–18 CAAP days) effects by age and gender using a single air pollutant model with a lag of 2 days (lag2)

Fig. 5

Averaged percentage change (%) in daily COPD admissions per 10-unit increase in PM_2.5, PM₁₀, and AQI concentrations on different lag days in Chengdu city, 2015–2017. For instance, lag1 represented that the COPD data of the candidate day paired with 1 day’s air pollutant concentration data lagged 1 day behind the day of the air pollutant concentration data; lag2 denoted 2 days after