Time series analysis of ambient air pollution effects on daily mortality

Abstract

Although the growths of ambient pollutants have been attracting public concern, the characteristic of the associations between air pollutants and mortality remains elusive. Time series analysis with a generalized additive model was performed to estimate the associations between ambient air pollutants and mortality outcomes in Shenzhen City for the period of 2012-2014. The results showed that nitrogen dioxide (NO)-induced excess risks (ER) of total non-accidental mortality and cardiovascular mortality were significantly increased (6.05% (95% CI 3.38%, 8.78%); 6.88% (95% CI 2.98%, 10.93%), respectively) in interquartile range (IQR) increase analysis. Also, these associations were strengthened after adjusting for other pollutants. Moreover, similar associations were estimated for sulfur dioxide (SO), particulate matter with an aerodynamic diameter of <10 μm (PM), and total non-accidental mortality. There were significant higher ERs of associations between PM and mortality for men than women; while there were significant higher ERs of associations between PM/NO and mortality for elders (65 or elder) than youngers (64 or younger). Season analyses showed that associations between NO and total non-accidental mortality were more pronounced in hot seasons than in warm seasons. Taken together, NO was positively associated with total non-accidental mortality and cardiovascular mortality in Shenzhen even when the concentrations were below the ambient air quality standard. Policy measures should aim at reducing residents' exposure to anthropogenic NO emissions.

Long-term effect of exposure to ambient air pollution on the risk of active tuberculosis

“…Nonlinear exposure-lag-response relationships have been demonstrated between outdoor air pollution and multiple health outcomes (Chen et al, 2017;Guo et al, 2017). The distributed-lag nonlinear model (DLNM) is an advanced time-series analysis method proposed by Gasparrini (Gasparrini, 2011;Gasparrini et al, 2010), that has been widely used to explore the relationships between meteorological factors and health outcomes (Buteau et al, 2018;Gasparrini, 2014;Neophytou et al, 2018), but has seldom been applied to evaluate the relationship between outdoor exposure to air pollution and active TB risk.…”

Time series analysis of ambient air pollution effects on dynamic stroke mortality

“…A study in Taipei found significant correlations between short‐term exposure to PM2.5 and increased hospital admissions for ischemic stroke on warm (>23°C) and cool days (<23°C) . Guo et al demonstrated that NO 2 , SO 2 , and PM10 had adverse effects on the total non‐accidental mortality and cardiovascular mortality in Shenzhen, especially for men and the elderly during hot seasons . Montresor‐López et al observed a strong correlation between ozone and the risk of hospitalization for stroke …”

“…A number of studies have also demonstrated that short‐term exposure to PM2.5 and PM10 (particulate matter with a grain size of 2.5 and 10 μm or less, respectively) increased the risk of ischemic and hemorrhagic stroke . Similarly, NO 2 and PM10 are significantly associated with cardiovascular mortality, with increasing concentrations of NO 2 noted to have a greater impact on cardiovascular mortality in men and the elderly . In addition, the rate of mortality from a stroke varies significantly across seasons; many studies have, therefore, investigated the seasonality of correlations between air pollution and cardiovascular mortality.…”

“…Most studies that have assessed the correlation between air pollution and stroke have applied a time‐series analysis approach using generalized additive Poisson regression models . Meanwhile, Spearman's rank correlation analysis has also been used to determine correlations among different air pollutants and meteorological factors .…”

“…Most studies that have assessed the correlation between air pollution and stroke have applied a time‐series analysis approach using generalized additive Poisson regression models . Meanwhile, Spearman's rank correlation analysis has also been used to determine correlations among different air pollutants and meteorological factors . While these methods are appropriate, other risk factors including age, gender, and time‐of‐the‐week have rarely been considered—and never in combination—despite likely having significance for the relationships between air pollution and dynamic stroke mortality.…”