Risks of exposure to ambient air pollutants on the admission of respiratory and cardiovascular diseases in Kuala Lumpur

Association of air pollutants and hospital admissions for respiratory diseases in Lanzhou, China, 2014–2019

“…Therefore, we were not able to use spatial interpolation or pollution data from the nearest air quality monitoring station to reflect the exposure level of the hospital population (Dong, Wang, et al, 2021b ). After consulting the relevant literature (Chang et al, 2020 ; Phosri et al, 2019 ; Sofwan et al, 2021 ), daily 24 h concentrations were calculated from hourly data taken at the 3 monitoring stations for all five pollutants except O 3 , which were calculated as 8 h maximum values (from 10 a.m. to 6 p.m.).…”

“…Hence, the respiratory system is the main organ affected by air pollution. Numerous epidemiological studies have indicated that air pollutants such as PM, SO 2 , NO 2 , O 3 , and CO are responsible for increasing respiratory morbidity in different populations around the world, especially from the megacities (Çapraz et al, 2017 ; Chang et al, 2020 ; Luo et al, 2018 ; Phosri et al, 2019 ; Phung et al, 2016 ; Qiu et al, 2018 ; Sofwan et al, 2021 ; Vahedian et al, 2017 ). For example, a study conducted by Phung et al ( 2016 ) reported effect estimates (relative risks, RRs) of 1.007 (95% CI 1.002, 1.013), 1.020 (95% CI 1.010, 1.030), and 1.080 (95% CI 1.060, 1.011) for each 10 μg/m 3 increment of PM 10 , SO 2 , and NO 2 , respectively.…”

“…And in Shenyang, a time-series study found that per 10 μg/m 3 increase in PM 2.5 , PM 10 , NO 2 , SO 2 , O 3 , and 1 mg/m 3 in CO concentrations were associated with a RR of 1.003 (95% CI 0.998–1.007), 1.009 (95% CI 1.005–1.013), 1.041 (95% CI 1.035–1.048), 1.007 (95% CI 1.001–1.014), 0.989(95% CI 0.976, 0.993), and 1.021 (95% CI 1.016–1.025) for respiratory admissions, respectively (Chang et al, 2020 ). In Kuala Lumpur, an epidemiological study of the health consequences due to air pollution exposures concluded that respiratory morbidity in the local residents was significantly associated with PM 10 (RR: 1.055; 95% CI 1.013, 1.098) (per 10 μg/m 3 increment), SO 2 (RR: 0.993; 95% CI 0.931, 1.059) (per 10 μg/m 3 increment), NO 2 (RR: 1.100; 95% CI 1.014, 1.93) (per 10 μg/m 3 increment), and CO (RR: 1.116; 95% CI 1.016, 1.226) exposure (per 1 mg/m 3 increment) (Sofwan et al, 2021 ).…”

“…Overall, studies mentioned above mainly discussed the effects of different ambient air pollutants on risk of respiratory disease in megacities including Ho Chi Minh City (Phung et al, 2016 ), Istanbul (Çapraz et al, 2017 ), Bangkok (Phosri et al, 2019 ), Kuala Lumpur (Sofwan et al, 2021 ), as well as in the Chinese cities of Taiyuan (Luo et al, 2018 ), Shenyang (Chang et al, 2020 ), and Sichuan (Qiu et al, 2018 ), and less research was available in the arid inland cities of northwest China, where the components of pollution, climate condition, and socio-demographic status (i.e., disease pattern and socioeconomic characteristic) differ from those cites. In addition, there are still some insufficiency in current researches.…”

“…These models can only observe the average risk estimates of RD morbidity related to exposure to air pollution during a single-exposure time window. In recent years, a distributed lag nonlinear model (DLNM), which attracted much attention from scholars, has revealed an exposure–response relationship, as well as a lag-response relationship from the lag dimension, which indicated the duration of lag effects (Sofwan et al, 2021 ; Xie et al, 2019 ; Xu et al, 2021 ). To avoid the limitations of Poisson regression model, we adopted an advanced method, a distributed lag nonlinear model (DLNM) which can represent both nonlinear exposure–response dependencies and delay effects.…”