Associations of ambient PM2.5 and O3 with cardiovascular mortality: a time-series study in Hefei, China

Sun

et al. 2021

Environ Sci Pollut Res

Self Cite

BackgroundThe topic of in ammatory bowel disease (IBD) has attracted more and more attentions. Accumulating evidence suggests that exposure to air pollutants is associated with IBD, yet the results are inconsistent and study about daily exposure is few. This study evaluated the association between daily air pollution and IBD in Hefei, China. MethodsDaily IBD admission data were obtained from two hospitals in Hefei from January 1, 2019 to December 31, 2019. Daily concentrations of major air pollutants were provided by the Hefei Environmental Protection Bureau. Meteorological data were collected from China Meteorological Data Network.Distributed lag nonlinear model (DLNM) considering both the lag effects of exposure factors and nonlinear relationship of exposure-reaction was used to assess the effect of daily air pollutants exposure on IBD admission. ResultsDuring the study period, totally 886 cases of IBD were recruited, including 313 cases of ulcerative colitis (UC) and 573 cases of Crohn's disease (CD). The ndings showed PM 2.5 , O 3 and CO exposure signi cantly increased the risk of IBD. Mean concentrations of PM 2.5 , O 3 and CO in Hefei were 43.85ug/m³, 100.78ug/m³, and 0.76mg/m³, respectively. Each increase of 10mg/m³ in PM 2.5 / O 3 and 0.1mg/m³ in CO increased the risk of IBD. The strongest effects of these three pollutants on IBD were observed in lag2-lag3 (RR = 1.037, 95% CI: 1.005-1.070%), lag3 (RR = 1.020, 95% CI: 1.002-1.038%) and lag2 (RR = 1.036, 95% CI: 1.003-1.071%), respectively. In warm seasons, PM 2.5 , O 3 and CO had a stronger effect increased the risk of IBD, which were observed in lag2 (RR = 1.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Association between exposure to air pollutants and the risk of inflammatory bowel diseases visits

Sun

et al. 2021

Environ Sci Pollut Res

Self Cite

“…These exogenous pollutants are easily dissolved in water; thus, allowing them to easily corrode the mucous membrane lining. Inhalation of these pollutants can subsequently produce irritation and inflammation of the airways on the upper respiratory tract [ 100 ]. These pollutant matter may weaken the body’s immune system and increase the risk of TB infections through (i) reduction of tumour necrosis factor-α (TNF-α) and interferon-gamma production (IFN-g) in peripheral blood monocytes [ 101 , 102 ]; (ii) inhibition of serum neutralizing antibodies formation [ 103 ]; (iii) alterations in alveolar macrophage phagocytosis and blood mononuclear cells [ 25 ]; and (d) increased susceptibility to infection via upregulation of receptors involved in pathogen invasion [ 104 ].…”

Section: Discussionmentioning

confidence: 99%

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

et al. 2021

Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran’s I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.

“…Air pollution was identified as the fourth leading risk factor for death worldwide ( 3 ). Short-term exposure to PM 2.5 can increase the risk of death from chronic diseases ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Interactive Effects Between Temperature and PM<sub>2.5</sub> on Mortality: A Study of Varying Coefficient Distributed Lag Model — Guangzhou, Guangdong Province, China, 2013–2020

Chen

Hua

et al. 2022

China CDC Weekly

Introduction: There is a large body of epidemiological evidence showing significantly increased mortality risks from air pollution and temperature. However, findings on the modification of the association between air pollution and mortality by temperature are mixed.Methods: We used a varying coefficient distributed lag model to assess the complex interplay between air temperature and PM 2.5 on daily mortality in Guangzhou City from 2013 to 2020, with the aim of establishing the PM 2.5 -mortality association at different temperatures and exploring synergetic mortality risks from PM 2.5 and temperature on vulnerable populations.Results: We observed near-linear concentrationresponse associations between PM 2.5 and mortality across different temperature levels. Each 10 μg/m³ increase of PM 2.5 in low, medium, and high temperature strata was associated with increments of 0.73% [95% confidence interval (CI): 0.38%, 1.09%], 0.12% (95% CI: −0.27%, 0.52%), and 0.46% (95% CI: 0.11%, 0.81%) in non-accidental mortality, with a statistically significant difference between low and medium temperatures (P=0.02). There were significant modification effects of PM 2.5 by low temperature for cardiovascular mortality and among individuals 75 years or older.Conclusions: Low temperatures may exacerbate physiological responses to short-term PM 2.5 exposure in Guangzhou, China. METHODSThe study period was 2013-2020. We obtained daily mortality data in Guangzhou from Guangzhou Center for Disease Control and Prevention. Causes of death were classified according to International