Association between exposure to air pollutants and the risk of hospitalization for pulmonary embolism in Beijing, China: A case-crossover design using a distributed lag nonlinear model

Abstract Background: Air pollution is a severe and dangerous public health problem. However, the effect of ambient gaseous air pollution exposure on years of life lost (YLL) attributable to chronic obstructive pulmonary disease (COPD) mortality has not been quantitatively verified.Methods: We collected the data of 12,781 COPD deaths and ambient gaseous air pollutants, including sulfur dioxide (SO2), nitrogen dioxide (NO2), Carbon monoxide (CO), and ozone (O3), during the years 2013-2019 in the Shanghai Pudong New Area (PNA). Then we performed a time-stratified case-crossover study combined with a distributed lag nonlinear model (DLNM) to estimate the impact of those air pollutants on daily COPD deaths counts and YLL. The confounders including long-term trend and meteorological factors have been controlled for, and effects of age and educational attainment as effect modifiers have also been evaluated.Results: During the 2013-2019 time frame, increases of 10μg/m3 in SO2 and NO2 were associated with a 4.93% (95% CI: 1.47%, 8.50%) and 1.47% (95% CI: 0.14%, 2.82%) in daily COPD death counts at lag0-1day, respectively, a 2.52 (95% CI: 0.31, 4.72) YLL increase and 0.85 (95% CI: 0.01, 1.68) YLL increase at lag0-1day, respectively. A 1mg/m3 increase in CO was associated with a 9.46% (95% CI: 0.40%, 19.35%) at lag0 increase in daily COPD death counts. No significant impact from O3 on both daily COPD deaths counts and YLL (P>0.05). The impact of gaseous air pollutants on the daily COPD death count and YLL were significant in populations of older adults and the lower educated population, while an insignificant effect was observed in the younger population and higher educated population. The YLL due to COPD related to SO2 and CO for the lower educated population was significantly higher than those for the higher educated population.Conclusion: Reducing specific gaseous air pollutants will help to control COPD deaths and improve the population’s life expectancy.

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Association between short-term exposure to air pollution and ischemic stroke onset: a time-stratified case-crossover analysis using a distributed lag nonlinear model in Shenzhen, China

Environmental Health ◽

10.1186/s12940-019-0557-4 ◽

2020 ◽

Vol 19 (1) ◽

Cited By ~ 1

Author(s):

Zhinghui Wang ◽

Ji Peng ◽

Peiyi Liu ◽

Yanran Duan ◽

Suli Huang ◽

...

Keyword(s):

Public Health ◽

Air Pollution ◽

Ischemic Stroke ◽

Nonlinear Model ◽

The Elderly ◽

Cumulative Exposure ◽

Distributed Lag ◽

Case Crossover ◽

Gender And Age ◽

Distributed Lag Nonlinear Model

Abstract Background Stroke, especially ischemic stroke (IS), has been a severe public health problem around the world. However, the association between air pollution and ischemic stroke remains ambiguous. Methods A total of 63, 997 IS cases aged 18 years or above in Shenzhen were collected from 2008 to 2014. We used the time-stratified case-crossover design combining with distributed lag nonlinear model (DLNM) to estimate the association between air pollution and IS onset. Furthermore, this study explored the variability across gender and age groups. Results The cumulative exposure-response curves were J-shaped for SO2, NO2 and PM10, and V-shaped for O3, and crossed over the relative risk (RR) of one. The 99th, 50th (median) and 1st percentiles of concentration (μg/m3) respectively were 37.86, 10.06, 3.71 for SO2, 116.26, 41.29, 18.51 for NO2, 145.94, 48.29, 16.14 for PM10, and 111.57, 49.82, 16.00 for O3. Extreme high-SO2, high-NO2, high-PM10, high-O3, and low-O3 concentration increased the risk of IS, with the maximum RR values and 95% CIs: 1.50(1.22, 1.84) (99th vs median) at 0–12 lag days, 1.37(1.13, 1.67) (99th vs median) at 0–10 lag days, 1.26(1.04, 1.53) (99th vs median) at 0–12 lag days, 1.25(1.04, 1.49) (99th vs median) at 0–14 lag days, and 1.29(1.03, 1.61) (1st vs median) at 0–14 lag days, respectively. The statistically significant minimal RR value and 95% CI was 0.79(0.66,0.94) at 0–10 lag days for extreme low-PM10. The elderly aged over 65 years were susceptible to extreme pollution conditions. Difference from the vulnerability of males to extreme high-SO2, high-NO2 and low-O3, females were vulnerable to extreme high-PM10 and high-O3. Comparing with the elderly, adults aged 18–64 year were immune to extreme low-NO2 and low-PM10. However, no association between CO and IS onset was found. Conclusions SO2, NO2, PM10 and O3 exerted non-linear and delayed influence on IS, and such influence varied with gender and age. These findings may have significant public health implications for the prevention of IS.

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Heat and Cold-related Hospitalization Risk in North-East of Iran: A Time-stratified Case Crossover Design

10.21203/rs.3.rs-513713/v1 ◽

2021 ◽

Author(s):

Omid Aboubakri ◽

Hamid Reza Shoraka ◽

Joan Ballester ◽

Rahim Sharafkhani

Keyword(s):

Linear Model ◽

Hospital Admissions ◽

Extreme Values ◽

Crossover Design ◽

Preventive Program ◽

North East ◽

Distributed Lag ◽

Case Crossover ◽

Non Linear ◽

The Impact

Abstract Background: This study aimed to estimate hospitalization risk/number attributed to air extreme temperatures using time-stratified case crossover study and distributed lag non-linear model in a region of Iran during 2015-2019.Methods: A time-stratified case crossover design based on aggregated exposure data was used in this study. In order to have no overlap bias in the estimations, a fixed and disjointed window by using one-month strata was used in the design. A conditional Poisson regression model allowing for over dispersion (Quasi-Poisson) was applied into Distributed Lag Non-linear Model (DLNM). Different approaches were applied to estimate Optimum Temperature (OT). In the model, the interaction effect between temperature and humidity was assessed to see if the impact of heat or cold on Hospital Admissions (HAs) are different between different levels of humidity.Results: The cumulative effect of heat during 21 days was not significant and it was the cold that had significant cumulative adverse effect on all groups. While the number of HAs attributed to any ranges of heat, including medium, high, extreme and even all values were negligible, but a large number was attributable to cold values; about 10000 HAs were attributable to all values of cold temperature, of which about 9000 were attributed to medium range and about 1000 and less than 500 were attributed to high and extreme values of cold, respectively.Conclusion: This study highlights the need for interventions in cold seasons by policymakers. The results inform researchers as well as policy makers to address both men and women and elderly when any plan or preventive program is developed in the area under study.

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