The relation between air pollution and respiratory deaths in Tehran, Iran- using generalized additive models

Dehghan, Azizallah; Khanjani, Narges; Bahrampour, Abbas; Goudarzi, Gholamreza; Yunesian, Masud

doi:10.1186/s12890-018-0613-9

Cited by 80 publications

(43 citation statements)

References 27 publications

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“…Generalized additive models (GAM) were used to assess the associations of exposure to PM 2.5 and SWT with CVD mortality [30][31][32]. The regression models included the following components:…”

Section: Statistical Analysesmentioning

confidence: 99%

Short-Term Associations of Fine Particulate Matter and Synoptic Weather Types with Cardiovascular Mortality: An Ecological Time-Series Study in Shanghai, China

Qing

Montgomery

et al. 2020

IJERPH

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Background: Exposures to both ambient fine particulate matter (PM2.5) and extreme weather conditions have been associated with cardiovascular disease (CVD) deaths in numerous epidemiologic studies. However, evidence on the associations with CVD deaths for interaction effects between PM2.5 and weather conditions is still limited. This study aimed to investigate associations of exposures to PM2.5 and weather conditions with cardiovascular mortality, and further to investigate the synergistic or antagonistic effects of ambient air pollutants and synoptic weather types (SWTs). Methods: Information on daily CVD deaths, air pollution, and meteorological conditions between 1 January 2012 and 31 December 2014 was obtained in Shanghai, China. Generalized additive models were used to assess the associations of daily PM2.5 concentrations and meteorological factors with CVD deaths. A 15-day lag analysis was conducted using a polynomial distributed lag model to access the lag patterns for associations with PM2.5. Results: During the study period, the total number of CVD deaths in Shanghai was 59,486, with a daily mean of 54.3 deaths. The average daily PM2.5 concentration was 55.0 µg/m3. Each 10 µg/m3 increase in PM2.5 concentration was associated with a 1.26% (95% confidence interval (CI): 0.40%, 2.12%) increase in CVD mortality. No SWT was statistically significantly associated with CVD deaths. For the interaction between PM2.5 and SWT, statistically significant interactions were found between PM2.5 and cold weather, with risk for PM2.5 in cold dry SWT decreasing by 1.47% (95% CI: 0.54%, 2.39%), and in cold humid SWT the risk decreased by 1.45% (95% CI: 0.52%, 2.36%). In the lag effect analysis, statistically significant positive associations were found for PM2.5 in the 1–3 lag days, while no statistically significant effects were found for other lag day periods. Conclusions: Exposure to PM2.5 was associated with short-term increased risk of cardiovascular deaths with some lag effects, while the cold weather may have an antagonistic effect with PM2.5. However, the ecological study design limited the possibility to identify a causal relationship, so prospective studies with individual level data are warranted.

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Section: Statistical Analysesmentioning

confidence: 99%

Short-Term Associations of Fine Particulate Matter and Synoptic Weather Types with Cardiovascular Mortality: An Ecological Time-Series Study in Shanghai, China

Qing

Montgomery

et al. 2020

IJERPH

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“…Taghvaee et al [12] reported that diesel exhaust and industrial emissions have a greater impact on cancer risks (~70%) than other air pollution sources in Tehran. Tehran is not the city in Iran with the worst air pollution, however, it has received more attention [8,[12][13][14][15][16][17][18] because of its large population (estimated to be 9 million in 2019 [19]). Dehghan et al [18] investigated the impact of Tehran's air pollution on the mortality rate related to respiratory diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Tehran is not the city in Iran with the worst air pollution, however, it has received more attention [8,[12][13][14][15][16][17][18] because of its large population (estimated to be 9 million in 2019 [19]). Dehghan et al [18] investigated the impact of Tehran's air pollution on the mortality rate related to respiratory diseases. They reported that from 2005 to 2014, high concentrations of O 3 , NO 2 , PM 10 , and PM 2.5 were strongly associated with 46%, annual precipitation of 429 mm, and temperature range from −5 to 38 • C. Elevation in Tehran varies significantly from 1117 to 1712.6 m above sea level.…”

Section: Introductionmentioning

confidence: 99%

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

et al. 2019

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In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.

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“…Except for the known advantages of ecological studies as have been discussed extensively [45][46][47], the major advantage of our study is the application of CGAM in analysis. Generalized additive models (GAM) have been widely implemented in time-series studies to explore the relationship between environmental factors and health outcomes because they can control for seasonal trends and nonlinear modi cation effects of multiple variables, adding to the fact that they are more maneuverable than full-parameter alternatives, generalized linear models [48,49]. However, the traditional GAM only assumes the smoothness of the nonlinear associations and cannot restrict their shape.…”

Section: Discussionmentioning

confidence: 99%

Sex differences in Associations of Fine Particulate Matter with Non-accidental Deaths: An Ecological Time-Series Study

Xia

Fang

Montgomery

et al. 2020

Preprint

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Background: Sex differences in the impact of exposure to air pollution have been reported previously and epidemiological studies indicate that fine particulate matter (PM2.5) effects on non-accidental death are modified by sex, however, the results are not conclusive. This study aimed to investigate the sex difference in the effects of PM2.5 on non-accidental death, considering the nonlinear exposure-response relationships between PM2.5 and smoking and death.Methods: Information on daily non-accidental deaths, air pollution, meteorological data, and smoking prevalence between 1 January, 2012 and 31 December, 2014 was obtained in Shanghai, China. Constrained generalized additive models were used to assess the association of interaction between sex and daily PM2.5 concentrations with daily non-accidental deaths, adjusting for weather type and smoking rate. A two-week lag analysis was conducted as a sensitivity analysis.Results: During the study period, the total number of non-accidental deaths in Shanghai was 336,379, with a daily mean of 163 deaths and 144 deaths for men and women, respectively. The average daily concentration of PM2.5 in Shanghai was 55.0 µg/m3 during the same time period. Older people and men showed a higher risk for non-accidental death (risk ratio (RR) = 1.025 per year increase in age, 95% confidence interval (CI): 1.021 – 1.029, and RR = 0.892 for women, 95% CI: 0.802 – 0.993, respectively). Compared with men, the risk for non-accidental death in relation to increasing PM2.5 concentration was smaller in women (RR = 0.998, 95% CI: 0.996 – 1.000, per 10 µg increase in PM2.5 concentration. The difference is consistent during the two lag weeks and more obvious when adjusting for the interaction between PM2.5 concentration and smoking prevalence.Conclusion: The effects of PM2.5 on daily non-accidental death are different between men and women in Shanghai, China, and women tend to have a lower risk. The underlying mechanisms of the sex difference of PM2.5 effects on death need further investigation.

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The relation between air pollution and respiratory deaths in Tehran, Iran- using generalized additive models

Cited by 80 publications

References 27 publications

Short-Term Associations of Fine Particulate Matter and Synoptic Weather Types with Cardiovascular Mortality: An Ecological Time-Series Study in Shanghai, China

Short-Term Associations of Fine Particulate Matter and Synoptic Weather Types with Cardiovascular Mortality: An Ecological Time-Series Study in Shanghai, China

PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

Sex differences in Associations of Fine Particulate Matter with Non-accidental Deaths: An Ecological Time-Series Study

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