scholarly journals The association between ambient air pollution and scarlet fever in Qingdao, China, 2014–2018: a quantitative analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fachun Jiang ◽  
Tao Wei ◽  
Xiaowen Hu ◽  
Yalin Han ◽  
Jing Jia ◽  
...  
Keyword(s):  
Air Pollution ◽  
Scarlet Fever ◽  
Mixed Model ◽  
Linear Time ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Lag Effects ◽  
Distributed Lag ◽  
Non Linear ◽  
Air Pollutions

Abstract Background We conducted a distributed lag non-linear time series analysis to quantify the association between air pollution and scarlet fever in Qingdao city during 2014–2018. Methods A distributed lag non-linear model (DLNM) combined with a generalized additive mixed model (GAMM) was applied to quantify the distributed lag effects of air pollutions on scarlet fever, with daily incidence of scarlet fever as the dependent variable and air pollutions as the independent variable adjusted for potential confounders. Results A total of 6316 cases of scarlet fever were notified, and there were 376 days occurring air pollution during the study period. Scarlet fever was significantly associated with air pollutions at a lag of 7 days with different relative risk (RR) of air pollution degrees [1.172, 95% confidence interval (CI): 1.038–1.323 in mild air pollution; 1.374, 95% CI 1.078–1.749 in moderate air pollution; 1.610, 95% CI 1.163–2.314 in severe air pollution; 1.887, 95% CI 1.163–3.061 in most severe air pollution]. Conclusions Our findings show that air pollution is positively associated with scarlet fever in Qingdao, and the risk of scarlet fever could be increased along with the degrees of air pollution. It contributes to developing strategies to prevent and reduce health impact from scarlet fever and other non-vaccine-preventable respiratory infectious diseases in air polluted areas.

scholarly journals The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

2020 ◽  
Author(s):  
Xiaowen Hu ◽  
Tao Wei ◽  
Yalin Han ◽  
Jing Jia ◽  
Bei Pan ◽  
...  
Keyword(s):  
Air Pollution ◽  
Scarlet Fever ◽  
Mixed Model ◽  
Linear Time ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Lag Effects ◽  
Distributed Lag ◽  
Non Linear ◽  
Air Pollutions

Abstract Background: We conducted a distributed lag non-linear time series analysis to quantify the association between air pollution and scarlet fever in Qingdao city during 2014-2018. Methods: A generalized additive Mixed Model (GAMM) combined with a distributed lag non-linear model (DLNM) was applied to quantify the distributed lag effects of air pollutions on scarlet fever, with daily incidence of scarlet fever as the dependent variable and air pollutions as the independent variable adjusted for potential confounders. Results: A total of 6,316 cases of scarlet fever were notified, and there were 376 days occurring air pollution during the study period. Scarlet fever was significantly associated with air pollutions at a lag of 7 days with different RRs of air pollution degrees (1.172, 95%CI: 1.038-1.323 in mild air pollution; 1.374, 95%CI: 1.078-1.749 in moderate air pollution; 1.610, 95%CI: 1.163-2.314 in severe air pollution; 1.887, 95%CI: 1.163-3.061 in most severe air pollution). Conclusions: Our findings show that air pollution is positively associated with scarlet fever in Qingdao, and the risk of scarlet fever could be increased along with the degrees of air pollution. It contributes to developing strategies to prevent and reduce health impact from scarlet fever and other non-vaccine-preventable respiratory infectious diseases in air polluted areas.

scholarly journals The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

2020 ◽  
Author(s):  
Xiaowen Hu ◽  
Tao Wei ◽  
Yalin Han ◽  
Jing Jia ◽  
Bei Pan ◽  
...  
Keyword(s):  
Air Pollution ◽  
Scarlet Fever ◽  
Mixed Model ◽  
Linear Time ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Lag Effects ◽  
Distributed Lag ◽  
Non Linear ◽  
Air Pollutions

Abstract Background: We conducted a distributed lag non-linear time series analysis to quantify the association between air pollution and scarlet fever in Qingdao city during 2014-2018. Methods: A generalized additive Mixed Model (GAMM) combined with a distributed lag non-linear model (DLNM) was applied to quantify the distributed lag effects of air pollutions on scarlet fever, with daily incidence of scarlet fever as the dependent variable and air pollutions as the independent variable adjusted for potential confounders. Results: A total of 6,316 cases of scarlet fever were notified, and there were 376 days occurring air pollution during the study period. Scarlet fever was significantly associated with air pollutions at a lag of 7 days with different RRs of air pollution degrees (1.172, 95%CI: 1.038-1.323 in mild air pollution; 1.374, 95%CI: 1.078-1.749 in moderate air pollution; 1.610, 95%CI: 1.163-2.314 in severe air pollution; 1.887, 95%CI: 1.163-3.061 in most severe air pollution). Conclusions: Our findings show that air pollution is positively associated with scarlet fever in Qingdao, and the risk of scarlet fever could be increased along with the degrees of air pollution. It contributes to developing strategies to prevent and reduce health impact from scarlet fever and other non-vaccine-preventable respiratory infectious diseases in air polluted areas.

scholarly journals The association between ambient air pollution and scarlet fever in Qingdao, China, 2014-2018: a quantitative analysis

2019 ◽  
Author(s):  
Xiaowen Hu ◽  
Tao Wei ◽  
Yalin Han ◽  
Jing Jia ◽  
Bei Pan ◽  
...  
Keyword(s):  
Air Pollution ◽  
Scarlet Fever ◽  
Mixed Model ◽  
Linear Time ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Lag Effects ◽  
Distributed Lag ◽  
Non Linear ◽  
Air Pollutions

Abstract Background: We conducted a distributed lag non-linear time series analysis to quantify the association between air pollution and scarlet fever in Qingdao city during 2014-2018. Methods: A generalized additive Mixed Model (GAMM) combined with a distributed lag non-linear model (DLNM) was applied to quantify the distributed lag effects of air pollutions on scarlet fever, with daily incidence of scarlet fever as the dependent variable and air pollutions as the independent variable adjusted for potential confounders. Results: A total of 6,316 cases of scarlet fever were notified, and there were 376 days occurring air pollution during the study period. Scarlet fever was significantly associated with air pollutions at lag 7 days with different RRs of air pollution degrees (1.172, 95%CI: 1.038-1.323 in mild air pollution; 1.374, 95%CI: 1.078-1.749 in moderate air pollution; 1.610, 95%CI: 1.163-2.314 in severe air pollution; 1.887, 95%CI: 1.163-3.061 in most severe air pollution). Conclusions: Our findings show that air pollution is positively associated with scarlet fever in Qingdao. Moreover, the risk of scarlet fever could be increased along with the degrees of air pollution. It contributes to developing strategies to prevent and reduce health impact from scarlet fever and other non-vaccine-preventable respiratory infectious diseases in air polluted areas.

scholarly journals Nexus Between Foreign Direct Investment Inflow, Renewable Energy Consumption, Ambient Air Pollution, and Human Mortality: A Public Health Perspective From Non-linear ARDL Approach

2022 ◽  
Vol 9 ◽  
Author(s):  
Muhammad Haroon Shah ◽  
Sultan Salem ◽  
Bilal Ahmed ◽  
Irfan Ullah ◽  
Alam Rehman ◽  
...  
Keyword(s):  
Air Pollution ◽  
Foreign Direct Investment ◽  
Renewable Energy ◽  
Direct Investment ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Series Data ◽  
Human Mortality ◽  
Ardl Approach ◽  
Non Linear

A huge foreign direct investment (FDI) inflow has been witnessed in China, though on the one hand, it brings a significant contribution to economic growth. On the other hand, it adversely affects the ambient air pollution that may affect human mortality in the country. Renewable energy (RE) usage meets the country's energy needs with no adverse effect on the environment. Therefore, this study is trying to empirically analyze the effect of FDI inflow on human morality and RE consumption in China. We used time-series data for 1998–2020 and applied a non-linear ARDL approach for the estimations. The empirical outcomes suggest that FDI inflow positively affects mortality and RE. There is also unidirectional causality running from RE and pollution to mortality. In addition, the relationship among the variable verifies the existence of a non-linear relationship. The government needs policy guidelines to further boost FDI inflow due to its positive aspects. However, to reduce the negative effect on the environment and human morality, the extensive usage of RE should be adopted. Indeed, proper legislation for foreign firms might be a good step toward quality environmental and longevity of human health in society.

scholarly journals The Relationship Between Air Pollution and All-Cause Mortality in Singapore

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 9 ◽  
Author(s):  
Ho ◽  
Zheng ◽  
Cheong ◽  
En ◽  
Pek ◽  
...  
Keyword(s):  
Air Pollution ◽  
Health Hazard ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
World Population ◽  
Distributed Lag ◽  
Increased Risk ◽  
All Cause Mortality ◽  
Unit Increase ◽  
The Relationship

Ambient air pollution is a risk factor for both acute and chronic diseases and poses serious health threats to the world population. We aim to study the relationship between air pollution and all-cause mortality in the context of a city-state exposed to the Southeast Asian haze problem. The primary exposure was ambient air pollution, as measured by the Pollutants Standards Index (PSI). The outcome of interest was all-cause mortality from 2010–2015. A time-stratified case-crossover design was performed. A conditional Poisson regression model, including environmental variables such as PSI, temperature, wind speed, and rainfall, was fitted to the daily count of deaths to estimate the incidence rate ratio (IRR) of mortality per unit increase in PSI, accounting for overdispersion and autocorrelation. To account for intermediate exposure effects (maximum lag of 10 days), a distributed lag non-linear model was used. There were 105,504 deaths during the study period. Increment in PSI was significantly associated with an increased risk of mortality. The adjusted IRR of mortality per the 10-unit increase in PSI was 1.01 (95%CI = 1.00–1.01). The lag effect was stronger when PSI was in the unhealthy range compared to the good and moderate ranges. At lag = 7 days, PSI appeared to have an adverse effect on mortality, although the effect was not significant. These findings provide evidence on the general health hazard of exposure to air pollution and can potentially guide public health policies in the region.

scholarly journals Associations between ambient air pollution and daily incidence of pediatric hand, foot and mouth disease in Ningbo, 2014–2016: a distributed lag nonlinear model

2020 ◽  
Vol 148 ◽  
Author(s):  
Shaohua Gu ◽  
Decheng Li ◽  
Beibei Lu ◽  
Ruixue Huang ◽  
Guozhang Xu
Keyword(s):  
Air Pollution ◽  
Nonlinear Models ◽  
Critical Role ◽  
Foot And Mouth Disease ◽  
Ambient Air ◽  
Mouth Disease ◽  
Ambient Air Pollution ◽  
Targeted Prevention ◽  
Distributed Lag ◽  
Foot And Mouth

Abstract Hand, foot and mouth disease (HFMD) has high prevalence around the world, with serious consequences for children. Due to the long survival period of HFMD virus in ambient air, air pollutants may play a critical role in HFMD epidemics. We collected data on daily cases of HFMD among children aged 0–14 years in Ningbo City between 2014 and 2016. Distributed lag nonlinear models were used to assess the effects of particulate matter (PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) on the daily incidence of HFMD among children, with analyses stratified by gender and age. Compared with moderate levels of air pollution, high SO2 levels had a relative risk (RR) of 2.32 (95% CI 1.42–3.79) and high NO2 levels had a RR of 2.01 (95% CI 1.22–3.31). The RR of O3 was 2.12 (95% CI 1.47–3.05) and that of PM2.5 was 0.77 (95% CI 0.64–0.92) at moderate levels of air pollution. Specifically, high levels of SO2 and NO2 had RRs of 2.39 (95% CI 1.44–3.96) and 2.02 (95% CI 1.21–3.39), respectively, among 0–4-year-old children, while high O3 had an RR of 2.31 (95% CI 1.09–4.89) among 5–14-year-old children. Our findings suggest significant associations of high SO2 and NO2 levels and moderate O3 levels in HFMD epidemics, and also indicate that air pollution causes lagged effects on HFMD epidemics. Our study provides practical and useful data for targeted prevention and control of HMFD based on environmental evidence.

THE DISTRIBUTED LAG BETWEEN AMBIENT AIR POLLUTION AND CARDIORESPIRATORY EMERGENCY DEPARTMENT VISITS

Epidemiology ◽  
2004 ◽  
Vol 15 (4) ◽  
pp. S61-S62 ◽  
Author(s):  
Mitch Klein ◽  
Paige E. Tolbert ◽  
Jennifer L. Peel ◽  
Kristina B. Metzger ◽  
W Dana Flanders
Keyword(s):  
Emergency Department ◽  
Air Pollution ◽  
Ambient Air ◽  
Emergency Department Visits ◽  
Ambient Air Pollution ◽  
Distributed Lag

scholarly journals 477Long-term exposure to low PM2.5 and NO2 and all-cause mortality in Australia: a difference-in-differences analyses

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Rongbin Xu ◽  
Shanshan Li ◽  
Michael Abramson ◽  
Yuming Guo
Keyword(s):  
Moving Average ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Long Term Effects ◽  
Distributed Lag ◽  
All Cause Mortality ◽  
Non Linear ◽  
Residual Confounding ◽  
Level 2

Abstract Background Although many cohort studies have documented the long-term effects of ambient air pollution on mortality, but they suffer from residual confounding, being unable to control unmeasured confounders, and are often not population representative. A recently developed variant of difference-in-difference (DID) approach is promising to address these limitations. Methods We collected annual all-cause death data from 2,193 statistical areas level-2 (SA2) in Australia during 2001-2015. Area-level annual mean concentrations of PM2.5 and NO2 were derived from widely used grid (0.01°×0.01° and 0.1°×0.1°, respectively) datasets. The variant of DID method was used to evaluate the causal relationship between annual PM2.5 and NO2 and all-cause mortality. We further developed this method by considering non-linear associations and lag impacts using distributed lag non-linear model. Results The impacts of low PM2.5 (1.94-12.00 µg/m3) and NO2 (0-7.41 µg/m3) on all-cause mortality were non-linear and lasted for 0-3 year and 0-6 year, respectively. The moving average PM2.5 (0-3 year) and NO2 (0-6 year) showed non-significant impacts below the thresholds (4.44 µg/m3 and 1.10 µg/m3) observed, while every 1 µg/m3 increase above the thresholds were associated with 2.4% (95%CI: 1.6-3.3%) and 9.4% (95%CI: 7.9-10.8%) increase in all-cause mortality, respectively. We estimated that 3.0% (95%CI: 2.0-3.9%) and 9.9% (8.3-11.3%) deaths were attributable to PM2.5 and NO2, respectively. Conclusions We further developed the causal DID model and documented the deadly impacts of long-term exposure to low PM2.5 and NO2 with thresholds and lag periods. Key messages Long-term exposure to low PM2.5 and NO2 are still deadly but have thresholds.

scholarly journals The Impact of Air Pollution on Hospitalization for Cardiovascular and Cerebrovascular Disease in Shenyang, China

2020 ◽  
Author(s):  
Qingquan REN ◽  
Shuyin LI ◽  
Chunling XIAO ◽  
Jiazhi ZHANG ◽  
Hong LIN ◽  
...  
Keyword(s):  
Air Pollution ◽  
Cerebrovascular Disease ◽  
Additive Model ◽  
Ambient Air ◽  
Cerebrovascular Diseases ◽  
Ambient Air Pollution ◽  
Lag Effects ◽  
Short Term Exposure ◽  
Increased Risk ◽  
The Impact

Background: The aim of this study was to investigate the overall impact of PM2.5, PM10, NO2, SO2, CO, and O3 on the admission of cardiovascular and cerebrovascular disease. Methods: We collected data on cardiovascular and cerebrovascular disease admissions from two hospitals in Shenyang Liaoning, China from Jan 2014 to Dec 2017, as well as daily measurements of six pollutants at 11 sites in Shenyang. The generalized additive model was used to assess the association between daily contaminants and admission to cardiovascular and cerebrovascular disease. Results: The single-contamination model showed a significant correlation between NO2, O3, PM10 and cardiovascular and cerebrovascular diseases at lag0 day. Air pollutants had lag effects on different gender groups. Excess relative risks (ERs) associated with a 10 μg/m3 increase were 1.522(1.057, 1.988) on lag02 for NO2, 0.547% (0.367%, 0.728%), 0.133% (0.061%, 0.205%) on lag3 for O3 and PM10. The dual pollutant model showed that the effects of NO2, O3, and PM10 after adjusting the influence of other pollutants were still statistically significant. Conclusion: Short-term exposure to ambient air pollution (NO2, O3, and PM10) may be associated with an increased risk of daily cardiovascular and cerebrovascular admission, which may provide reliable evidence for further understanding of the potential adverse effects of air pollution on cardiovascular and cerebrovascular diseases.

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