Effect of changes in season and temperature on cardiovascular mortality associated with nitrogen dioxide air pollution in Shenzhen, China

Duan, Yanran; Liao, Yi‐Chu; Li, Hongyan; Yan, Siyu; Zhao, Zhiguang; Yu, Shuyuan; Fu, Yingbin; Wang, Zhihui; Yin, Ping; Cheng, Jinquan; Jiang, Hongwei

doi:10.1016/j.scitotenv.2019.134051

Cited by 75 publications

(47 citation statements)

References 54 publications

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“…The generalized additive model (GAM) is a semi-parametric extension of the generalized linear model (GLM), which is useful to explore the nonlinear relationship between weather factors and health outcomes Liu et al, 2020;Peng et al, 2006;Talmoudi et al, 2017;Wu et al, 2018). Because the temperature effect could last for several days and the incubation period of COVID-19 ranges from 1 day to 14 days (reported by National Health Commission in China), it is a reasonable choice to use a moving-average approach to account for the cumulative lag effect of temperature (Duan et al, 2019;Li et al, 2018;Lu et al, 2015). Therefore, in this study, a GAM with a Gaussian distribution family (Hastie, 2017;Liu et al, 2020) was applied to examine the moving average lag effect (lag0-7, lag0-14, lag0-21) of mean temperature on daily confirmed cases of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Association between ambient temperature and COVID-19 infection in 122 cities from China

Xie

Zhu

2020

Science of The Total Environment

741

652

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Background: Coronavirus disease 2019 has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus. Methods: Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail. Results: The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of b3°C and became flat above 3°C. When mean temperature (lag0-14) was below 3°C, each 1°C rise was associated with a 4.861% (95% CI: 3.209-6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses. Conclusions: Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3°C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.

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Section: Discussionmentioning

confidence: 99%

Association between ambient temperature and COVID-19 infection in 122 cities from China

Xie

Zhu

2020

Science of The Total Environment

741

652

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“…In stratification analysis, all of these outpatients were classified into different sex (boys and girls) and age (0-3 years, 4-6 years and 7-13 years), and season [cold season (November to March), hot season (June to August) and transition season (April, May, September and October)] [23,30]. According to the AIC and previous studies [23,31], the df of time was 3, 2, 3 per year for the cold, hot and transition season, respectively. We also conducted a sensitivity analysis by changing the df from 5 to 9 per year for calendar time and from 3 to 8 for temperature and relative humidity.…”

Section: Methodsmentioning

confidence: 99%

Association of air pollution with outpatient visits for respiratory diseases of children in an ex-heavily polluted Northwestern city, China

Liu

et al. 2020

BMC Public Health

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Background: A great number of studies have confirmed that children are a particularly vulnerable population to air pollution. Methods: In the present study, 332,337 outpatient visits of 15 hospitals for respiratory diseases among children (0-13 years), as well as the simultaneous meteorological and air pollution data, were obtained from 2014 to 2016 in Lanzhou, China. The generalized additive model was used to examine the effects of air pollutants on children's respiratory outpatient visits, including the stratified analysis of age, gender and season. Results: We found that PM 2.5 , NO 2 and SO 2 were significantly associated with the increased total respiratory outpatient visits. The increments of total respiratory outpatient visits were the highest in lag 05 for NO 2 and SO 2 , a 10 μg/m 3 increase in NO 2 and SO 2 was associated with a 2.50% (95% CI: 1.54, 3.48%) and 3.50% (95% CI: 1.51, 5.53%) increase in total respiratory outpatient visits, respectively. Those associations remained stable in twopollutant models. Through stratification analysis, all air pollutants other than PM 10 were significantly positive associated with the outpatients of bronchitis and upper respiratory tract infection. Besides, both NO 2 and SO 2 were positively related to the pneumonia outpatient visits. PM 2.5 and SO 2 were significantly related to the outpatient visits of other respiratory diseases, while only NO 2 was positively associated with the asthma outpatients. We found these associations were stronger in girls than in boys, particularly in younger (0-3 years) children. Interestingly, season stratification analysis indicated that these associations were stronger in the cold season than in the transition or the hot season for PM 10 , PM 2.5 and SO 2. Conclusions: Our results indicate that the air pollution exposure may account for the increased risk of outpatient visits for respiratory diseases among children in Lanzhou, particularly for younger children and in the cold season.

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“…We rely on a generalised additive model (GAM) estimation procedure to estimate the daily meteorological factors in Africa and its associative consequence for confirmed cases of coronavirus. It is plausible to employ GAM to obtain asymptotically consistent estimates that could inform containment and risk analysis approach ( Duan et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal analysis of meteorological factors in abating the spread of COVID-19 in Africa

Adekunle

Tella

Oyesiku

et al. 2020

Heliyon

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In Asia, Europe and South America, the role of atmospheric condition in aiding or abating the growth curve of COVID-19 has been analysed. However, no study to date has examined such climatic extensions for the growth or otherwise of the novel coronavirus in Africa. Africa, with a mostly relatively warmer temperature differs from other regions of the world and in addition, has recorded far fewer cases compared to Asian, Europeans and the Americans (North and South). It then becomes imperative to examine the influence of meteorological indices in the growth or otherwise of coronavirus diseases in Africa to establish whether findings on the climatic conditions-COVID-19 growth are regionally specific. In this study, we examined the influence of meteorological factors for aiding or abating the spread of the aerosolised pathogen of COVID-19 in Africa. We rely on the generalised additive model (GAM) and found wind speed to positively relate to COVID-19 growth while mean temperature and relative humidity to inversely relates to COVID-19 growth curve in Africa. We accounted for potential cofounders in the core GAM model and discuss policy implications.

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Effect of changes in season and temperature on cardiovascular mortality associated with nitrogen dioxide air pollution in Shenzhen, China

Cited by 75 publications

References 54 publications

Association between ambient temperature and COVID-19 infection in 122 cities from China

Association between ambient temperature and COVID-19 infection in 122 cities from China

Association of air pollution with outpatient visits for respiratory diseases of children in an ex-heavily polluted Northwestern city, China

Spatio-temporal analysis of meteorological factors in abating the spread of COVID-19 in Africa

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