scholarly journals COVID-19 and Smoking: What Evidence Needs Our Attention?

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianghua Xie ◽  
Rui Zhong ◽  
Wei Wang ◽  
Ouying Chen ◽  
Yanhui Zou
Keyword(s):  
Smoking Status ◽  
Smoking Behavior ◽  
Control Measures ◽  
Current Smoking Status ◽  
Current Smoking ◽  
Health Measures ◽  
Medical Institutions ◽  
Prevention And Control Measures ◽  
And Control ◽  
The Relationship

The current COVID-19 pandemic has caused severe morbidity and mortality worldwide. Although relevant studies show that the smoking rate of COVID-19 patients is relatively low, the current smoking status of people with COVID-19 cannot be accurately measured for reasons. Thus, it is difficult to assess the relationship between smoking and COVID-19. Smoking can increase the risk of severe COVID-19 symptoms and aggravate the condition of patients with COVID-19. Nicotine upregulates the expression of ACE2, which can also increase susceptibility to COVID-19, aggravatiing the disease. Although nicotine has certain anti-inflammatory effects, there is no evidence that it is related to COVID-19 treatment; therefore, smoking cannot be considered a preventative measure. Furthermore, smokers gathering and sharing tobacco may promote the spread of viruses. Despite the COVID-19 epidemic, the findings suggested that COVID-19 has not encouraged smokers to quit. Additionally, there is evidence that isolation at home has contributed to increased smoking behavior and increased quantities. Therefore, it is recommended that governments increase smoking cessation messaging as part of public health measures to contain the COVID-19 pandemic. This review analyzes the existing research on smoking’s impact on COVID-19 so that governments and medical institutions can develop evidence-based smoking-related prevention and control measures for COVID-19.

scholarly journals The Effect of Smoking on COVID-19 Symptom Severity: Systematic Review and Meta-Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Askin Gülsen ◽  
Burcu Arpinar Yigitbas ◽  
Berat Uslu ◽  
Daniel Drömann ◽  
Oguz Kilinc
Keyword(s):  
Web Of Science ◽  
Smoking Status ◽  
Meta Analysis ◽  
Good Health ◽  
Common Type ◽  
Current Smoking Status ◽  
Current Smoking ◽  
The World ◽  
History Of ◽  
The Relationship

Background. Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SAR2-COV-2) and was first identified in Wuhan, China, in December of 2019, but quickly spread to the rest of the world, causing a pandemic. While some studies have found no link between smoking status and severe COVID-19, others demonstrated a significant one. The present study aimed to determine the relationship between smoking and clinical COVID-19 severity via a systematic meta-analysis approach. Methods. We searched the Google Scholar, PubMed, Scopus, Web of Science, and Embase databases to identify clinical studies suitable for inclusion in this meta-analysis. Studies reporting smoking status and comparing nonsevere and severe patients were included. Nonsevere cases were described as mild, common type, nonintensive care unit (ICU) treatment, survivors, and severe cases as critical, need for ICU, refractory, and nonsurvivors. Results. A total of 16 articles detailing 11322 COVID-19 patients were included. Our meta-analysis revealed a relationship between a history of smoking and severe COVID-19 cases (OR=2.17; 95% CI: 1.37–3.46; P<.001). Additionally, we found an association between the current smoking status and severe COVID-19 (OR=1.51; 95% CI: 1.12–2.05; P<.008). In 10.7% (978/9067) of nonsmokers, COVID-19 was severe, while in active smokers, severe COVID-19 occurred in 21.2% (65/305) of cases. Conclusion. Active smoking and a history of smoking are clearly associated with severe COVID-19. The SARS-COV-2 epidemic should serve as an impetus for patients and those at risk to maintain good health practices and discontinue smoking. The trial is registered with the International Prospective Register of Systematic Reviews (PROSPERO) CRD42020180173.

scholarly journals Clinical Guidelines for Dentistry in China During the Coronavirus Disease 2019 Pandemic

2021 ◽  
Vol 2 ◽  
Author(s):  
Wen Luo ◽  
Jing Wang ◽  
Maoxue Tang ◽  
Jiaming Peng ◽  
Wenmin Ma ◽  
...  
Keyword(s):  
Prevention And Control ◽  
Dental Treatment ◽  
Control Measures ◽  
Oral Health Education ◽  
Dental Procedures ◽  
Dental Hospital ◽  
Prevention And Control Measures ◽  
Treatment Procedures ◽  
And Control ◽  
The Relationship

Preventing the spread of Coronavirus Disease 2019 (COVID-19) has become the focus of epidemiologists as the highly infectious respiratory disease spreads primarily by close, person-to-person contact via droplets or the skin. Aerosol dissemination may occur in a closed, high-aerosol environment. The aerosols generated in dental procedures can pollute surrounding air and device surfaces. In this paper, we summarize prevention and control measures relating to dentistry. We focus on the relationship between COVID-19 and dental disease prevention and control in dental treatment procedures and imaging examinations, oral health education and perspectives, and guidance for the practice of dentistry during the COVID-19 pandemic to provide a consistent and broadly endorsed standard for dental hospital and clinics.

scholarly journals The effect of smoking on COVID-19 symptom severity: Systematic review and meta-analysis

2020 ◽  
Author(s):  
Askin Gulsen ◽  
Burcu Arpinar Yigitbas ◽  
Berat Uslu ◽  
Daniel Droemann ◽  
Oguz Kilinc
Keyword(s):  
Web Of Science ◽  
Smoking Status ◽  
Meta Analysis ◽  
Good Health ◽  
Common Type ◽  
Current Smoking Status ◽  
Current Smoking ◽  
The World ◽  
History Of ◽  
The Relationship

Background: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SAR2-COV-2), and was first identified in Wuhan, China in December of 2019, but quickly spread to the rest of the world, causing a pandemic. While some studies have found no link between smoking status and severe COVID-19, others demonstrated a significant one. The present study aimed to determine the relationship between smoking and clinical COVID-19 severity via a systematic meta-analysis approach. Methods: We searched the Google Scholar, PubMed, Scopus, Web of Science, and Embase databases to identify clinical studies suitable for inclusion in this meta-analysis. Studies reporting smoking status and comparing non-severe and severe patients were included. Non-severe cases were described as mild, common type, non-intensive care unit (ICU) treatment, survivors, and severe cases as critical, need for ICU, refractory, and non-survivors. Results: A total of 16 articles detailing 11322 COVID-19 patients were included. Our meta-analysis revealed a relationship between a history of smoking and severe COVID-19 cases (OR=2.17; 95% CI: 1.37-3.46; P <.001). Additionally, we found an association between the current smoking status and severe COVID-19 (OR=1.51; 95% CI: 1.12-2.05; P <.008). In 10.7% (978/9067) of non-smokers, COVID-19 was severe, while in active smokers, severe COVID-19 occurred in 21.2% (65/305) of cases. Conclusion: Active smoking and a history of smoking are clearly associated with severe COVID-19. The SARS-COV-2 epidemic should serve as an impetus for patients and those at risk to maintain good health practices and discontinue smoking.

scholarly journals Phase-adjusted estimation of the number of Coronavirus Disease 2019 cases in Wuhan, China

2020 ◽  
Author(s):  
Huwen Wang ◽  
Zezhou Wang ◽  
Yinqiao Dong ◽  
Ruijie Chang ◽  
Chen Xu ◽  
...  
Keyword(s):  
Prevention And Control ◽  
Control Measures ◽  
Disease Dynamics ◽  
Health Measures ◽  
Important Index ◽  
Prevention And Control Measures ◽  
Novel Coronavirus ◽  
High Level ◽  
And Control ◽  
Infectious Disease Dynamics

AbstractAn outbreak of clusters of viral pneumonia due to a novel coronavirus (2019-nCoV / SARS-CoV-2) happened in Wuhan, Hubei Province in China in December 2019. Since the outbreak, several groups reported estimated R0 of Coronavirus Disease 2019 (COVID-19) and generated valuable prediction for the early phase of this outbreak. After implementation of strict prevention and control measures in China, new estimation is needed. An infectious disease dynamics SEIR (Susceptible, Exposed, Infectious and Removed) model was applied to estimate the epidemic trend in Wuhan, China under two assumptions of Rt. In the first assumption, Rt was assumed to maintain over 1. The estimated number of infections would continue to increase throughout February without any indication of dropping with Rt = 1.9, 2.6 or 3.1. The number of infections would reach 11,044, 70,258 and 227,989, respectively, by 29 February 2020. In the second assumption, Rt was assumed to gradually decrease at different phases from high level of transmission (Rt = 3.1, 2.6 and 1.9) to below 1 (Rt = 0.9 or 0.5) owing to increasingly implemented public heath intervention. Several phases were divided by the dates when various levels of prevention and control measures were taken in effect in Wuhan. The estimated number of infections would reach the peak in late February, which is 58,077–84,520 or 55,869–81,393. Whether or not the peak of the number of infections would occur in February 2020 may be an important index for evaluating the sufficiency of the current measures taken in China. Regardless of the occurrence of the peak, the currently strict measures in Wuhan should be continuously implemented and necessary strict public health measures should be applied in other locations in China with high number of COVID-19 cases, in order to reduce Rt to an ideal level and control the infection.

Knowledge and Awareness of Dengue Fever

2018 ◽  
pp. 219-222
Author(s):  
Y. Arockia Suganthi ◽  
Chitra K. ◽  
J. Magelin Mary
Keyword(s):  
Dengue Fever ◽  
Prevention And Control ◽  
Control Measures ◽  
Infection Transmission ◽  
Standard Guideline ◽  
The World ◽  
Different Types ◽  
Prevention And Control Measures ◽  
Dengue Prevention ◽  
And Control

Dengue fever is a painful mosquito-borne infection caused by different types of virus in various localities of the world. There is no particular medicine or vaccine to treat person suffering from dengue fever. Dengue viruses are transmitted by the bite of female Aedes (Ae) mosquitoes. Dengue fever viruses are mainly transmitted by Aedes which can be active in tropical or subtropical climates. Aedes Aegypti is the key step to avoid infection transmission to save millions of people in all over the world. This paper provides a standard guideline in the planning of dengue prevention and control measures. At the same time gives the priorities including clinical management and hospitalized dengue patients have to address essentially.

An evaluation model of 2019-nCoV prevention effectiveness based on immigration data in China (Preprint)

2020 ◽  
Author(s):  
Qiangsheng Huang
Keyword(s):  
Prevention And Control ◽  
Time Window ◽  
Evaluation Model ◽  
Control Measures ◽  
Related Data ◽  
The Face ◽  
Prevention And Control Measures ◽  
And Control ◽  
Best Fit

BACKGROUND As of the end of February 2020, 2019-nCoV is currently well controlled in China. However, the virus is now spreading globally. OBJECTIVE This study aimed to evaluate the effectiveness of outbreak prevention and control measures in a region. METHODS A model is built for find the best fit for two sets of data (the number of daily new diagnosed, and the risk value of incoming immigration population). The parameters (offset and time window) in the model can be used as the evaluation of effectiveness of outbreak prevention and control. RESULTS Through study, it is found that the parameter offset and time window in the model can accurately reflect the prevention effectiveness. Some related data and public news confirm this result. And this method has advantages over the method using R0 in two aspects. CONCLUSIONS If the epidemic situation is well controlled, the virus is not terrible. Now the daily new diagnosed patients in most regions of China is quickly reduced to zero or close to zero. Chinese can do a good job in the face of huge epidemic pressure. Therefore, if other countries can do well in prevention and control, the epidemic in those places can also pass quickly.

Extremes of Temperature

2017 ◽  
Author(s):  
Ann M. Krake
Keyword(s):  
Control Measures ◽  
Temperature Extremes ◽  
Physiological Effects ◽  
Assessment And Evaluation ◽  
The Public ◽  
Adverse Health Effects ◽  
Extreme Cold ◽  
Prevention And Control Measures ◽  
And Control ◽  
Extremes Of Temperature

This chapter covers extremes of temperature conditions, physiological effects, and prevention. All deaths caused by exposure to hot and cold temperature extremes are preventable when proper measures are taken. Described in this chapter are the effects of extreme heat and extreme cold on the health of members of the public, particularly older people and young people, and workers employed in various workplace settings. The differences between heat stress and heat strain are also discussed, as are various regulations governing exposure to temperature extremes. The nature and magnitude of heat- and cold-related conditions and symptoms are described in detail. Final sections of the chapter address various assessment and evaluation tools as well as prevention and control measures. In addition, an appendix describes the hazards related to hyperbaric and hypobaric environments and adverse health effects.

scholarly journals A Legionnaires’ Disease Cluster in a Private Building in Italy

2021 ◽  
Vol 18 (13) ◽  
pp. 6922
Author(s):  
Maria Luisa Ricci ◽  
Maria Cristina Rota ◽  
Maria Grazia Caporali ◽  
Antonietta Girolamo ◽  
Maria Scaturro
Keyword(s):  
Severe Pneumonia ◽  
Control Measures ◽  
Health Concern ◽  
Human Consumption ◽  
Domestic Water ◽  
Quality Of Water ◽  
Legionnaires Disease ◽  
Prevention And Control Measures ◽  
Private Homes ◽  
And Control

Legionnaires’ disease (LD) is a severe pneumonia caused by bacteria belonging to the genus Legionella. This is a major public health concern and infections are steadily increasing worldwide. Several sources of infection have been identified, but they have not always been linked to human isolates by molecular match. The well-known Legionella contamination of private homes has rarely been associated with the acquisition of the disease, although some patients never left their homes during the incubation period. This study demonstrated by genomic matching between clinical and environmental Legionella isolates that the source of an LD cluster was a private building. Monoclonal antibodies and sequence-based typing were used to type the isolates, and the results clearly demonstrated the molecular relationship between the strains highlighting the risk of contracting LD at home. To contain this risk, the new European directive on the quality of water intended for human consumption has introduced for the first time Legionella as a microbiological parameter to be investigated in domestic water systems. This should lead to a greater attention to prevention and control measures for domestic Legionella contamination and, consequently, to a possible reduction in community acquired LD cases.

scholarly journals Etiological, epidemiological, and clinical features of acute diarrhea in China

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Li-Ping Wang ◽  
◽  
Shi-Xia Zhou ◽  
Xin Wang ◽  
Qing-Bin Lu ◽  
...  
Keyword(s):  
Clinical Features ◽  
Acute Diarrhea ◽  
Vibrio Parahaemolyticus ◽  
Bacterial Pathogens ◽  
Control Measures ◽  
Viral Pathogens ◽  
Rotavirus A ◽  
Positive Rate ◽  
Prevention And Control Measures ◽  
And Control

AbstractNational-based prospective surveillance of all-age patients with acute diarrhea was conducted in China between 2009‒2018. Here we report the etiological, epidemiological, and clinical features of the 152,792 eligible patients enrolled in this analysis. Rotavirus A and norovirus are the two leading viral pathogens detected in the patients, followed by adenovirus and astrovirus. Diarrheagenic Escherichia coli and nontyphoidal Salmonella are the two leading bacterial pathogens, followed by Shigella and Vibrio parahaemolyticus. Patients aged <5 years had higher overall positive rate of viral pathogens, while bacterial pathogens were more common in patients aged 18‒45 years. A joinpoint analysis revealed the age-specific positivity rate and how this varied for individual pathogens. Our findings fill crucial gaps of how the distributions of enteropathogens change across China in patients with diarrhea. This allows enhanced identification of the predominant diarrheal pathogen candidates for diagnosis in clinical practice and more targeted application of prevention and control measures.

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