Archives of clinical and biomedical research最新文献

Objectives: To balance the costs and effects comparing a strict lockdown versus a flexible social distancing strategy for societies affected by Coronavirus-19 Disease (COVID-19).

Design: Cost-effectiveness analysis.

Participants: We used societal data and COVID-19 mortality rates from the public domain.

Interventions: The intervention was a strict lockdown strategy that has been followed by Denmark. Reference strategy was flexible social distancing policy as was applied by Sweden. We derived mortality rates from COVID-19 national statistics, assumed the expected life years lost from each COVID-19 death to be 11 years and calculated lost life years until 31^st August 2020. Expected economic costs were derived from gross domestic productivity (GDP) statistics from each country's official statistics bureau and forecasted GDP. The incremental financial costs of the strict lockdown were calculated by comparing Sweden with Denmark using externally available market information. Calculations were projected per one million inhabitants. In sensitivity analyses we varied the total cost of the lockdown (range -50% to +100%).

Main outcome measure: Financial costs per life years saved.

Results: In Sweden, the number of people who died with COVID-19 was 577 per million inhabitants, resulting in an estimated 6,350 life years lost per million inhabitants. In Denmark, where a strict lockdown strategy was installed for months, the number of people dying with COVID-19 was on average 111 per million, resulting in an estimated 1,216 life years per million inhabitants lost. The incremental costs of strict lockdown to save one life year was US$ 137,285, and higher in most of the sensitivity analyses.

Conclusions: Comparisons of public health interventions for COVID-19 should take into account life years saved and not only lost lives. Strict lockdown costs more than US$ 130,000 per life year saved. As our all our assumptions were in favour of strict lockdown, a flexible social distancing policy in response to COVID19 is defendable.

Introduction: Younger generations are an important market for the tobacco products industry since most smokers try their first cigarette before the age of 18. Electronic cigarettes (e-cigarettes) are a common mode of smoking among teens, and the number of users is increasing exponentially.

Objective: This study aimed to estimate the current prevalence of e-cigarettes and vaping usage among adolescents between the ages of 15 and 19 in the city of Mecca, Saudi Arabia.

Methods: This study was conducted among 534 students at four high schools. They were asked to complete a 23-item questionnaire retrieved from the Global Youth Tobacco Survey. Descriptive statistics and regression analysis were conducted. The study was approved by the Ministry of Health Saudi Arabia Medical Research Center Institutional Review Board committee on October 10, 2018 (research number 18-506E).

Results: A total of 109 (20.6%) of the participants reported being smokers of e-cigarettes. Being a male (OR = 1.55; 95% CI: [1.01-2.37]), in the second year of high school (OR = 2.91; 95% CI: [1.61-5.24], ever experimenting with regular tobacco cigarettes, being a current shisha smoker, living with a smoker, and believing that e-cigarettes are less addictive than traditional cigarettes are all factors independently associated with e-cigarette use in this sample of adolescents.

Conclusion: Among adolescent smokers, even minimal experience with smoking is correlated with pro-smoking attitudes. E-cigarette use is common in adolescents and related to the use of other combustible tobacco products. Tobacco control efforts at all levels should eliminate factors fostering future tobacco use to minimize the burden of disease and disability in vulnerable populations.

ReadUntil enables Oxford Nanopore Technology's (ONT) sequencers to selectively sequence reads of target species in real-time. This enables efficient microbial enrichment for applications such as microbial abundance estimation and is particularly beneficial for metagenomic samples with a very high fraction of non-target reads (> 99% can be human reads). However, read-until requires a fast and accurate software filter that analyzes a short prefix of a read and determines if it belongs to a microbe of interest (target) or not. The baseline Read Until pipeline uses a deep neural network-based basecaller called Guppy and is slow and inaccurate for this task (~60% of bases sequenced are unclassified). We present RawMap, an efficient CPU-only microbial species-agnostic Read Until classifier for filtering non-target human reads in the squiggle space. RawMap uses a Support Vector Machine (SVM), which is trained to distinguish human from microbe using non-linear and non-stationary characteristics of ONT's squiggle output (continuous electrical signals). Compared to the baseline Read Until pipeline, RawMap is a 1327X faster classifier and significantly improves the sequencing time and cost, and compute time savings. We show that RawMap augmented pipelines reduce sequencing time and cost by ~24% and computing cost by 22%. Additionally, since RawMap is agnostic to microbial species, it can also classify microbial species it is not trained on. We also discuss how RawMap may be used as an alternative to the RT-PCR test for viral load quantification of SARS-CoV-2.