scholarly journals Variants in SARS-CoV-2 Associated with Mild or Severe Outcome

2020 ◽  
Author(s):  
Jameson D. Voss ◽  
Martin Skarzynski ◽  
Erin M. McAuley ◽  
Ezekiel J. Maier ◽  
Thomas Gibbons ◽  
...  
Keyword(s):  
Public Health ◽  
Regression Models ◽  
Molecular Characteristics ◽  
Median Frequency ◽  
Global Public Health ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Logistic Regression Models ◽  
Health Measures ◽  
And Gender

AbstractIntroductionThe coronavirus disease 2019 (COVID-19) pandemic is a global public health emergency causing a disparate burden of death and disability around the world. The molecular characteristics of the virus that predict better or worse outcome are largely still being discovered.MethodsWe downloaded 155,958 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from GISAID and evaluated whether variants improved prediction of reported severity beyond age and region. We also evaluated specific variants to determine the magnitude of association with severity and the frequency of these variants among the genomes.ResultsLogistic regression models that included viral genomic variants outperformed other models (AUC=0.91 as compared with 0.68 for age and gender alone; p<0.001). Among individual variants, we found 17 single nucleotide variants in SARS-CoV-2 have more than two-fold greater odds of being associated with higher severity and 67 variants associated with ≤ 0.5 times the odds of severity. The median frequency of associated variants was 0.15% (interquartile range 0.09%-0.45%). Altogether 85% of genomes had at least one variant associated with patient outcome.ConclusionNumerous SARS-CoV-2 variants have two-fold or greater association with odds of mild or severe outcome and collectively, these variants are common. In addition to comprehensive mitigation efforts, public health measures should be prioritized to control the more severe manifestations of COVID-19 and the transmission chains linked to these severe cases.

scholarly journals Intra-host evolution provides for continuous emergence of SARS-CoV-2 variants

2021 ◽  
Author(s):  
Justin Landis ◽  
Razia Moorad ◽  
Linda J. Pluta ◽  
Carolina Caro-Vegas ◽  
Ryan P. McNamara ◽  
...  
Keyword(s):  
Public Health ◽  
Sequence Evolution ◽  
Clinical Progression ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Viral Genomes ◽  
Synonymous Mutations ◽  
Health Measures ◽  
Broad Public Health ◽  
Host Evolution

Variants of concern (VOC) in SARS-CoV-2 refer to viral genomes that differ significantly from the ancestor virus and that show the potential for higher transmissibility and/or worse clinical progression. VOC have the potential to disrupt ongoing public health measures and vaccine efforts. Yet, little is known regarding how frequently different viral variants emerge and under what circumstances. We report a longitudinal study to determine the degree of SARS-CoV-2 sequence evolution in 94 COVID-19 cases and to estimate the frequency at which highly diverse variants emerge. 2 cases accumulated 9 single-nucleotide variants (SNVs) over a two-week period and 1 case accumulated 23 SNVs over a three-week period, including three non-synonymous mutations in the Spike protein (D138H, E554D, D614G). We estimate that in 2% of COVID cases, viral variants with multiple mutations, including in the Spike glycoprotein, can become the dominant strains in as little as one month of persistent in patient virus replication. This suggests the continued local emergence of VOC independent of travel patterns. Surveillance by sequencing for (i) viremic COVID-19 patients, (ii) patients suspected of re-infection, and (iii) patients with diminished immune function may offer broad public health benefits.

scholarly journals COVID-19 Real-Time Tracker and Analytical Report

2020 ◽  
Author(s):  
Jiawei Long
Keyword(s):  
Public Health ◽  
Real Time ◽  
Regression Models ◽  
Data Retrieval ◽  
Global Public Health ◽  
Time Data ◽  
General Audience ◽  
The Real ◽  
Logistic Regression Models ◽  
Data Visualizations

While the COVID-19 outbreak was reported to first originate from Wuhan, China, it has been declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by WHO, and it has spread to over 180 countries by the time of this paper was being composed. As the disease spreads around the globe, it has evolved into a worldwide pandemic, endangering the state of global public health and becoming a serious threat to the global community. To combat and prevent the spread of the disease, all individuals should be well-informed of the rapidly changing state of COVID-19. In the endeavor of accomplishing this objective, a COVID-19 real-time analytical tracker has been built to provide the latest status of the disease and relevant analytical insights. The real-time tracker is designed to cater to the general audience without advanced statistical aptitude. It aims to communicate insights through various straightforward and concise data visualizations that are supported by sound statistical foundations and reliable data sources. This paper aims to discuss the major methodologies which are utilized to generate the insights displayed on the real-time tracker, which include real-time data retrieval, normalization techniques, ARIMA time-series forecasting, and logistic regression models. In addition to introducing the details and motivations of the utilized methodologies, the paper additionally features some key discoveries that have been derived in regard to COVID-19 using the methodologies.

Beverage intake and obesity in early childhood: evidence form primary health care clients in Northwest Argentina

2015 ◽  
Vol 7 (3) ◽  
pp. 244-252 ◽  
Author(s):  
E. Alderete ◽  
I. Bejarano ◽  
A. Rodríguez
Keyword(s):  
Public Health ◽  
Nutritional Status ◽  
Regression Models ◽  
Task Force ◽  
Normal Weight ◽  
The Body ◽  
Logistic Regression Models ◽  
Dietary Recall ◽  
Northwest Argentina ◽  
Artificial Fruit

Sugar sweetened beverages (SSB) are thought to play an important role in weight gain. We examined the relationship between the intake of caloric and noncaloric beverages (SSB and water) and the nutritional status of children. In 2014, we randomly selected 16 public health clinics in four cities of Northwest Argentina and conducted a survey among mothers of children 0–6 years of age. Children’s beverage intake was ascertained by 24-h dietary recall provided by the mothers. Children’s weight and height measures were obtained from clinic’s registries. We calculated the body mass index using the International Obesity Task Force standards. The analysis included 562 children 25 months to 6 years of age with normal or above normal nutritional status. Children’s beverage consumption was as follows, water 81.8%, carbonated soft drinks (CSD) 49.7%, coffee/tea/cocoa 44.0%, artificial fruit drinks 35.6%, flavored water 17.9%, natural fruit juice 14.5%. In multivariate logistic regression models the likelihood of being obese v. being overweight or having normal weight doubled with an intake of one to five glasses of CSD (OR=2.2) and increased by more than three-fold with an intake of more than five glasses (OR=3.5). Drinking more than five glasses of water decreased the likelihood of being obese by less than half (OR=0.3). The percentage of children drinking more than five glasses of other beverages was low (3.3–0.9%) and regression models did not yield significant results. The study contributed evidence for reducing children’s CSD intake and for promoting water consumption, together with the implementation of comprehensive regulatory public health policies.

scholarly journals 1384Factors related to neck and shoulder pain during the COVID-19 pandemic in Japan

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Sayo Tanaka ◽  
Sachiko Ohde ◽  
Kota Katanoda ◽  
Sarah Krull Abe ◽  
Takahiro Tabuchi
Keyword(s):  
Shoulder Pain ◽  
Regression Models ◽  
Cross Sectional Study ◽  
Lifestyle Changes ◽  
Internet Survey ◽  
Cross Sectional ◽  
Shoulder Stiffness ◽  
Logistic Regression Models ◽  
Health Measures ◽  
Neck And Shoulder Pain

Abstract Background Shoulder stiffness is a common health issue in Japan. During the COVID-19 pandemic, people were forced to stay home which possibly escalated the development of shoulder stiffness. We aimed to assess associations of lifestyle changes and newly developed neck and shoulder pain (NSP) during the pandemic. Methods A cross-sectional study was conducted, analyzing the data from the Japan COVID-19 and Society Internet Survey (JACSIS), an internet survey conducted from August to September 2020. Data included sociodemographic, lifestyle and health measures related to NSP during the pandemic. Multivariable logistic regression models were fitted to calculate adjusted odds ratio (AOR) with 95% confidence interval (CI) for newly developed NSP with adjustment for lifestyle, physiological and psychosocial changes. Results After exclusion of participants with existing pain, 25,482 participants (12,673 male, 12,809 female) aged 15-79 years were included in the analysis. Of these, 4.1% reported newly developed NSP. Weight gain and long sedentary time were strongly associated with the NSP (AOR (95%CI): 2.4 (2.0-2.8) and 1.81 (1.4-2.3)). Stratified analyses of 1,751 students and 4,211 teleworkers showed significant associations (AOR (95%CI): 2.1 (1.4-2.8) and 2.5 (1.9-3.2)) compared to non-workers and non-teleworkers, respectively. Conclusions Students and teleworkers became prone to NSP during the pandemic. However, factors which were associated to pain did not differ during the pandemic compared with the previous studies. Key messages Preventive measures for NSP should be taken in students and teleworkers.

scholarly journals Coffee Consumption, Genetic Polymorphisms, and the Risk of Type 2 Diabetes Mellitus: A Pooled Analysis of Four Prospective Cohort Studies

2020 ◽  
Vol 17 (15) ◽  
pp. 5379
Author(s):  
An Na Kim ◽  
Hyun Jeong Cho ◽  
Jiyoung Youn ◽  
Taiyue Jin ◽  
Moonil Kang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Regression Models ◽  
Coffee Consumption ◽  
Pooled Analysis ◽  
Random Effects Model ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Logistic Regression Models

The association between coffee consumption and the risk of type 2 diabetes may vary by genetic variants. Our study addresses the question of whether the incidence of type 2 diabetes is related to the consumption of coffee and whether this relationship is modified by polymorphisms related to type 2 diabetes. We performed a pooled analysis of four Korean prospective studies that included 71,527 participants; median follow-up periods ranged between 2 and 13 years. All participants had completed a validated food-frequency questionnaire (FFQ) at baseline. The odds ratios (ORs) and 95% confidence intervals (CIs) for type 2 diabetes were calculated using logistic regression models. The ORs were combined using a fixed or random effects model depending on the heterogeneity across the studies. Compared with 0 to <0.5 cups/day of coffee consumption, the OR for type 2 diabetes was 0.89 (95% CI: 0.80–0.98, p for trend = 0.01) for ≥3 cups/day of coffee consumption. We did not observe significant interactions by five single nucleotide polymorphisms (SNPs) related to type 2 diabetes (CDKAL1 rs7756992, CDKN2A/B rs10811661, KCNJ11 rs5215, KCNQ1 rs163184, and PEPD rs3786897) in the association between coffee and the risk of type 2 diabetes. We found that coffee consumption was inversely associated with the risk of type 2 diabetes.

scholarly journals How Incarceration Influences Native-Born Black Men’s Risk of Obesity

2018 ◽  
Vol 28 (2) ◽  
pp. 69
Author(s):  
Tony N. Brown ◽  
Julian Culver ◽  
Asia Bento
Keyword(s):  
Public Health ◽  
Black Men ◽  
Lower Risk ◽  
Regression Models ◽  
African American Men ◽  
Mass Incarceration ◽  
Obesity Risk ◽  
Logistic Regression Models ◽  
Demographic Group ◽  
Health Significance

<p> <strong>Objective: </strong>To build upon research that inves­tigates the health significance of familial and former incarceration with special emphasis on obesity risk among native-born Black (ie, African American) men.</p><p><strong>Methods: </strong>We analyzed data from the 2001-2003 National Survey of American Life (NSAL), focusing on native-born Black men (n=1140), the demographic group that bears the brunt of mass incarceration. The out­come of interest was obesity as measured by body mass index (BMI)&gt;30. Principal predic­tors were familial and former incarceration, and their statistical interaction.</p><p><strong>Results: </strong>In survey-adjusted binomial logistic regression models, familial incarceration ap­peared an unimportant predictor; whereas, former incarceration associated with a lower risk of obesity. However, former incarceration modifies the association between familial incarceration and obesity, such that native-born Black men experiencing both familial and former incarceration were significantly more likely to be obese.</p><p><strong>Conclusions: </strong>Public health researchers should treat former incarceration with greater care in studies including native-born Black men because time spent incarcerated has lingering physical health significance.</p><p><em>Ethn Dis. </em>2018;28(2):69-74; doi:10.18865/ ed.28.2.69.</p>

scholarly journals Differential Methylation in APOE Genes (Chr19; exon four; from 44,909,188 to 44,909,373) and Increased Apolipoprotein E Plasmatic Levels in Subjects with Mild Cognitive Impairment”

2019 ◽  
Author(s):  
Oscar Mancera Páez ◽  
Kelly Estrada Orozco ◽  
Maria Fernanda Mahecha ◽  
Francy Cruz Sanabria ◽  
Kely Bonilla-Vargas ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Apolipoprotein E ◽  
Social History ◽  
Regression Models ◽  
Neuropsychological Testing ◽  
Logistic Regression Models ◽  
And Gender ◽  
Epigenetic Patterns ◽  
Risk Association

Background: Biomarkers are essential for identification of individuals at high risk of mild cognitive impairment (MCI) for potential prevention of dementia. We investigated DNA methylation in the ApoE gene and plasmatic apolipoprotein E (ApoE) levels as MCI biomarkers in Colombian subjects with MCI and controls. Methods: 100 participants were included (71% women, average age, 70 yrs., range 43-91). MCI was diagnosed by neuropsychological testing, medical and social history, activities of daily living, cognitive symptoms and neuroimaging. Multivariate logistic regression models adjusted by age and gender were performed to examine the risk association of MCI with plasma ApoE and APOE methylation Results: MCI was diagnosed in 41 subjects (average age, 66.5±9.6 yrs.) and compared with 59 controls. Elevated plasma ApoE and APOE methylation of CpGs 165, 190, and 198 were risk factors for MCI (P&lt;0.05). Higher CpG-227 methylation correlated with lower risk for MCI (P=0.002). Only CpG-227 was significantly correlated with plasmatic ApoE levels (correlation coefficient=-0.665; P=0.008). Conclusion: Differential APOE methylation and increased plasma ApoE levels were correlated with MCI. These epigenetic patterns can be used as potential biomarkers to identify early stages of MCI.

scholarly journals Relationship between Kidney Stone Disease and Arterial Stiffness in a Taiwanese Population

2020 ◽  
Vol 9 (6) ◽  
pp. 1693
Author(s):  
Zih-Jie Sun ◽  
Hsuan-Jung Hsiao ◽  
Hsiang-Ju Cheng ◽  
Chieh-Ying Chou ◽  
Feng-Hwa Lu ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Kidney Stone ◽  
Regression Models ◽  
Pulse Wave ◽  
Stone Disease ◽  
Taiwanese Population ◽  
Kidney Stone Disease ◽  
Logistic Regression Models ◽  
And Gender ◽  
The Relationship

Previous studies examining the association between kidney stone disease (KSD) and arterial stiffness have been limited. Both age and gender have been found to have an impact on KSD, but their influence on the relationship between KSD and increased arterial stiffness is unclear. This study included 6694 subjects from October 2006 to August 2009. The diagnosis of kidney stone was based on the results of ultrasonographic examination. Increased arterial stiffness was defined as right-sided brachial-ankle pulse wave velocity (baPWV) ≥ 14 m/s. Associations between KSD and increased arterial stiffness were analyzed using multiple logistic regression models. KSD was positively related to increased arterial stiffness in both male and female groups (males: odds ratio [OR], 1.306; 95% confidence interval [CI], 1.035–1.649; females: OR, 1.585; 95% CI, 1.038–2.419) after adjusting for confounding factors. Subgroup analysis by age group (<50 and ≥50 years) showed a significant positive relationship only in the groups ≥ 50 years for both genders (males: OR, 1.546; 95% CI, 1.111–2.151; females: OR, 1.783; 95% CI, 1.042–3.054), but not in the groups < 50 years. In conclusion, KSD is associated with a higher risk of increased arterial stiffness in individuals aged ≥ 50 years, but not in those aged < 50 years for both genders.

scholarly journals Analyzing the Epidemiological Outbreak of COVID-19: Real-time, Visual Data Analysis, Short-term Forecasting, and Risk Factor Identification

2021 ◽  
Vol 2 (3) ◽  
pp. 246-261
Author(s):  
Jiawei Long
Keyword(s):  
Public Health ◽  
Language Processing ◽  
Moving Average ◽  
Global Public Health ◽  
Automatic Data ◽  
Logistic Regression Models ◽  
Data Ingestion ◽  
Short Term Forecasting ◽  
Processing Techniques ◽  
Data Visualizations

The COVID-19 outbreak was initially reported in Wuhan, China, and it has been declared as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 by WHO. It has now spread to over 180 countries, and it has gradually evolved into a world-wide pandemic, endangering the state of global public health and becoming a serious threat to the global community. To combat and prevent the spread of the disease, all individuals should be well-informed of the rapidly changing state of COVID-19. To accomplish this objective, I have built a website to analyze and deliver the latest state of the disease and relevant analytical insights. The website is designed to cater to the general audience, and it aims to communicate insights through various straightforward and concise data visualizations that are supported by sound statistical methods, accurate data modeling, state-of-the-art natural language processing techniques, and reliable data sources. This paper discusses the major methodologies which are utilized to generate the insights displayed on the website, which include an automatic data ingestion pipeline, normalization techniques, moving average computation, ARIMA time-series forecasting, and logistic regression models. In addition, the paper highlights key discoveries that have been derived in regard to COVID-19 using the methodologies. Doi: 10.28991/HIJ-2021-02-03-09 Full Text: PDF

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