scholarly journals The dynamic antibody responses on COVID-19 patients with different severity: A retrospective research

2020 ◽  
Author(s):  
Wanrong Lu ◽  
Ping Wu ◽  
Liang He ◽  
Yifan Meng ◽  
Peng Wu ◽  
...  
Keyword(s):  
Regression Models ◽  
Severe Disease ◽  
Disease Onset ◽  
Antibody Responses ◽  
Clinical Severity ◽  
Serum Samples ◽  
Logistic Regression Models ◽  
The Difference ◽  
Igg Level

Abstract ObjectivesWe aimed to explore the association between dynamic antibody responses and the clinical severity of COVID-19. MethodsWe collected complete follow-up data of 777 pathogen-confirmed COVID-19 patients with corresponding IgG/IgM testing results. ResultsWe found the overall positive rates of IgG and IgM in severe patients were slightly higher than those in non-severe patients. In addition, higher IgG levels were detected in severe patients compared with non-severe patients (P=0.026). Through further analysis, our results showed that the statistical difference in the IgG only significant in serum samples taken ≤14 days from disease onset (P<0.001). In 74 patients who taken detection more than three times, by analyzing the antibody expression levels at different time points, we found that the difference between IgG was more obvious than that of IgM among severe/non-severe patients. In multivariate logistic regression models, after adjusting for cofactors, the higher anti-SARS-CoV-2 IgG level before 14 days from disease onset was independently associated with severe disease in COVID-19 (OR=1.310, 95%CI= 1.137-1.509).ConclusionWe observed differences in antibody responses among COVID-19 patients with different disease severity. A high IgG level in the first 14 days from disease onset might positively associate with severe disease.

Demographic factors associated with missed follow-up among solid tumor patients treated at a large multi-site academic institution

2020 ◽  
Vol 16 (32) ◽  
pp. 2635-2643
Author(s):  
Samantha L Freije ◽  
Jordan A Holmes ◽  
Saleh Rachidi ◽  
Susannah G Ellsworth ◽  
Richard C Zellars ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Odds Ratio ◽  
Regression Models ◽  
Tumor Patients ◽  
Logistic Regression Models ◽  
Factors Associated ◽  
Follow Up Care ◽  
Risk Patients ◽  
African American Race

Aim: To identify demographic predictors of patients who miss oncology follow-up, considering that missed follow-up has not been well studies in cancer patients. Methods: Patients with solid tumors diagnosed from 2007 to 2016 were analyzed (n = 16,080). Univariate and multivariable logistic regression models were constructed to examine predictors of missed follow-up. Results: Our study revealed that 21.2% of patients missed ≥1 follow-up appointment. African–American race (odds ratio [OR] 1.33; 95% CI: 1.17–1.51), Medicaid insurance (OR 1.59; 1.36–1.87), no insurance (OR 1.66; 1.32–2.10) and rural residence (OR 1.78; 1.49–2.13) were associated with missed follow-up. Conclusion: Many cancer patients miss follow-up, and inadequate follow-up may influence cancer outcomes. Further research is needed on how to address disparities in follow-up care in high-risk patients.

scholarly journals Low HbA1c levels and all-cause or cardiovascular mortality among people without diabetes: the US National Health and Nutrition Examination Survey 1999–2015

2020 ◽  
Author(s):  
Kosuke Inoue ◽  
Roch Nianogo ◽  
Donatello Telesca ◽  
Atsushi Goto ◽  
Vahe Khachadourian ◽  
...  
Keyword(s):  
Cardiovascular Mortality ◽  
Regression Models ◽  
Machine Learning Algorithms ◽  
Logistic Regression Models ◽  
Health And Nutrition ◽  
Increased Risk ◽  
The Us ◽  
All Cause Mortality ◽  
Hba1c Levels

Abstract Objective It is unclear whether relatively low glycated haemoglobin (HbA1c) levels are beneficial or harmful for the long-term health outcomes among people without diabetes. We aimed to investigate the association between low HbA1c levels and mortality among the US general population. Methods This study includes a nationally representative sample of 39 453 US adults from the National Health and Nutrition Examination Surveys 1999–2014, linked to mortality data through 2015. We employed the parametric g-formula with pooled logistic regression models and the ensemble machine learning algorithms to estimate the time-varying risk of all-cause and cardiovascular mortality by HbA1c categories (low, 4.0 to &lt;5.0%; mid-level, 5.0 to &lt;5.7%; prediabetes, 5.7 to &lt;6.5%; and diabetes, ≥6.5% or taking antidiabetic medication), adjusting for 72 potential confounders including demographic characteristics, lifestyle, biomarkers, comorbidities and medications. Results Over a median follow-up of 7.5 years, 5118 (13%) all-cause deaths, and 1116 (3%) cardiovascular deaths were observed. Logistic regression models and machine learning algorithms showed nearly identical predictive performance of death and risk estimates. Compared with mid-level HbA1c, low HbA1c was associated with a 30% (95% CI, 16 to 48) and a 12% (95% CI, 3 to 22) increased risk of all-cause mortality at 5 years and 10 years of follow-up, respectively. We found no evidence that low HbA1c levels were associated with cardiovascular mortality risk. The diabetes group, but not the prediabetes group, also showed an increased risk of all-cause mortality. Conclusions Using the US national database and adjusting for an extensive set of potential confounders with flexible modelling, we found that adults with low HbA1c were at increased risk of all-cause mortality. Further evaluation and careful monitoring of low HbA1c levels need to be considered.

scholarly journals Ethnic differences in the incidence of pterygium in a multi-ethnic Asian population: the Singapore Epidemiology of Eye Diseases Study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiao Ling Fang ◽  
Crystal Chun Yuen Chong ◽  
Sahil Thakur ◽  
Zhi Da Soh ◽  
Zhen Ling Teo ◽  
...  
Keyword(s):  
Ethnic Differences ◽  
Regression Models ◽  
Multivariable Analysis ◽  
Anterior Segment ◽  
Asian Population ◽  
Eye Diseases ◽  
Logistic Regression Models ◽  
Income Status ◽  
Baseline Visit

AbstractWe evaluated the 6-year incidence and risk factors of pterygium in a multi-ethnic Asian population. Participants who attended the baseline visit of the Singapore Epidemiology of Eye Diseases Study (year 2004–2011) and returned six years later, were included in this study. Pterygium was diagnosed based on anterior segment photographs. Incident pterygium was defined as presence of pterygium at 6-year follow-up in either eye, among individuals without pterygium at baseline. Multivariable logistic regression models were used to determine factors associated with incident pterygium, adjusting for baseline age, gender, ethnicity, body mass index, occupation type, educational level, income status, smoking, alcohol consumption, presence of hypertension, diabetes and hyperlipidemia. The overall age-adjusted 6-year incidence of pterygium was 1.2% (95% confidence interval [CI] 1.0–1.6%); with Chinese (1.9%; 95% CI 1.4%-2.5%) having the highest incidence rate followed by Malays (1.4%; 95% CI 0.9%-2.1%) and Indians (0.3%; 95% CI 0.3–0.7%). In multivariable analysis, Chinese (compared with Indians; odds ratio [OR] = 4.21; 95% CI 2.12–9.35) and Malays (OR 3.22; 95% CI 1.52–7.45), male (OR 2.13; 95% CI 1.26–3.63), outdoor occupation (OR 2.33; 95% CI 1.16–4.38), and smoking (OR 0.41; 95% CI 0.16–0.87) were significantly associated with incident pterygium. Findings from this multi-ethnic Asian population provide useful information in identifying at-risk individuals for pterygium.

scholarly journals The prospective relationship between social cohesion and depressive symptoms among older adults from Central and Eastern Europe

2018 ◽  
Vol 73 (2) ◽  
pp. 117-122 ◽  
Author(s):  
Carla Bertossi Urzua ◽  
Milagros A Ruiz ◽  
Andrzej Pajak ◽  
Magdalena Kozela ◽  
Ruzena Kubinova ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Eastern Europe ◽  
Social Cohesion ◽  
Regression Models ◽  
Central And Eastern Europe ◽  
The Czech Republic ◽  
Logistic Regression Models ◽  
Z Scores ◽  
Prospective Association

BackgroundSocial cohesion has a potential protective effect against depression, but evidence for Central and Eastern Europe is lacking. We investigated the prospective association between social cohesion and elevated depressive symptoms in the Czech Republic, Russia and Poland, and assessed whether alcohol drinking and smoking mediated this association.MethodsCohort data from 15 438 older urban participants from the Health, Alcohol and Psychosocial factors In Eastern Europe project were analysed. Baseline social cohesion was measured by five questions, and depressive symptoms were measured 3 years later by the 10-item Center for Epidemiological Depression (CES-D) Scale. Nested logistic regression models estimated ORs of elevated depressive symptoms (CES-D 10 score ≥4) by z-scores and tertiles of social cohesion.ResultsPer 1 SD decrease in social cohesion score, adjusted ORs of elevated depressive symptoms were 1.13 (95% CI 1.05 to 1.23) and 1.05 (95% CI 0.99 to 1.13) in men and women, respectively. Further adjustment for smoking and drinking did not attenuate these associations in either men (OR=1.13, 95% CI 1.05 to 1.22) or women (OR=1.05, 95% CI 0.99 to 1.13). Similarly, the fully adjusted ORs comparing the lowest versus highest social cohesion tertile were 1.33 (95% CI 1.10 to 1.62) in men and 1.18 (95% CI 1.01 to 1.39) in women.ConclusionsLower levels of social cohesion was associated with heightened depressive symptoms after a 3-year follow-up among older Czech, Russian and Polish adults. These effects appeared stronger in men, and alcohol and smoking played no appreciable role in this association.

scholarly journals Alcohol consumption and pregnancy in the Mexican national addiction survey

1997 ◽  
Vol 13 (2) ◽  
pp. 205-211 ◽  
Author(s):  
Guilherme Borges ◽  
Roberto Tapia-Conyer ◽  
Malaquías López-Cervantes ◽  
María Elena Medina-Mora ◽  
Blanca Pelcastre ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Spontaneous Abortion ◽  
Regression Models ◽  
Congenital Abnormalities ◽  
Adverse Outcomes ◽  
Multiple Logistic Regression ◽  
National Data ◽  
Logistic Regression Models ◽  
Important Relationship

In 1988, the General Directorate of Epidemiology and the Mexican Institute of Psychiatry conducted the first National Addiction Survey (ENA), providing regional and national data on alcohol, tobacco, and drug use. The ENA providing a subsample of women who have been pregnant at some time in their lives. There were 5,234 affirmative responses. Women were asked if they had suffered any of three adverse outcomes during their last pregnancy: spontaneous abortion, stillbirth, and congenital abnormalities. Prevalence of spontaneous abortion was 3.8%, stillbirth 1.2%, and congenital abnormalities 1.1 %. Multiple logistic-regression models were used to analyze the effect of alcohol consumption on these problems. Consumption during pregnancy was related only with the prevalence of congenital abnormalities, with prevalence odds of 3.4. Among habitual users during the last 12 months, oniy women in the highest use category showed an important relationship with the three problems mentioned. Follow-up studies on the Mexican population are recommended in order to obtain more conclusive findings.

scholarly journals Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

scholarly journals Can we predict antibody responses in SARS-CoV-2? A cohort analysis

2021 ◽  
Author(s):  
Mary Gaeddert ◽  
Philip Kitchen ◽  
Tobias Broger ◽  
Stefan Weber ◽  
Ralf Bartenschlager ◽  
...  
Keyword(s):  
Neutralizing Antibodies ◽  
Severe Disease ◽  
Cohort Analysis ◽  
Antibody Responses ◽  
High Antibody ◽  
Igg Antibodies ◽  
Rt Pcr ◽  
Median Increase ◽  
Antibody Titers

AbstractBackgroundAfter infection with severe acute respiratory syndrome coronavirus (SARS-CoV-2), Immunoglobulin G (IgG) antibodies and virus-specific neutralizing antibodies (nAbs) develop. This study describes antibody responses in a cohort of recovered COVID-19 patients to identify predictors.MethodsWe recruited patients with confirmed SARS-CoV-2 infection from Heidelberg, Germany. Blood samples were collected three weeks after COVID-19 symptoms ended. Participants with high antibody titers were invited for follow-up visits. IgG titers were measured by the Euroimmun Assay, and nAbs titers in a SARS-CoV-2 infection-based assay.Results281 participants were enrolled between April and August 2020 with IgG testing, 145 (51.6%) had nAbs, and 35 (12.5%) had follow-up. The median IgG optical density (OD) ratio was 3.1 (Interquartile range (IQR) 1.6-5.1), and 24.1% (35/145) had a nAb titer>1:80. Higher IgG titers were associated with increased age and more severe disease, and higher nAbs were associated with male gender and CT-value of 25-30 on RT-PCR at diagnosis. The median IgG OD ratio on follow-up was 3.7 (IQR 2.9-5.9), a median increase of 0.5 (IQR −0.3-1.7). Six participants with follow-up nAbs all had titers ≤ 1:80.ConclusionsWhile age and disease severity were correlated with IgG responses, predictive factors for nAbs in convalescent patients remain unclear.

scholarly journals Differences in Sickness Allowance Receipt between Swedish Speakers and Finnish Speakers in Finland

2017 ◽  
Vol 52 ◽  
pp. 43-58 ◽  
Author(s):  
Kaarina S. Reini ◽  
Jan Saarela
Keyword(s):  
Regression Models ◽  
Health Condition ◽  
Logistic Regression Models ◽  
Individual Level ◽  
Objective Health ◽  
Language Groups ◽  
Health Measure ◽  
All Cause Mortality ◽  
The Difference ◽  
The Individual

Previous research has documented lower disability retirement and mortality rates of Swedish speakers as compared with Finnish speakers in Finland. This paper is the first to compare the two language groups with regard to the receipt of sickness allowance, which is an objective health measure that reflects a less severe poor health condition. Register-based data covering the years 1988-2011 are used. We estimate logistic regression models with generalized estimating equations to account for repeated observations at the individual level. We find that Swedish-speaking men have approximately 30 percent lower odds of receiving sickness allowance than Finnish-speaking men, whereas the difference in women is about 15 percent. In correspondence with previous research on all-cause mortality at working ages, we find no language-group difference in sickness allowance receipt in the socially most successful subgroup of the population.

scholarly journals Asymmetric Fixed-effects Models for Panel Data

2019 ◽  
Vol 5 ◽  
pp. 237802311982644 ◽  
Author(s):  
Paul D. Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

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.

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