scholarly journals Estimating seroprevalence of SARS-CoV-2 antibodies using three self-reported symptoms: Development of a prediction model based on data from Ischgl, Austria

2020 ◽  
Author(s):  
Jens Lehmann ◽  
Johannes M Giesinger ◽  
Gerhard Rumpold ◽  
Wegene Borena ◽  
Ludwig Knabl ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Multivariate Analysis ◽  
Prediction Model ◽  
Regression Model ◽  
Web Application ◽  
Binary Logistic Regression ◽  
Population Level ◽  
Limb Pain ◽  
The Past ◽  
Model Based

We report the development of a regression model to predict prevalence of SARS-CoV-2 antibodies on a population level based on self-reported symptoms.We assessed participant-reported symptoms in the past twelve weeks, as well as presence of SARS-CoV-2 antibodies during a study conducted in April 2020 in Ischgl, Austria. We conducted multivariate binary logistic regression to predict seroprevalence in the sample. Participants (n=451) were on average 47.4 years old (SD 16.8) and 52.5% female. SARS-CoV-2 antibodies were found in n=197 (43.7%) participants. In the multivariate analysis, three significant predictors were included: Odds ratios (OR) for the most predictive categories were: cough (OR 3.34, CI 1.70 - 6.58), gustatory/olfactory alterations (OR 13.78, CI 5.90 - 32.17), and limb pain (OR 2.55, CI 1.20 - 6.50). The AUC was 0.773 (95% CI: 0.727-0.820).Our regression model may be used to estimate seroprevalence on a population-level and a web application is being developed to facilitate use of the model.

scholarly journals Estimating seroprevalence of SARS-CoV-2 antibodies using three self-reported symptoms: development of a prediction model based on data from Ischgl, Austria

2021 ◽  
Vol 149 ◽  
Author(s):  
Jens Lehmann ◽  
Johannes M. Giesinger ◽  
Gerhard Rumpold ◽  
Wegene Borena ◽  
Ludwig Knabl ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Regression Model ◽  
Web Application ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Binary Logistic Regression ◽  
Population Level ◽  
Limb Pain ◽  
The Past ◽  
Operating Characteristic Curve

Abstract We report the development of a regression model to predict the prevalence of severe acute respiratory syndrome coronavirus (SARS-CoV-2) antibodies on a population level based on self-reported symptoms. We assessed participant-reported symptoms in the past 12 weeks, as well as the presence of SARS-CoV-2 antibodies during a study conducted in April 2020 in Ischgl, Austria. We conducted multivariate binary logistic regression to predict seroprevalence in the sample. Participants (n = 451) were on average 47.4 years old (s.d. 16.8) and 52.5% female. SARS-CoV-2 antibodies were found in n = 197 (43.7%) participants. In the multivariate analysis, three significant predictors were included and the odds ratios (OR) for the most predictive categories were cough (OR 3.34, CI 1.70–6.58), gustatory/olfactory alterations (OR 13.78, CI 5.90–32.17) and limb pain (OR 2.55, CI 1.20–6.50). The area under the receiver operating characteristic curve was 0.773 (95% CI 0.727–0.820). Our regression model may be used to estimate the seroprevalence on a population level and a web application is being developed to facilitate the use of the model.

scholarly journals Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

An Optimized Logistic Regression Model Based on the Maximum Entropy Estimation Under the Hesitant Fuzzy Environment

2021 ◽  
pp. 1-25
Author(s):  
Chenyang Song ◽  
Liguo Wang ◽  
Zeshui Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Maximum Entropy ◽  
Logistic Regression Model ◽  
Uncertain Information ◽  
Entropy Estimation ◽  
Model Based ◽  
Fuzzy Environment ◽  
Maximum Entropy Estimation ◽  
Specific Implementation

The logistic regression model is one of the most widely used classification models. In some practical situations, few samples and massive uncertain information bring more challenges to the application of the traditional logistic regression. This paper takes advantages of the hesitant fuzzy set (HFS) in depicting uncertain information and develops the logistic regression model under hesitant fuzzy environment. Considering the complexity and uncertainty in the application of this logistic regression, the concept of hesitant fuzzy information flow (HFIF) and the correlation coefficient between HFSs are introduced to determine the main factors. In order to better manage situations with small samples, a new optimized method based on the maximum entropy estimation is also proposed to determine the parameters. Then the Levenberg–Marquardt Algorithm (LMA) under hesitant fuzzy environment is developed to solve the parameter estimation problem with fewer samples and uncertain information in the logistic regression model. A specific implementation process for the optimized logistic regression model based on the maximum entropy estimation under the hesitant fuzzy environment is also provided. Moreover, we apply the proposed model to the prediction problem of Emergency Extreme Air Pollution Event (EEAPE). A comparative analysis and a sensitivity analysis are further conducted to illustrate the advantages of the optimized logistic regression model under hesitant fuzzy environment.

The relationship between disability and suicide: prospective evidence from the Ten to Men cohort

2018 ◽  
Vol 41 (4) ◽  
pp. 707-713 ◽  
Author(s):  
Allison Milner ◽  
Anne-Marie Bollier ◽  
Eric Emerson ◽  
Anne Kavanagh
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
People With Disabilities ◽  
Cross Sectional ◽  
Longitudinal Cohort ◽  
The Past ◽  
The Relationship

Abstract Background People with disabilities often face a range of social and economic adversities. Evidence suggests that these disadvantages result in poorer mental health. Some research also indicates that people with disabilities are more likely experience thoughts about suicide than people without disability, although most of this research is based on small cross-sectional samples. Methods We explored the relationship between self-reported disability (measured at baseline) and likelihood of reporting thoughts of suicide (measured at follow up) using a large longitudinal cohort of Australian males. A logistic regression model was conducted with thoughts of suicide within the past 12 months (yes or no) as the outcome and disability as the exposure. The models adjusted for relevant confounders, including mental health using the SF-12 MCS, and excluded males who reported thoughts of suicide at baseline. Results After adjustment, there was a 1.48 (95% CI: 0.98–2.23, P = 0.063) increase in the odds of thoughts of suicide among men who also reported a disability. The size of association was similar to that of being unemployed. Conclusions Males reporting disability may also suffer from thoughts of suicide. We speculate that discrimination may be one explanation for the observed association. More research on this topic is needed.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PEROLEHAN KREDIT OLEH PENGUSAHA MIKRO

2018 ◽  
Vol 4 (3) ◽  
Author(s):  
Abdul Azis Safii ◽  
Tri Suwarno
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Large Scale ◽  
Logistic Regression Model ◽  
Banking Sector ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Access To Credit ◽  
The Poor ◽  
Micro Enterprises

Abstract: The number of micro-entrepreneurs and the dominant number of micro enterprises compared to medium and large-scale enterprises in Indonesia are not balanced by the provision of access to credit and venture capital for micro businesses. This resulted in a micro-sector sector identical to the poor being vulnerable to exploitation by moneylenders who exploit the difficulties of micro entrepreneurs accessing credit from the banking sector. This study examines the factors that determine the accessibility of credit by micro entrepreneur in Bojonegoro regency. A total sum of 270 micro entrepreneurs who have applied for banking loan were sampled from the study area. With an binary logistic regression model the research resulting that education, skill on entrepreneur, and monthly net profits generated by the microenterprise are significant in determining the accessibility of microcredit. Keywords: micro entrepreneur, microcredit, credit accessibility Abstrak: Perkembangan jumlah pengusaha mikro serta dominannya jumlah usaha mikro dibandingkan dengan usaha menengah dan usaha besar di Indonesia, tidak diimbingi dengan penyediaan akses kredit dan modal usaha bagi para pelaku usaha mikro. Hal tersebut mengakibatkan sektor usaha mikro yang identik dengan masyarakat miskin rentan dieksploitasi oleh rentenir yang memanfaatkan sulitnya para pengusaha mikro mengakses kredit dari sektor perbankan. Penelitian ini menggunakan data primer yang di ambil langsung dari pengusaha mikro dengan teknik kuesioner. Analisis data dengan metode binary logistic regression mendapatkan hasil variabel yang berpengaruh signifikan terhadap akses kredit para pengusaha mikro adalah variabel usia pengusaha, laba bersih usaha tiap bulan, dan jumlah karyawan yang di pekerjakan. Kata kunci : usaha mikro, microcredit, akses kredit

scholarly journals Hierarchy of Covid-19-Related Flu Symptoms According to Sex and Color or Race in Reports of Patients with Severe Acute Respiratory Syndrome in Brazil

2020 ◽  
pp. 67-78
Author(s):  
João Francisco Severo- Santos ◽  
Dimítria Dahmer Santos
Keyword(s):  
Logistic Regression ◽  
Black Women ◽  
Severe Acute Respiratory Syndrome ◽  
Prediction Model ◽  
Black Men ◽  
Muscle Pain ◽  
Socioeconomic Inequality ◽  
Racial Diversity ◽  
Binary Logistic Regression ◽  
White Men

The COVID-19 is a disease that presents a wide variety of combinations and intensities of symptoms, characteristic of a Flu Syndrome (FS), which can quickly evolve to a Severe Acute Respiratory Syndrome (SARS). The objectives of this study were to evaluate the hierarchy of symptoms of FS in patients with SARS caused by COVID-19 and to develop a prediction model for potential cases based on sex and race. Binary logistic regression modeling was used in 405,419 records selected from the database of the Ministry of Health of Brazil. It was found that men were more affected by the disease, with a 15.5% higher risk than women. They also died more, with a 13.8% and 15% higher risk for all causes and for COVID-19, respectively. The chances of more than one non-white patient dying from all causes ranged from 18.4% to 38.7% and for Covid-19 it ranged from 16.7% to 64.3% according to race. Fever, muscle pain and loss of smell or taste alternate in the first three positions of the symptom hierarchy, according to sex and race. Cough was only relevant for white men and sore throat for black men. Vomiting was only relevant for black women. The best prediction model developed encompassed seven symptoms adjusted for age, sex and race, but was able to explain only 63% of the cases of COVID-19. Possibly racial diversity, and the socioeconomic inequality associated with it, make the challenge of estimating probabilities of infection by COVID-19, based on symptoms, more complicated in Brazil than in other countries.

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