scholarly journals The Emergence of a New Form of Headache in COVID-19 Pandemic Era

2020 ◽  
Author(s):  
Özge Uygun ◽  
Mustafa Ertas ◽  
Esme Ekizoğlu ◽  
Hayrunnisa Bolay ◽  
Aynur Özge ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Healthcare Workers ◽  
Binary Logistic Regression ◽  
Male Gender ◽  
Primary Headaches ◽  
Binary Logistic Regression Model ◽  
Web Based ◽  
Gastrointestinal Complaints ◽  
Different Characteristics ◽  
New Onset

Abstract BackgroundHeadache is the most common COVID-19-related neurological symptom. But, diagnostic clues of headache for COVID-19 infection are not well known. MethodsWe developed a detailed web-based questionnaire screening the characteristics and course of headaches besides clinical COVID-19 features. The participants were grouped according to being diagnosed with COVID-19 infection or not, and having previous or new-onset headaches. The COVID-19 related headache features and their associations with other clinical features were investigated. A binary logistic regression model was performed to differentiate the characteristics of headache related to COVID-19. Findings 3458 participants (2341 females;67.7%, 1495 healthcare workers;43.2%) with a mean age of 43.21±11.2 years experiencing headache during pandemic contributed to the survey. Among them, 262 participants had COVID-19 and 126(48.1%) were male. The rate of males in the group without COVID-19 was 31% (991 out of 3196 participants) showing significant gender difference between groups (p<0.000). COVID-19 related headaches were more closely associated with anosmia/ageusia and gastrointestinal complaints (RR=3.7 and RR=1.33, respectively), showed different characteristics like pulsating, pressing, and even stabbing quality. Logistic regression analyses showed that bilateral headache, duration over 72 hours, analgesic resistance and having male gender were significant variables to differentiate COVID-19 positive patients from those without COVID-19 (p<0.000 for all variables). A worsening of previous primary headaches due to the pandemic-related problems was not the rule in majority of patients.Interpretation Bilateral, long-lasting headaches, resistance to analgesics and having male gender were more frequent in people with COVID-19 in conjunction with the anosmia/ageusia and gastrointestinal complaints. These features may be helpful for COVID-19 diagnosis in the clinical evaluation of headache patients during the pandemic.Funding: This study was funded by Scientific Research Projects Coordination Unit of Istanbul University with a project number of GAP-36802.

scholarly journals Headache characteristics in COVID-19 pandemic-a survey study

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Özge Uygun ◽  
Mustafa Ertaş ◽  
Esme Ekizoğlu ◽  
Hayrunnisa Bolay ◽  
Aynur Özge ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Binary Logistic Regression ◽  
Male Gender ◽  
Survey Study ◽  
Primary Headaches ◽  
Binary Logistic Regression Model ◽  
Web Based ◽  
Long Duration ◽  
Gastrointestinal Complaints ◽  
Different Characteristics

Abstract Background Headache is the most common COVID-19-related neurological symptom. We aimed to reveal diagnostic clues of headache for COVID-19 infection and to investigate the course of primary headaches during the pandemic. Methods We developed a detailed web-based questionnaire screening the characteristics and course of headaches besides clinical COVID-19 features. The participants were grouped according to being diagnosed with COVID-19 infection or not, and having previous or new-onset headaches. The COVID-19 related headache features and their associations with other clinical features were investigated. A binary logistic regression model was performed to differentiate the characteristics of headache related to COVID-19. Findings A total of 3458 participants (2341 females;67.7%, 1495 healthcare workers;43.2%) with a mean age of 43.21 ± 11.2 years contributed to the survey. Among them, 262 participants had COVID-19 diagnosis and 126 (48.1%) were male. The rate of males in the group without COVID-19 was 31% (991 out of 3196 participants) showing significant gender difference between groups (p < 0.000). COVID-19 related headaches were more closely associated with anosmia/ageusia and gastrointestinal complaints (p < 0.000 and p < 0.000), and showed different characteristics like pulsating, pressing, and even stabbing quality. Logistic regression analyses showed that bilateral headache, duration over 72 h, analgesic resistance and having male gender were significant variables to differentiate COVID-19 positive patients from those without COVID-19 (p = 0.04 for long duration and p < 0.000 for others). A worsening of previous primary headaches due to the pandemic-related problems was not reported in the majority of patients. Interpretation Bilateral, long-lasting headaches, resistance to analgesics and having male gender were more frequent in people with COVID-19 in conjunction with anosmia/ageusia and gastrointestinal complaints. These features may be helpful for diagnosing the headache related to COVID-19 during the pandemic.

scholarly journals Headache Characteristics In COVID-19 Pandemic-A Survey Study

2020 ◽  
Author(s):  
Özge Uygun ◽  
Mustafa Ertas ◽  
Esme Ekizoğlu ◽  
Hayrunnisa Bolay ◽  
Aynur Özge ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Binary Logistic Regression ◽  
Male Gender ◽  
Survey Study ◽  
Primary Headaches ◽  
Binary Logistic Regression Model ◽  
Web Based ◽  
Long Duration ◽  
Gastrointestinal Complaints ◽  
Different Characteristics

Abstract BackgroundHeadache is the most common COVID-19-related neurological symptom. We aimed to reveal diagnostic clues of headache for COVID-19 infection and to investigate the course of primary headaches during the pandemic. MethodsWe developed a detailed web-based questionnaire screening the characteristics and course of headaches besides clinical COVID-19 features. The participants were grouped according to being diagnosed with COVID-19 infection or not, and having previous or new-onset headaches. The COVID-19 related headache features and their associations with other clinical features were investigated. A binary logistic regression model was performed to differentiate the characteristics of headache related to COVID-19. Findings A total of 3458 participants (2341 females;67.7%, 1495 healthcare workers;43.2%) with a mean age of 43.21±11.2 years contributed to the survey. Among them, 262 participants had COVID-19 diagnosis and 126 (48.1%) were male. The rate of males in the group without COVID-19 was 31% (991 out of 3196 participants) showing significant gender difference between groups (p<0.000). COVID-19 related headaches were more closely associated with anosmia/ageusia and gastrointestinal complaints (p<0.000 and p<0.000), and showed different characteristics like pulsating, pressing, and even stabbing quality. Logistic regression analyses showed that bilateral headache, duration over 72 hours, analgesic resistance and having male gender were significant variables to differentiate COVID-19 positive patients from those without COVID-19 (p=0.04 for long duration and p<0.000 for others). A worsening of previous primary headaches due to the pandemic-related problems was not reported in the majority of patients.Interpretation Bilateral, long-lasting headaches, resistance to analgesics and having male gender were more frequent in people with COVID-19 in conjunction with anosmia/ageusia and gastrointestinal complaints. These features may be helpful for diagnosing the headache related to COVID-19 during the pandemic.

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

scholarly journals A predictive model for early diagnosis of keratoconus

2020 ◽  
Author(s):  
GRACIA CASTRO-LUNA ◽  
ANTONIO PÉREZ-RUEDA
Keyword(s):  
Logistic Regression ◽  
Predictive Model ◽  
Early Stage ◽  
Corneal Thickness ◽  
Binary Logistic Regression ◽  
Cross Sectional Study ◽  
Bivariate Analysis ◽  
Diagnostic Model ◽  
Binary Logistic Regression Model ◽  
Cross Sectional

Abstract Background: The diagnosis of keratoconus in the early stages of the disease is necessary to initiate an early treatment of keratoconus. Furthermore, to avoid possible refractive surgery that could produce ectasias. This study aims to describe the topographic, pachymetric and aberrometry characteristics in patients with keratoconus, subclinical keratoconus and normal corneas. Additionally to propose a diagnostic model of subclinical keratoconus based in binary logistic regression models Methods: The design was a cross-sectional study. It included 205 eyes from 205 patients distributed in 82 normal corneas, 40 early-stage keratoconus and 83 established keratoconus. The rotary Scheimpflug camera (Pentacam® type) analyzed the topographic, pachymetric and aberrometry variables. It performed a descriptive and bivariate analysis of the recorded data. A diagnostic and predictive model of early-stage keratoconus was calculated with the statistically significant variables Results: Statistically significant differences were observed when comparing normal corneas with early-stage keratoconus/ in variables of the vertical asymmetry to 90º and the central corneal thickness. The binary logistic regression model included the minimal corneal thickness, the anterior coma to 90º and posterior coma to 90º. The model properly diagnosed 92% of cases with a sensitivity of 97.59%, specificity 98.78%, accuracy 98.18% and precision 98.78%Conclusions: The differential diagnosis between normal cases and subclinical keratoconus depends on the mínimum corneal thickness, the anterior coma to 90º and the posterior coma to 90º.

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 Using the Econometric Models for Identification of Risk Factors for Albanian Smes (Case Study: Smes of Gjirokastra Region)

2021 ◽  
Vol 18 ◽  
pp. 163-170
Author(s):  
Lorenc Koçiu ◽  
Kledian Kodra
Keyword(s):  
Logistic Regression ◽  
Statistical Models ◽  
Logistic Regression Model ◽  
Qualitative Data ◽  
Linear Models ◽  
Binary Logistic Regression ◽  
Random Variable ◽  
Econometric Models ◽  
Binary Logistic Regression Model

Using the econometric models, this paper addresses the ability of Albanian Small and Medium-sizedEnterprises (SMEs) to identify the risks they face. To write this paper, we studied SMEs operating in theGjirokastra region. First, qualitative data gathered through a questionnaire was used. Next, the 5-level Likertscale was used to measure it. Finally, the data was processed through statistical software SPSS version 21,using the binary logistic regression model, which reveals the probability of occurrence of an event when allindependent variables are included. Logistic regression is an integral part of a category of statistical models,which are called General Linear Models. Logistic regression is used to analyze problems in which one or moreindependent variables interfere, which influences the dichotomous dependent variable. In such cases, the latteris seen as the random variable and is dependent on them. To evaluate whether Albanian SMEs can identifyrisks, we analyzed the factors that SMEs perceive as directly affecting the risks they face. At the end of thepaper, we conclude that Albanian SMEs can identify risk

scholarly journals ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING

2018 ◽  
Vol 48 (3) ◽  
pp. 199-204 ◽  
Author(s):  
R. LI ◽  
J. ZHOU ◽  
L. WANG
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Parametric Bootstrap ◽  
Binary Logistic Regression Model ◽  
Bayesian Bootstrap ◽  
Non Parametric

In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the maximum likelihood method for parameter estimation in the binary logistic regression model.

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