scholarly journals Association of dyslipidemia with chronic non-malignant pain in elderly patients with femoral neck fractures treated by primary total hip arthroplasty: a retrospective study

2021 ◽  
Vol 49 (9) ◽  
pp. 030006052110452
Author(s):  
Chen Wang ◽  
Bo Xu ◽  
Pengfei Wang ◽  
Weiguang Yu ◽  
Xianshang Zeng ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Retrospective Study ◽  
Total Hip Arthroplasty ◽  
Femoral Neck ◽  
Elderly Patients ◽  
Logistic Regression Model ◽  
Density Lipoprotein ◽  
Binary Logistic Regression ◽  
Femoral Neck Fractures ◽  
Binary Logistic Regression Model

Objective The association of chronic non-malignant pain (CNP) with dyslipidemia is unclear. This retrospective study was performed to evaluate the association between CNP and dyslipidemia in elderly patients with femoral neck fractures (FNFs) treated by primary unilateral total hip arthroplasty (THA). Methods We retrospectively identified 521 consecutive patients with FNFs (AO/OTA type 31B) who underwent primary unilateral THA from 2009 to 2021. The study population was divided into patients with and without CNP. Serum lipids were measured for each patient. The association between CNP and dyslipidemia was assessed using a multivariate binary logistic regression model. Results In total, 436 patients (220 with CNP, 216 without CNP) were eligible for analysis. In the quantile regression, the adverse effect of CNP was significantly attenuated by resilience in patients with a high high-density lipoprotein (HDL) concentration and low low-density lipoprotein (LDL) concentration. The multivariate binary logistic regression model showed that the HDL and LDL concentrations were the only variables significantly associated with the development of CNP. Conclusion Both a low HDL and high LDL concentration may result in the occurrence of CNP in elderly patients with FNFs treated by primary unilateral THA.

scholarly journals Association of dyslipidaemia with osteoporosis in postmenopausal women

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199955
Author(s):  
Lingnan Zhang ◽  
Qilong Liu ◽  
Xianshang Zeng ◽  
Wenshan Gao ◽  
Yanan Niu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Confidence Interval ◽  
Regression Model ◽  
Postmenopausal Women ◽  
Odds Ratio ◽  
Logistic Regression Model ◽  
Density Lipoprotein ◽  
Binary Logistic Regression ◽  
Control Group ◽  
Binary Logistic Regression Model

Objective To assess the association of dyslipidaemia with osteoporosis in postmenopausal women. Methods Data from 160 postmenopausal women with newly diagnosed osteoporosis (osteoporosis group) and 156 healthy controls (control group) were retrospectively reviewed from 2016 to 2020. The primary outcomes were laboratory values assessed by a multivariate binary logistic regression model. Results Factors that greatly increased the risk of being in the osteoporosis group included high low-density lipoprotein (LDL) and low high-density lipoprotein (HDL) levels. The osteoporosis group had lower HDL and higher LDL levels than the control group. A multivariate binary logistic regression model showed that lower HDL and higher LDL levels were the only variables that were significantly associated with osteoporosis (odds ratio 1.86, 95% confidence interval: 3.66–4.25 and odds ratio 1.47, 95% confidence interval: 1.25–2.74, respectively). Conclusion Low HDL and high LDL levels may be associated with the occurrence of osteoporosis in postmenopausal women.

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.

Die Flexion der Indefinita jemand und niemand

2021 ◽  
Vol 49 (2) ◽  
pp. 209-243
Author(s):  
Linnéa Weitkamp
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Influencing Factors ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Corpus Study ◽  
Frequent Use ◽  
Current Variation ◽  
Reference Corpus

Abstract This article investigates the inflection of the German indefinite pronouns jemand and niemand in the accusative and dative. The pronouns are used both with inflectional suffix (jemanden/jemandem, niemanden/niemandem) and without (jemand, niemand) and are thus an example of current variation in contemporary German. The grammars take an unusually liberal stance and describe both forms as correct, partially even with preference to the uninflected form. A corpus study which examines conceptually written data of the DeReKo (German reference corpus) and conceptually oral data of the DECOW16B (German web corpus), shows that over 90 % of occurrences are inflected. But almost 10 % of uninflected forms show that these formations are no arbitrary errors either. To find out what influences the presence or absence of the inflectional ending, a binary logistic regression model was calculated. The following factors proved to be significant influencing factors for inflection: the degree of formality (DeReKo vs. DECOW16B), the lexeme (jemand vs. niemand), the case (acc vs. dat), government by preposition vs. government by verb and the following nominalized adjective (jemand anderen). With regard to the different inflectional suffixes, the frequent use of -en in the dative stood out in particular. Although this form is classified as erroneous in all grammars, almost 30 % of the dative occurrences in informal DECOW16B data are formed in this way.

scholarly journals Determinants of adaptation strategies to climate change among the smallholder farmers in Adama District, Ethiopia

2020 ◽  
Vol 12 (4) ◽  
pp. 463-476
Author(s):  
Hurgesa Hundera Hirpha ◽  
Sylvester Mpandeli ◽  
Amare Bantider
Keyword(s):  
Climate Change ◽  
Logistic Regression ◽  
Regression Model ◽  
Climate Change Adaptation ◽  
Logistic Regression Model ◽  
Smallholder Farmers ◽  
Binary Logistic Regression ◽  
Adaptation Strategies ◽  
Binary Logistic Regression Model ◽  
Content Type

Purpose The Ethiopian economy is mainly based on the rain-fed agriculture practiced by smallholder farmers. The sector is highly vulnerable to climate change impacts. This study aims to examine the determinants of adaptation strategies to climate change among the smallholder farmers in Adama District, Ethiopia. Design/methodology/approach A cross-sectional survey design was used to collect quantitative data using questionnaire with 351 randomly selected smallholder farmers. To collect qualitative data focus group discussions, key informant interviews and field observations were also used. Triangulated with thematic analysis, descriptive statistics and binary logistic regression model were used for the analysis. Findings The result indicated that the majority of the smallholder farmers use at least one climate change adaptation strategy in their local areas though the strategy is generally weak. In this regard, some of the dominant climate change adaptation activities identified in the study area are using improved crop varieties, planting trees, watershed management, adjusting planting date and terracing. The result from binary logistic regression model showed that age and sex of household head, as well as their education, family size, access to agricultural extension services and training on climate change significantly influence the practices of adaptation measures. Originality/value This study would help the practitioners to modify the existing weak adaptation activities by introducing advanced and technological-based adaptation strategies to the rural farming communities.

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