Pemodelan Status Usaha (Pengusaha  dan Pekerja/Karyawan) Menggunakan Regresi Logistik Multilevel

The level of a country's economy is directly proportional to the number of entrepreneurs in the country. According to the World Bank standard number of entrepreneurs, the ideal of a country is at least 4% of the total population. Based on data from the Indonesian Young Entrepreneurs Association (HIPMI), the number of entrepreneurs in Indonesia is only about 1.5%. Of course not easy to achieve the ideal number of bank standards-based world that is 4%. This study aims to determine what factors are driving someone in determining a career as an entrepreneur or not (worker/employee). The data used is the Adult Population Survey (APS) in 2013 conducted by the Global Entrepreneurship Monitor (GEM). the survey conducted in 16 provinces, 51 districts/cities, and 176 subdistricts. Data generated hierarchical modeling that will be performed using multilevel logistic regression. The variables studied were the state variable effort (Y), variable knowent (X1), variable opport (X2), variable suskill (X3), variable fearfail (X4), the variable gender (X5) at level 1 and the variable sub-district at level 2. the analysis showed that the logistic regression model 2-level produce a better model than the ordinary logistic regression model. Based on modeling results we concluded that all predictor variables (knowent, opport, suskill, fearfail, gender, etc.) affect the status of one's business.

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Effect of fluid administration on scene to traffic accident patients by EMS personnel: a propensity score-matched study using population-based ambulance records and nationwide trauma registry in Japan

European Journal of Trauma and Emergency Surgery ◽

10.1007/s00068-020-01590-z ◽

2021 ◽

Author(s):

Yusuke Katayama ◽

Tetsuhisa Kitamura ◽

Kosuke Kiyohara ◽

Kenichiro Ishida ◽

Tomoya Hirose ◽

...

Keyword(s):

Logistic Regression ◽

Propensity Score ◽

Hospital Mortality ◽

Regression Model ◽

Traffic Accident ◽

Logistic Regression Model ◽

Cardiopulmonary Arrest ◽

Population Based ◽

Fluid Administration ◽

Secondary Outcome

Abstract Purpose The aim of this study was to assess the effect of fluid administration by emergency life-saving technicians (ELST) on the prognosis of traffic accident patients by using a propensity score (PS)-matching method. Methods The study included traffic accident patients registered in the JTDB database from January 2016 to December 2017. The main outcome was hospital mortality, and the secondary outcome was cardiopulmonary arrest on hospital arrival (CPAOA). To reduce potential confounding effects in the comparisons between two groups, we estimated a propensity score (PS) by fitting a logistic regression model that was adjusted for 17 variables before the implementation of fluid administration by ELST at the scene. Results During the study period, 10,908 traffic accident patients were registered in the JTDB database, and we included 3502 patients in this study. Of these patients, 142 were administered fluid by ELST and 3360 were not administered fluid by ELST. After PS matching, 141 patients were selected from each group. In the PS-matched model, fluid administration by ELST at the scene was not associated with discharge to death (crude OR: 0.859 [95% CI, 0.500–1.475]; p = 0.582). However, the fluid group showed statistically better outcome for CPAOA than the no fluid group in the multiple logistic regression model (adjusted OR: 0.231 [95% CI, 0.055–0.967]; p = 0.045). Conclusion In this study, fluid administration to traffic accident patients by ELST was associated not with hospital mortality but with a lower proportion of CPAOA.

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Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

Frontiers in Pediatrics ◽

10.3389/fped.2020.585868 ◽

2021 ◽

Vol 8 ◽

Author(s):

Robert A. Reed ◽

Andrei S. Morgan ◽

Jennifer Zeitlin ◽

Pierre-Henri Jarreau ◽

Héloïse Torchin ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Regression Model ◽

Expert Opinion ◽

Logistic Regression Model ◽

Population Based ◽

Regression Modelling ◽

Preterm Babies ◽

Logistic Regression Modelling

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

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Perceived Institutional Campus Image and its Entrance Edifice

Open House International ◽

10.1108/ohi-03-2018-b0007 ◽

2018 ◽

Vol 43 (3) ◽

pp. 52-58

Author(s):

Babu Rajeswaran ◽

Thirumaran Kesavaperumal

Keyword(s):

Logistic Regression ◽

Visual Perception ◽

Regression Model ◽

Logistic Regression Model ◽

Belief System ◽

Architectural Elements ◽

The Status ◽

Physical Features

Entrance edifice of the institutional campus artifice as the visible representation of status in their society. The design shall stand to show the status of the person who develops the setting, it could signify the power of the person or the entire place, and it could sometimes support certain cosmological belief system that prevails in that culture too. This study intended to identify the entrance edifice Physical features that influence observer's perception of the image of the institutional campus. To accomplish this, physical features of the entrance edifice and its characters were identified and developed into questionnaire material. A logistic regression model was adopted to analyze the Physical features of Entrance Edifice. The findings found the followings: (i) Physical features of Entrance Edifice influence the Visual perception of the Institutional Campus. (ii) The Physical features were found to impact the Visual perception of the Institutional campus in the order of Form Identity, Architectural Elements, Scale and Portion, color and material. These variances are based on the observers' perception, which can be integrated into the Institutional Campus Entrance Edifice design to strengthen the Image of the Institutional campus. It is clear that Entrance Edifices have the potential to build the image for a greater content and purpose through its Form Identity, Architectural Elements, Scale and Portion, color and material. Nevertheless, the entrance design could be a powerful element to build up the image of the physical settings it serves.

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Association between Maternal Pre-pregnancy Body Mass Index and Breastfeeding Duration in Taiwan: A Population-Based Cohort Study

Nutrients ◽

10.3390/nu12082361 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2361

Author(s):

Chi-Nien Chen ◽

Hung-Chen Yu ◽

An-Kuo Chou

Keyword(s):

Body Mass Index ◽

Logistic Regression ◽

Regression Model ◽

Body Mass ◽

Logistic Regression Model ◽

Maternal Obesity ◽

Population Based ◽

Normal Weight ◽

Breastfeeding Duration ◽

Breastfeeding Cessation

An association between high pre-pregnancy body mass index (BMI) and early breastfeeding cessation has been previously observed, but studies examining the effect of underweight are still scant and remain inconclusive. This study analyzed data from a nationally representative cohort of 18,312 women (mean age 28.3 years; underweight 20.1%; overweight 8.2%; obesity 1.9%) who delivered singleton live births in 2005 in Taiwan. Comprehensive face-to-face interviews and surveys were completed at 6 and 18 months postpartum. BMI status and breastfeeding duration were calculated from the self-reported data in the questionnaires. In the adjusted ordinal logistic regression model, maternal obesity and underweight had a higher odds of shorter breastfeeding duration compared with normal-weight women. The risk of breastfeeding cessation was significantly higher in underweight women than in normal-weight women after adjustments in the logistic regression model (2 m: aOR = 1.11, 95% CI = 1.03–1.2; 4 m: aOR = 1.32, 95% CI = 1.21–1.43; 6 m: aOR = 1.3, 95% CI = 1.18–1.42). Our findings indicated that maternal underweight and obesity are associated with earlier breastfeeding cessation in Taiwan. Optimizing maternal BMI during the pre-conception period is essential, and future interventions to promote and support breastfeeding in underweight mothers are necessary to improve maternal and child health.

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Glaucoma may be a non-memory manifestation of Alzheimer's disease in older people

International Psychogeriatrics ◽

10.1017/s1041610217000801 ◽

2017 ◽

Vol 29 (9) ◽

pp. 1535-1541 ◽

Cited By ~ 12

Author(s):

Shih-Wei Lai ◽

Cheng-Li Lin ◽

Kuan-Fu Liao

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Logistic Regression ◽

Older People ◽

Regression Model ◽

Logistic Regression Model ◽

Population Based ◽

Multivariable Logistic Regression Model ◽

Retrospective Case ◽

Taiwan National Health Insurance

ABSTRACTBackground:The purpose of this paper was to examine whether glaucoma could be a non-memory manifestation of Alzheimer's disease in older people.Methods:We conducted a population-based, retrospective, case-control study to analyze the database of the Taiwan National Health Insurance Program. There were 1,351 subjects ≥65 years old with newly diagnosed Alzheimer's disease as the cases, and 5,329 subjects without any type of dementias as the controls during 2000–2011. The odds ratio (OR) and 95% confidence interval (CI) for the risk of Alzheimer's disease associated with glaucoma was estimated by the multivariable unconditional logistic regression model.Results:After controlling for confounders, the multivariable logistic regression model demonstrated that the adjusted OR of Alzheimer's disease was 1.50 in subjects with glaucoma (95% CI 1.19, 1.89), compared to subjects without glaucoma.Conclusions:Older people with glaucoma are associated with 1.5-fold increased odds of Alzheimer's disease in Taiwan. Glaucoma may be a non-memory manifestation of Alzheimer's disease in older people. Further research is needed to confirm this issue.

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COVID-19 associated hospitalization in 571 patients with fibromyalgia—A population-based study

PLoS ONE ◽

10.1371/journal.pone.0261772 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0261772

Author(s):

Mor Amital ◽

Niv Ben-Shabat ◽

Howard Amital ◽

Dan Buskila ◽

Arnon D. Cohen ◽

...

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Multivariable Analysis ◽

Population Based ◽

Female Dominance ◽

Categorical Variables ◽

Continuous Variables ◽

Multivariable Logistic Regression Model ◽

Population Based Study

Objective To identify predicators of patients with fibromyalgia (FM) that are associated with a severe COVID-19 disease course. Methods We utilized the data base of the Clalit Health Services (CHS); the largest public organization in Israel, and extracted data concerning patients with FM. We matched two subjects without FM to each subject with FM by sex and age and geographic location. Baseline characteristics were evaluated by t-test for continuous variables and chi-square for categorical variables. Predictors of COVID-19 associated hospitalization were identified using univariable logistic regression model, significant variables were selected and analyzed by a multivariable logistic regression model. Results The initial cohort comprised 18,598 patients with FM and 36,985 matched controls. The mean age was 57.5± 14.5(SD), with a female dominance of 91%. Out of this cohort we extracted the study population, which included all patients contracted with COVID-19, and consisted of 571 patients with FM and 1008 controls. By multivariable analysis, the following variables were found to predict COVID-19 associated hospitalization in patients with FM: older age (OR, 1.25; CI, 1.13–1.39; p<0.001), male sex (OR, 2.63; CI, 1.18–5.88; p<0.05) and hypertension (OR, 1.75; CI, 1.04–2.95; p<0.05). Conclusion The current population-based study revealed that FM per se was not directly associated with COVID-19 hospitalization or related mortality. Yet classical risk factors endangering the general population were also relevant among patients with FM.

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Predicting aneurysm rupture probabilities through the application of a computed tomography angiography–derived binary logistic regression model

Journal of Neurosurgery ◽

10.3171/2008.5.17558 ◽

2009 ◽

Vol 110 (1) ◽

pp. 1-6 ◽

Cited By ~ 33

Author(s):

Charles J. Prestigiacomo ◽

Wenzhuan He ◽

Jeffrey Catrambone ◽

Stephanie Chung ◽

Lydia Kasper ◽

...

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Prospective Cohort ◽

Logistic Regression Model ◽

Binary Logistic Regression ◽

Aneurysm Rupture ◽

Binary Logistic Regression Model ◽

Unruptured Aneurysms ◽

Neck Size ◽

The Status

Object The goal of this study was to establish a biomathematical model to accurately predict the probability of aneurysm rupture. Biomathematical models incorporate various physical and dynamic phenomena that provide insight into why certain aneurysms grow or rupture. Prior studies have demonstrated that regression models may determine which parameters of an aneurysm contribute to rupture. In this study, the authors derived a modified binary logistic regression model and then validated it in a distinct cohort of patients to assess the model's stability. Methods Patients were examined with CT angiography. Three-dimensional reconstructions were generated and aneurysm height, width, and neck size were obtained in 2 orthogonal planes. Forward stepwise binary logistic regression was performed and then applied to a prospective cohort of 49 aneurysms in 37 patients (not included in the original derivation of the equation) to determine the log-odds of rupture for this aneurysm. Results A total of 279 aneurysms (156 ruptured and 123 unruptured) were observed in 217 patients. Four of 6 linear dimensions and the aspect ratio were significantly larger (each with p < 0.01) in ruptured aneurysms than unruptured aneurysms. Calculated volume and aneurysm location were correlated with rupture risk. Binary logistic regression applied to an independent prospective cohort demonstrated the model's stability, showing 83% sensitivity and 80% accuracy. Conclusions This binary logistic regression model of aneurysm rupture identified the status of an aneurysm with good accuracy. The use of this technique and its validation suggests that biomorphometric data and their relationships may be valuable in determining the status of an aneurysm.

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Last Minute Medical Appointments No-Show Management

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.2020100102 ◽

2020 ◽

Vol 15 (4) ◽

pp. 18-37

Author(s):

Daniel M. Sousa ◽

André Vasconcelos

Keyword(s):

Logistic Regression ◽

Bayesian Inference ◽

Regression Model ◽

Logistic Regression Model ◽

Time Slot ◽

Population Based ◽

Behavior Prediction ◽

Hybrid Solution ◽

To Come ◽

Prediction Techniques

A no-show occurs when a client has an appointment of some sort with another entity, and voluntarily or not, the client does not show up to that appointment. A patient missing an appointment will mean that the clinic's and health professional's time slot will be wasted. The goal of this research is to find a solution that minimizes no-shows, detecting when a patient is not going to come to the appointment and finding an appropriate replacement. The authors propose a hybrid solution which combines two different behavior prediction techniques: population-based behavior and individual-based behavior. The algorithm starts by computing a no-show probability based on the population's behavior using a logistic regression model. After that, using Bayesian inference, that probability is personalized for each patient. After computing the no-show probabilities for every candidate patient, the algorithm checks if any of them are interested on taking the appointment. The proposed algorithm was assessed using lab data and healthcare provider data.

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