Risk factors for temporomandibular disorder: Binary logistic regression analysis

Background: The cognitive function of the elderly has become a focus of public health research. Little is known about the changes of cognitive function and the risk factors for cognitive impairment in the Chinese elderly; thus, the purposes of this study are as follows: (1) to describe changes in cognitive function in the Chinese elderly from 2005–2014 and (2) to explore risk factors for cognitive impairment of the Chinese elderly. Design and setting: A total of 2603 participants aged 64 years and above participated in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and were followed up from 2005 to 2014. Cognitive function and cognitive impairment were assessed using the Chinese version of the Mini-Mental State Examination (MMSE). Binary logistic regression analysis was used to estimate the odds ratio (OR) and 95% confidence intervals (CI) of cognitive impairment. Results: Results revealed that the cognitive function of the Chinese elderly shows diversified changes: deterioration (55.09%), unchanged (17.21%) and improvement (27.70%). In addition, there are significant demographic differences in gender, age, education, marriage and other aspects when it comes to the changes of cognitive function in Chinese elderly. In the binary logistic regression analysis, female, increased age, lower education level, no spouse, less income, worse PWB (psychological well-being), less fresh fruit and vegetable intake, more activities of daily living (ADL) limitations, lower social engagement were significantly associated with higher odds for cognitive impairment. Conclusions: Various interventions should be implemented to maintain cognitive function in Chinese elderly.

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Distal Adding On In Lenke 2AR Adolescent Idiopathic Scoliosis:risk Factors Analysis and Clinical Assessment

10.21203/rs.3.rs-34362/v1 ◽

2020 ◽

Author(s):

Yulei Dong ◽

Ning Tang ◽

Shengru Wang ◽

Jianguo Zhang ◽

Hong Zhao

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Clinical Assessment ◽

Lumbar Lordosis ◽

Binary Logistic Regression ◽

Thoracic Curve ◽

Binary Logistic Regression Analysis

Abstract Background: Risk factors and clinical outcome associated with distal adding on in Lenke 2AR adolescent idiopathic scoliosis (AIS) are not fully elucidated.Methods: Radiographic data of Lenke 2AR AIS patients treated by posterior pedicle screws in a single institution were retrospectively analyzed. Patients who developed distal adding on were compared with patients who did not. Clinical assessment were carried out by SF-36 and SRS-22 questionnaire. Risk factors of distal adding on were explored by binary logistic regression analysis.Results: A total of 88 patients completed an average of 2.9 (2-12.9) years of follow-up were included. 18 (20.5%) patients met the criterion of distal adding on while the rest of 70 cases did not at follow up.Clinical parameters were comparable before surgery and became lower at follow up in adding on group in dimensions of vitality (63.1±11.4Vs.67.5±11.8,P=0.026), mental health (73.9±9.2Vs.77.8±10.2,P=0.039), self-image (3.9±0.3Vs.4.2±0.3,P=0.021) and satisfaction (4.0±0.3Vs.4.5±0.4,P=0.035). The proportion of selection lowest instrumented vertebra (LIV) higher than last touching vertebra(LTV) was significantly higher in adding on group. (6/18vs 4/70, P=0.004) Binary logistic regression analysis revealed that preoperative main thoracic curve magnitude (OR=1.118,P=0.041), preoperative lumbar lordosis (OR=1.091,P=0.029), and the gap between LIV and LTV(OR=6.123,P=0.043) were independent risk factors associated with adding on.Conclusions: In Lenke 2AR AIS cases, the selection of LIV higher than LTV is closely correlated with the development of adding-on. Patients with minor main thoracic curve and lumbar lordosis are more likely to develop distal adding on. Adding on may cause discontent of patients .

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Predicting Burn Mortality Using a Simple Novel Prediction Model

Indian Journal of Plastic Surgery ◽

10.1055/s-0040-1721867 ◽

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.

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Predicting organizational performance from motivation in Oromia Seed Enterprise Bale branch

Humanities and Social Sciences Communications ◽

10.1057/s41599-021-00742-9 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Askalech Feyisa Jobira ◽

Abdulnasir Abdulmelike Mohammed

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Intrinsic Motivation ◽

Organizational Performance ◽

Logistic Regression Analysis ◽

Extrinsic Motivation ◽

Binary Logistic Regression ◽

Moderate Level ◽

Binary Logistic Regression Analysis ◽

Study Results

AbstractMotivation is one of the most researched yet crucial topics in academia from various perspectives. Despite this, researches show mixed results about the effect of extrinsic motivation on intrinsic motivation and organizational performance. Studies in Ethiopia also lack causal analysis and theoretical underpinning that made contributions from academia very little. Hence, this research is important to assess the effect of extrinsic motivation on intrinsic motivation and organizational performance from a cognitive evaluation theory perspective. The researchers adopted an explanatory research design with a quantitative approach. The entire 119 employees of the Oromia Seed Enterprise, Bale branch were included in the study to collect primary data through a close-ended questionnaire. The collected data was processed by SPSS software version 20. The relationship analysis was addressed by correlation and binary logistic regression analysis. Seen from extrinsic and intrinsic motivation aspects, the findings of the study showed that Oromia Seed Enterprise had a moderate level of organizational performance and a moderate level of employees’ motivation. The correlation analysis result indicated that employees’ extrinsic and intrinsic motivation had a positive relationship with organizational performance. The binary logistic regression analysis also indicated that extrinsic and intrinsic motivation had a positive and significant influence on organizational performance. However, the interaction effect of intrinsic and extrinsic motivation on organizational performance was not significant, implying the absence of influence when both intrinsic and extrinsic motivations happen at the same time. Finally, the study results have a theoretical contribution for compensating the lack of actual experience in the Ethiopian organization’s context. Equally, the understanding of the moderated relationship among the study variables may encourage Oromia Seed Enterprise and its managers to develop a practical motivation system, which entertains the complex interaction of motivation variables to improve organizational performance. In addition, studies of this nature can inform policymakers to strengthen an incentive system as well as other motivation veins in the Ethiopian public organizations.

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Binary Logistic Regression Analysis of Variables That Influence Poverty in Central Java

Journal of Intelligent Computing & Health Informatics ◽

10.26714/jichi.v1i1.5381 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Sendi Nugraha Nurdiansah ◽

Laelatul Khikmah

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Binary Logistic Regression ◽

Equation Model ◽

Binary Logistic Regression Analysis ◽

Poor People ◽

Regional Government ◽

Central Java ◽

The Government

The phenomenon of poverty is a serious problem faced by almost every country in the world. This is because poverty can affect various aspects of people's lives. One of the causes of poverty is due to lack of income and assets to meet basic needs such as food, clothing, housing, health level and acceptable education. In addition, poverty occurs because of the powerlessness of society to get out of the problems it faces. The Central Java regional government incorporated poverty issues into the Regional Medium-Term Development Plan (RPJMD) because Central Java has a high number of poor people. This was done as an effort by the Central Java government to reduce poverty. Therefore, research is needed to find out the variables that most influence poverty in order to assist the government in developing the RPJMD. To find out what factors influence poverty in Central Java with the dichotomous categorical response variable, binary logistic regression analysis was used. The results showed that based on the analysis conducted did not obtain a logistic regression equation model because there were no significant parameters because there were no variables that had a sig value <0.05. Existing variables are Number of Population, Female Head of Household, Number of Children not in School, Number of Disabled Individuals, Number of Chronic Disease Individuals, Unemployment, Non-Electricity Lighting Sources, Unprotected Drinking Water Sources, Kerosene and Wood Cooking Fuels, Location Facilities Defecation (BAB) Not Available, so there are no variables that affect the level of poverty in Central Java Province.

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Factors affecting internet use among university students in Sarawak, Malaysia: an empirical study

International Journal of Community Medicine and Public Health ◽

10.18203/2394-6040.ijcmph20200933 ◽

2020 ◽

Vol 7 (3) ◽

pp. 848

Author(s):

M. Mizanur Rahman ◽

M. Taha Arif ◽

Fready Luke ◽

Santha Letchumi ◽

Fatin Nabila ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

University Students ◽

Logistic Regression Analysis ◽

Internet Use ◽

Binary Logistic Regression ◽

Problematic Internet Use ◽

The Internet ◽

Binary Logistic Regression Analysis ◽

Factors Affecting

Background: The internet has become an indispensable tool for communication, academic research, information and entertainment. However, heavy users of the internet lead to less confidence in social skills and the tendency to be isolated. The study aimed to assess the pattern of internet use and factors affecting problematic internet use among university students.Methods: This cross-sectional study conducted among the students of a university in Sarawak, Malaysia. A multistage cluster sampling technique was adapted to select the participants. Data were collected from 463 students by self-administered questionnaire. Hierarchical binary logistic regression analysis was done to determine the potential factors for problematic internet use.Results: The mean age of the students was 22 years, with a standard deviation of 1.6 years. Two-fifths (61.8%) of the students had no problematic internet use. However, 35.4% had moderate and 2.8% had severe problematic internet use. Hierarchical binary logistic regression analysis found that age of the students, year of study, duration of daily internet use and use of social networking like Skype appeared to be potential predictors of problematic internet use (p<0.05).Conclusions: This study was conducted in only one university, thus did not depict the overall scenarios of the country. The implications of the findings are still worth noting in the process of designing internet addiction studies among university students. Overall, this study has unearthed some useful insights which can serve as a guide to more elaborate studies.

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Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies

Journal of International Medical Research ◽

10.1177/0300060518777173 ◽

2018 ◽

Vol 46 (9) ◽

pp. 3656-3664 ◽

Cited By ~ 5

Author(s):

Wenbo Xu ◽

Yang Zhao ◽

Shiyan Nian ◽

Lei Feng ◽

Xuejing Bai ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Logistic Regression Analysis ◽

Density Lipoprotein ◽

Binary Logistic Regression ◽

Lipoprotein Cholesterol ◽

Binary Logistic Regression Analysis ◽

Confounding Factors ◽

Analysis Strategies ◽

Glutamyl Transferase

Objective To investigate the importance of controlling confounding factors during binary logistic regression analysis. Methods Male coronary heart disease (CHD) patients (n = 664) and healthy control subjects (n = 400) were enrolled. Fourteen indexes were collected: age, uric acid, cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B100, lipoprotein a, homocysteine, total bilirubin, direct bilirubin, indirect bilirubin, and γ-glutamyl transferase. Associations between these indexes and CHD were assessed by logistic regression, and results were compared by using different analysis strategies. Results 1) Without controlling for confounding factors, 14 indexes were directly inputted in the analysis process, and 11 indexes were finally retained. A model was obtained with conflicting results. 2) According to the application conditions for logistic regression analysis, all 14 indexes were weighed according to their variances and the results of correlation analysis. Seven indexes were finally included in the model. The model was verified by receiver operating characteristic curve, with an area under the curve of 0.927. Conclusions When binary logistic regression analysis is used to evaluate the complex relationships between risk factors and CHD, strict control of confounding factors can improve the reliability and validity of the analysis.

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