scholarly journals Predictors of Undiagnosed Diabetes among Middle-Aged and Seniors in China: Application of Andersen’s Behavioral Model

2021 ◽  
Vol 18 (16) ◽  
pp. 8396
Author(s):  
Chaozhou Mou ◽  
Minlan Xu ◽  
Juncheng Lyu
Keyword(s):  
Logistic Regression ◽  
Health Service ◽  
Regression Model ◽  
Roc Curve ◽  
Behavioral Model ◽  
Undiagnosed Diabetes ◽  
Multiple Logistic Regression ◽  
Andersen’S Behavioral Model ◽  
Andersen Model ◽  
Geographical Regions

Undiagnosed diabetes is a threat to public health. This study aims to identify potential variables related to undiagnosed diabetes using Andersen’s behavioral model. Baseline data including blood test data from the China Health and Retirement Longitudinal Study (CHARLS) were adopted. First, we constructed health service related variables based on Andersen model. Second, univariate analysis and multiple logistic regression were used to analyze the relations of variables to undiagnosed diabetes. The strength of relationships was presented by odds ratios (ORs) and 95% confidence intervals (CIs). Finally, the prediction of multiple logistic regression model was assessed using the Receiver Operating Characteristic (ROC) curve and the area under the ROC curve (AUC). According to diagnosis standards, 1234 respondents had diabetes, among which 560 were undiagnosed and 674 were previously diagnosed. Further analysis showed that the following variables were significantly associated with undiagnosed diabetes: age as the predisposing factor; medical insurance, residential places and geographical regions as enabling factors; having other chronic diseases and self-perceived health status as need factors. Moreover, the prediction of regression model was assessed well in the form of ROC and AUC. Andersen model provided a theoretical framework for detecting variables of health service utilization, which may not only explain the undiagnosed reasons but also provide clues for policy-makers to balance health services among diverse social groups in China.

scholarly journals Association Between Red Cell Distribution Width and Hospital Mortality in Patients with Sepsis

2021 ◽  
Vol 49 (4) ◽  
pp. 030006052110042
Author(s):  
Yide Li ◽  
Yingfang She ◽  
Le Fu ◽  
Ruitong Zhou ◽  
Wendi Xiang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Hospital Mortality ◽  
Regression Model ◽  
Roc Curve ◽  
Prognostic Value ◽  
Logistic Regression Model ◽  
Cell Distribution ◽  
Multiple Logistic Regression ◽  
Distribution Width ◽  
Apache Iv

Objective Sepsis is the leading cause of death in patients admitted to adult intensive care units (ICUs). We aimed to determine the predictive value of red blood cell distribution width (RDW) in patients with sepsis in a large cohort. Methods This retrospective observational study used data from the eICU Collaborative Research Database. The prognostic value of RDW was investigated using the receiver operating characteristic (ROC) curve, multiple logistic regression model, integrated discriminatory index (IDI), and net reclassification index (NRI). Results In total, 9743 patients were included. The area under the ROC curve of the RDW for predicting hospital mortality was 0.631 (95% confidence interval [CI]: 0.616–0.645). Based on the multiple logistic regression model, an RDW of ≥14.5% was correlated with hospital mortality, regardless of Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation IV (APACHE IV) scores (odds ratio [OR]: 1.838, 95% CI: 1.598–2.119). Using SOFA and APACHE IV scores as reference, the IDI and continuous NRI of RDW for hospital mortality was about 0.3 and 0.014, respectively. Conclusions The RDW may be useful in predicting hospital mortality in patients with sepsis, offering extra prognostic value beyond SOFA and APACHE IV scores.

scholarly journals Aplicación de modelos de regresión logística en metodología observacional: modalidades de competición en la iniciación al fútbol.

2015 ◽  
Vol 32 (1) ◽  
pp. 288 ◽  
Author(s):  
Daniel Lapresa ◽  
Javier Arana ◽  
M.Teresa Anguera ◽  
J.Ignacio Pérez-Castellanos ◽  
Mario Amatria
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Multiple Logistic Regression ◽  
Observational Methodology ◽  
Logistic Regression Models ◽  
Game Format ◽  
Simple Logistic

This study shows how simple and multiple logistic regression can be used in observational methodology and more specifically, in the fields of physical activity and sport. We demonstrate this in a study designed to determine whether three-a-side futsal or five-a-side futsal is more suited to the needs and potential of children aged 6-to-8 years. We constructed a multiple logistic regression model to analyze use of space (depth of play) and three simple logistic regression models to determine which game format is more likely to potentiate effective technical and tactical performance.

Temporal and farm-management-associated variation in faecal-pat prevalence ofCampylobacter fetusin sheep and cattle

2013 ◽  
Vol 142 (6) ◽  
pp. 1196-1204 ◽  
Author(s):  
J. S. DUNCAN ◽  
A. J. H. LEATHERBARROW ◽  
N. P. FRENCH ◽  
D. H. GROVE-WHITE
Keyword(s):  
Logistic Regression ◽  
Farm Management ◽  
Multiple Logistic Regression ◽  
Seasonal Periodicity ◽  
Culture Techniques ◽  
Regression Modelling ◽  
Increased Risk ◽  
Geographical Regions ◽  
Logistic Regression Modelling ◽  
Cattle And Sheep

SUMMARYThe faecal-pat prevalence (as estimated by culture) ofCampylobacter fetusfrom cattle and sheep on 19 farms in rural Lancashire was investigated using standardCampylobacterculture techniques and PCR during a 2-year longitudinal study.C. fetuswas isolated from 9·48% [95% confidence interval (CI) 8·48–10·48] of cattle faecal pats and 7·29% (95% CI 6·21–9·62) of sheep faecal pats. There was evidence of significant differences in shedding prevalence between geographical regions; cows in geographical zone 3 had an increased risk of sheddingC. fetuscompared to cows in geographical zones 1 and 2 (OR 6·64, 95% CI 1·67–26·5,P = 0·007), as did cows at pasture (OR 1·66, 95% CI 1·01–2·73,P = 0·046) compared to when housed. Multiple logistic regression modelling demonstrated underlying seasonal periodicity in both species.

scholarly journals Modeling Incidence Rate by Region: Analysis of Asthma and Other Chronic Obstructive Lung Diseases

2021 ◽  
Author(s):  
Ronald Wesonga ◽  
Khidir Abdelbasit
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Lung Diseases ◽  
Logistic Regression Model ◽  
Incidence Rates ◽  
Chronic Obstructive ◽  
Binary Logistic Regression Model ◽  
Obstructive Lung Diseases ◽  
Geographical Regions ◽  
Chronic Obstructive Lung Diseases

Abstract This study aimed to examine regional differences for asthma and other chronic obstructive lung diseases. The study was based on data collected from annual reports produced by the Ministry of Health over a ten-year period beginning from 2010 to 2019. Incidence rates for eleven regions in the Sultanate of Oman were analyzed using statistical tools including; analysis of variance and binary logistic regression model to determine the effect of region on asthma and other chronic obstructive lung diseases. The incident rates were found to be significantly different by region (F-value = 27.07, p = 0.00). There was no significant variation by year (F-value = 1.05, p > 0.407). Overall over the ten-year period the incidence rates stagnated between 250 and 300 per 10000 of the population, but showed a reducing trend between 2016 and 2019. The logistic regression model shows that compared to the Muscat region, all the other regions had significant increased odd ratios. There is a significant evidence of regional variation in the incidence of asthma and other chronic obstructive lung diseases. This implies probable characteristics in geographical regions that are associated with asthma and other chronic obstructive lung diseases’ exacerbation. Five asthma-region classifications were identified from our analysis. Findings of this study may be used to guide decision making towards the management and control of asthma and chronic obstructive lung diseases.

scholarly journals Clinical Characteristics and Prognostic Factors of Posterior Segment Intraocular Foreign Body: Canadian Experience from a Tertiary University Hospital in Quebec

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jean-Philippe Rozon ◽  
Guillaume Lavertu ◽  
Mélanie Hébert ◽  
Eunice You ◽  
Serge Bourgault ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Foreign Body ◽  
Regression Model ◽  
Predictive Factors ◽  
Clinical Characteristics ◽  
Logistic Regression Model ◽  
Visual Outcome ◽  
Posterior Segment ◽  
Multiple Logistic Regression ◽  
Multiple Logistic Regression Model

Purpose. To identify predictive factors for visual outcomes of patients presenting with a posterior segment intraocular foreign body (IOFB). Methods. A retrospective chart review was performed for all consecutive patients operated for posterior segment IOFB removal between January 2009 and December 2018. Data were collected for patient demographics, clinical characteristics at presentation, IOFB characteristics, surgical procedures, and postoperative outcomes. A multiple logistic regression model was built for poor final visual acuity (VA) as an outcome (defined as final VA 50 letters or worse [Snellen equivalent: 20/100]). Results. Fifty-four patients were included in our study. Ninety-three percent of patients were men, with a mean age of 40.4 ± 12.6 years. Metallic IOFB comprised 88% of cases with a mean ± standard deviation (SD) size of 5.31 ± 4.62 mm. VA improved in 70% of patients after IOFB removal. Predictive factors for poor VA outcome included poor baseline VA, larger IOFB size, high number of additional diagnoses, an anterior chamber extraction, a second intervention, the use of C3F8 or silicone tamponade, and the presence of vitreous hemorrhage, hyphema, and iris damage. Predictive factors for a better visual outcome included first intention intraocular lens (IOL) implantation and the use of air tamponade. In the multiple logistic regression model, both baseline VA ( p  = 0.009) and number of additional complications ( p  = 0.01) were independent risk factors for a poor final VA. Conclusions. A high number of concomitant complications and poor baseline VA following posterior segment IOFB were significant predictive factors of poor visual outcome.

scholarly journals Exploring the Value of Lung Texture Features in Distinguishing Usual and Non-specific Interstitial Pneumonia

2021 ◽  
Author(s):  
Xinhui Chen ◽  
Ge Cheng ◽  
Xinguan Yang ◽  
Yuting Liao ◽  
Zhipeng Zhou
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Interstitial Pneumonia ◽  
Roc Curve ◽  
Logistic Regression Model ◽  
Usual Interstitial Pneumonia ◽  
Texture Features ◽  
Feature Reduction ◽  
Multivariate Logistic Regression ◽  
Lung Segmentation

Abstract Backgground At present, the most common types of interstitial pneumonia are usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP), and different types have different prognosis. In addition, if there is a mixture of different classifications, it will be difficult for radiologists to diagnose, and it will make clinical treatment difficult. Therefore, clinicians urgently need new imaging methods to solve such problems. This article aims to explore the CT lung texture images of UIP and NSIP to provide evidence for the identification of UIP and NSIP. Methods A retrospective analysis of 96 cases of interstitial pneumonia diagnosed by the Department of Pathology and the Affiliated Hospital of Guilin Medical College. Among them, there are 40 cases of UIP and 56 cases of NSIP. All patients are scanned by CT. Lung Intelligence Kit was utilized to perform lung segmentation and texture feature extraction. Variance analysis, least absolute shrinkage and selection operator (Lasso) and multivariate logistic regression were used to select effective features. Finally, a multivariate logistic regression model was constructed to identify two kinds of interstitial pneumonia. Receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, specificity were used to evaluate performance of the constructed model. We used the LK software to segment the two sets of lungs. Feature calculation and selection were performed on the data of the two groups of interstitial pneumonia after lung segmentation, the logistic regression model was established for the selected features, and the ROC curve was drawn. Results A total of 100 texture features are extracted from the whole lung segmented by LK, and finally 8 features are left after feature reduction. The above-mentioned values of UIP and NSIP of the training group are greater than those of the test group. Conclusions It is possible to distinguish UIP and NSIP by using LK software to extract lung texture in CT images.

scholarly journals Association between peripheral blood/bronchoalveolar lavage eosinophilia and significant oxygen requirements in patients with acute eosinophilic pneumonia

2020 ◽  
Author(s):  
Joon Young Choi ◽  
Jeong Uk Lim ◽  
Ho Jung Jeong ◽  
Ji Eun Lee ◽  
Chin Kook Rhee
Keyword(s):  
Logistic Regression ◽  
Bronchoalveolar Lavage ◽  
Roc Curve ◽  
Oxygen Supply ◽  
Eosinophilic Pneumonia ◽  
Blood Eosinophil ◽  
Multiple Logistic Regression ◽  
Acute Eosinophilic Pneumonia ◽  
Correlation Analyses ◽  
Oxygen Requirements

Abstract Background We investigated the association between a combination of two markers, peripheral (PEC) and bronchoalveolar lavage (BAL) eosinophil percentage (BEP), and oxygen requirements in patients with acute eosinophilic pneumonia (AEP). Methods We retrospectively reviewed the medical records of patients with AEP treated at the Armed Forces Capital Hospital between May 2012 and May 2017. We used correlation analyses to assess the association between PEC/BEP and clinical outcomes in AEP patients. Receiver operating characteristic (ROC) curve analyses were used to calculate the cut-off value for BEP that categorised patients requiring a significant oxygen supply. The BAL/blood eosinophil (BBE) score was introduced to stratify patients with peripheral eosinophilia and elevated BEP. Clinical characteristics and outcomes were compared between the different groups. Multiple logistic regression was performed for significant oxygen requirements using two different models using age, C-reactive protein (CRP), smoking duration, and BBE score (model 1) and age, CRP, BEP, and PEC (model 2). Results Among the 338 patients, 99.7% were male, and their mean age was 20.4 ± 1.4 years. Only 0.6% of patients were never smokers and the mean number of smoking days was 26.2 ± 25.4. Correlation analyses revealed that both the PaO2/FiO2 ratio and duration of oxygen supply were associated with BEP. ROC curve analyses indicated a cut-off level of 41.5%. Patients with a high BBE score had favourable outcomes in terms of hypoxemia, hospital days, intensive care unit admission, oxygen supply days, and steroid treatment days. Multiple logistic regression revealed that BEP and BBE score tended to be associated with significant oxygen requirements. Conclusions In this study, we revealed that both peripheral and BAL eosinophilia is associated with favourable outcomes in AEP patients.

Sign in / Sign up

Export Citation Format

Share Document