scholarly journals Bivariate Response Logistic Regression for Categorical Data

2019 ◽  
Vol 1 ◽  
pp. 193-199
Author(s):  
V Adah ◽  
S C Nwaosu ◽  
M E Nja
Keyword(s):  
Logistic Regression ◽  
Categorical Data ◽  
Malaria Infection ◽  
Logistic Model ◽  
Joint Infection ◽  
New Approach ◽  
Explanatory Variables ◽  
Joint Infections ◽  
Bivariate Logistic Regression ◽  
Model Approach

The bivariate logistic regression model can be used to obtain the probability of joint events as well as individual events where there are two response variables and several explanatory variables. The existing bivariate logistic model approach appears intractable. This paper provides a modeling procedure that addresses this problem. This approach compares favourably with the existing procedure. The new approach is used to model the probability of malaria and typhoid infections, using age, sex and location of the patients as associated factors. The marginal probabilities showed a decrease in malaria infection with age. Sex and location showed a significant impact on the probability of malaria infection. Typhoid fever infection on the other hand indicates an increase with age. Sex has no significant impact on the probability of typhoid infection. The joint model shows that all variables are statistically significant with odds value greater than 1 indicating higher likelihood of joint infection and odds value that are less than one indicating lower likelihood of joint infections, χ2:12.02828 (0.00729)

scholarly journals Analysis of Haematological Parameters as Predictors of Malaria Infection Using a Logistic Regression Model: A Case Study of a Hospital in the Ashanti Region of Ghana

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Ellis Kobina Paintsil ◽  
Akoto Yaw Omari-Sasu ◽  
Matthew Glover Addo ◽  
Maxwell Akwasi Boateng
Keyword(s):  
Logistic Regression ◽  
Microscopic Examination ◽  
Peripheral Blood ◽  
Malaria Infection ◽  
Logistic Model ◽  
Malaria Diagnosis ◽  
Blood Film ◽  
Haematological Parameters ◽  
Peripheral Blood Film ◽  
Binary Logistic Model

Malaria is the leading cause of morbidity in Ghana representing 40-60% of outpatient hospital attendance with about 10% ending up on admission. Microscopic examination of peripheral blood film remains the most preferred and reliable method for malaria diagnosis worldwide. But the level of skills required for microscopic examination of peripheral blood film is often lacking in Ghana. This study looked at determining the extent to which haematological parameters and demographic characteristics of patients could be used to predict malaria infection using logistic regression. The overall prevalence of malaria in the study area was determined to be 25.96%; nonetheless, 45.30% of children between the ages of 5 and 14 tested positive. The binary logistic model developed for this study identified age, haemoglobin, platelet, and lymphocyte as the most significant predictors. The sensitivity and specificity of the model were 77.4% and 75.7%, respectively, with a PPV and NPV of 52.72% and 90.51%, respectively. Similar to RDT this logistic model when used will reduce the waiting time and improve the diagnosis of malaria.

Incorporation of Twins in the Regressive Logistic Model for Pedigree Disease Data

1990 ◽  
Vol 39 (2) ◽  
pp. 173-180 ◽  
Author(s):  
J.L. Hopper ◽  
J.B. Carlin ◽  
G.T. Macaskill ◽  
P.L. Derrick ◽  
L.B. Flander ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Logistic Model ◽  
Disease Prevalence ◽  
Conditional Probabilities ◽  
Multivariate Normal ◽  
Explanatory Variables ◽  
Regression Modelling ◽  
Genes And Environment ◽  
The Family ◽  
Logistic Regression Modelling

AbstractSegregation and twin disease concordance analyses have assumed a theoretical underlying liability following a multivariate normal distribution. For reasons of computation, of incorporation of measured explanatory variables, and of testing of fit and assumptions, newer analytical methods are being developed. The regressive logistic model (RLM) relies on expressing the pedigree likelihood as a product of conditional probabilities, one for each individual. In addition to logistic regression modelling of measured epidemiological variables on disease prevalence, there is modelling of vertical transmission, of transmission of unmeasured genotypes and of sibship environment. This paper discusses methods for the analysis of binary traits in twins and in pedigrees. Some extensions to the RLM for pedigrees which include twins are proposed. These enable exploration of twin concordance in the context of the twins' common parenthood, the sibship similarities within the family, and the twins' similarity in age, sex, genes and environment.

scholarly journals Logistic Regression to Evaluate the Marketability of Pepper Cultivars

Agronomy ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 125 ◽  
Author(s):  
Manuel Díaz-Pérez ◽  
Ángel Carreño-Ortega ◽  
José-Antonio Salinas-Andújar ◽  
Ángel-Jesús Callejón-Ferre
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fruit Quality ◽  
Logistic Model ◽  
Sweet Pepper ◽  
Continuous Variables ◽  
Statistical Tool ◽  
Explanatory Variables ◽  
Fruit Samples ◽  
Binary Logistic Model

The goal of this paper is to show that logistic regression is an analytical method of interest to evaluate the marketability of different pepper (Capsicum annuum L.) cultivars. Two studies were conducted on “Italian sweet” pepper cultivars. Fruit samples were introduced in storage chambers and kept at 9 °C and 85–95% relative humidity during the study period. The fruits were evaluated individually and periodically by measuring the deterioration of fruit quality (rot, ageing, etc.). In this study, categorical explanatory variables (rot, etc.) and continuous explanatory variables (days of storage) were integrated and combined to determine the probability of marketability of the fruit. The results show that the binary logistic model is a useful statistical tool to analyse together both categorical and continuous variables in the study of the marketability of pepper cultivars.

scholarly journals Online health information-seeking behaviors and skills of Chinese college students

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dangui Zhang ◽  
Weixin Zhan ◽  
Chunwen Zheng ◽  
Jinsheng Zhang ◽  
Anqi Huang ◽  
...  
Keyword(s):  
College Students ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Health Information ◽  
Information Seeking ◽  
Online Health Information ◽  
Chinese College Students ◽  
Link Type ◽  
Bivariate Logistic Regression ◽  
Information Seeking Behaviors

Abstract Background Seeking online health information (OHI) has become a common practice globally. The information seekers could face health risks if they are not proficient in OHI literacy. The OHI-seeking behaviors and skills of Chinese college students, the largest proportion of college students in the world, are understudied. This study was aimed to describe OHI-seeking behaviors and skills of college students in Guangdong, China. Methods College students in the Guangdong province with OHI-seeking experience were invited via WeChat, QQ, and Sina Weibo using QR code posters and flyers for participation in this online anonymized questionnaire-based study. Data on demographics, OHI literacy, information resources, search approaches, and behaviors were collected. The relationship between perceived OHI literacy and high-risk behaviors was investigated by bivariate logistic regression analysis. Results Respondents were 1203 college students with a mean age of 20.6 years, females (60.2%), and undergraduates (97.2%). They sought health information via websites (20.3%), WeChat (2.6%), or both (77.1%). Baidu was the main search engine, and baike.baidu.com (80.3%), Zhihu.com (48.4%), and Zhidao.baidu.com (35.8%) were top three among 20 searched websites for information about self-care (80.7%), general health (79.5%), disease prevention (77.7%), self-medication (61.2%), family treatment (40.9%), drugs (37.7%), western medications (26.6%), hospitals (22.7%), physicians (21.4%), and Traditional Chinese Medicine (15.6%). Despite most respondents (78%) lacked confidence in the evidence quality and satisfaction with the results, only 32.4% further consulted doctors. Many (> 50%) would recommend the retrieved information to others. About 20% experienced hacking/Internet fraud. Cronbach’s alpha for the internal consistency of OHI literacy was 0.786. Bivariate logistic regression analysis showed that students who believed they can judge the evidence level of OHI were more likely to self-diagnose (OR = 2.2, 95%CI, 1.6–3.1) and look for drug usage (OR = 3.1, 95%CI, 1.9–5.0). Conclusions This study reveals Chinese college students’ heavy reliance on OHI to manage their own and others’ health without sufficient knowledge/skills to identify misinformation and disinformation. The apparent risky information-seeking behaviors of Chinese college students warrant the provision of regulated, accurate, and actionable health information; assurance of cybersecurity; and health information literacy promotion in colleges by concerned authorities.

scholarly journals Nanotechnology as an Anti-Infection Strategy in Periprosthetic Joint Infections (PJI)

2021 ◽  
Vol 6 (2) ◽  
pp. 91
Author(s):  
Pier Francesco Indelli ◽  
Stefano Ghirardelli ◽  
Ferdinando Iannotti ◽  
Alessia Maria Indelli ◽  
Gennaro Pipino
Keyword(s):  
Joint Infection ◽  
Treatment Strategies ◽  
High Morbidity ◽  
Treatment Protocols ◽  
Joint Infections ◽  
Prevention And Treatment ◽  
Efficient Control ◽  
Cement Joint ◽  
Clay Nanotubes

Background: Periprosthetic joint infection (PJI) represents a devastating consequence of total joint arthroplasty (TJA) because of its high morbidity and its high impact on patient quality of life. The lack of standardized preventive and treatment strategies is a major challenge for arthroplasty surgeons. The purpose of this article was to explore the potential and future uses of nanotechnology as a tool for the prevention and treatment of PJI. Methods: Multiple review articles from the PubMed, Scopus and Google Scholar databases were reviewed in order to establish the current efficacy of nanotechnology in PJI preventive or therapeutic scenarios. Results: As a prevention tool, anti-biofilm implants equipped with nanoparticles (silver, silk fibroin, poly nanofibers, nanophase selenium) have shown promising antibacterial functionality. As a therapeutic tool, drug-loaded nanomolecules have been created and a wide variety of carrier materials (chitosan, titanium, calcium phosphate) have shown precise drug targeting and efficient control of drug release. Other nanotechnology-based antibiotic carriers (lipid nanoparticles, silica, clay nanotubes), when added to common bone cements, enhanced prolonged drug delivery, making this technology promising for the creation of antibiotic-added cement joint spacers. Conclusion: Although still in its infancy, nanotechnology has the potential to revolutionize prevention and treatment protocols of PJI. Nevertheless, extensive basic science and clinical research will be needed to investigate the potential toxicities of nanoparticles.

scholarly journals Bone and Joint Infections among Hematopoietic Stem Cell Transplant Recipients

2019 ◽  
Vol 4 (5) ◽  
pp. 209-215
Author(s):  
Cybele Lara Abad ◽  
Vania Phuoc ◽  
Prashant Kapoor ◽  
Pritish K. Tosh ◽  
Irene G. Sia ◽  
...  
Keyword(s):  
Stem Cell ◽  
Septic Arthritis ◽  
Hematopoietic Stem Cell ◽  
Joint Infection ◽  
Cell Transplant ◽  
Hematopoietic Stem ◽  
Joint Infections ◽  
Bone And Joint Infections ◽  
Prosthetic Joint ◽  
Increased Risk

Abstract. Background: Hematopoietic stem cell transplantation (HSCT) recipients are at increased risk for infection. This study describes bone and joint infections (BJI) among HSCT recipients.Methods: We reviewed 5861 patients who underwent HSCT at Mayo Clinic, Rochester, MN from January 1, 2005 through January 1, 2015 for study inclusion. BJI was defined as native septic arthritis, prosthetic joint infection, osteomyelitis, and orthopedic implant infection. All adults with BJI after HSCT were included in the analysis.Results: Of 5861 patients, 33 (0.6%) developed BJI. Native joint septic arthritis was the most common BJI occurring in 15/33 (45.4%) patients. Patients were predominantly male (24/33, 72.7%), with median age of 58 (range 20-72) years. BJI was diagnosed a median of 39 (range 1-114) months after allogeneic (14/33, 42.4%) or autologous (19/33, 57.6%) HSCT. Organisms were recovered via tissue (24/27, 88.9%), synovial fluid (13/17, 76.5%), and/or blood cultures (16/25, 64%). Most underwent surgical debridement (23/33, 69.7%). Patients were followed a median of 78.3 months (range 74-119). Therapy was unsuccessful in 4/33 (12.1%), with death related to the underlying BJI in two (50%). Failure occurred a median of 3.4 (0.1-48.5) months from diagnosis. At last follow up, 7/33 (21.2%) patients were alive. Median overall survival was 13 months (0.07-70.6).Conclusion: BJI among HSCT recipients is infrequent. The most common infection is native joint septic arthritis. Pathogens appear similar to patients without HSCT. Treatment involving surgical-medical modalities is successful, with most patients surviving >1 year after BJI.

Modelling shallow landslide susceptibility: a new approach in logistic regression by using favourability assessment

2009 ◽  
Vol 99 (3) ◽  
pp. 661-674 ◽  
Author(s):  
María José Domínguez-Cuesta ◽  
Montserrat Jiménez-Sánchez ◽  
Ana Colubi ◽  
Gil González-Rodríguez
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Shallow Landslide ◽  
New Approach

Outcomes of single- and two-stage revision total knee arthroplasty for chronic periprosthetic joint infection

2021 ◽  
Vol 103-B (8) ◽  
pp. 1373-1379
Author(s):  
Hosam E. Matar ◽  
Benjamin V. Bloch ◽  
Susan E. Snape ◽  
Peter J. James
Keyword(s):  
Total Knee Arthroplasty ◽  
Knee Arthroplasty ◽  
Joint Infection ◽  
Recurrent Infection ◽  
Primary Outcome Measure ◽  
Revision Total Knee Arthroplasty ◽  
Single Stage ◽  
Two Stage ◽  
Joint Infections ◽  
Total Knee

Aims Single-stage revision total knee arthroplasty (rTKA) is gaining popularity in treating chronic periprosthetic joint infections (PJIs). We have introduced this approach to our clinical practice and sought to evaluate rates of reinfection and re-revision, along with predictors of failure of both single- and two-stage rTKA for chronic PJI. Methods A retrospective comparative cohort study of all rTKAs for chronic PJI between 1 April 2003 and 31 December 2018 was undertaken using prospective databases. Patients with acute infections were excluded; rTKAs were classified as single-stage, stage 1, or stage 2 of two-stage revision. The primary outcome measure was failure to eradicate or recurrent infection. Variables evaluated for failure by regression analysis included age, BMI, American Society of Anesthesiologists grade, infecting organisms, and the presence of a sinus. Patient survivorship was also compared between the groups. Results A total of 292 consecutive first-time rTKAs for chronic PJI were included: 82 single-stage (28.1%); and 210 two-stage (71.9%) revisions. The mean age was 71 years (27 to 90), with 165 females (57.4%), and a mean BMI of 30.9 kg/m2 (20 to 53). Significantly more patients with a known infecting organism were in the single-stage group (93.9% vs 80.47%; p = 0.004). The infecting organism was identified preoperatively in 246 cases (84.2%). At a mean follow-up of 6.3 years (2.0 to 17.6), the failure rate was 6.1% in the single-stage, and 12% in the two-stage groups. All failures occurred within four years of treatment. The presence of a sinus was an independent risk factor for failure (odds ratio (OR) 4.97; 95% confidence interval (CI) 1.593 to 15.505; p = 0.006), as well as age > 80 years (OR 5.962; 95% CI 1.156 to 30.73; p = 0.033). The ten-year patient survivorship rate was 72% in the single-stage group compared with 70.5% in the two-stage group. This difference was not significant (p = 0.517). Conclusion Single-stage rTKA is an effective strategy with a high success rate comparable to two-stage approach in appropriately selected patients. Cite this article: Bone Joint J 2021;103-B(8):1373–1379.

The multi-model approach for fractional-order systems modelling

2016 ◽  
Vol 40 (1) ◽  
pp. 331-340 ◽  
Author(s):  
Samia Talmoudi ◽  
Moufida Lahmari
Keyword(s):  
Transfer Function ◽  
Fractional Order ◽  
Global Model ◽  
Fractional Order Systems ◽  
New Approach ◽  
Physical Systems ◽  
Integer Order ◽  
Systems Modelling ◽  
Model Approach

Currently, fractional-order systems are attracting the attention of many researchers because they present a better representation of many physical systems in several areas, compared with integer-order models. This article contains two main contributions. In the first one, we suggest a new approach to fractional-order systems modelling. This model is represented by an explicit transfer function based on the multi-model approach. In the second contribution, a new method of computation of the validity of library models, according to the frequency [Formula: see text], is exposed. Finally, a global model is obtained by fusion of library models weighted by their respective validities. Illustrative examples are presented to show the advantages and the quality of the proposed strategy.

Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil's Federal District

2019 ◽  
Vol 28 (1) ◽  
pp. 35 ◽  
Author(s):  
Pablo Pozzobon de Bem ◽  
Osmar Abílio de Carvalho Júnior ◽  
Eraldo Aparecido Trondoli Matricardi ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes
Keyword(s):  
Logistic Regression ◽  
Characteristic Curve ◽  
Federal District ◽  
Burned Area ◽  
Ann Model ◽  
Wildfire Management ◽  
Explanatory Variables ◽  
Burned Areas ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. In this work, we applied two data-mining models commonly used to predict fire occurrence – logistic regression (LR) and an artificial neural network (ANN) – to Brazil’s Federal District, located inside the Brazilian Cerrado. We used Landsat-based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors as explanatory variables. The models were optimised via feature selection for best area under receiver operating characteristic curve (AUC) and then validated with real burn area data. The models had similar performance, but the ANN model showed better AUC (0.77) and accuracy values when evaluating exclusively non-burned areas (73.39%), whereas it had worse accuracy overall (66.55%) when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. The main driving aspects of the burned area distribution were land-use type and elevation. The results showed good performance for both models tested. These studies are still scarce despite the importance of the Brazilian savanna.

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