Logistic Regression Coefficients Research Articles

Barriers to Follow-Up Retinal Care During the COVID-19 Pandemic: A Survey Study.

To characterize patient-identified barriers to care in those non-compliant with retina appointments during the coronavirus disease 2019 (COVID-19) pandemic. Inclusion criteria included non-compliant patients from March 1, 2020 to May 1, 2020. Ultimately, 1,345 patients were invited to complete a 14-question survey. A retrospective chart review correlated clinical and demographic information. Univariate logistic regression, independent-samples t-test, and Pearson correlation coefficient identified differences among subgroups. Of the 1,345 patients, 181 (13.5%) completed the survey. The most significant barriers to care included fear of COVID (76/181; 42.0%), wait times (21/181; 11.6%), and costs (11/181; 6.1%). Patients who got their COVID information from the Centers for Disease Control and Prevention (7.8 ± 2.4) and televised news (8.0 ± 2.0) had higher levels of fear. Finally, patients with diabetic retinopathy and higher Charlson Comorbidity Index scores had greater concerns of COVID (P = .034 and P = .047, respectively). This survey study suggests fear of COVID-19 is a prominent new barrier to retinal care. Identifying those at risk for loss to follow-up can guide practices as the pandemic continues. [Ophthalmic Surg Lasers Imaging Retina. 2021;52:526-533.].

MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer.

ObjectiveTo reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa).MethodsMRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2020. PA, central gland sectional area (CGA), and peripheral zone sectional area (PZA) were measured on axial T2-weighted prostate MRI. Multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curve were performed to evaluate and integrate the predictors of PCa. Based on multivariate logistic regression coefficients after excluding combinations of collinear variables, three models and nomograms were generated and intercompared by Delong test, calibration curve, and decision curve analysis (DCA).ResultsThe positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, MRI, transCGA, coroPZA, transPA, and transPAI (transverse PZA-to-CGA ratio) were independent predictors of PCa. Compared with no PCa patients, transCGA (AUC = 0.801) was significantly lower and transPAI (AUC = 0.749) was significantly higher in PCa patients. Both of them have a significantly higher AUC than PSA (AUC = 0.714) and PV (AUC = 0.725). Our best predictive model included the factors age, PSA, MRI, transCGA, and coroPZA with the AUC of 0.918 for predicting PCa status. Based on this predictive model, a novel nomogram for predicting PCa was conducted and internally validated (C-index = 0.913).ConclusionsWe found the potential clinical utility of transCGA and transPAI in predicting PCa. Then, we firstly built the nomogram based on PA and its zone area to evaluate its diagnostic efficacy for PCa, which could reduce unnecessary prostate biopsies.

Interaction between cognitive leisure activity and long-chain polyunsaturated fatty acid intake on global cognitive decline in a Japanese longitudinal cohort study: National Institute for Longevity Sciences-Longitudinal Study of Aging

BackgroundThere is a growing interest in the significance of adopting a variety of lifestyle habits for maintaining cognitive function among older adults. A lifestyle that is easy to modify, simple, and less burdensome for older people is ideal. We investigated the longitudinal association between global cognitive decline and cognitive leisure activities (CLAs) combined with long-chain polyunsaturated fatty acids (LCPUFAs) intake.MethodsThe National Institute for Longevity Sciences-Longitudinal Study of Aging (NILS-LSA) enrolled community-dwelling middle-aged and older men and women who were randomly selected from Obu-City and Higashiura Town, Aichi, Japan. Baseline data (2006–2008), including CLAs and dietary intake, were obtained from 517 participants (aged 60–84 years) with normal cognition. Global cognitive decline, defined as the Mini-Mental State Examination (MMSE) score ≤ 27, was assessed at baseline and four years later. Interaction between CLAs and LCPUFAs on cognitive decline was investigated using a multiple logistic analysis with adjustment for confounders. CLA engagement and LCPUFA intake were divided into high and low groups according to the frequency at which each participant engaged in the activity and the median intake level according to sex, respectively.ResultsA significant interaction was detected for the combination of CLA engagement and LCPUFA intake. Logistic regression coefficients revealed significant interactions when participants engaged in more than five CLA varieties. One of the CLAs, art appreciation, produced a significant main effect against cognitive decline and a significant interaction in combination with LCPUFA intake. The major LCPUFAs—docosahexaenoic acid and arachidonic acid—also exhibited a significant interaction. The combination of high LCPUFA intake and high art appreciation frequency yielded a lower adjusted odds ratio for cognitive decline than the combination of low LCPUFA and low art appreciation [0.25 (95 % confidence intervals, 0.11–0.56)].ConclusionsPreserving cognitive function might be associated with a combination of varied and high-frequency engagement in CLAs combined with high LCPUFA intake.

A nomogram for predicting late radiation-induced xerostomia among locoregionally advanced nasopharyngeal carcinoma in intensity modulated radiation therapy era

Background: Dry mouth sensation cannot be improved completely even though parotids are spared correctly. Our purpose is to develop a nomogram to predict the moderate-to-severe late radiation xerostomia for patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC) in intensity modulated radiation therapy (IMRT) / volumetric modulated arc radiotherapy (VMAT) era.Methods: A dataset of 311 patients was retrospectively collected between January 2010 and February 2013. The binary logistic regression was to estimate each factor’s prognostic value for development of moderate-to-severe patient-reported xerostomia at least 2 years (Xer2y) after completion of radiotherapy. Therefore, we can develop a nomogram according to binary logistic regression coefficients. This novel model was validated by bootstrapping analyses.Results: Contralateral Parotid mean dose (coMD<24.4Gy), VMAT (yes), and platinum-based concurrent chemoradiotherapy (no) were significantly related to patient-reported xerostomia at least 2 years (Xer2y) (all p < 0.001), and were included in the nomogram. Receiver operating characteristic (ROC) analysis revealed AUC (area under the ROC curve) with the value of 0.811 (0.710-0.912) of the nomogram, which was significantly higher than coMD 0.698 (0.560-0.840) from QUANTEC2010 (p<0.001). Calibration plots illustrated that the predicted Xer2y was close to the actual observation, and decision curve analyses (DCA) indicated valid positive net benefits.Conclusion: We developed a feasible nomogram to predict patient-rated Xer2y based on comprehensive individual data in patients with LA-NPC in the real world. The proposed model is able to facilitate the development of treatment plan and quality of life improvement.

Noncollapsibility and its role in quantifying confounding bias in logistic regression

BackgroundConfounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias.MethodsA Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects.ResultsThe simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect.ConclusionIn logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model.

Vovel metrics-novel coupling metrics for improved software fault prediction.

Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability.

Comparison of various approaches to combine logistic regression with genetic algorithms in survival prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common liver cancer in adults. Many different factors make it difficult to diagnose in humans.. In this paper, a novel diagnostics approach based on machine learning techniques is presented. Logistic regression is one of the most classic machine learning models used to solve the problem of binary classification. In typical implementations, logistic regression coefficients are optimized using iterative methods. Additionally, parameters such as solver, C - a regularization parameter or the number of iterations of the algorithm operation should be selected. In our research, we propose a combination of logistic regression with genetic algorithms. We present three experiments showing the fusion of those methods. In the first experiment, we genetically select the logistic regression parameters, while the second experiment extends this approach by including a genetic selection of features. The third experiment presents a novel approach to train the logistic regression model - the genetic selection of coefficients (weights). Our models are tested for the survival prediction of hepatocellular carcinoma based on patient data collected at Coimbra's Hospital and Universitary Center (CHUC), Portugal. The model we proposed achieved a classification accuracy of 94.55% and an f1-score of 93.56%. Our algorithm shows that machine learning techniques optimized by the proposed concept can bring a new and accurate approach in HCC diagnosis with high accuracy.

Examining Farmers’ Resilience to Climate Change and Policy Ramifications in North-West Cameroon

Over half of the world’s population depends on smallholder farms for their daily food needs. However, high levels of vulnerability and low levels of resilience to the adverse effects of climatic variations and changes constitute major threats to smallholder farms and farmers. It is within this context that this paper assessed the levels of resilience of smallholder farmers as well as the factors affecting smallholder farmers’ resilience to climate change in north western Cameroon. A mixed research approach was adopted during data collection, and data analysis was done using descriptive and inferential statistics. It was found that climate extremes were the order of the day, and farmers perceived income level, practice of agroforestry and land accessibility as the main determinants of resilience. The main resilience option practiced by most smallholder farmers was agroforestry. Chi-square and t-test statistics showed the existence of a statistically significant difference (p&lt;0.05) between smallholder farmers resilience to climate change and different environmental, institutional and socio-economic variables. Logistic regression coefficients showed the existence of a statistically significant cause-effect relationship (p&lt;0.05) between farmers’ resilience to climate change and different environmental, institutional and socio-economic variables such as income level, land accessibility, credit accessibility, information accessibility and number of farms. From the foregoing, income level, land accessibility, credit accessibility, information accessibility and number of farms play a significant role towards enhancing smallholder farmers’ resilience to climate change. Policy makers therefore need to factor in these variables when crafting policies geared towards improving smallholder farmers’ resilience to climate change.

Predicting wildfire burns from big geodata using deep learning

Wildfire continues to be a major environmental problem in the world. To help land and fire management agencies manage and mitigate wildfire-related risks, we need to develop tools for mapping those risks. Big geodata—in the form of remotely sensed images, ground-based sensor observations, and topographical datasets—can help us characterize the dynamics of wildfire related events. In this study, we design a deep fully convolutional network, called AllConvNet, to produce daily maps of the probability of a wildfire burn over the next 7 days. We applied it to burns in Victoria, Australia for the period of 2006–2017. Fifteen factors that were extracted from six different datasets and resulted into 29 quantitative features, were selected as input to the network. We compared it with three baseline methods: SegNet, multilayer perceptron, and logistic regression. AllConvNet outperforms the other three baseline methods in four of the six quantitative metrics considered. AllConvNet and SegNet provide smoother and more regularized predicted maps, with SegNet providing greater sensitivity in dificriminating less wildfire-prone locations. Input feature statistical importance was measured for all the networks and compared against logistic regression coefficients. Total precipitation, lightning flash density, and land surface temperature occur to be consistently highly weighted by all models while terrain aspect components, wind direction components, certain land cover classes (such as crop field and woodland), and distance from power lines are ranked on the lower end. We conclude that wild-fire burn prediction methods based on deep learning present quantitative and qualitative gains.

Robust Bayesian approach to logistic regression modeling in small sample size utilizing a weakly informative student’s t prior distribution

This work discourages using the logistic regression (LR) model for estimative purposes when the sample size is small. We propose a new model called the “Robust Bayesian Logistic (RBL) model” that minimizes bias in the estimated logistic regression coefficients when the sample size is small, and covariate corruption is suspected. For the prior specification in the proposed RBL model, all the logistic regression coefficients are assigned independent Student’s t-distribution with the location parameter 0, scale parameter 1, and degree of freedom 7 for the constant term and degree of freedom of 1 for all other regression coefficients. In our experimental study, the proposed RBL model outperforms the Logistic Regression (LR) model by having a lower mean squared error (MSE) in the regression coefficients estimated for all the sample sizes considered. The proposed RBL model has a lower standard deviation than the LR model for all the estimated regression coefficients on the real-life dataset. We suggest that the proposed RBL model be considered for logistic modeling since it generates stable, consistent, and reliable estimates, especially when the sample size is small. The proposed RBL model is a fully Bayesian method implemented in the R environment using the RJAGS package.

A pilot agreement study of a new classification system for Peri-implant femoral fractures

BackgroundPeri-implant fractures occur in association with an implant, used to treat a previous injury that is still attached to the bone. We recently published a proposal for a classification system for peri-implant femoral fractures [Videla-Cés, Injury,2019]. AimTo assess the agreement of our classification system for peri-implant femoral fractures among a group of future users of said classification system. MethodsA prospective, multicentre, international agreement pilot study was conducted among a group of independent traumatologists/orthopaedic surgeons (evaluators): senior (a minimum of 10 years' experience) and junior (in fourth or fifth training year). A set of 30 radiographs of peri-implant femoral fractures were selected (stratified into 3 levels of difficulty: low, medium and high). Each evaluator interpreted the radiographs on 2 different occasions separated by a period of one month and in a different order each time. The level of difficulty was masked from the evaluators and they had one week to classify the radiographs each time. Logistic regression and Cohen's kappa coefficient (κ) and its 95% confidence interval (95%CI) were used to assess the accuracy and agreement, both intra- and inter-evaluator. Between senior and junior surgeons was performed an exploratory analysis. Results35 traumatologists/orthopaedic surgeons (17 senior and 18 junior) from 16 tertiary hospitals from 4 countries acted as evaluators.The accuracy, percentage of correct classifications (2 digits and 3 letters), in the first and second evaluation was: 56% (95%CI: 53-59%) and 55% (95%CI: 51-57%), respectively. (insert space)Negligible differences were found between junior and senior evaluators (first evaluation, OR: 1.46, 95%CI: 0.82-2.61, p-value: 0.199; second evaluation, OR: 1.06, 95%CI: 0.56-2.00, p-value: 0.860). (insert space)Both medium and low radiograph difficulty were associated with a lower probability of an incorrect classification compared with those of high difficulty (first evaluation, OR: 7.60, 95%CI: 5.24-11.05, p-value: <0.001; OR: 14.15, 95%CI: 9.12-21.96, p-value: <0.001, respectively; second evaluation, OR: 7.11, 95%CI: 4.88-10.38, p-value: <0.001, OR: 15.28, 95%CI: 9.77-23.89, p-value: <0.001). (insert space)The kappa for intra-observer agreement between the first and second evaluation was: 0.66, 95%CI: 0.63, 0.69. The kappa for inter-observer agreement considering all 30 radiographs was: 0.40, 95%CI: 0.40, 0.41 in the first evaluation and 0.39, 95%CI: 0.39, 0.40 in the second evaluation. ConclusionThe proposed classification for peri-implant femoral fractures may be useful and user-friendly. Future studies are needed to assess the how clinically useful this classification system may be (the third phase in the validation process).

CEO Bonuses as Studied by Robust Logistic Regression

This investigation utilized a robust logistic regression method (BYLOGREG) to investigate CEO bonuses prior to the 2007-2009 financial crisis. The robust logistic regression analysis determined that the year and CEO tenure affected the probability that a CEO received a bonus in the 2004-2006 study period. The analysis refuted that ”management entrenchment” widely influenced CEO bonus compensation because the probability of receiving a bonus was negatively related to CEO tenure. The probability of receiving of bonus declined during the 2004-2006 study period because the percentage of CEOs that received a bonus was lowest in 2006. The robust logistic regression analysis found that the current year stock return was positively and statistically significantly related to the probability that a CEO received a bonus. The analysis also showed that managerial (financial) performance in the areas of growth of sales, ROE, and growth in earnings per share increased the probability that a CEO received a bonus. In this investigation, the size of the firm and the growth rate of equity were not statistically significant. Overall, robust logistic regression correctly classified 77% of the observations on the basis of the model variables, which indicated that most CEO bonuses could be explained by firm, CEO, and financial variables.The BY robust logistic regression proved to be robust to outliers in the CEO bonus sample studied. Interestingly, the relationship between stock return and the probability of a bonus was completely missed by a maximum likelihood (ML) logistic regression with the full CEO bonus sample, which contained outliers. After trimming the CEO bonus data set to remove outliers, the ML logistic regression coefficients changed dramatically. However, the BY robust logistic regression coefficients changed very little. Use of the residuals from the BY robust logistic equation should facilitate further inquiry into CEOs that received a bonus but were predicted to have a low probability of a bonus.

Do neuroticism and extraversion personality traits influence disease-specific risk factors for mortality from cancer and cardiovascular disease in a Japanese population?

ObjectiveAlthough many reports discussing the associations between personality traits and mortality have been published internationally, those evaluating the relationships among personality, risk factors, and mortality of cancer and CVD are limited. MethodsIn a prospective cohort study, we assessed the relationship of neuroticism and extraversion traits with mortality from cancer and cardiovascular disease (CVD) in 11,554 Japanese residents (male, n = 4995; female, n = 6559), and whether the risk factors that have been validated in the Japanese population mediated the relationship. The baseline survey was conducted between February 2004 and August 2007, and the participants were followed until the date of death or December 31, 2013. ResultsNeuroticism was positively associated with risk factors for cancer and negatively associated with the risk score for CVD in both sexes. The relationship between extraversion and cancer risk factors differed depending on the factors, and a positive association between extraversion and the CVD risk score was observed only in men. Among cancer mortality, CVD mortality, and mortality due to other causes, cancer mortality showed remarkably negative association with neuroticism in women; unadjusted hazard ratio for the highest tertile versus the lowest tertile was 0.41 (95% confidence interval [CI], 0.23–0.73). While the logistic regression coefficients changed 19% after adjustment for age, it changed no more than 19% after adjustment for age and risk factors. ConclusionWhile neuroticism was negatively associated with cancer mortality in women, the mediating effect of the risk factors was small.

Risk factors affecting the surgical outcome of severe tracheal stenosis

Objective:To investigate the risk factors affecting the surgical outcome of severe tracheal stenosis. Methods:The data of 36 patients with severe tracheal stenosis were analyzed retrospectively. All patients underwent tracheal stenosis resection with primary end-to-end anastomosis.Six factors including the location of tracheal stenosis, the length of resected trachea, the degree of stenosis, scar constitution, diabetes and gastroesophageal reflux disease were analyzed by univariate analysis and multivariate Logistic regression analysis. Results:Finally, 23 cases were extubated,including 19 cases with successful extubation and 4 cases with granulation tissue removed by bracing laryngoscope.Univariate analysis showed that the location of tracheal stenosis, the length of resected trachea,scar constitution, diabetes and gastroesophageal reflux disease were the risk factors affecting the surgical efficacy of severe tracheal stenosis.The multivariate Logistic regression coefficients of each factor were 2.857, 1.761, 3.123, -1.066, 3.545 respectively. Conclution: The risk factors affecting the outcome of severe tracheal decannulation rate were the location of tracheal stenosis, the length of resected trachea, scar constitution, diabetes and gastroesophageal reflux disease. Among them, the stenosis position, the length of the resected trachea, scar constitution and gastroesophageal reflux disease had more significant effects on the prognosis of the operation, and the comprehensive evaluation of these risk factors before operation was conducive to improve the surgical effect.

New insights into modeling exposure measurements below the limit of detection.

Background:In environmental epidemiology, it is of interest to assess the health effects of environmental exposures. Some exposure analytes present values that are below the laboratory limit of detection (LOD). There have been many methods proposed for handling this issue to incorporate exposures subject to LOD in risk modeling using logistic regression. We present a fresh look at proposed methods to handle exposure analytes that present values that are below the LOD.Methods:We performed comparisons through an extensive simulation study and a cancer epidemiology example. The methods we considered were a maximum-likelihood approach, multiple imputation, Cox regression, complete case analysis, filling in values below the LOD with , and a missing indicator method.Results:We found that the logistic regression coefficient associated with the exposure (subject to LOD) can be severely biased when underlying assumptions are not met, even with a relatively small proportion (under 20%) of measurements below the LOD.Conclusions:We propose the use of a simple method where the relationship between the analyte and disease risk is modeled only above the detection limit with an intercept term at the LOD and a slope parameter, which makes no assumptions about what happens below the LOD. In most practical situations, our results suggest that this simple method may be the best choice for analyzing analytes with detection limits.

Prediction Modelling of COVID-19 Outbreak in Indonesia using a Logistic Regression Model

The COVID-19 outbreak has changed the world at large since it was announced by the World Health Organization (WHO). Many policies in various countries were then implemented to control its spread. Most aspects of human life and the environment are affected by this pandemic. This paper aims to determine the prediction model for the COVID-19 outbreak in Indonesia. The approach used for this modelling employs a logistic regression model. The data modeled in this paper is data on the distribution of COVID-19 sufferers and data on patients who have recovered from COVID-19 in Indonesia. The data obtained as research material were taken from March 2, 2020, to November 12, 2020. From the results of this paper, this prediction model obtained logistic regression coefficient values for data on COVID-19 sufferers in Indonesia of 8.114748 and 0.750743, while the coefficient values for data on sufferers who recovered from COVID-19 were 9.360925 and 0.788334. The results of the prediction model for sufferers and people who have recovered from COVID-19 have the same accuracy value, namely mean absolute error (MAE) of 0.02, mean square error (MSE) of 0.00, and R2 of 0.99.

«DIARISK» — the first national prediabetes and diabetes mellitus type 2 risk calculator

BACKGRAUND: The need to create a national questionnaire for screening the population for disorders of carbohydrate metabolism.AIMS: Develop a highly specific and sensitive questionnaire-calculator of the risk of prediabetes (PreD) and diabetes mellitus type 2 (DM2T) based on depersonalized data of individuals who have these diagnoses confirmed in the national epidemiological study NATION.METHODS: The express method of prediabetes (PreD) and diabetes mellitus type 2 (DM2T) risks has been created, validated and proposed for clinical usage for the first time in the Russian Federation, it was based of NATION epidemiologic study database (more than 26 000 participants). The following mathematical instruments have been used: mathematic modelling, main components methodology, binary variables, correlation analysis and single-factor analysis of variance.RESULTS: Highest coefficients of logistic regression for PreD (p&lt;0.05) were identified for age — ≥52 years old for women and ≥59 years old for men (1,48); body mass index ≥31 (0.75); waist circumference ≥100 сm for women and ≥112 cm for men (0.90). In the same time coefficients for DM2T were for for age — ≥52 years old for women and ≥59 years old for men (1,24); body mass index ≥31 (1,19), high blood fasting glucose analysis in the past (1,37); waist circumference / hip circumference ratio ≥0.85 for women and ≥0.92 for men (1,05), high blood pressure (0.69). Correlation coefficients were taken into account and score has been used for specialized questionnaire creation. Sensitivity and specificity for PreD were 83,8% and 59,3% accordingly, for DM2T — 85,4% and 73,6% accordingly. DM2T may be suspected when total score of questionnaire is 11 and higher, and PreD — when total score will be ≥7 and &lt;11.CONCLUSIONS: The calculator based on questionnaire (DIARISK) allows to separate of potential patient for PreD and DM2T confirmation during first examination or dispanserization. A patent for innovation has been issued.

Determinants and Policy Ramifications of Cocoa Farmers’ Use of Agrochemicals in Cocoa-Based (Theobroma cacao) Agroforestry Systems in Cameroon

The application of agrochemicals in agroforestry systems in general and cocoa-based agroforestry systems in particular is unprecedented as agroforestry systems are considered as environmentally benign, agro-ecological and sustainable agricultural systems. It was within this context that this study was undertaken to examine the determinants of cocoa farmers’ use of agrochemical in cocoa-based agroforestry systems. Through a mixed research approach, and data analysis using appropriate descriptive and inferential statistical tools, it was found that the main types of herbicides used by cocoa farmers in cocoa-based agroforestry systems are Paraquat (50%) and Glyphosate (40%). For fungicides, the main types used were Mancozeb (70%), Maneb (65%), Ridomil (60%), Caocobre (50%), and Nordox (40%). For insecticides, the main types used were Methyl (65%), Imidacloprid (60%), Endosulfan (50%), Cypermethrin (50%), and Fenobucarp (40%). The most common pests and diseases affecting cocoa perceived by cocoa farmers in cocoa-based agroforestry systems were Black pod – Phytophtora spp (100%), Capsid/Mirids – Distantiella theobroma and Sahlbergella singularis (100%), Cocoa Swollen Shoot Virus Disease – CSSVD (60%), Witches’ broom – Crinipellis perniciosa (50%) and Vascular streak dieback (40%). Most cocoa farmers perceived that the use of all three agrochemicals (herbicides, fungicides, and insecticides) in cocoa-based agroforestry systems have negative socio-economic and environmental impacts. From Spearman rank correlation and logistic regression coefficients, it was noticed that the main explanatory variables having a statistically significant direct non-cause-effect and cause-effect relationship (p&lt;0.05) with cocoa farmers’ use of agrochemicals in cocoa-based agroforestry systems were gender, income, availability of agrochemicals, access to information, membership in farming groups, and access to extension services. Meanwhile the main explanatory variables having a statistically significant indirect or inverse non-cause-effect and cause-effect relationship (p&lt;0.05) with cocoa farmers’ use of agrochemicals in cocoa-based agroforestry systems were farm size and number of farms. It is recommended that policy makers take a critical look at the different agrochemicals used by cocoa farmers as well as the determinants of their use when formulating policies geared towards ensuring the sustainable use of these agrochemicals in cocoa-based agroforestry systems.

Predicting dropout in online learning environments

Online learning environments became popular in recent years. Due to high attrition rates, the problem of student dropouts became of immense importance for course designers, and course makers. In this paper, we utilized lasso and ridge logistic regression to create a prediction model for dropout on the Open University database. We investigated how early dropout can be predicted, and why dropouts occur. To answer the first question, we created models for eight different time frames, ranging from the beginning of the course to the mid-term. There are two results based on two definitions of dropout. Results show that at the beginning AUC of the prediction model is 0.549 and 0.661 and rises to 0.681 and 0.869 at mid-term. By analyzing logistic regression coefficients, we showed that at the beginning of the course demographic features of the student and course description features are the most important variables for dropout prediction, while later student activity gains more importance.

«ДИАРИСК» — первый отечественный калькулятор риска предиабета и сахарного диабета 2 типа

BACKGRAUND: The need to create a national questionnaire for screening the population for disorders of carbohydrate metabolism. AIMS: Develop a highly specific and sensitive questionnaire-calculator of the risk of prediabetes (PreD) and diabetes mellitus type 2 (DM2T) based on depersonalized data of individuals who have these diagnoses confirmed in the national epidemiological study NATION . METHODS: The express method of prediabetes (PreD) and diabetes mellitus type 2 (DM2T) risks has been created, validated and proposed for clinical usage for the first time in the Russian Federation, it was based of NATION epidemiologic study database (more than 26 000 participants). The following mathematical instruments have been used: mathematic modelling, main components methodology, binary variables, correlation analysis and single-factor analysis of variance . RESULTS: Highest coefficients of logistic regression for PreD (p<0.05) were identified for age — ≥52 years old for women and ≥59 years old for men (1,48); body mass index ≥31 (0.75); waist circumference ≥100 сm for women and ≥112 cm for men (0.90). In the same time coefficients for DM2T were for for age — ≥52 years old for women and ≥59 years old for men (1,24); body mass index ≥31 (1,19), high blood fasting glucose analysis in the past (1,37); waist circumference / hip circumference ratio ≥0.85 for women and ≥0.92 for men (1,05), high blood pressure (0.69). Correlation coefficients were taken into account and score has been used for specialized questionnaire creation. Sensitivity and specificity for PreD were 83,8% and 59,3% accordingly, for DM2T — 85,4% and 73,6% accordingly. DM2T may be suspected when total score of questionnaire is 11 and higher, and PreD — when total score will be ≥7 and <11. CONCLUSIONS: The calculator based on questionnaire (DIARISK) allows to separate of potential patient for PreD and DM2T confirmation during first examination or dispanserization. A patent for innovation has been issued.