Search results for: logistic regression model

Authors: Julide Yildirim, Nadir Ocal

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Even though there are many studies investigating the extent and determinants of poverty, there is paucity of research investigating the issue of energy poverty in Turkey. The aim of this paper is threefold: First to investigate the extend of energy poverty in Turkey by using Household Budget Survey datasets belonging to 2005 - 2016 period. Second, to examine the risk factors for energy poverty. Finally, to assess the impact of social assistance program participation on energy poverty. Existing literature employs alternative methods to measure energy poverty. In this study energy poverty is measured by employing expenditure approach, where people are considered as energy poor if they disburse more than 10 per cent of their income to meet their energy requirements. Empirical results indicate that energy poverty rate is around 20 per cent during the time period under consideration. Since Household Budget Survey panel data is not available for 2005 - 2016 period, a pseudo panel has been constructed. Panel logistic regression method is utilized to determine the risk factors for energy poverty. The empirical results demonstrate that there is a statistically significant impact of work status and education level on energy poverty likelihood. In the final part of the paper the impact of social transfers on energy poverty has been examined by utilizing panel biprobit model, where social transfer participation and energy poverty incidences are jointly modeled. The empirical findings indicate that social transfer program participation reduces energy poverty. The negative association between energy poverty and social transfer program participation is more pronounced in urban areas compared with the rural areas.

Keywords: energy poverty, social transfers, panel data models, Turkey

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Authors: Areej Al-Khabbaz, Swapna Goerge, Majedah Abdul-Rasoul

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Introduction: The most prevalent periodontal disease among children is gingivitis, and it usually becomes more severe in adolescence. A number of intervention studies suggested that resolution of periodontal inflammation can improve metabolic control in patients diagnosed with diabetes mellitus. Aim: to assess the effect of non-surgical periodontal therapy on glycemic control of children diagnosed with diabetes mellitus. Method: Twenty-eight children diagnosed with diabetes mellitus were recruited with established diagnosis diabetes for at least 1 year. Informed consent and child assent form were obtained from children and parents prior to enrolment. The dental examination for the participants was performed on the same week directly following their annual medical assessment. All patients had their glycosylated hemoglobin (HbA1c%) test one week prior to their annual medical and dental visit and 3 months following non-surgical periodontal therapy. All patients received a comprehensive periodontal examination The periodontal assessment included clinical attachment loss, bleeding on probing, plaque score, plaque index and gingival index. All patients were referred for non-surgical periodontal therapy, which included oral hygiene instruction and motivation followed by supra-gingival and subg-ingival scaling using ultrasonic and hand instruments. Statistical Analysis: Data were entered and analyzed using the Statistical Package for Social Science software (SPSS, Chicago, USA), version 18. Statistical analysis of clinical findings was performed to detect differences between the two groups in term of periodontal findings and HbA1c%. Binary logistic regression analysis was performed in order to examine which factors were significant in multivariate analysis after adjusting for confounding between effects. The regression model used the dependent variable ‘Improved glycemic control’, and the independent variables entered in the model were plaque index, gingival index, bleeding %, plaque Statistical significance was set at p < 0.05. Result: A total of 28 children. The mean age of the participants was 13.3±1.92 years. The study participants were divided into two groups; Compliant group (received dental scaling) and non-complaints group (received oral hygiene instructions only). No statistical difference was found between compliant and non-compliant group in age, gender distribution, oral hygiene practice and the level of diabetes control. There was a significant difference between compliant and non-compliant group in term of improvement of HBa1c before and after periodontal therapy. Mean gingival index was the only significant variable associated with improved glycemic control level. In conclusion, this study has demonstrated that non-surgical mechanical periodontal therapy can improve HbA1c% control. The result of this study confirmed that children with diabetes mellitus who are compliant to dental care and have routine professional scaling may have better metabolic control compared to diabetic children who are erratic with dental care.

Keywords: children, diabetes, metabolic control, periodontal therapy

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Authors: M. Bodner, M. Scampicchio

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Fourier Transform Infrared (FT-IR) spectroscopy coupled with chemometrics was used to distinguish between butter samples and non-butter samples. Further, quantification of the content of margarine in adulterated butter samples was investigated. Fingerprinting region (1400-800 cm^–1) was used to develop unsupervised pattern recognition (Principal Component Analysis, PCA), supervised modeling (Soft Independent Modelling by Class Analogy, SIMCA), classification (Partial Least Squares Discriminant Analysis, PLS-DA) and regression (Partial Least Squares Regression, PLS-R) models. PCA of the fingerprinting region shows a clustering of the two sample types. All samples were classified in their rightful class by SIMCA approach; however, nine adulterated samples (between 1% and 30% w/w of margarine) were classified as belonging both at the butter class and at the non-butter one. In the two-class PLS-DA model’s (R² = 0.73, RMSEP, Root Mean Square Error of Prediction = 0.26% w/w) sensitivity was 71.4% and Positive Predictive Value (PPV) 100%. Its threshold was calculated at 7% w/w of margarine in adulterated butter samples. Finally, PLS-R model (R² = 0.84, RMSEP = 16.54%) was developed. PLS-DA was a suitable classification tool and PLS-R a proper quantification approach. Results demonstrate that FT-IR spectroscopy combined with PLS-R can be used as a rapid, simple and safe method to identify pure butter samples from adulterated ones and to determine the grade of adulteration of margarine in butter samples.

Keywords: adulterated butter, margarine, PCA, PLS-DA, PLS-R, SIMCA

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Authors: Priya Kedia, Kiranmoy Das

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There is a rich literature on variable selection in regression setting. However, most of these methods assume normality for the response variable under consideration for implementing the methodology and establishing the statistical properties of the estimates. In many real applications, the distribution for the response variable may be non-Gaussian, and one might be interested in finding the best subset of covariates at some predetermined quantile level. We develop dynamic Bayesian approach for variable selection in quantile regression framework. We use a zero-inflated mixture prior for the regression coefficients, and consider the asymmetric Laplace distribution for the response variable for modeling different quantiles of its distribution. An efficient Gibbs sampler is developed for our computation. Our proposed approach is assessed through extensive simulation studies, and real application of the proposed approach is also illustrated. We consider the data from health and retirement study conducted by the University of Michigan, and select the important predictors when the outcome of interest is out-of-pocket medical cost, which is considered as an important measure for financial risk. Our analysis finds important predictors at different quantiles of the outcome, and thus enhance our understanding on the effects of different predictors on the out-of-pocket medical cost.

Keywords: variable selection, quantile regression, Gibbs sampler, asymmetric Laplace distribution

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Authors: Hsien Hua Kuo, Wen Chun Lin, Chia Chi Hsu, Hsien Wen Kuo

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Objective: Despite the high prevalence of musculoskeletal disorders (MSDs) among nurses, which has been consistently observed in the studies of Western countries, very little information regarding intensity of workload and work-related quality of life (WRQOL) related to MSDs among nurses is available in Taiwan. The objective of this study is to investigate the factors affecting musculoskeletal disorders in nurses from a hospital. Methods: 550 nurses from a hospital in Taoyuan were interviewed using a modified standardized Nordic Musculoskeletal (NMQ) questionnaire which contained the demographic information, workplace condition and musculoskeletal disorders. Results: Response rate of nurses were 92.5% from a teaching hospital. Based on medical diagnosis by physician, neck of musculoskeletal disorders had the highest percentage in nine body portions. The higher percentage of musculoskeletal disorders in nurses found from wards of internal and surgery. Severity and symptoms of musculoskeletal disorders diagnosed by self-reported questionnaire significantly correlated with WRQOL, job satisfaction and intensity of workload among nurses based on the logistic regression model. Conclusion: The severity and symptoms of musculoskeletal disorders among nurses showed a dose-dependent with WRQOL and workload. When work characteristics in hospital were modified, the severity of musculoskeletal disorders among nurses will be decreased and alleviated. Comment: Multifaceted ergonomic intervention programme to reduce the prevalence of MSDs among nurses was by encouraging nurses to do more physical activity which will make them more flexible and increase their strength. Therefore, the head nurse should encourage nurses to regularly physical activity and to modify unfitting ergonomic environment in order to reduce the prevalence of MSDs.

Keywords: musculoskeletal disorders, nurse, WRQOL, job satisfaction

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Authors: Mohammed Abdullah Alshahrani

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The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

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Authors: Yuzhi Cai

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In this paper, we discuss a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. Together with a combining forecasts technique, we then predict USD to GBP currency exchange rates. Combined forecasts contain all the information captured by the fitted QAR models at different quantile levels and are therefore better than those obtained from individual models. Our results show that an unequally weighted combining method performs better than other forecasting methodology. We found that a median AR model can perform well in point forecasting when the predictive density functions are symmetric. However, in practice, using the median AR model alone may involve the loss of information about the data captured by other QAR models. We recommend that combined forecasts should be used whenever possible.

Keywords: combining forecasts, MCMC, predictive density functions, quantile forecasting, quantile modelling

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Authors: Joseph Marfo Boaheng, Daniel Ansong, Eugenia Amporfo

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Health Insurance is proposed to provide financial protection against catastrophic health care cost arising from disease. Ghana has had a National Health Insurance Scheme (NHIS) since 2003 with the current enrolment/retention rate of 36%. The main goal of the scheme is to provide equity in the health sector as well as ensuring affordable health care for the poor. However, the current payment system is not flexible to attract significant proportion of the poor informal sector onto the scheme. Looking at the extensive use of mobiles in the Ghana where about 29,220,602.00 registered mobile phone lines are actively in used as of June 2014, paying health insurance premium through mobile phone could be feasible to attract larger proportion of the informal sector onto the scheme. Methodology: The quantitative cross-sectional survey was used to solicit the required information from 877 respondents living in Kumasi, the second capital city of Ghana. The magnitude of the effect of Pro-rata system (flexible payment terms) on NHIS enrollment rate was estimated with binary logistic regression model. Results: The odds for an individual to enroll onto NHIS with mobile phone increases about 2 times more when payment of insurance premium is on pro-rata basis ie. flexible payment terms (p=0.008, CI=1.212-3.565). Conclusion: The study advocates the National Health Insurance Authority consider this alternative payment system that has the potential of attracting a greater proportion of the informal sector to be enrolled or retained onto the scheme.

Keywords: enrollment, health insurance, mobile phone, pro-rata

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Authors: Endang Jati Mat Sahid, Hussain Ali Bekhet

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Ability to supply reliable and secure electricity has been one of the crucial components of economic development for any country. Forecasting of electricity production is therefore very important for accurate investment planning of generation power plants. In this study, we aim to examine and analyze the factors that affect electricity generation. Multiple regression models were used to find the relationship between various variables and electricity production. The models will simultaneously determine the effects of the variables on electricity generation. Many variables influencing electricity generation, i.e. natural gas (NG), coal (CO), fuel oil (FO), renewable energy (RE), gross domestic product (GDP) and fuel prices (FP), were examined for Malaysia. The results demonstrate that NG, CO, and FO were the main factors influencing electricity generation growth. This study then identified a number of policy implications resulting from the empirical results.

Keywords: energy policy, energy security, electricity production, Malaysia, the regression model

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Authors: Wenting Zhang, Shishi Liu, Peihong Fu

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As an indicator of air quality and directly related to concentration of ground PM2.5, the spatial-temporal variation and impact factor analysis of Aerosol Optical Depth (AOD) have been a hot spot in air pollution. This paper concerns the non-stationarity and the autocorrelation (with Moran’s I index of 0.75) of the AOD in Wuhan agglomeration (WHA), in central China, uses the geographically weighted regression (GRW) to identify the spatial relationship of AOD and its impact factors. The 3 km AOD product of Moderate Resolution Imaging Spectrometer (MODIS) is used in this study. Beyond the economic-social factor, land use density factors, vegetable cover, and elevation, the landscape metric is also considered as one factor. The results suggest that the GWR model is capable of dealing with spatial varying relationship, with R square, corrected Akaike Information Criterion (AICc) and standard residual better than that of ordinary least square (OLS) model. The results of GWR suggest that the urban developing, forest, landscape metric, and elevation are the major driving factors of AOD. Generally, the higher AOD trends to located in the place with higher urban developing, less forest, and flat area.

Keywords: aerosol optical depth, geographically weighted regression, land use change, Wuhan agglomeration

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Authors: Desplanches Maxime

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Accurate estimation of the remaining useful life of lithium-ion batteries for electronic devices is crucial. Data-driven methodologies encounter challenges related to data volume and acquisition protocols, particularly in capturing a comprehensive range of aging indicators. To address these limitations, we propose a hybrid approach that integrates an electrochemical model with state-of-the-art data analysis techniques, yielding a comprehensive database. Our methodology involves infusing an aging phenomenon into a Newman model, leading to the creation of an extensive database capturing various aging states based on non-destructive parameters. This database serves as a robust foundation for subsequent analysis. Leveraging advanced data analysis techniques, notably principal component analysis and t-Distributed Stochastic Neighbor Embedding, we extract pivotal information from the data. This information is harnessed to construct a regression function using either random forest or support vector machine algorithms. The resulting predictor demonstrates a 5% error margin in estimating remaining battery life, providing actionable insights for optimizing usage. Furthermore, the database was built from the Newman model calibrated for aging and performance using data from a European project called Teesmat. The model was then initialized numerous times with different aging values, for instance, with varying thicknesses of SEI (Solid Electrolyte Interphase). This comprehensive approach ensures a thorough exploration of battery aging dynamics, enhancing the accuracy and reliability of our predictive model. Of particular importance is our reliance on the database generated through the integration of the electrochemical model. This database serves as a crucial asset in advancing our understanding of aging states. Beyond its capability for precise remaining life predictions, this database-driven approach offers valuable insights for optimizing battery usage and adapting the predictor to various scenarios. This underscores the practical significance of our method in facilitating better decision-making regarding lithium-ion battery management.

Keywords: Li-ion battery, aging, diagnostics, data analysis, prediction, machine learning, electrochemical model, regression

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Authors: Mohammad Tahir Yousafzai, Rubina Qasim

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We assessed Hepatitis B vaccination and its determinants among health care workers (HCW) in Northwest Pakistan. HCWs from both public and private clinics were interviewed about hepatitis B vaccination, socio-demographic, hepatitis B virus transmission modes, disease threat and benefits of vaccination. Logistic regression was performed. Hepatitis B vaccination was 40% (Qualified Physicians: 86% and non-qualified Dispensers:16%). Being Qualified Physician (Adj. OR 26.6; 95%CI 9.3-73.2), Non-qualified Physician (Adj.OR 1.9; 95%CI 0.8-4.6), qualified Dispensers (Adj. OR 3.6; 95%CI 1.3-9.5) compared to non-qualified Dispensers, working in public clinics (Adj. OR 2.5; 95%CI 1.1-5.7) compared to private, perceived disease threat after exposure to blood and body fluids (Adj. OR 1.1; 95%CI 1.1-1.2) and perceived benefits of vaccination (Adj. OR 1.1; 95%CI 1.1-1.2) were significant predictors of hepatitis B vaccination. Improved perception of disease threat and benefits of vaccination and qualification of HCWs are associated with hepatitis B vaccination.

Keywords: Hepatitis B vaccine, immunization, healthcare workers, primary health

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Authors: Mohamed Elkoushy, Sameer Munshi, Waseem Tayeb

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Background: Flexible ureteroscopy (fURS) has dramatically improved the minimally invasive management of complex nephrolithiasis. fUR is increasingly being used as the first-line treatment for patients with renal stones. Stone-free status (SFS) is the primary goal in the management of patients with urolithiasis. However, substantial variations exist in the reported SFS following fURS. Objectives: This study determines the predictors of SFS after a single session of fURS with holmium laser lithotripsy (HLL) for renal calculi. Methods: A retrospective review of prospectively collected data was performed for all consecutive patients undergoing fURS and HLL for renal calculi at a tertiary care center. Patients with previous ipsilateral URS for the same stones were excluded. All patients underwent JJ ureteral stent insertion at the end of the procedure. SFS was defined as the presence of no residuals or ≤4-mm non-obstructing stone and was assessed by CT/KUB imaging after 3-4 weeks post-operatively. Multivariate logistic regression was used to detect possible predictors of SFS. Results: A total of 212 patients were included with a mean age of 52.3±8.3 years and a stone burden <20 mm (49.1%), 20-30 mm (41.0%) and >30 mm (9.9%). Overall SFS after a single session of fURS was 71.7%, 92% and 52% for stones less and larger than 20 mm, respectively. Patients with stones> 20 mm need retreatment with a mean number of 1.8 (1.3-2.7) fURS. SFS was significantly associated with male gender, stone bulk <20 mm (95.7% vs. 56.2%), non-lower pole stones, hydronephrotic kidney, low stone intensity, ureteral access sheath, and preoperative stenting. SFS was associated with a lower readmission rate (5.9% vs. 38.9%) and urinary tract infections (3.8% vs. 25.9%). In multivariate regression analysis, SFS maintains its significant association with low stone burden of <20 mm (OR: 5.21), stone intensity <600 HFU (OR: 2.87), and non-lower caliceal stones (OR: 3.84). Conclusion: Best results after a single-session fURS for renal stone were obtained for the stone burden of less than 20 mm and low stone attenuation. Lower calyceal stones may influence stone clearance and need a different approach than fURS, especially for higher stone burden.

Keywords: ureteroscopy, kidney stone, lithotripsy, stone-free, predictors

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Authors: Himansu, Bedanga Talukdar

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The aim of present study is to investigate the prevalence of various health outcomes and quality of life and analyzes the moderating role of social capital on health outcomes (i.e., self-rated good health (SRH), depression, functional health and quality of life) among elderly in India. Using WHO Study on Global AGEing and adults health (SAGE) data, with sample of 6559 elderly between 50 and above (Mage=61.81, SD=9.00) age were selected for analysis. Multivariate analysis accessed the prevalence of SRH, depression, functional limitation and quality of life among older adults. Logistic regression evaluates the effect of social capital along with other co-founders on SRH, depression, and functional limitation, whereas linear regression evaluates the effect of social capital with other co-founders on quality of life (QoL) among elderly. Empirical results reveal that (74%) of respondents were married, (70%) having low social action, (46%) medium sociability, (45%) low trust-solidarity, (58%) high safety, (65%) medium civic engagement and 37% reported medium psychological resources. The multivariate analysis, explains (SRH) is associated with age, female, having education, higher social action great trust, safety and greater psychological resources. Depression among elderly is greatly related to age, sex, education and higher wealth, higher sociability, having psychological resources. QoL is negatively associated with age, sex, being Muslim, whereas positive associated with higher education, currently married, civic engagement, having wealth, social action, trust and solidarity, safeness, and strong psychological resources.

Keywords: depressive symptom, functional limitation, older adults, quality of life, self rated health, social capital

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Authors: Shady Gamal Aziz Shehata

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Difficulty in defining construction quality arises due to perception based on the nature and requirements of the market, the different partners themselves and the results they want. Quantitative research was used in this constructivist research. A case-based study was conducted to assess the structures of positive attitudes and expectations in the context of quality improvement. A survey based on expert opinions was analyzed among construction organizations/companies operating in the construction industry in Pakistan. The financial strength, management structure and construction experience of the construction companies formed the basis of their selection. A good concept is visible at the project level and is seen as the most valuable part of the construction project. Each quality improvement technique was expected to increase the user's profits by improving the efficiency of the construction project. The Survey is useful for construction professionals to evaluate current construction concepts and expectations for the application of quality improvement techniques in construction projects.

Keywords: correlation analysis, lean construction tools, lean construction, logistic regression analysis, risk management, safety construction quality, expectation, improvement, perception

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Authors: S. K. Ashiquer Rahman

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Recently, oil has become more influential in almost every economic sector as a key material. As can be seen from the news, when there are some changes in an oil price or OPEC announces a new strategy, its effect spreads to every part of the economy directly and indirectly. That’s a reason why people always observe the oil price and try to forecast the changes of it. The most important factor affecting the price is its supply which is determined by the number of wildcats drilled. Therefore, a study about the number of wellheads and other economic variables may give us some understanding of the mechanism indicated by the amount of oil supplies. In this paper, we will consider a relationship between the number of wellheads and three key factors: the price of the wellhead, domestic output, and GNP constant dollars. We also add trend variables in the models because the consumption of oil varies from time to time. Moreover, this paper will use an econometrics method to estimate parameters in the model, apply some tests to verify the result we acquire, and then conclude the model.

Keywords: price, domestic output, GNP, trend variable, wildcat activity

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Authors: Maryam Khazaee-Pool, Tahereh Pashaei, Koen Ponnet

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Background: The global prevalence and morbidity of Coronavirus disease 2019 (COVID-19) are high. Preventive behaviors are proven to reduce the damage caused by the disease. There is a paucity of information on determinants of preventive behaviors in response to COVID-19 in Mazandaran province, north of Iran. So, we aimed to evaluate the protection motivation theory (PMT) in promoting preventive behaviors of COVID-19 in Mazandaran province. Materials and Methods: In this descriptive cross-sectional study, 1220 individuals participated. They were selected via social networks using convenience sampling in 2020. Data were collected online using a demographic questionnaire and a valid and reliable scale based on PMT. Data analysis was done using the Pearson correlation coefficient and linear regression in SPSS V24. Result: The mean age of the participants was 39.34±8.74 years. The regression model showed perceived threat (ß =0.033, P =0.007), perceived costs (ß=0.039, P=0.045), perceived self-efficacy (ß =0.116, P>0.001), and perceived fear (ß=0.131, P>0.001) as the significant predictors of COVID-19 preventive behaviors. This model accounted for 78% of the variance in these behaviors. Conclusion: According to constructs of the PMT associated with protection against COVID-19, educational programs and health promotion based on the theory and benefiting from social networks could be helpful in increasing the motivation of people towards protective behaviors against COVID-19.

Keywords: questionnaire development, validation, intention, prevention, covid-19

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Authors: Syed Afrozuddin Ahmed, Junaid Saghir Siddiqi, Sabah Quaiser

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Various studies have reported that global warming causes unstable climate and many serious impact to physical environment and public health. The increasing incidence of dengue incidence is now a priority health issue and become a health burden of Pakistan. In this study it has been investigated that spatial pattern of environment causes the emergence or increasing rate of dengue fever incidence that effects the population and its health. The climatic or environmental structure data and the Dengue Fever (DF) data was processed by coding, editing, tabulating, recoding, restructuring in terms of re-tabulating was carried out, and finally applying different statistical methods, techniques, and procedures for the evaluation. Five climatic variables which we have studied are precipitation (P), Maximum temperature (Mx), Minimum temperature (Mn), Humidity (H) and Wind speed (W) collected from 1980-2012. The dengue cases in Karachi from 2010 to 2012 are reported on weekly basis. Principal component analysis is applied to explore the climatic variables and/or the climatic (structure) which may influence in the increase or decrease in the number of dengue fever cases in Karachi. PC1 for all the period is General atmospheric condition. PC2 for dengue period is contrast between precipitation and wind speed. PC3 is the weighted difference between maximum temperature and wind speed. PC4 for dengue period contrast between maximum and wind speed. Negative binomial and Poisson regression model are used to correlate the dengue fever incidence to climatic variable and principal component score. Relative humidity is estimated to positively influence on the chances of dengue occurrence by 1.71% times. Maximum temperature positively influence on the chances dengue occurrence by 19.48% times. Minimum temperature affects positively on the chances of dengue occurrence by 11.51% times. Wind speed is effecting negatively on the weekly occurrence of dengue fever by 7.41% times.

Keywords: principal component analysis, dengue fever, negative binomial regression model, poisson regression model

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Authors: Gizachew Assefa Tessema, Mohammad Afzal Mahmood, Judith Streak Gomersall, Caroline O. Laurence

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Introduction: Improving access to quality family planning services is the key to improving health of women and children. However, there is currently little evidence on the quality and scope of family planning services provided by private facilities, and this compares to the services provided in public facilities in Ethiopia. This is important, particularly in determining whether the government should further expand the roles of the private sector in the delivery of family planning facility. Methods: This study used the 2014 Ethiopian Services Provision Assessment Plus (ESPA+) survey dataset for comparing the structural aspects of quality of care in family planning services. The present analysis used a weighted sample of 1093 primary health care facilities (955 public and 138 private). This study employed logistic regression analysis to compare key structural variables between public and private facilities. While taking the structural variables as an outcome for comparison, the facility type (public vs private) were used as the key exposure of interest. Results: When comparing availability of basic amenities (infrastructure), public facilities were less likely to have functional cell phones (AOR=0.12; 95% CI: 0.07-0.21), and water supply (AOR=0.29; 95% CI: 0.15-0.58) than private facilities. However, public facilities were more likely to have staff available 24 hours in the facility (AOR=0.12; 95% CI: 0.07-0.21), providers having family planning related training in the past 24 months (AOR=4.4; 95% CI: 2.51, 7.64) and possessing guidelines/protocols (AOR= 3.1 95% CI: 1.87, 5.24) than private facilities. Moreover, comparing the availability of equipment, public facilities had higher odds of having pelvic model for IUD demonstration (AOR=2.60; 95% CI: 1.35, 5.01) and penile model for condom demonstration (AOR=2.51; 95% CI: 1.32, 4.78) than private facilities. Conclusion: The present study suggests that Ethiopian government needs to provide emphasis towards the private sector in terms of providing family planning guidelines and training on family planning services for their staff. It is also worthwhile for the public health facilities to allocate funding for improving the availability of basic amenities. Implications for policy and/ or practice: This study calls policy makers to design appropriate strategies in providing opportunities for training a health care providers working in private health facility.

Keywords: quality of care, family planning, public-private, Ethiopia

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Authors: Jacqueline Y. Thompson, Sam Watson, Lee Middleton, Karla Hemming

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Introduction: The odds ratio (estimated via logistic regression) is a well-established and common approach for estimating covariate-adjusted binary treatment effects when comparing a treatment and control group with dichotomous outcomes. Its popularity is primarily because of its stability and robustness to model misspecification. However, the situation is different for the relative risk and risk difference, which are arguably easier to interpret and better suited to specific designs such as non-inferiority studies. So far, there is no equivalent, widely acceptable approach to estimate an adjusted relative risk and risk difference when conducting clinical trials. This is partly due to the lack of a comprehensive evaluation of available candidate methods. Methods/Approach: A simulation study is designed to evaluate the performance of relevant candidate methods to estimate relative risks to represent conditional and marginal estimation approaches. We consider the log-binomial, generalised linear models (GLM) with iteratively weighted least-squares (IWLS) and model-based standard errors (SE); log-binomial GLM with convex optimisation and model-based SEs; log-binomial GLM with convex optimisation and permutation tests; modified-Poisson GLM IWLS and robust SEs; log-binomial generalised estimation equations (GEE) and robust SEs; marginal standardisation and delta method SEs; and marginal standardisation and permutation test SEs. Independent and identically distributed datasets are simulated from a randomised controlled trial to evaluate these candidate methods. Simulations are replicated 10000 times for each scenario across all possible combinations of sample sizes (200, 1000, and 5000), outcomes (10%, 50%, and 80%), and covariates (ranging from -0.05 to 0.7) representing weak, moderate or strong relationships. Treatment effects (ranging from 0, -0.5, 1; on the log-scale) will consider null (H0) and alternative (H1) hypotheses to evaluate coverage and power in realistic scenarios. Performance measures (bias, mean square error (MSE), relative efficiency, and convergence rates) are evaluated across scenarios covering a range of sample sizes, event rates, covariate prognostic strength, and model misspecifications. Potential Results, Relevance & Impact: There are several methods for estimating unadjusted and adjusted relative risks. However, it is unclear which method(s) is the most efficient, preserves type-I error rate, is robust to model misspecification, or is the most powerful when adjusting for non-prognostic and prognostic covariates. GEE estimations may be biased when the outcome distributions are not from marginal binary data. Also, it seems that marginal standardisation and convex optimisation may perform better than GLM IWLS log-binomial.

Keywords: binary outcomes, statistical methods, clinical trials, simulation study

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Authors: Anan Jarab, Riham Almrayat, Salam Alqudah, Maher Khdour, Tareq Mukattash, Sharell Pinto

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Background: The prevalence of diabetes in Jordan is among the highest in the world, making it a particularly alarming health problem there. It has been indicated that poor adherence to the prescribed therapy lead to poor glycemic control and enhance the development of diabetes complications and unnecessary hospitalization. Purpose: To explore factors associated with medication non-adherence in patients with type 2 diabetes in Jordan. Materials and Methods: Variables including socio-demographics, disease and therapy factors, diabetes knowledge, and health-related quality of life in addition to adherence assessment were collected for 171 patients with type 2 diabetes using custom-designed and validated questionnaires. Logistic regression was performed to develop a model with variables that best predicted medication non-adherence in patients with type 2 diabetes in Jordan. Results: The majority of the patients (72.5%) were non-adherent. Patients were found four times less likely to adhere to their medications with each unit increase in the number of prescribed medications (OR = 0.244, CI = 0.08-0.63) and nine times less likely to adhere to their medications with each unit increase in the frequency of administration of diabetic medication (OR = 0.111, CI = 0.04-2.01). Patients in the present study were also approximately three times less likely (OR = 0.362, CI = 0.24-0.87) to adhere to their medications if they reported having concerns about side effects and twice more likely to adhere to medications (OR = 0.493, CI = 0.08-1.16) if they had one or more micro-vascular complication. Conclusion: The current study revealed low adherence rate to the prescribed therapy among Jordanians with type 2 diabetes. Simplifying dosage regimen, selecting treatments with lower side effects along with an emphasis on diabetes complications should be taken into account when developing care plans for patients with type 2 diabetes.

Keywords: type 2 diabetes, adherence, glycemic control, clinical pharmacist, Jordan

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Authors: Fasanmi Samuel Sunday

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This study investigated the influence of gender and personality traits on emotional distress among awaiting trial inmates in Nigeria. Participants were three hundred and twenty (320) awaiting trial inmates, drawn from three main correctional centres in Northeast Nigeria, namely: Gashua Correctional Centre, Postiskum Correctional Centre, and Bauchi Correctional Centre. Expo facto research design was adopted. Questionnaires such as the Big Five Inventory and the Perceived Emotional Distress Inventory (PEDI) were used to measure the variables of the study. Three hypotheses were tested. Logistic regression was used for data analysis. Results of the analysis indicated that conscientiousness significantly predicted emotional distress among awaiting trial inmates. However, most of the identified personality traits did not significantly predict emotional distress among awaiting trial inmates. There was no significant gender difference in emotional distress among awaiting-trial inmates. The implications of the study were discussed.

Keywords: personality traits, emotional distress, awaiting-trial inmates, gender

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Petra Buzkova, Milos Kopa

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Reliability of the credit default swap market had been questioned repeatedly during the EMU debt crisis. The article examines whether this development influenced sovereign EMU CDS prices in general. We regress the CDS market price on a model risk neutral CDS price obtained from an adopted reduced form valuation model in the 2009-2013 period. We look for a break point in the single-equation and multi-equation econometric models in order to show the changes in relations between CDS market and model prices. Our results differ according to the risk profile of a country. We find that in the case of riskier countries, the relationship between the market and model price changed when market participants started to question the ability of CDS contracts to protect their buyers. Specifically, it weakened after the change. In the case of less risky countries, the change happened earlier and the effect of a weakened relationship is not observed.

Keywords: chow stability test, credit default swap, debt crisis, reduced form valuation model, seemingly unrelated regression

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Authors: Zeytu G. Asfaw, Serkalem K. Abrha, Demisew G. Degefu

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Background: HIV/AIDS remains a major public health problem in Ethiopia and heavily affecting people of productive and reproductive age. We aimed to compare the performance of Parametric Survival Analysis and Bayesian Survival Analysis using simulations and in a real dataset application focused on determining predictors of HIV patient survival. Methods: A Parametric Survival Models - Exponential, Weibull, Log-normal, Log-logistic, Gompertz and Generalized gamma distributions were considered. Simulation study was carried out with two different algorithms that were informative and noninformative priors. A retrospective cohort study was implemented for HIV infected patients under Highly Active Antiretroviral Therapy in Alamata General Hospital, North Ethiopia. Results: A total of 320 HIV patients were included in the study where 52.19% females and 47.81% males. According to Kaplan-Meier survival estimates for the two sex groups, females has shown better survival time in comparison with their male counterparts. The median survival time of HIV patients was 79 months. During the follow-up period 89 (27.81%) deaths and 231 (72.19%) censored individuals registered. The average baseline cluster of differentiation 4 (CD4) cells count for HIV/AIDS patients were 126.01 but after a three-year antiretroviral therapy follow-up the average cluster of differentiation 4 (CD4) cells counts were 305.74, which was quite encouraging. Age, functional status, tuberculosis screen, past opportunistic infection, baseline cluster of differentiation 4 (CD4) cells, World Health Organization clinical stage, sex, marital status, employment status, occupation type, baseline weight were found statistically significant factors for longer survival of HIV patients. The standard error of all covariate in Bayesian log-normal survival model is less than the classical one. Hence, Bayesian survival analysis showed better performance than classical parametric survival analysis, when subjective data analysis was performed by considering expert opinions and historical knowledge about the parameters. Conclusions: Thus, HIV/AIDS patient mortality rate could be reduced through timely antiretroviral therapy with special care on the potential factors. Moreover, Bayesian log-normal survival model was preferable than the classical log-normal survival model for determining predictors of HIV patients survival.

Keywords: antiretroviral therapy (ART), Bayesian analysis, HIV, log-normal, parametric survival models

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Authors: Vadim A. Kuznetsov, Anna M. Soldatova, Tatyana N. Enina, Elena A. Gorbatenko, Dmitrii V. Krinochkin

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The aim of the study was to evaluate potential parameters related with super-response to CRT. Methods: 60 CRT patients (mean age 54.3 ± 9.8 years; 80% men) with congestive heart failure (CHF) II-IV NYHA functional class, left ventricular ejection fraction < 35% were enrolled. At baseline, 1 month, 3 months and each 6 months after implantation clinical, electrocardiographic and echocardiographic parameters, NT-proBNP level were evaluated. According to the best decrease of left ventricular end-systolic volume (LVESV) (mean follow-up period 33.7 ± 15.1 months) patients were classified as super-responders (SR) (n=28; reduction in LVESV ≥ 30%) and non-SR (n=32; reduction in LVESV < 30%). Results: At baseline groups differed in age (58.1 ± 5.8 years in SR vs 50.8 ± 11.4 years in non-SR; p=0.003), gender (female gender 32.1% vs 9.4% respectively; p=0.028), width of QRS complex (157.6 ± 40.6 ms in SR vs 137.6 ± 33.9 ms in non-SR; p=0.044). Percentage of LBBB was equal between groups (75% in SR vs 59.4% in non-SR; p=0.274). All parameters of mechanical dyssynchrony were higher in SR, but only difference in left ventricular pre-ejection period (LVPEP) was statistically significant (153.0 ± 35.9 ms vs. 129.3 ± 28.7 ms p=0.032). NT-proBNP level was lower in SR (1581 ± 1369 pg/ml vs 3024 ± 2431 pg/ml; p=0.006). The survival rates were 100% in SR and 90.6% in non-SR (log-rank test P=0.002). Multiple logistic regression analysis showed that LVPEP (HR 1.024; 95% CI 1.004–1.044; P = 0.017), baseline NT-proBNP level (HR 0.628; 95% CI 0.414–0.953; P=0.029) and age at baseline (HR 1.094; 95% CI 1.009-1.168; P=0.30) were independent predictors for CRT super-response. ROC curve analysis demonstrated sensitivity 71.9% and specificity 82.1% (AUC=0.827; p < 0.001) of this model in prediction of super-response to CRT. Conclusion: Super-response to CRT is associated with better survival in long-term period. Presence of LBBB was not associated with super-response. LVPEP, NT-proBNP level, and age at baseline can be used as independent predictors of CRT super-response.

Keywords: cardiac resynchronisation therapy, superresponse, congestive heart failure, left bundle branch block

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Diabetes in India is growing at an alarming rate and the complications caused by it need to be controlled. Coronary heart disease (CHD) is one of the complications that will be discussed for prediction in this study. India has the second most number of diabetes patients in the world. To the best of our knowledge, there is no CHD risk score for Indian type 2 diabetes patients. Any form of CHD has been taken as the event of interest. A sample of 750 was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of CHD. Predictive risk scores of CHD events are designed by cox proportional hazard regression. Model calibration and discrimination is assessed from Hosmer Lemeshow and area under receiver operating characteristic (ROC) curve. Overfitting and underfitting of the model is checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Youden’s index is used to choose the optimal cut off point from the scores. Five year probability of CHD is predicted by both survival function and Markov chain two state model and the better technique is concluded. The risk scores for CHD developed can be calculated by doctors and patients for self-control of diabetes. Furthermore, the five-year probabilities can be implemented as well to forecast and maintain the condition of patients.

Keywords: coronary heart disease, cox proportional hazard regression, ROC curve, type 2 diabetes Mellitus

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Authors: L. S. Chathurika

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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

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Authors: Fidelis Chigondo

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This study focuses on investigating the effectiveness of watermelon rind in phenol removal from aqueous solution. The effects of various parameters (pH, initial phenol concentration, biosorbent dosage and contact time) on phenol adsorption were investigated. The pH of 2, initial phenol concentration of 40 ppm, the biosorbent dosage of 0.6 g and contact time of 6 h also deduced to be the optimum conditions for the adsorption process. The maximum phenol removal under optimized conditions was 85%. The sorption data fitted to the Freundlich isotherm with a regression coefficient of 0.9824. The kinetics was best described by the intraparticle diffusion model and Elovich Equation with regression coefficients of 1 and 0.8461 respectively showing that the reaction is chemisorption on a heterogeneous surface and the intraparticle diffusion rate only is the rate determining step. The study revealed that watermelon rind has a potential of removing phenol from industrial wastewaters.

Keywords: biosorption, phenol, biosorbent, watermelon rind

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Authors: Wei Yang, Xiaohui Cui, Weiping Zhu, Liqun Liu

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The rapid development of the Confucius Institute is attracting more and more attention from mainstream media around the world. Mainstream media plays a large role in public information dissemination and public opinion. This study presents efforts to analyze the correlation and functional relationship between domestic and foreign mainstream media by analyzing the amount of reports on the Confucius Institute. Three kinds of correlation calculation methods, the Pearson correlation coefficient (PCC), the Spearman correlation coefficient (SCC), and the Kendall rank correlation coefficient (KCC), were applied to analyze the correlations among mainstream media from three regions: mainland of China; Hong Kong and Macao (the two special administration regions of China denoted as SARs); and overseas countries excluding China, such as the United States, England, and Canada. Further, the paper measures the functional relationships among the regions using a regression model. The experimental analyses found high correlations among mainstream media from the different regions. Additionally, we found that there is a linear relationship between the mainstream media of overseas countries and those of the SARs by analyzing the amount of reports on the Confucius Institute based on a data set obtained by crawling the websites of 106 mainstream media during the years 2004 to 2014.

Keywords: mainstream media, Confucius institute, correlation analysis, regression model

Procedia PDF Downloads 318