Binary Logistic Regression Approach Research Articles

Prediction model of factors causing traffic accidents on rural arterial roads: A binary logistic regression approach

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.

Association between sarcopenia grade and fall history among older adults in West China: A retrospective study

ObjectivesThe association between sarcopenia severity and fall history remains under-researched at present. Accordingly, this study was developed to evaluate the relationship between sarcopenic status and prior fall events in a...

Examining factors predicting sexual exploitation among victims of human trafficking

The primary aim of this study is to investigate the factors predicting sexual exploitation among victims of human trafficking, specifically examining how traffickers’ means of control, types of sexual services, nature of recruiter relationships and female age groups play a role in this phenomenon. The data analysed spanned from 2002 to 2019 and were sourced from anonymised public data provided by the Counter-Trafficking Data Collaborative (CTDC), a global hub collecting information from counter-trafficking organisations worldwide. Utilising a binary logistic regression approach, the study identified that traffickers employ various means of control, such as debt bondage, taking earnings, threats, sexual abuse, false promises, use of psychoactive substances, exploitation of children, threat of law enforcement and withholding necessities, aligning with Biderman's Theory of Coercion. Additionally, victims experience prostitution, pornography and private sexual services, often recruited by intimate partners and friends. Furthermore, the study revealed that young women aged 30 years and above were less likely to experience sexual exploitation compared to younger girls.

Factors affecting awareness on good agriculture practices among citrus growers in Palpa, Nepal: Through binary logistic regression approach

Being an indigenous high-value commodity, with significant market demand in the mid-hills of Nepal, citrus requires the incorporation of sustainable techniques in orchard to increase fruit production. For farmers in Nepal, Good Agricultural Practices (GAP) is a novel concept. The majority of farmers are unaware of it, and those who are aware also have not fully embraced the techniques. Therefore, the study was conducted in summer 2022 to assess the factors that affect the awareness of GAP among the farmers at Palpa, Nepal. Rainadevi Chahara and Ribdikot rural municipality of Palpa district, Nepal was purposively selected, and altogether, 64 commercial citrus growers from the municipalities were taken by simple random sampling technique. The binary logistic regression model was used for analyzing the effect of different variables on the awareness of GAP among citrus growers. Different demographic and socioeconomic variables have been found associated for odds of being GAP aware. The findings revealed that, farmers’ who are GAP aware have an access to trainings and contact with extension agents, 4.164 and 10.293 times higher than those farmers who are GAP unaware respectively. Therefore, the study suggests that farmers knowledge on GAP can be further expanded through trainings and frequent contact with extension agents as they are the major factors affecting commercial citrus growers’ awareness to GAP in Palpa district, Nepal.

Demographic factors lead to mobile phone ownership in Tanzania mainland: A binary logistic regression approach

The purpose of this study was to determine the demographic factors that influence mobile phone ownership in Tanzania Mainland. Secondary data from the 2017-2018 Household Budget Survey (HBS), which was conducted by the Tanzania National Bureau of Statistics (NBS), were used in this study. In order to determine which demographic factors influence Tanzanian mobile phone ownership, the Binary logistic regression method was utilized. According to the findings, a person's age, marital status, sex, residence area, and household size are all demographic factors that influence mobile phone ownership. Out of 9463 households, approximately 77.3 percent (7311) have at least one mobile phone, while only 22.7 percent (2152) do not. More than just a means of communication, the mobile phone has changed how people live and keep in touch with one another.

Illicit and Corruption Mitigation Strategy in the Financial Sector: A Study with a Hybrid Methodological Approach

In recent decades, experts from all around the world have been increasingly interested in illegal conduct and corruption in financial organizations. Utilizing the illegal and corruption hypothesis, we investigate the core traits and trends of this phenomenon in European financial institutions. We also examined how specific aspects are incorporated into financial firms’ anti-corruption strategies. The current study makes an effort to comprehend the afore-mentioned challenges using the tools of scientometrics and data analysis. More precisely, our empirical analysis was based on 336 European financial institutions for the years of 2018–2020, and our study also employed bibliographic data from 687 scientific documents indexed in the Scopus database in 2010–2021. The R language’s Bibliometrix tool was applied to analyze the body of the existing literature. A binary logistic regression approach was used to analyze the data. While the goal of our empirical study is to unveil the factors causing or mitigating illegal activity and corruption in the financial sector, the bibliographic analysis revealed the determinants validated in previous work. Our results highlight the need of policy makers to implement measures to limit illegal activity and reduce corruption in financial institutions to improve reputations and customers’ fidelity, which are necessary for the achievement of the sustainable development goals concerning financial institutions. Another conclusion is the emergence of a new source of corruption, which South Africa’s experience has confirmed. Our findings also support two components of the contemporary approach for reducing corruption and illicit activity, namely, the usage of new technology specialists and the incorporation of ESG factors.

Determinants of access to trainings on post – harvest loss management among maize farmers in Uganda: a binary logistic regression approach

Determinants of access to trainings on post – harvest loss management among maize farmers in Uganda: a binary logistic regression approach

ANALISIS KLASIFIKASI MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART) (Studi Kasus: Nasabah Koperasi Simpan Pinjam Dan Pembiayaan Syariah (KSPPS))

The Save Loan and Sharia Financing Cooperatives (KSPPS) is a financial institution that offers deposits, loans, and financing to its members while adhering to Islamic sharia rules. Customers payment behaviour is influenced by their background differences, such as age, gender, occupation, and so on. The classification method is used to determine the characteristics of members who are currently in arears or are stuck in arears. Binary Logistic Regression and Bootstrap Aggregating Classification and Regression Trees were utilized as classification methods (BAGGING CART). A Logistic Regression with binary response variables is known as a Binary Logistic Regression. By resampling 50 times, the technique with the BAGGING process is used to improve the performance of the classification using CART. Customer data from one of the KSPPS in Central Java in 2021 was used in this investigation. Gender, age, marital status, employment, education level, time period, and income were the independent variables in this study, whereas payment status was the dependent variable (not stuck and stuck). The Binary Logistic Regression approach had an accuracy of 78.67 percent with an APER 21.33 percent, a Press's Q of 24.65, and a specificity of 98.30 percent, according to the classification accuracy statistics. The accuracy of the classification produced by CART with an accuracy value of 77.33 percent with an APER 22.67 percent, the value of Press's Q is 22,413, and specificity is 94.91 percent, then approached by BAGGING process the accuracy of the resulting classification by predicting data testing accuracy value of 78.67 percent with an APER 21.33 percent, press's Q value of 24.65, and specificity of 96.61 percent. Based on these findings, it can be inferred that using the BAGGING process can increase the CART method's performance to the point where it is nearly as good as Binary Logistic Regression, which has a slightly higher classification accuracy

Binary logistic regression approach for decision making in bridge management

Bridge management professionals need effective tools to help guide the decision making process and maintain quality infrastructure in a region. Using logistic regression for a novel binary response defined by at-risk and not-at-risk bridges based on their existing overall condition scores, the probability of a bridge being at risk is expressed in terms of the primary bridge factors age, load, the type of construction material and structural design and conditions of the deck, superstructure and substructure. These estimated probabilities multiplied by specified consequence values are used to introduce the risk classes and their ranks. Employing the method for training and validating sets of sizes 13 540 and 3385 in 2017 and 13 481 and 3370 in 2018 data in the National Bridge Inventory (NBI) Indiana, a statistically significant model is established containing the age, load and conditions of both superstructure and substructure. Moreover, at-risk bridges are identified from Indiana NBI data in both years and for a subset from Connecticut in 2017. The innovative bridge-ranking tool prioritises bridges for maintenance purposes such as replacing or repairing and hence efficiently guides the management in the decision making process for capital expenditures and perhaps for predicting the missing overall bridge condition.

P-OGC40 Enhanced recovery after oesophagectomy at Oxford University Hospitals: the benefit of having a discharge target

Abstract Background Enhanced recovery after surgery (ERAS) is a multimodal approach to decreasing morbidity and accelerating return-of-function after major surgery. However, the majority of patients undergoing oesophagectomy for cancer experience some kind of complication, which extends their admission and causes pathway deviation. Thus, the value of having a discharge target (Day 8 at our institution) could be challenged. In this study we reviewed our experience using this pathway, with a focus on: (i) whether length of stay and morbidity has improved since pathway implementation (ii) describing the medical and non-medical reasons for extended admission (iii) identifying pre-operative predictors of pathway deviation. Methods A retrospective review of a prospectively maintained database was performed. Complications are recorded during routine clinical care using standardised definitions set out by the Esophageal Complications Consensus group, and are updated in the database at a weekly departmental meeting. Median and interquartile range were used for descriptors and differences tested with Mann-Whitney U test or Chi squared tests, using SPSS ver 26. Multivariable models were fitted to establish independent predictors of pathway deviation, using a binary logistic regression approach with bootstrapping. Results 150 of 448 patients met the 8-day target. Patients treated since 2018 were significantly more likely meet their discharge date (OR 1.28, P = 0.041) compared to 2015-2017, and have less complications. Surgical quality was unchanged. Thirty-day mortality increased but was still low (0.9% vs 1.8%, P = 0.067). Aside from having a complication (OR 4.15, P < 0.001), independent predictor of delayed discharge was female sex (OR 2.6, P = 0.003), squamous cell cancer histology (OR 2.8, P = 0.014) and low socio-economic status (OR 1.33, P = 0.014), but not age, smoking, BMI, co-morbidities and other baseline variables. Conclusions Enhanced recovery after oesophagectomy provides clear goals for the patient and multi-disciplinary team, however the target is often not met owing to the frequent nature of post-operative complications. Nonetheless these targets provide a culture for accelerated recovery, with steady improvement in short-term outcomes over time. A number of specific patient-groups are more likely to have extended admissions independent of complications, which has implications for optimising prehabilitation services. Overall , these findings suggest that having a discharge target is valuable, even though it often is not met.

Accelerated adoption of sugar palm farming technology to supports sustainable resource utilization in North Sulawesi

The purpose of this study was to examine the characteristics and opportunities of accelerating the adoption of palm sugar farming technology in sustainable resource use in North Sulawesi Province on July to November 2016. The data used are secondary data and primary data derived from respondents as many as 120 palm-based farming households in the central district of palm plantations in Tareran, Tomohon, and Motoling, Analysis used cross tabulation for farmer characteristics and binary logistic regression approach for accelerated adoption opportunities. The results showed that the farmers cultivated sugar palm plants with an intercropping farming system with a variety of plantation and forestry food crops. Good knowledge of palm plant cultivation system (64.84%) in cultivating sugar palm with a good understanding of the sustainability of palm plant-based farming. Opportunities for accelerating the appreciation and adoption of farmers to sugar palm-based farming technology can be realized in the form of participatory technology assistance on all technology components, both basic and optional technology, by expanding the business scale, increasing various processed products, bringing the location of farming closer to residential and information sources technology. To increase the production of aren and their derivative products and to maintain the continuity and preservation of the environment, it is necessary to cultivate and expand the palm area in harmony with regional spatial planning for conservation and sustainable management.

Nexus between perceptions of urban environmental planning and solid waste management practices in Debre Markos Town, Amhara Region, Northwest Ethiopia

Lack of integration between urban environmental planning and solid waste management is a challenge for cities in developing countries. The aim of the research was to analyze the nexus between local communities’ perceptions of urban environmental planning and solid waste management practices in Debre Markos Town, Northwest Ethiopia. The study applied a binary logistic regression approach to identify the factors that influence local communities’ perceptions about urban environmental planning and solid waste management practices. Results show that education level, family size, income and availability of safe disposal sites are statistically significant (either at P < 0.01 or P < 0.05) predictors of local communities’ perceptions. The challenges and prospects of urban environmental management were varied and rated from least to very big by respondents. Lack of commitment is reported at 41% as a very big challenge and at 43% as a big challenge of urban environmental management. The paper concludes by proposing proper implementation of the integration of solid waste management and urban environmental planning for sustainable urban development.

Binary logistic regression for identification of high school student interest in Banda Aceh city in continuing study at Universitas Syiah Kuala

Universitas Syiah Kuala is the oldest university in Aceh. The campus has the nickname of the jantong hate campus to the Acehnese people, which of course can be interpreted as the campus of pride of the Acehnese people. Universitas Syiah Kuala increased its accreditation status from C to A accreditation awarded by the research, technology and higher education ministry. This showed a very significant quality of improvement. With this condition a study was conducted on the interest of high school students in the Banda Aceh City in choosing Universitas Syiah Kuala as a place to study. This study aims to determine the factors that influence the interest of Banda high school students in continuing their studies to Universitas Syiah Kuala and get a model of interest in the Banda Aceh city students through a binary logistic regression approach. The results show that the factors that influence students’ interest in continuing their studies at Universitas Syiah Kuala are the ability to be affiliated with others, goals, and expectations. With the binary logistic regression model obtained, it can be seen the tendency of students to continue their studies at Universitas Syiah Kuala.

Determinants of youth unemployment in Uganda: The role of gender, education, residence, and age

Abstract Youth unemployment in Uganda increased from 12.7% in 2012/13 to 13.3 in 2016/17, despite a decline in the overall national unemployment rate from 11.1% to 9.2%. This poses serious development challenges, particularly to the ongoing efforts to poverty reduction. The main objective of the current study is to examine the extent to which gender, education, residence, and age determine youth unemployment in Uganda. Using recent data from the Uganda National Household Survey 2016/17 collected by the Uganda National Bureau of Statistics, we obtained a sample of 5,912 respondents for the ages between 18 years and 30 years. The main findings based on a binary logistic regression approach, reveal that education, gender, residence, and age are all critical in driving youth unemployment. The Ugandan youth who has some level of education is more likely to be unemployed compared to those with no education. But the youth that attended post-secondary education is associated with the highest unemployment probability followed by those with secondary school education and finally by primary education. While an increase in age appears to increase youth unemployment for females, the married youth have less chances of being unemployed compared to the unmarried youth. Moreover, as the probability of being unemployed reduces for the married youth, being divorced increases that probability. Similarly, the male youth are found more likely to be unemployed than their female counterparts. Additionally, the urban youth increased their chances of unemployment compared to the rural ones. Likewise, males are far more likely to remain in unemployment relative to females, just as living in the northern, eastern, or western region as a youth is less risky in terms of unemployment compared to living in the central region. On the other hand, whereas the education level of the household head is not important for youth unemployment, the marital status and gender of the household head are critical. The indirect effects of education, gender, residence, and age are clearly notable. Implications for policy and research are drawn.

Corporate governance mechanism as income smoothing suppressor

Income smoothing is an act of accounting engineering by exploiting gaps in accounting standards. This study aims to determine the motives for income-shifting management. Based on agency theory, this study tested three hypotheses on two income-smoothing objects: operating income and net income. This research is a quantitative study with data in Indonesian public manufacturing companies’ financial statements dated December 31, 2009 - 2018 obtained from the Indonesian Capital Market Directory. Hypothesis testing uses a binary logistic regression approach. The practice of income smoothing exists in manufacturing companies in Indonesia. Management shift income with object engineering is gross profit by 30.2% and net income by 21.7%. Hypothesis testing confirms that the commissionaire board size is not a mechanism of supervision effectiveness. The independent commissioners’ size was able to suppress income smoothing in manufacturing companies. Audit tenure has a negative effect on income smoothing. The audit period is directly proportional to the auditor’s ability to limit income smoothing. These results contribute to the formulation of policies, especially in improving the quality of corporate governance. Even the public and investors can understand the indications of income smoothing practices. New evidence suggests that income smoothing is less likely to be desired by corporate governance mechanisms. The motive for income smoothing is considered opportunistic. Audit tenure improves the quality of oversight of accounting engineering actions, contrary to the previous opinion that tenure reduces auditor independence.

Development of a predictive model for work-relatedness of MSDs among semiconductor back-end workers

Objective. Limited models are available to predict work-relatedness of musculoskeletal disorders (MSDs) among semiconductor back-end workers. This study aims to develop a model to predict the MSDs development among back-end workers. Method. Potential MSD risk factors were extracted from 277 work compensation investigation reports conducted between 2011–2019. Binary logistic regression approach was used to determine significant predictors. Results. Significant predictors (p < 0.05) include poor posture (odds ratio [OR] = 1.822; 95% confidence interval [CI] [1.261, 2.632]), forceful exertion (OR = 1.741; 95% CI [1.281, 2.367]), static posture (OR = 1.796; 95% CI [1.367, 2.378]), lifting and lowering (OR = 1.438; 95% CI [0.966, 1.880]), transferring (OR = 1.533; 95% CI [1.101, 2.136]), pushing and pulling (OR = 0.990; 95% CI [0.744, 1.317]), repairing machines (OR = 0.845; 95% CI 76 [0.616, 1.159]), preventive maintenance (OR = 1.061; 95% CI [0.765, 1.471]) and quality inspection (OR = 0.982; 95% CI [0.729, 1.322]). Confounding factors and employment duration played crucial roles in the model. Cross-validation of predictive model was 86.2%, while face validation among 30 experts was 7.9/10 (SD 1.9). Conclusion. The model allows practitioners to predict potential MSD cases among semiconductor back-end workers and proactively plan appropriate mitigation measures.

Low and very low birth weight in puppies: definitions, risk factors and survival in a large-scale population

BackgroundNeonatal mortality (over the first three weeks of life) is a major concern in canine breeding facilities as an economic and welfare issue. Since low birth weight (LBW) dramatically increases the risk of neonatal death, the risk factors of occurrence need to be identified together with the chances and determinants of survival of newborns at-risk.ResultsData from 4971 puppies from 10 breeds were analysed. Two birth weight thresholds regarding the risk of neonatal mortality were identified by breed, using respectively Receiver Operating Characteristics and Classification and Regression Tree method. Puppies were qualified as LBW and very low birth weight (VLBW) when their birth weight value was respectively between the two thresholds and lower than the two thresholds. Mortality rates were 4.2, 8.8 and 55.3%, in the normal, LBW and VLBW groups, accounting for 48.7, 47.9 and 3.4% of the included puppies, respectively. A separate binary logistic regression approach allowed to identify breed, gender and litter size as determinants of LBW. The increase in litter size and being a female were associated with a higher risk for LBW. Survival for LBW puppies was reduced in litters with at least one stillborn, compared to litters with no stillborn, and was also reduced when the dam was more than 6 years old. Concerning VLBW puppies, occurrence and survival were influenced by litter size. Surprisingly, the decrease in litter size was a risk factor for VLBW and also reduced their survival. The results of this study suggest that VLBW and LBW puppies are two distinct populations. Moreover, it indicates that events and factors affecting intrauterine growth (leading to birth weight reduction) also affect their ability to adapt to extrauterine life.ConclusionThese findings could help veterinarians and breeders to improve the management of their facility and more specifically of LBW puppies. Possible recommendations would be to only select for reproduction dams of optimal age and to pay particular attention to LBW puppies born in small litters. Further studies are required to understand the origin of LBW in dogs.

Effect of socioeconomic factors on malnutrition among children in Pakistan

BackgroundIn the current era, malnutrition among children considers main reason of morbidity and mortality in the world scenario while more specifically in developing countries. Malnutrition in children severely affects their physical growth and academic achievements. This study aimed to find out the effect of socioeconomic factors on malnutrition, children aged under 5 years in Multan district of Punjab province, Pakistan. The study used the data of 2497 children, obtained from Multiple Integrated Cluster Survey 2018 and employed Binary logistic regression approach for empirical estimation the effects of socioeconomic factors on malnutrition among children.ResultsEmpirical estimates of the study specified the prevalence of higher frequency of stunting (18.58), wasting (28.43) and underweight (19.54) among children particularly in rural areas of the study district Multan. Stunting status was significantly associated with children of some month (OR = 1.247, CI 95% 0.543–1.546), family size (OR = 0.589, CI 95% 0.431–3.715), maternal education (OR = 1.432, CI 95% 0.528–1.972), wealth quintile (OR = 2.174, CI 95% 1.234–3.376) and sanitation facility (OR = 0.789, CI 95% 0.627–1.987). Wasting status was mostly correlated with male children (OR = 1.208, CI 95% 0.769–1.352), children of urban areas (OR = 0.594, CI 95% 0.476–1.987) and food shortage (OR = 1.367, CI 95% 0.782–2.543). Underweight status was close relationship with male gender (OR = 1.213, CI 95% 0.821–2.897), incomplete immunization (OR = 1.342, CI 95% 1.041–2.658) and treated water access (OR = 0.689, CI 95% 0.542–2.743).ConclusionIn this study, prevalence of higher-level malnutrition was estimated due to lower socioeconomic status among major population in the community specifically in rural areas. The government needs to enhance economic opportunities, promote healthcare education, increase nutritional access and generate improving personal hygiene habits through appropriate economic, political and social policy measures. The outcome of these policies will enhance individual economic resources; ensure nutritional safety, increasing maternal schooling and helpful for improving child health.

Financial performance of the United Kingdom's national non-profit sport federations: a binary logistic regression approach

PurposeThe aim of this study was to predict the financial performance of the United Kingdom's (UK) national non-profit sport federations (NNSFs) using financial effectiveness indicators and financial efficiency ratios, as framed by the resource dependency theory and stakeholder theory.Design/methodology/approachThe dependent variable was financial performance quantified as net income. The independent variables were financial effectiveness (measured as total assets and revenues) and financial efficiency (indicated as return on assets, sponsorship efficiency and donation efficiency). With the help of panel data, the study utilised binary logistic regression and Kendall’ tau correlations.FindingsBinary regression results reported a Nagelkerke R2 of 87.5%, with ROA and donation efficiency being the best predictors of financial performance. Results from Kendall’ tau correlations indicated a positive and statistical association between financial performance and financial effectiveness and financial efficiency.Research limitations/implicationsThe study was delimited to UK non-profit sports organisations that had free, useable and publicly available financial data. For top management, donors and policy advocates, the study highlighted the superiority of financial efficiency over financial effectiveness.Originality/valueThe study adds to research, theory and practitioners' perspectives by offering a new way of evaluating financial performance with the combination of financial effectiveness and efficiency and not opinions, a factor uncommon in previous studies.

A fuzzy DEMATEL – Fuzzy Binary Logistic Regression approach to evaluate and prioritize risks and simulated annealing optimization algorithm (an empirical study in energy projects)

PurposeThe aim of this paper is to predict and minimize the risks of oil, gas and petrochemical projects. Besides, reducing the likelihood of occurrence and minimizing risks impact on the projects to reduce the probable costs and improve the economic situation is another purpose of this paper.Design/methodology/approachThis paper provides a fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) – a technique that assist to solve decision-making problems – and IP (Impact & Probability) table methods to identify and analyze critical risks in energy projects, and then fuzzy Binary Logistic Regression (BLR) in order to predict the probability of each level of risk for more efficient risk management in projects. Furthermore, in this paper, the fuzzy BLR (FBLR) is optimized such that the probability of a high level of risk for the implementation of the project has been minimized using meta-heuristic algorithm.FindingsThe results from the point of view of experts show that combination of fuzzy DEMATEL with FBLR approach as well as using SA algorithm, in order to optimize the high level of risks, can provide a smart approach to managing risks with more success.Practical implicationsThe application of the proposed method is illustrated via a real data set from energy projects.Originality/valueWe propose combined fuzzy DEMATEL and FBLR methods to predict and optimize the risks of the energy projects, which is the innovation of this paper.