Journal of Biostatistics and Epidemiology

Clustering Undergraduate Students Based on Their Self-Esteem and Academic Achieve- ment Via The K-Means Approach

2025-04-27T08:19:35+00:00

Introduction: Self-esteem is one of the key foundations of human personality and is known as an important component in mental health and social psychology. Students with high self-esteem tend to be more engaged and persistent in areas of achievement. This study was devoted to cluster undergraduate students of Shahrekord University of Medical Sciences based on their self-esteem and academic achievement.

Methods: The multi-stage cluster sampling method (three faculties and three departments in each) was used to select 260 undergraduate students from various fields in 2022. The data collection tool included a background information checklist, a 10-item self-esteem questionnaire, and a 39-item academic achievement questionnaire. The elbow method was used to estimate the number of optimal clusters. The NbClust package in R 4.2.1 software was used for clustering analysis based on the k-means approach.

Results: In this study, out of 260 participating students, 176 (67.7%) were girls and 84 (32.3%) were boys. The overall mean ± standard deviation of academic achievement was 105.2 ± 10.3. There is a positive and significant correlation (r = 0.44) between academic achievement and self-esteem (P-value <0.001). The optimal number of clusters was estimated as four based on the elbow method. Self-esteem in cluster number 1 with 35 students was at the lowest level at -2.6±2.9. The academic achievement was significantly different among the obtained clusters (P<0.001). Cluster number 4 with 48 students had less academic progress with 94.0 ± 6.1 than the other three clusters.

Conclusion: Based on the obtained findings, performing effective interventions for promoting self-esteem and academic achievement seems necessary.

Epidemiological Study of The Physical Ability to Practice Physical Education in Children with School Pathologies

2025-04-28T07:27:10+00:00

Introduction: The term "school pathologies" encompasses two distinct categories of health disorders: those that are caused or exacerbated by a lack of physical activity, and those that predominantly affect children of school age. This study employs an epidemiological approach to examine the physical aptitude of students in relation to their capacity to engage in physical education and sports (PE) classes. Our approach is based on an analysis of 93,870 medical records.

Methods: The survey is comprised of four distinct sections. The initial stage of the analysis entails an examination of the prevalence of confirmed impairments among school-aged children. Secondly, an evaluation of the physical aptitude to engage in physical education will be conducted. Thirdly, an analysis of the physical inaptitude of students to participate in physical education will be conducted.

Results: The results indicated a range of prevalence rates for various diagnosed and confirmed impairments, though no notable differences were observed between the sexes. Similarly, the majority of respondents attended school in urban areas (64.38%), and the most prevalent age group in this study was 16-18 years (59.59%;p< .05). In contrast, a prevalence of 40.20% of students with SEN (or 3.93‰ of the diagnosed population who are totally unfit for physical practice in PE) has been observed. However, this figure varies according to the types and characteristics of impairment. Three children with one type of impairment out of 1,000 pupils are unfit, which engenders physical inactivity at school due to medical restrictions. This phenomenon is not influenced by gender; however, it differs between geographical areas and age groups. This indicates a correlation between urbanization and age-related changes in physical disability and inactivity.

Conclusion: This study underscores the necessity of monitoring the physical activity of students with SEN, whether at school or elsewhere, to gain a more comprehensive understanding of well-being.

Unraveling Growth: Analyzing the Key Factors Influencing Growth Rate of Children Under Two Years

2025-04-27T08:19:34+00:00

Introduction: According to the significance of children in the culture, economy, and human resources for the country's future, their growth in the first 2 years and its influencing factors are crucial for the country's progress. The study investigates and identifies the factors influencing growth rates through the transitional and marginal longitudinal models.

Methods: This retrospective cohort study evaluates the determinants that impact children's growth in their first two years. We used longitudinal models (transmission-random-marginal) and SPSS software version 26. the Corrected Quasi Likelihood under the Independence Model Criterion (QICC) was used to evaluate the models, with a significance level of 0.05.

Results: The mean weight was 3257 ± 491 grams at birth and 12105 ± 1633 grams at two years old. The mean height was 50.4 ± 2.6 cm at birth and 87.6 ± 0.3 cm at two years old. Factors such as the child's gender, place of residence, mother's education, type of breastfeeding, gestational age, singleton births, and mother's weight all significantly affect children's growth. Evaluating these factors using a marginal model was also meaningful. The results from the transfer model indicate that when controlling for the child's previous weight, factors such as the child's gender, mother's age, and exclusive breastfeeding with breast milk impact weight growth in children. Similarly, when controlling for the child's previous height, the child's gender, mother's age, and level of education significantly influence children's height. A comparison between the transfer and marginal models revealed that the transfer model provides a better fit.

Conclusion: A comparison between the transfer and marginal models revealed that the transfer model provides a better fit. According to the results of this study, adequate training on factors affecting children's growth should be provided to both mothers and health workers, which may reduce the risk of developing disorders.

Deep Neural Network for Cure Fraction Survival Analysis Using Pseudo Values

2025-04-27T08:19:33+00:00

Introduction: The hidden assumption in most of survival analysis models is the occurrence of the event of interest for all study units. The violation of this assumption occurs in several situations. For example, in medicine, some patients may never have cancer, and some may never face Alzheimer. Ignoring such information and analyzing the data with traditional survival models may lead to misleading results. Analyzing long term survivals can be performed using both traditional and neural networks. There has been an increasing interest in modeling lifetime data using neural network due to its ability to handle complex covariates if any. Also, in several numerical results it provides a better prediction. However, for long-term survivors only one neural network was introduced to estimate the uncured proportion together with the EM algorithm to account for the latency part. Neural network in survival analysis requires special cost function to account for censoring.

Methods: In this paper, we extend the neural network using pseudo values to analyze cure fraction model. It neither requires the use of special cost function nor the EM algorithm

Results: The network is applied on both synthetic and Melanoma real datasets to evaluate its performance. We compared the results using goodness of fit methods in both datasets with cox proportional model using EM algorithm.

Conclusion: The proposed neural network has the flexibility of analyzing data without parametric assumption or special cost function. Also, it has the advantage of analyzing the data without the need of EM algorithm. Comparing the results with cox proportional model using EM algorithm, the proposed neural network performed better

Machine Learning Models for Prognostic Assessment of Covid-19 Mortality Using Computed Tomography-Based Radiomics

2025-04-27T08:19:32+00:00

Introduction: This study examines the significance of developing a predictive approach for assessing theprognosis of individuals diagnosed with COVID-19. This method can help physicians make treatment decisionsthat decrease mortality and prevent unnecessary treatments. This study also emphasizes the significance ofradiomics features. Therefore, our objective was to assess the predictive capabilities of Computed Tomography-based radiomics models using a dataset comprising 577 individuals diagnosed with COVID-19.

Methods: The U-net model was applied to automatically perform whole lung segmentations, extracting 107 texture, intensity, and morphological features. We utilized two feature selectors and three classifiers. We assessed the random forest, logistic regression, and support vector machines by implementing a five-fold cross-validation approach. Precision, sensitivity, specificity, accuracy, F1-score, and area under the receiver operating characteristic curve were reported.

Results: The random forest model achieved an area under the receiver operating characteristic curve, precision, sensitivity, specificity, accuracy, and F1-score in the range of 0.85 (CI 95%: 0.76–0.91), 0.75, 0.82, 0.78, 0.68, and 0.71, respectively. Logistic regression attained an area under the receiver operating characteristic curve of 0.80 (CI 95%: 0.72–0.88), corresponding to values of 0.88, 0.62, 0.74, 0.55, and 0.67, respectively. Support Vector Machines computed the above six metrics as an area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, precision, and F1-score in the range of 0.69 (CI 95%: 0.59–0.79), 0.68, 0.64, 0.66, 0.5, and 0.57, respectively.

Conclusion: We are developing a robust radiomics classifier that predicts mortality in COVID-19 patients. Lung Computed Tomography radiomics features may aid in identifying high-risk individuals who need supplementary therapy and decrease the propagation of the virus

Barriers and Determinants of Diabetes Self-Management Among Palestinian Refugees in Jordan: A Mixed-Methods Study

2025-04-27T08:19:31+00:00

Introduction: Diabetes mellitus is one of the highest causes of death around the world as one out of eleventh adults have diabetes mellitus. In Jordan, the prevalence of diabetes mellitus was projected to be around 16% in 2020. Our study aims to understand the compliance and efficacy for self-management among refugees living with diabetes mellitus in the Jordanian Nuzha health centers.

Methods: Structured interviews with short questionnaires, focus group discussions (FGDs), and semi- structured interviews with healthcare providers. The study population was based on a sample of patients who visited the Nuzha health centers.

Results: A total of 30 participants at UNRWA Nuzha Health Center participated in the questionnaire. Notably, most participants demonstrated high self-efficacy for controlling one’s DM (83%) and high perceived ability to find the support and medical resources for management (87%). Additionally, most participants showed robust knowledge in the importance of diet and exercise for the management of DM (93% for both variables). This study also reports that 11 participants were overweight, 9 had Class I obesity and 6 had Class II obesity.

Conclusion: Limitations of this study included a low number of female patients during FGDs, limited number of Type I DM patients, and limited ages. Our main findings are that patients of Nuzha HC have high perceived self-efficacy and structural support for managing DM, level of education impacts management of diabetes, transportation is a major barrier to receiving consistent care and healthy dietary options are not affordable.

Additive Value of Computed Tomography Severity Scores to Predict Lengths of Stay in Hospital and ICU for Covid-19 Patients: A Machine Learning Study

2025-04-27T08:19:29+00:00

Introduction: During the outbreak of COVID-19, most hospitals faced resource shortages due to the greatsurges in the influx of infected COVID-19 patients and demand exceeding capacities. Predicting the lengths ofstay (LOS) of the patients can help to make proper resource-planning decisions. CT-SS accurately determinesthe disease severity and could be considered an appropriate prognostic factor to predict patients’ LOS.In this study, we evaluate the additive value of CT-SS in the prediction of hospital and ICU LOSs of COVID-19patients.

Methods: This single-center study retrospectively reviewed a hospital-based COVID-19 registry database from 6854 cases of suspected COVID-19. Four well-known ML classification models including kNN, MLP, SVM, and C4.5 decision tree algorithms were used to predict hospital and ICU LOSs of COVID-19 patients. The confusion matrix-based performance measures were used to evaluate the classification performances of the ML algorithms.

Results: For predicting hospital LOS, the kNN model with an accuracy of 77.1%, sensitivity of 100.0%, precision of 68.6%, specificity of 54.2%, and AUC of around 99.4% had the best performance among the other three ML techniques. This algorithm with 94.4% sensitivity, 74.6% specificity, 84.5% accuracy, 78.8% precision, 85.9% F-Measure, and an AUC of 95.3% had also the best performance for predicting ICU LOS of the patients.

Conclusion: The performances of the ML predictive models for predicting hospital and ICU LOSs of COVID-19 patients were improved when CT-SS data was integrated into the input dataset.

Scientific Knowledge of Wegener's Granulomatosis: A Scientometric Analysis, From 1970 to 2023

2025-04-27T08:19:28+00:00

Introduction: Granulomatosis with polyangiitis (GPA), previously known as Wegener's granulomatosis is a systemic, necrotizing vasculitis. To our knowledge, there have been no previous attempts to assess the literature on GPA through scientometric analysis. In our study, we utilized scientometric analysis to explore the geographical, institutional, publication, authorship, citation, and keyword dimensions of GPA research, with the goal of uncovering the current state and emerging trends in the field.

Methods: The bibliographic information for studies on GPA was obtained from Scopus up to 2024. VOSviewer software was used to analyze publication characteristics, including countries, institutions, journals, authors, core references, and keywords.

Results: The literature review yielded 15092 publications in the Title-abstract-keyword fields related to GPA. The number of published articles increased from 2014 to 2021, and decreased since 2021. The United States (n=3672, 24.3%), has the highest publication number. There was a strong and significant positive correlation between the number of articles produced by countries on GPA and their gross domestic product (GDP) (r = 0.7103, P < 0.001). Mayo Clinic (n=353) is the most active institution and the Journal of Rheumatology (n=248) is the most active journal. The analysis of the co-occurrences of keywords was performed by VOSviewer. The most frequent author keyword was “Wegener’s Granulomatosis” (n=1718).

Conclusion: The current study comprehensively reviewed GPA research from 1970 to 2024 using Scopus- indexed articles. Results highlighted leading countries, institutions, journals, influential publications, and key authors, identifying impactful research avenues. This scientometric review offers valuable insights for future research directions and publishing strategies in GPA. By recognizing trends and emerging themes, clinicians can enhance their practice, engage in relevant research, and contribute to improved patient outcomes.