International Journal of Statistics in Medical Research
International Journal of Statistics in Medical Research Editor In Chief: Kartlos Joseph Kachiashvili View Profile Abbreviated key title: Int. J. Stats. Med. Res. ISSN (online): 1929-6029 Published by: Lifescience Global Periodicity: Continuous, One Volume per Year contact us for more information Scope of the Journal The International Journal of Statistics in Medical Research is a peer-reviewed scientific journal that broadly encompasses applied statistical methodologies relevant to medical, biological, environmental, epidemiological, and public health sciences. The journal aims to serve as a platform for the timely dissemination of novel bio-statistical models and methods, as well as original applications of established statistical techniques that advance knowledge in scientific research. The journal welcomes original research and review articles, as well as supplements, short communications, conference proceedings, and case studies exploring real-world scenarios that involve various aspects of biostatistics, such as data analysis, statistical modeling, and hypothesis testing etc. Indexed and Abstracted in OCLC (WorldCat), EZB Nutzeranfragen, Genamics JournalSeek, Scilit, OpenAIRE | EXPLORE, Index Copernicus International, Embase
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Extreme Heterogeneity in Global Prevalence Meta-Analyses: Evaluating Current Practices and Exploring Bayesian Alternatives - an Umbrella Review
Introduction: Global prevalence meta-analyses often exhibit extreme heterogeneity (I² > 90%), yet criteria designed for clinical trials, where homogeneity is desirable, continue to be applied without recognizing that in prevalence studies, variability reflects real differences between populations. Objective: To document the magnitude of heterogeneity in global prevalence meta-analyses, evaluate the methodological strategies employed for its exploration and management, and explore through illustrative application how Bayesian methods—rarely employed in prevalence meta-analyses—compare with standard frequentist approaches. Methods: Umbrella review conducted according to PRIOR guidelines. Systematic search in SCOPUS for systematic reviews with global/worldwide prevalence meta-analyses published between 2015-2025. Data were extracted on I², statistical models, subgroup analyses, sensitivity analyses, meta-regression, and prediction intervals. Three meta-analyses were randomly selected for illustrative Bayesian re-analysis using hierarchical models with weakly informative priors, and the results were compared with those from frequentist approaches. Results: Of 53 included meta-analyses, 52 (98.1%) presented I²≥75%, 47 (88.7%) I²≥90%, and 34 (64.2%) I²>99%. Management strategies showed a decreasing implementation rate: subgroup analyses (96.2%), sensitivity analyses (64.2%), meta-regression (34.0%), and prediction intervals (5.8%). Among studies with I²≥75%, 63.5% provided explicit justification for proceeding with pooling. The illustrative Bayesian analysis of three randomly selected studies demonstrated excellent concordance with frequentist estimates (differences <0.1%), while providing additional uncertainty quantification for heterogeneity parameters unavailable from standard approaches. Conclusions: Extreme heterogeneity constitutes the norm in global prevalence meta-analyses. The underutilization of prediction intervals (5.8%) and meta-regression (34.0%) represents the critical gap for improving statistical rigor. An exploratory Bayesian analysis demonstrated concordance with frequentist estimates, while providing additional uncertainty quantification. This illustrates that alternative methods are feasible, though their value lies primarily in specific scenarios rather than routine application. Prevalence-specific frameworks should recognize high heterogeneity as an expected characteristic requiring comprehensive exploration rather than elimination.
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Mortality Prediction and Survival Estimation in Dialysis Patients Using Logistic and Cox Regression with Machine Learning Feature Selection
Mortality remains high among patients undergoing maintenance dialysis for end-stage renal disease (ESRD). Identification of key mortality predictors is paramount for improving prognosis and guiding care. Recent advances in machine learning (ML) offer potential to enhance risk stratification beyond traditional statistical models. This study compares feature selection methods—LASSO, Random Forest, and Gradient Boosting—in predicting mortality risk among dialysis patients, integrating logistic regression and Cox proportional hazards modelling. Retrospective data from 224 ESRD patients on maintenance haemodialysis were analysed. Thirty-three clinical and demographic variables were evaluated. Feature subsets were generated using ML algorithms and used for building predictive models. Model performance was assessed via discrimination (AUC), accuracy, sensitivity, specificity, and survival prediction concordance index (C-index). LASSO-selected features yielded an AUC of 0.82 and C-index of 0.81, demonstrating strong discriminatory ability. Random Forest showed highest AUC (0.85) but lower sensitivity. Gradient Boosting offered balanced sensitivity and specificity with an AUC of 0.81. The parsimonious common-feature model (dialysis session frequency, diabetes) achieved the best survival discrimination (C-index 0.83). Full models with all variables demonstrated moderate performance, highlighting potential overfitting. Key mortality predictors included dialysis adequacy, diabetes status, respiratory comorbidities, and hemodynamic parameters. Machine learning–aided feature selection enhances mortality risk prediction in dialysis patients. Parsimonious models focusing on consistent predictors may optimize clinical applicability. These findings support integrating ML and traditional regression approaches to refine prognostic tools and inform personalized care strategies in ESRD.
A Hybrid Bayesian-PDE Constrained Optimization Framework for High-Dimensional Image Reconstruction
High-dimensional image reconstruction problems in fields such as medical imaging, astrophysics, and remote sensing are typically ill-posed inverse problems affected by noise, under sampling, and imperfections in the physical forward model. Traditional methods for resolving these conflicts suffer from an inherent trade-off: pure physics-based PDE-constrained models impose physical consistency but are deterministic and do not represent uncertainty, and fully Bayesian models provide principled uncertainty quantification but tend to become computationally intractable in very high-dimensional spaces. In response to these challenges, we propose the Hybrid Bayesian - PDE Constrained Optimization Framework. The Hybrid Bayesian - PDE Constrained Optimization Framework leverages the physical fidelity of PDE-based forward models with the expressive capability of Bayesian inference to model uncertainty. The reconstruction problem is cast as an optimization problem whereby a variational or hierarchical Bayesian prior is combined with a PDE-constrained data fidelity term, and the optimization objective is solved by an efficient stochastic variational optimization scheme. Experiments using a representative CT example and MRI datasets demonstrated how the hybrid methods provided (i) better reconstructions, preserving fine structures for substantially undersampled data and robust performance in noise when compared to the pure physics model along with providing (ii) clinically meaningful pixel-wise uncertainty maps. These results support the view that the proposed hybrid method provides a principled, computationally efficient, reliable approach to the challenge of solving large-scale inversion problems, while addressing the fundamental limitations of both deterministic, physics-based methods and probabilistic Bayesian inversion.
HLA-DQ Allele Carriers as Genetic Risk Factors for Pulmonary Tuberculosis: A Meta-Analysis
Several studies have shown that pulmonary tuberculosis (PTB) is a major global health issue, affecting various countries around the world. Susceptibility to the disease has been reported to be influenced by host genetic variables, including Human Leukocyte Antigen (HLA) class II genes, specifically HLA-DQ. Despite the association, studies on the relationship between HLA-DQ allele carriers and the risk of PTB are still not consistent among various populations. This meta-analysis aims to assess the association between carrier status (phenotype frequency) of HLA-DQA1 and HLA-DQB1 alleles and susceptibility to pulmonary tuberculosis. Several HLA-DQ alleles were examined, and the pooled effect size estimates were calculated based on carrier (phenotype) frequencies of HLA-DQA1 and HLA-DQB1 alleles, using odds ratios (ORs) and 95% confidence intervals (CI). A total of 21 high-quality studies (NOS ≥7) were included in the review, with 25,896 controls and 3,927 cases. The results showed that the risk of PTB was significantly increased by allele carriers of HLA-DQA1*01:01 (OR = 1.79; 95% CI: 1.22–2.62) and HLA-DQA1*03:01 (OR = 1.64; 95% CI: 1.08–2.48). Risk factors for HLA-DQB1 allele carriers were also found to be the *02:01 (OR = 1.36; 95% CI: 1.04–1.79), *05:03 (OR = 1.35; 95% CI: 1.01–1.80), and *06:01 (OR = 1.41; 95% CI: 1.00–1.97) allele carriers. Meanwhile, HLA-DQA1*02:01, *04:01, *05:01, and *06:01 allele carriers had protective effects. The majority of analyses found no indications of publication bias. This meta-analysis demonstrates that carrier status of specific HLA-DQA1 and HLA-DQB1 alleles is significantly associated with susceptibility to pulmonary tuberculosis
Circular Statistical Analysis of Emergency Department Admissions
Health-care administrators face ongoing challenges managing emergency department (ED) operations, particularly in understanding how patient arrival trends fluctuate within the 24-hour day. Although prior research has examined the times at which patients seek emergency care, most of these studies have used simple statistical methods that do not account for time as a periodic variable. As a result, many significant time-of-day patterns may not be detected. We use circular statistics on 142,005 hourly emergency department admissions at a large hospital in Iowa from January 2014 to August 2017. Overall, the pattern of ED visits presents an anisotropic distribution that is statistically significant according to both Rayleigh and Kuiper tests. Patient arrival times show a circular mean in the early to mid-afternoon, a marked late-afternoon modal peak, and a diffuse distribution across the day. Adjusted circular probability models such as the von Mises distribution, cardioid distribution, and the wrapped normal distribution perform significantly better than the circular uniform model when the AIC, BIC, CAIC, and HQIC criteria are considered. The circular summary charts help in understanding the various trends observed in the time series graph. In pointing out how the use of a circular method is a mathematically appropriate and more interpretable approach for describing trends related to admissions on an hourly basis, this piece of research also points out the benefits of such a method as being a useful tool for health-care planners.
Comparative Evaluation of Inception V3 and ResNet 50 for Pneumonia Prediction
Pneumonia is a fatal respiratory infection that has become the leading cause of death among many people across the world. Its widespread has grabbed great attention making it a major topic for research under various domains. Its severity has led to the development of systems that can predict whether a patient has chances of being diagnosed with pneumonia or not, this is also called as computer aided diagnosis. However, current study intends to identify an Artificial Neural Network (ANN) model that has been able to provide the highest accuracy when it comes to predicting this life-threatening condition. The prediction was initially done with Machine learning techniques but with the introduction of ANN, it was observed that there are models that provided higher accuracy than the ML models. This study investigates how the concept of deep learning which is a vital part of ANN makes use of one of its most efficient models including Inception V3 and ResNet 50 for the prediction of pneumonia and compare their performance to suggest a better solution to the problem. Results indicate that ResNet50 offers clinically meaningful improvements in sensitivity and specificity, supporting its role as a decision-support tool for early pneumonia detection.
Perceptions of COVID-19 Spread Factors, Socio-Demographic Influences, and Vaccine Efficacy: Insights from Frontline Health Workers in Bhopal, India
Background: As frontline warriors against COVID-19, health workers have gained a significant, unique, and invaluable understanding of the virus through personal experience and various studies over the past five years. These insights can provide a more precise understanding of the factors influencing the spread of COVID-19, as well as socio-demographic factors and vaccine efficacy in the long term. Despite a growing body of pandemic literature, studies that capture healthcare workers’ perceptions of community-level transmission remain scarce, and even fewer examine those perceptions alongside socio-demographic drivers and vaccine performance. Objective: To understand what healthcare workers, think about COVID-19 spread factors, socio-demographic influences, and vaccine efficacy in the community, and to assess the key factors they identify, along with the differences and similarities in their perceptions across professional groups. Materials and Methods: A structured questionnaire comprising 32 questions was administered to 252 health workers in Bhopal, India, including doctors, nurses, laboratory technicians, pharmacists, and ambulance drivers. In this study, a descriptive and comparative analysis is performed to identify factors contributing to the spread of COVID-19, as well as significant differences and similarities in how various professions classified and identified these factors, and the demographic risks associated with them. The analysis also evaluates the long-term effectiveness of vaccines. Data were analyzed using the Borda Count technique to establish rankings, while Kendall’s W was used to quantify the strength of agreement across different medical roles. Results: The study revealed notable variations in how different healthcare professions perceived factors influencing the spread of COVID-19, socio-demographic influences, and vaccine efficacy. Doctors and nurses displayed the closest alignment, while pharmacists, lab technicians, and ambulance drivers offered distinct viewpoints shaped by their roles and exposure levels. Despite these differences, all groups agreed on the major transmission mechanisms, the effectiveness of preventive measures, and the importance of vaccination in reducing severity. Conclusion: Health workers across professions contributed diverse but meaningful perspectives on COVID-19, reflecting their clinical and operational experiences. While some differences emerged, there was shared recognition of the need for public cooperation, early detection, and sustained vaccination efforts. These insights underscore the importance of a multidisciplinary, locally responsive approach to enhancing pandemic preparedness and future public health strategies.
Health Belief Model Predictors of Behavioral Intention to Use Emergency Healthcare Services among Diabetic Patients: A Cross-Sectional Study
Background: Emergency healthcare services are critical for diabetic patients experiencing acute complications; however, timely utilization remains inconsistent. In Saudi Arabia, limited evidence exists regarding the cognitive factors influencing emergency care-seeking intentions. Aim: To examine Health Belief Model (HBM) predictors of behavioral intention to use emergency healthcare services among diabetic patients in the Jazan region. Methods: A cross-sectional survey of 250 diabetic patients was conducted using validated Arabic questionnaires assessing six HBM constructs. Hierarchical binary logistic regression evaluated HBM constructs (Model 1) and additional demographic/clinical variables (Model 2). Results: Most participants (64.4%) demonstrated low intention to use emergency services. HBM constructs significantly predicted behavioral intention (χ²=16.916, p=0.010). Significant predictors were Cues to Action (OR=1.174), Perceived Susceptibility (OR=1.149), Self-Efficacy (OR=1.126), and Perceived Barriers (OR=0.880). Adding demographic variables did not significantly improve model fit (p=0.475). Conclusion: Health beliefs, rather than demographic characteristics, primarily determine emergency service utilization intentions among diabetic patients. Interventions should target symptom recognition, perceived vulnerability, self-efficacy, and access barriers to enhance timely emergency care-seeking.
Determinants of Latent Tuberculosis Infection (LTBI) among Health Workers in Community Health Centers, Makassar City: A Case-Control Study
The prevalence of latent tuberculosis infection (LTBI) in Asia reaches 36% based on Tuberculin Skin Test (TST) results, and healthcare workers represent a high-risk group for occupational TB exposure. In South Sulawesi Province, 2,488 LTBI cases were reported in 2024, with Makassar City contributing the highest number. This study aimed to identify determinants of LTBI among healthcare workers in community health centers in Makassar City using a retrospective case-control design. Cases were healthcare workers with positive TST results recorded in the SITB database, while controls were healthcare workers from the same centers with negative TST results selected through simple random sampling with a 1:1 ratio. Bivariate analysis was conducted using Odds Ratios (OR), and variables with p < 0.25 were included in a multivariate logistic regression model to determine independent factors. A total of 316 cases and 316 controls were analyzed. Inconsistent mask use (AOR = 5.20; 95% CI: 3.60–7.51), history of contact with TB patients (AOR = 2.63; 95% CI: 1.82–3.80), and absence of BCG immunization (AOR = 2.30; 95% CI: 1.50–3.53) were independently associated with LTBI. Although smoking was significant in bivariate analysis, it was not retained in the final multivariate model. These findings highlight the importance of strengthening infection prevention and control practices and routine LTBI screening among healthcare workers in primary healthcare settings.
Enhanced Prediction of Chronic Kidney Disease using XGBoost Machine Learning Model
Chronic kidney disease (CKD) might progress to end stage renal disease; moreover, cardiovascular dangers are dire. Machine learning used in for more speed and accurate diagnosis of CKD. The CKD prediction model proposed in this paper was developed using the XGBoost algorithm, which is quite effective in classification problems. Other clinical parameters such as blood urea, serum creatinine and white blood cell count are some of the 24 indices identified from among the 400 patient records in the dataset. Feature selection using SelectKBest was relevant, and hyperparameter tuning was done by RandomizedSearchCV Both quantitative and categorical data were preprocessed. Altogether, 75% of data used for training, while 25% of data used for testing. The XGBoost model had a better result with 96.88 % recall, 100% precision, and 98% accuracy. However, the proposed approach has disadvantages; namely, a small sample cross-section and possibly an imbalanced class. Further, the dataset will be increased, the methods of dealing with class imbalance will be applied using SMOTE algorithm, and the effectiveness of the proposed model will be tested in real clinical practice. This work also highlight how crucial it is to employ and enhance machine learning, especially XGBoost to detect early stage of CKD, proper treatment, low mortality rate, and increased survival rate among patients.
A Study on Postpartum Depression in Tertiary Care Hospital: Implication for Maternal Mental Health
Background: Postpartum depression (PPD) is a significant maternal health issue often overlooked in India. This study assesses its prevalence, risk factors, and implications in a tertiary care hospital in Pune. Methods: A cross-sectional study was conducted from March 2025 to June 2025, involving 102 postpartum women. The Edinburgh Postnatal Depression Scale (EPDS) was used to assess depression immediately postpartum and at six months Results: The incidence of PPD was 12.75% immediately postpartum and 15.69% at six months. Significant risk factors included young maternal age (<20 years), low family income, unplanned pregnancy, preterm birth, antenatal complications, and low self-esteem. Employment status and lack of antenatal care were also associated with higher EPDS scores. However, factors such as education level, delivery mode, and baby’s gender did not show statistical significance. Conclusion: PPD is a prevalent yet underdiagnosed condition that requires urgent integration into maternal healthcare programs. Early screening, mental health support, and targeted interventions can improve outcomes for mothers and infants. Further research and policy focus on maternal mental health are necessary for better healthcare delivery.
Improving Adolescent Mental Health Care in Indonesia: A Systematic Review of Electronic Medical Record Implementation
Background: Adolescent mental health disorders represent a significant global public health challenge, with low rates of detection and treatment in many settings. Electronic Medical Records (EMRs) are increasingly recognized as pivotal tools for improving the integration of validated screening, supporting clinical decision-making, and enabling continuous quality monitoring in mental health care. However, the specific utility, effectiveness, and implementation challenges of EMRs in developing nations, particularly in Southeast Asia, such as Indonesia, remain underexplored. Methods: An extensive search was conducted across international databases (e.g., PubMed, ScienceDirect, Scopus) and relevant regional databases (e.g., Garuda, SINTA) to capture studies published in both English and Bahasa Indonesia. Search terms will encompass 'electronic medical records,' 'EMR,' 'EHR,' 'adolescent,' 'youth,' 'mental health,' 'depression,' 'anxiety,' 'suicide,' and 'Indonesia.' Inclusion criteria will focus on studies involving adolescents (aged 10-24 years) and EMR-based mental health interventions. Two independent reviewers will conduct title, abstract, and full-text screening. Data extraction will concentrate on study design, population characteristics, EMR functionalities, clinical outcomes, patient engagement, provider workflow, implementation barriers, and reported disparities. A qualitative synthesis will pooled effects and quantify EMR impact on clinical outcomes context-specific recommendations for Indonesia. Results: This review aims to provide a robust overview of EMR applications in adolescent mental health in the Indonesian context. Globally, EMR integration consistently improved screening efficiency, increasing rates by 20–30 percentage points compared to paper-based systems. Provider training amplified these effects, with odds ratios exceeding 40 for screening uptake and relative risks of 2.7 for new diagnoses. Validation studies confirmed diagnostic reliability, reporting positive predictive values up to 100% for lifetime psychiatric disorders. Conclusion: Large-scale surveillance networks revealed rising prevalence of anxiety, eating disorders, and suicidality, with detection occurring at younger ages. These findings highlight EMRs as not only efficiency tools but urgent safeguards against systemic fragility in Indonesia.
Enhancing Thyroid Cancer Detection in Ultrasound Images Using Augmentation-Driven Deep Learning Framework
Thyroid cancer has become the most rapidly increasing endocrine malignancy, and its incidence is on the rise globally. Deep learning methods demonstrated reliable prospects in improving thyroid cancer diagnosis based on ultrasound imaging. But data scarcity and class imbalance make it challenging to build robust models. In this paper, we propose an image augmentation approach for enhancing thyroid cancer classification and segmentation using deep learning. The Thyroid Ultrasound-Image Database, which contains 2,450 images with five ACR-TIRADS levels, was applied. Pre-processing: Noise reduction, inverse intensity, and normalization were performed. Geometric transformations, photometric augmentations, GAN-based synthetic image generation and domain-specific augmentations were utilized. For classification and segmentation tasks, CNN-based architectures (e.g., VGG, ResNet) as well as transformer-based models were employed, along with U-Net variants. Conclusions: Data augmentation was found to lead to a substantial improvement in the models' ability to generalize, with 10–15% gains in accuracy. Various augmentation techniques were performed differently, and the combination of multiple techniques was the most accurate. Qualitative results indicated the robustness of feature extraction, and quantitative comparisons showed that our method was competitive with several state-of-the-art methods. The present approach has potential clinical applications to help radiologists in the early detection of thyroid cancer. Potential future work involves adapting advanced augmentation methods and multimodal fusion to further increase the classification performance.
Adaptive Mean–Regression Modeling for Medical Diagnosis: A Hybrid Statistical Learning Framework with Interpretable and Adaptive Properties
This paper proposes a new statistical machine learning algorithm for medical diagnosis, called the Adaptive Mean-Regression Model (AMRM). The approach combines class-wise mean estimation with a basic linear regression correction mechanism to enhance diagnostic quality as precisely as possible, while retaining the entire interpretation. Compared with complex deep learning or probabilistic models, AMRM uses only simple statistical functions, such as calculating the mean, computing Euclidean distance, and performing linear regression. An adaptive update rule modifies the influence weights within classes based on observed diagnostic errors, allowing the model to learn from misclassifications without gradient-based optimization. Compared with more traditional machine learning models such as logistic regression, support vector machines, and random forests, the proposed AMRM achieves similar predictive accuracy while being much more interpretable and having lower computational complexity. This makes the model particularly applicable in clinical settings where transparency is crucial. The accuracy, precision, sensitivity, and specificity of the UCI Heart Disease dataset are 81.3%, 80.5%, 78.6%, and 83.7%, respectively. The proposed AMRM, in contrast to conventional regression-based classifiers, offers a hybrid statistical framework that combines the class-wise mean representation with regression-based correction and adaptive feature weighting. This combination improves interpretability while preserving competitive predictive performance. The findings indicate that AMRM can provide a good balance between statistical transparency and diagnostic accuracy.
Real-Time Blood Pressure Change Classification Using Enhanced PPG Signals and Convolutional Neural Networks
Photoplethysmography (PPG) is widely used as a non-invasive and cost-effective technique for monitoring cardiovascular activity and assessing blood pressure (BP) variations. However, PPG signals are often affected by noise and motion artifacts, which can reduce signal reliability and negatively impact clinical interpretation and machine learning performance. This study proposes a signal enhancement and classification framework to improve the accuracy of BP change classification using PPG signals. The proposed approach enhances signal quality by reducing noise while preserving important physiological waveform characteristics, enabling more reliable feature extraction. The enhanced signals are then utilized as input to a Convolutional Neural Network (CNN) to learn discriminative temporal and morphological features associated with BP variations. Experimental results demonstrate that the proposed framework achieves a training accuracy of 96.66% and a validation accuracy of 95.31%, outperforming conventional preprocessing approaches such as hard and soft thresholding. These findings highlight the potential of integrating adaptive signal enhancement with deep learning techniques to improve the robustness and reliability of non-invasive BP monitoring systems. The proposed framework offers promising applications for real-time and clinically relevant cardiovascular monitoring.
Choosing Exploratory, Predictive, or Causal Multivariable Models in Biomedical Research: A Practical Methodological Guide
Background: Multivariable regression is widely used in biomedical research, but models built for different purposes are often treated as if they were interchangeable. This confuses variable handling, covariate adjustment, model evaluation, and interpretation. Objective: To provide a practical guide for clinicians, biomedical researchers, and collaborating statisticians on how to choose and report multivariable models according to whether the aim is exploratory, predictive, or causal. Methods: We prepared a narrative methodological tutorial using a targeted search of PubMed, Scopus, and Google Scholar, together with key textbooks and reporting guidance (STROBE, TRIPOD+AI, and PROBAST+AI). We prioritized seminal papers and recent methodological references (2021-2025) on variable prespecification, continuous predictors, validation, calibration, and causal diagrams. Illustrative examples are simulated and are used only for didactic purposes. Results: The first step is to state the analytic objective explicitly. Exploratory models are used to describe adjusted associations and generate hypotheses; predictive models aim to estimate individual risk and therefore require attention to discrimination, calibration, and internal/external validation; causal models aim to estimate an effect and should rely on temporality, substantive knowledge, and directed acyclic graphs (DAGs) to define adjustment sets. Across objectives, arbitrary dichotomization and univariable screening are discouraged. Continuous predictors should usually be kept on their original scale, with flexible functions such as restricted cubic splines when nonlinearity is plausible. Penalization is generally preferable to stepwise procedures when overfitting is a concern in prediction, whereas full theory-based models are often preferable in causal analyses. Conclusions: The research question should determine the model, not the reverse. A practical workflow is to define the objective first, prespecify candidate variables, choose a functional form that preserves information, and evaluate the model with objective-specific criteria. Clear separation of exploratory, predictive, and causal aims improves transparency, interpretability, and clinical usefulness.
Causal Structural Equation Modeling of Climatic and Environmental Factors Influencing Malaria Incidence in Southeastern Senegal
Introduction: Malaria remains a major public health problem in tropical regions due to complex interactions between climatic, environmental, and epidemiological processes. Objective: To improve the quantitative understanding of these mechanisms, this study introduces a causal analysis framework based on structural equation modeling (SEM) to elucidate the direct and indirect pathways through which climatic and environmental variables influence malaria incidence. Methods: We analyzed weekly malaria case data collected in four districts of southeastern Senegal (Kédougou, Salémata, Saraya, and Dianké Makha) over the period 2018–2024 using structural equation modeling and climates factors. Climate predictors included temperature, precipitation, relative humidity, wind speed, atmospheric pressure, cloud cover, and lunar phase, obtained from Visual Crossing, complemented by vegetation indices derived from satellite imagery. Results: The results revealed significant spatial heterogeneity: rainfall and cloud cover had the strongest total effects on malaria incidence in three districts (standardized total effects ranging from 0.51 to 0.66), while high wind speed emerged as the primary determining factor in Kédougou, with a negative association (−0.51). Wind-related factors showed protective effects against malaria transmission, with notable spatial complexity, and atmospheric pressure exhibited consistent indirect effects (0.18 to 0.42), mediated by other climatic predictors, but without a significant direct association. Conclusion: These results underscore the importance of considering indirect causal pathways in environmental epidemiology and highlight district-specific climatic profiles that can inform localized malaria control strategies. The SEM framework presented here offers a rigorous statistical approach to integrating multi-source climatic and environmental data to advance causal inference in vector-borne disease research, ultimately supporting early warning systems and targeted vector control interventions at the district level.
Placental and Maternal Serum Expression of sFlt-1 e15a and PlGF Reveal Distinct Angiogenic Patterns in Early-Onset Preeclampsia
Background: Early-onset preeclampsia (EOPE) represents a severe pregnancy disorder occurring before 34 weeks of gestation and is strongly associated with impaired placental angiogenesis. This condition is characterized by an imbalance between antiangiogenic and proangiogenic factors, particularly soluble fms-like tyrosine kinase-1 (sFlt-1) e15a and placental growth factor (PlGF). Despite their recognized roles, data regarding the concurrent expression of these biomarkers in maternal serum and placental tissue remain limited. Methods: This analytical observational study used a comparative cross-sectional design involving 91 pregnant women with preeclampsia, categorized into EOPE (n=45) and late-onset preeclampsia (LOPE; n=46). Maternal serum and placental levels of sFlt-1 e15a and PlGF were measured using enzyme-linked immunosorbent assay (ELISA). Statistical analyses were performed using independent t-tests or Mann–Whitney tests depending on the data distribution. Receiver operating characteristic (ROC) analysis and Spearman correlation were conducted to evaluate diagnostic performance and biomarker relationships. Results: Serum sFlt-1 e15a levels were higher in EOPE than LOPE, although not statistically significant (p=0.082), while serum PlGF levels were significantly lower (p<0.001). Consequently, the serum sFlt-1/PlGF ratio was significantly elevated in EOPE (p<0.001). In placental tissue, sFlt-1 e15a levels were significantly lower, whereas PlGF levels were markedly reduced in EOPE compared to LOPE (both p<0.001), resulting in a higher placental sFlt-1/PlGF ratio (p<0.001). ROC analysis demonstrated good discriminative performance for both serum (AUC=0.772) and placental ratios (AUC=0.782). Significant correlations were observed between serum and placental biomarkers, particularly for the sFlt-1/PlGF ratio (ρ=0.859, p<0.001). Conclusion: EOPE is characterized by a pronounced angiogenic imbalance in both maternal serum and placental tissue. The sFlt-1/PlGF ratio demonstrates strong diagnostic potential and may serve as a reliable biomarker for early identification and risk stratification of EOPE.
Determinants of VIA Screening Utilization: A Cross-Sectional Logistic Regression Study in an Archipelagic Setting of Eastern Indonesia
Background: Cervical cancer remains a leading cause of morbidity and mortality among women in low- and middle-income countries. Visual Inspection with Acetic Acid (VIA) is recommended as an effective and affordable screening method; however, coverage remains low in archipelagic regions of Eastern Indonesia. Objective: To examine the association between reproductive factors and contraceptive use with VIA screening utilization among women of reproductive age. Methods: A cross-sectional study was conducted among 131 women at a primary healthcare center in Ambon City. Data were analyzed using chi-square tests and multivariable logistic regression to estimate crude and adjusted odds ratios. Variables with p < 0.25 were included in the multivariable model. Model fit was assessed using the Hosmer–Lemeshow test, and explanatory power using Nagelkerke R². Results: A total of 65.6% of respondents had undergone VIA screening. No variables were significantly associated with screening utilization (p > 0.05). Contraceptive use showed a non-significant tendency toward increased screening likelihood (AOR = 1.837; 95% CI: 0.443–7.610). The model demonstrated weak explanatory power (Nagelkerke R² = 0.05), and the omnibus test was not statistically significant (p > 0.05), indicating limited predictive capacity. Conclusion: The study did not identify significant predictors of VIA utilization. The findings suggest that the variables included in the model were insufficient to explain screening behavior. Further studies with larger samples and more comprehensive analytical models are needed.
Quantitative Mapping of Global Research on Mobile Health Interventions for Hypertension: A Bibliometric Study
Background: Mobile health (mHealth) technologies are increasingly reshaping the management of hypertension, which remains a leading contributor to global cardiovascular burden. Although a rapidly expanding body of research has explored digitally supported interventions, the structural evolution, intellectual foundations, and emerging trajectories of this field have not been comprehensively synthesized. Objective: This study aimed to map the global research landscape of mHealth interventions for hypertension and to identify the collaborative, intellectual, and conceptual structures guiding its development. Methods: A bibliometric analysis was conducted on 474 documents indexed in Scopus using VOSviewer and R Bibliometrix. Trends in scientific production were examined alongside contributions by countries, journals, authors, and highly cited documents. Co-authorship, co-citation, and keyword co-occurrence networks were analyzed to uncover the field’s knowledge architecture and thematic evolution. Results: Scientific output in mHealth intervention for hypertension has increased steadily, led by high-income countries with strong international collaboration. Highly cited studies are mainly randomized controlled trials, indicating a solid evidence base. Thematic trends show a shift from telemonitoring to scalable, patient-centred interventions, with growing focus on engagement, implementation, and equity. Conclusion: The research landscape on mHealth interventions for hypertension is rapidly advancing, with increasing methodological rigor and a shift toward scalable, patient-centred solutions. Nonetheless, disparities in global research contributions persist, underscoring the need for more inclusive and context-specific studies to support equitable implementation.
Work Fatigue as a Mediator between Occupational and Lifestyle Factors and Blood Pressure among PLN Sulawesi Distribution and Load Dispatch Center Employees
This study aimed to analyze the influence of Working Hours, Work Stress, Physical Activity, and Dietary Habit on Blood Pressure through Work Fatigue. This quantitative study used a cross-sectional design with path analysis. The sample consisted of 140 workers and employees of PT. PLN (Persero) UIP3B Sulawesi. Blood Pressure and Work Fatigue were measured directly, while Working Hours, Work Stress, Physical Activity, and Dietary Habit were obtained through questionnaires. The results showed that Working Hours significantly influenced Work Fatigue (p < 0.001), while its direct effect on Blood Pressure was not statistically significant at the 0.05 level, although it showed a borderline trend (p = 0.051). Work Stress significantly affected Work Fatigue (p = 0.017), but did not have a significant direct effect on Blood Pressure (p = 0.166). Physical Activity had no significant effect on either Work Fatigue (p = 0.652) or Blood Pressure (p = 0.915). Dietary Habit significantly influenced both Work Fatigue (p = 0.001) and Blood Pressure (p = 0.030). Furthermore, Work Fatigue significantly affected Blood Pressure (p < 0.001). In conclusion, Work Fatigue mediated the relationship between Working Hours, Work Stress, and Dietary Habit with Blood Pressure, while Physical Activity showed no significant direct or indirect effect. Dietary Habit indicated partial mediation because it had both significant direct and indirect effects on Blood Pressure.
Evaluating the Impact of Demographic Covariates on Healthcare Access and NCD Risk: Evidence from a Cross-Sectional Survey in Five Arab Countries
This study evaluated how demographic factors and socio-economic factor influence on the healthcare access and non-communicable disease (NCD) risk in the five different Arab countries. It has also investigating how Gender moderates this relationship. It has used quantitative cross-sectional design where data was collected from 400 respondents in Egypt, Saudi Arabia, Jordan, Morocco and the United Arab Emirates. The data was analysed using binary logistic regression and chi-square tests. The results indicated considerable variation across countries in terms of access to health care and NCD risk. In the access to health care services category, the highest frequency was observed in the UAE and Morocco, whereas the lowest frequency was recorded in Egypt and Saudi Arabia. With regard to NCD risk, the highest and lowest frequencies were observed in Egypt and Saudi Arabia and the UAE and Morocco, respectively. The logistic regression indicated that factors such as age, residence, education, employment status, income level, housing arrangements, and assets ownership had significant impacts on access to healthcare services and NCD risk. Factors like age positively influenced access to health services and NCD risk while education, high-income levels, and assets ownership negatively impacted NCD risk. Even though the effect of gender was directly influenced by the two health variables, moderation analysis revealed that gender did not influence the relationship between them. The study has highlighted importance of socioeconomic determinants in shaping health outcomes across Arab countries and suggests that policies aimed to improve education, economic conditions and healthcare accessibility are effective in reducing NCD risk and promoting equal healthcare access than gender specific intervention alone. Hence, this study neglects the theoretical norm of gender differences and rather indicates that other SES factors can be evaluated for having a moderating role in this regards.
A Linear Algebraic Framework for SIR Epidemic Modelling with Applications to Infectious Disease Dynamics and Healthcare Planning
The Susceptible–Infected–Recovered (SIR) model is a fundamental framework in mathematical epidemiology used to analyse the spread and control of infectious disease within a population. Although classical SIR models provide valuable insights into epidemic dynamics, their nonlinear structure can make analytical investigation of local stability and epidemic thresholds challenging. In this study, a linear algebraic framework for the SIR model is developed by expressing the system in matrix form and analysing it through eigenvalues and eigenvectors. By linearizing the system around equilibrium points, the proposed approach enables systematic evaluation of local stability conditions and epidemic thresholds, providing insight into the early-stage growth or decay of an infection. In addition to its theoretical advantages, the framework has important medical and public health applications. The eigenvalue-based analysis facilitates assessment of epidemic growth rates, local stability characteristics, and identification of critical control thresholds. These insights can support healthcare planning, including hospital resource allocation, intervention strategies, and epidemic preparedness. Overall, the study demonstrates that linear algebraic techniques provide a useful complementary framework for analysing local epidemic dynamics and supporting epidemiological decision-making. A comparison between the nonlinear SIR model and its linearized approximation is also presented to highlight the applicability and limitations of the linear algebraic approach.
Maternal and Child Determinants of Under-Five Mortality in India: Evidence from the National Family Health Survey (NFHS-5), 2019–21
Background: Under-five mortality is a critical indicator of child health and overall socioeconomic development. Although India has made substantial progress in reducing child mortality, significant disparities persist across regions and population groups. This study aimed to examine the maternal and child determinants associated with under-five mortality in India using nationally representative data from the National Family Health Survey (NFHS-5, 2019–21). Methods: A cross-sectional secondary analysis of NFHS-5 data was conducted, including 237,500 live births occurring within five years preceding the survey. Under-five mortality was the outcome variable. Maternal factors (age, education, residence, wealth status, and media exposure) and child-related factors (sex, birth order, birth weight, and type of birth) were examined. Descriptive statistics, chi-square tests, and multivariable logistic regression analyses were used to identify significant determinants. Statistical significance was considered at p < 0.05. Results: The prevalence of under-five mortality was 3.8%. Mortality was significantly higher among children born to mothers with no formal education (5.5%) and those belonging to the poorest wealth quintile (6.0%). Children residing in rural areas and those born to mothers aged below 20 years experienced higher mortality rates. High birth order (≥4), low birth weight, and multiple births were strongly associated with increased risk of death before five years of age. Male children showed slightly higher mortality (4.0%) compared with female children (3.5%). Maternal exposure to mass media was associated with improved child survival. Multivariable analysis identified maternal education, household wealth, place of residence, birth weight, birth order, and type of birth as significant predictors of under-five mortality. Conclusion: Under-five mortality in India continues to be influenced by socioeconomic, maternal, and child-related factors. Improving maternal education, reducing socioeconomic inequalities, enhancing maternal and child nutrition, and strengthening access to quality healthcare and health information are essential for further reducing child mortality and achieving the Sustainable Development Goal target of fewer than 25 under-five deaths per 1,000 live births by 2030.
Distribution Pattern Analysis of Daily Confirmed COVID-19 Cases Across Selected Countries and Indian Epidemic Waves
Daily confirmed COVID-19 cases have shown highly heterogeneous epidemic trajectories across different countries. The timing of the outbreak, the intensity of spread, the peak size, the duration of waves, and the reporting pattern varied substantially from one country to another. Due to this heterogeneity, the statistical analysis of daily confirmed cases is important not only to describe the epidemic burden but also to understand how cases are distributed over time. In this study, the distribution pattern of COVID-19 daily cases is analysed for six countries: India, the United States, Brazil, the United Kingdom, South Africa, and Japan. The daily confirmed COVID-19 case data are taken from the Our World in Data COVID-19 dataset. The study period is restricted to a common analytical window from 30 January 2020 to 20 February 2022. After preprocessing, 753 daily observations are obtained for each country. For cross-country comparison, daily cases per million population are used, while India-specific wave analysis is performed using raw daily confirmed case counts. The analysis evaluates descriptive distributional behaviour and parametric distribution fitting using selected candidate distributions, including Gamma, Generalised Gamma, Weibull, Log-Normal, Log-Logistic, Inverse Gaussian, Exponential, and Normal distributions. The results show that daily COVID-19 case distributions are right-skewed in all selected countries. The mean value is greater than the median value in all countries, showing that high-incidence days strongly affect the average case count. Country-wise distribution fitting shows that no single distribution is best for all countries. Log-Logistic distribution is selected for India, the United States and Japan; Inverse Gaussian distribution for Brazil; Generalised Gamma distribution for South Africa; and Log-Normal distribution for the United Kingdom. For India, the wave-wise analysis shows that the three epidemic waves have different distributional behaviour. Wave 1 is best approximated by the Weibull distribution, Wave 2 by the Log-Normal distribution, and Wave 3 by the Generalised Gamma distribution. These findings indicate that COVID-19 daily case patterns are asymmetric, heterogeneous, and wave-specific. Hence, flexible positive-support and right-skewed distributions are more useful for describing the empirical distribution of positive daily confirmed COVID-19 cases than symmetric assumptions. However, the selected distributions are interpreted only as relative empirical approximations among the evaluated candidate distributions, and not as exact distributional laws of COVID-19 daily cases.
Comparative Study of Burr-XII Nested Hazard Models: Simulation under Mixed Directional Effects and AML Data Analysis
Choosing an appropriate hazard-based regression model is critical in survival analysis. The proportional hazards (PH), accelerated failure time (AFT), and accelerated hazard (AH) models make distinct assumptions about covariate effects, yet real data often violate these assumptions. The general hazard (GH) model nests all three frameworks, allowing both time-scale and hazard-scale effects within a single structure. This paper presents a comparative study of the four nested models (PH, AFT, AH, GH) using the three-parameter Burr XII baseline hazard. Extensive simulations under ten scenarios, including both positive and negative covariate effects, evaluate maximum likelihood estimators via bias, standard errors, mean squared error, coverage probability, and information criteria. Sample sizes of 1000 and 5000 with 20% and 40% censoring are considered. A dedicated simulation examines the recovery of increasing, decreasing, and unimodal hazard shapes. Results show that the GH model consistently recovers the true data-generating structure and all three hazard patterns. The GH model yields the lowest information criterion values among its submodels, supporting its utility as a data-driven model selection tool. Practical relevance is illustrated using real survival data from 801 acute myeloid leukemia (AML) patients who received allogeneic stem cell transplantation. This comparative framework offers applied researchers a systematic, evidence‑based approach to select the most appropriate hazard structure for right-skewed, heavily censored survival data.