Journal of Biostatistics and Epidemiology

Diabetes Management with Wearable Continuous Glucose Monitoring Trackers: A Systematic Review and Meta-Analysis of Randomized, Controlled Trials

Patankar Chinmayee Nilesh, Jahnavi Hegde, Ramesh Athe — Fri, 01 Aug 2025 11:03:21 +0000

Introduction: Continuous glucose monitoring (CGM) has become an essential tool in diabetes management as it provides real-time information on blood glucose levels. Present study summarizes current evidence on the clinical outcomes, glycemic control, and patient-reported outcomes of CGM compared to non-CGM strategies among the included studies with diabetes.

Methods: The systematic review was performed following the PRISMA guidelines. PubMed, Google Scholar, JAMA Network, and SpringerLink, etc. were searched for relevant research published after 2010. Included research assessed the use of CGM with non-CGM treatments, such as traditional therapy or self-monitoring of blood glucose (SMBG) for diabetics. The study's design, participant characteristics, intervention specifics, glycemic outcomes (i.e. HbA1c and duration in range), and quality of life were all included in the extracted data.

Results: Twelve studies (1916 subjects) based on randomized controlled trials and satisfied the inclusion criteria. Findings show that using a CGM is linked to notable improvements in glycemic control, as evidenced by falls in HbA1c readings when compared to non-CGM approaches. The studies had adequate heterogeneity: I² = 32%, Chi² = 16.08, and Tau² = 0.00. An impact was found in the overall effect using a random effects model, with weighted mean difference (WMD) = 0.43; CI: 0.34-0.52 (p<0.001). To evaluate the cause of heterogeneity and publication bias, meta-regression and Egger's regression were used.

Conclusion: This study highlights the potential of CGM devices to enhance diabetes management by improving glycemic control and patient outcomes. Despite several obstacles, CGM shows promise as a substitute for conventional diabetes treatment approaches. Future studies should address these issues and assess the long-term advantages of using a CGM in more detail. This study is registered in PROSPERO (Registration ID: CRD42024518635).

A Quantitative Comparison Between Focal Loss and Binary Cross-Entropy Loss in Brain Tumor Auto-Segmentation Using U-Net

Fri, 01 Aug 2025 11:09:28 +0000

Introduction: Brain tumors are among the most fatal cancers and cause the deaths of many people annually. Early diagnosis of a brain tumor can help save the patient’s life.

Methods: We have collected a dataset consisting of 314 brain MRI images in all planes taken after administering a contrast medium with the dimension of 800*512, which offers the highest resolution. First, skull stripping has been implemented to separate the brain from other parts in the images. Next, we have annotated the tumors in the images under the supervision of experienced radiologists to create ground truth. To determine the most effective model versions for all three loss functions, hyperparameter tuning was performed. Following the comparison, the study further evaluates the effectiveness of two loss functions, Binary Cross-Entropy (BCE) and Focal loss, specifically in handling tumor regions within the dataset.

Results: The two proposed loss functions were evaluated using 5-fold cross-validation, and the average precision, recall, and F1-score were 76.16%, 71.9%, and 74.52 for BCE loss and 82.92%, 79.32%, and 81% for the Focal loss on the test data, respectively. Moreover, the accuracy for BCE loss was 99.03% and 99.44% for the Focal loss.

Conclusion: We recommend using BCE loss cautiously in classification tasks without data imbalance and emphasize the adoption of Focal loss for more accurate and reliable results in brain tumor segmentation.

Estimation of HIV Prevalence Among Women in KENYA in the Presence of Mediation Using Latent Trait Analysis

ilence Mola, Drinold Aluda Mbete, Job Sirengo — Fri, 01 Aug 2025 11:44:01 +0000

Introduction: Estimating prevalence in cause-effect relationships where the mediator variables are assumed to be latent is not usually easy. However, the use of proper indicators and statistical model can make the measurement and use of such constructs easy.

Methods: Structural Equation Modeling makes it possible to analyze simultaneously both the relationship between the latent variable and the links between the latent variable and their indicators. The 2018 Kenya AIDS Indicator Survey data was used to validate the model developed. The maximum likelihood was used to estimate the model parameters. The findings of the study were, there is a relationship between education attainment and knowledge /awareness of HIV/AIDS.

Results: The results further shows that education levels are not associated with HIV prevalence after controlling for a number of socio-demographic characteristics and behavioral factors.

Conclusion: These findings can inform policy makers in formulation of appropriate HIV/AIDS management (policies) and intervention strategies aimed at reducing HIV/AIDS prevalence that has remained a challenge in many developing countries.

Understanding Premature Mortality Patterns from Non– Communicable Diseases in Iran (2012-2020)

Batoul Rabbani, Elaheh Kazemi, Amin Ataey, Elham Rashidian, Ardeshir Khosravi — Fri, 01 Aug 2025 11:49:28 +0000

Introduction: More than 85% of premature deaths from major non-communicable diseases (NCDs) occur in low- and middle-income countries. This study aimed to investigate trends of premature deaths (30-70 years) due to the noncommunicable diseases in Iran, from 2012 to 2020.

Methods: The present study is a descriptive study. Data on the causes of death from 2012 to 2020 were extracted from the death registration system of the Ministry of Health and Medical Education, and the data is not based on sampling. After calculating the completeness of the death registration system using the method proposed by Lopez and Adair, the probability of premature deaths due to noncommunicable diseases was determined.

Results: From 2012 to 2020, non-communicable diseases accounted for a significant percentage of all deaths, ranging from approximately 54% to 72%. Premature deaths due to these diseases varied between 50% and 71% during the study period. The probability of premature deaths due to these diseases fluctuated between 14.95% and 17.35%.

Conclusion: With the knowledge that the most common cause of premature death in women is non-communicable disease and the most common cause in the general population is cardiovascular disease, evidence-based planning and policymaking should be done to achieve further reductions in premature mortality, with an approach to be adopted in a unified way by focusing on modifiable risk factors in different sectors and disciplines in Iran.

Sparse Variable Selection in Competing Risks Additive Hazards Regression: An Application for Identifying Biomarkers Related to Prognosis of Bladder Cancer

Leili Tapak, Michael R. Kosorok, Omid Hamidi, Mahya Arayeshgari — Fri, 01 Aug 2025 11:55:17 +0000

Introduction: Variable selection is increasingly becoming a key step in biomedical research, particularly in high-throughput genomic data analysis. One major focus is selecting relevant gene expression profiles associated with time-to-event outcomes, such as death. A significant challenge in this context is competing risks, where identifying a small subset of gene expression profiles related to the cumulative incidence function (CIF) is essential.

Methods: Several methods have been proposed for directly modeling CIF, primarily by modeling the subdistribution hazard function for the event of interest. We proposed a regularized method for variable selection in the additive subdistribution hazards model by integrating five penalized likelihood approaches—Least Absolute Shrinkage and Selection Operator (LASSO), Adaptive LASSO (ALASSO), Elastic Net (ENET), Adaptive Elastic Net (AENET), and Smoothly Clipped Absolute Deviation (SCAD)—with the pseudoscore method. We conducted Monte Carlo simulations to evaluate the performance of our proposed method. Furthermore, the method was applied to a publicly available dataset of 301 patients diagnosed with non-muscle-invasive bladder carcinoma from five countries between 1987 and 2000.

Results: Our proposed method was evaluated through simulation studies and applied to genomic data, focusing on progression-free survival as the endpoint and identifying the genes associated with the CIF of bladder cancer in the presence of competing events. Five genes, namely DCTD, IGF1R, NCF2, PLEK, and CDC20, were consistently identified across different penalties. Notably, the overexpression of DCTD and IGF1R was associated with a decreased cumulative incidence of bladder cancer progression or death. In contrast, the overexpression of NCF2, PLEK, and CDC20 correlated with an increased cumulative incidence of these events.

Conclusion: According to the findings of the simulation studies, all penalties yielded comparable results in terms of sensitivity and specificity. However, the AENET and ALASSO ppenalties demonstrated superior estimation accuracy.

Developing a Comparative Analysis Model by Risk Management Approach on Satellite Media Affecting Individual Health, Families, Social Health, Humans and Human Dignity: Comparing Countries with Iran

Sahar Naghshineh, Seyed Jamaledin Tabibi, Aliakbar Farhangi — Fri, 01 Aug 2025 12:01:20 +0000

Introduction: To ascertain the impact of risks arising from the technology of satellite media, whatsoever, in this era especially those connected to the internet (including Cyborgs, Metaverse and AI), on the health of individuals (mental, attitudes and behaviors that even may cause physical impacts such as violence, etc.), families, the social and as a whole, humans and human dignity, an authentic research done to deal with the related risks to prevent any kind of direct and/or indirect negative impacts of satellite media on the variables. The objective was to display a new way of assessing the related risks and developing a comparative analysis model by risk management approach to reach to a result for Iran.

Methods: Comparing five countries randomly selected from almost each continent along with Iran, focused on Tehran as a metropolitan counting different ethnic groups, their common risks of satellite media were identified by risk management approach. The questionnaires, provided by applying the Likert Scale, were distributed among the related experts, professionals, graduate and post-graduate students as well as the related authorities. The comparative study called for a qualitative research required a descriptive research along with case studies and an analytical study. Subsequently distributing questionnaires, exploratory research, The Mann-Whitney U Test using the SPSS statistics concluded as quantitative research. Finally, this research reached to an applied research too.

Results: Social and cultural risks of the countries understudy were assessed. The comparative and analytical studies and appraisals as well as statistical processes revealed that Iran’s current media approach and its performance towards the whole society are quite different from the other five countries with those Iran was compared to.

Conclusion: A new technical method to approach risks of satellite media to control them for preventing any kind of satellite media risks that impacts on variables. As applied research the new result showed that an immediate action is required to regulate satellite media standards protect the whole society and to modify the current situation of Iran’s media approach towards them.

Association of Sleep Health Metrics (Sleep Duration and Quality) and Mental Health Status: A Population-Based Cross-Sectional Survey

Fri, 01 Aug 2025 00:00:00 +0000

Introduction: Sleep pattern is a vital part of healthy lifestyle, with deviations from the recommended 7 to < 9 hours associated to negative outcomes. Limited studies have investigated the relationship between sleep patterns and mental health in underserved urban communities. This study explores the association between sleep health metrics, such as sleep duration and mental health among the population residing in Southern Tehran.

Methods: This population-based cross-sectional study utilized data from 1,311 participants in the "Study Protocol and Lessons from Iran for the Integrated and Repeated Public Health Surveillance System (IRPHS)" telephone survey participants to evaluate amount of sleep duration (< 7 h and ≥ 9 h) and sleep quality, along with their associations with anxiety, depression, suicidal ideation, and related sociodemographic factors.

Results: The mean ± standard deviation (SD) age of participants was 40.4±13.4 years, and 60.4% of the sample was female. Among our participants 15.0% had insufficient sleep (< 7 h) and 18.5% had long sleep duration (≥ 9 h). Sex, socioeconomic status (SES), and tobacco use were associated with sleep duration (p < 0.05). Moderate and high anxiety, moderate depression, and high suicidal ideation were associated with long sleep duration (p < 0.05).

Conclusion: In southern Tehran population, prevalence of both insufficient and long sleep duration is high. Poor sleep quality and oversleeping can happen due to underlying depression and anxiety, and targeting improvement of mental health can increase sleep health. The findings provide insights for prevention strategies tailored to the study

Robust Neighborhood Confidence Interval and Width to Evaluate the Outcome of a Binary Random Variable of Unequal Cluster Sizes

Fri, 01 Aug 2025 12:10:08 +0000

Introduction: Confidence intervals (CIs) provide a more precise evaluation of outcomes, especially when the risk of an event is influenced by cluster size. While confidence intervals are commonly used to assess uncertainty in future data, in this study, we focus on their role in quantifying variability within currently observed outcomes. Specifically, the width of the predicted confidence interval serves as an indicator of existing intra-cluster heterogeneity, highlighting the extent of variability across different cluster sizes. This study introduces a novel method for evaluating observed outcomes of dichotomous random variables in datasets with unequal binary cluster sizes. By employing a robust neighborhood confidence interval width, this approach ensures a more reliable and adaptive estimation of intra-cluster variability, allowing for a more accurate interpretation of current data distributions. Methods: We introduce a novel algorithm for constructing an intra-cluster robust neighborhood confidence interval and its corresponding width for each cluster. This method enables the ranking of clusters based on confidence interval width, from the narrowest to the widest, providing a systematic approach to quantifying intra-cluster variability. By evaluating observed values within these ranked intervals, the algorithm offers a more precise assessment of data heterogeneity. To illustrate the effectiveness of this method, we present both a simulated example assessing its finite-sample performance and a real-world application in the context of antimicrobial resistance data with unequal binary cluster sizes. Results: The robust neighborhood intra-cluster confidence interval (CI) width was successfully derived for interpreting binary outcome data with unequal cluster sizes. The analysis showed that narrow confidence intervals indicate minimal random variation, suggesting higher reliability in the observed results, whereas wider intervals highlight increased intracluster variability. Conclusion: The intra-cluster robust neighborhood CI and its corresponding width provide a valuable tool for analyzing binary outcome data with unequal cluster sizes. This method enhances the interpretation of observed results by systematically quantifying variability within clusters, allowing for more reliable intra-cluster comparisons.