Journal of Biostatistics and Epidemiology

Unmet Need for Family Planning and Associated Factors Among Married Women of Reproductive Age in Siranchowk Rural Municipality, Nepal: A Cross-Sectional Study

Rajesh Karki, Mausam Adhikari, Roshani Poudel, Maheshor Kaphle — Mon, 27 Apr 2026 08:05:04 +0000

Introduction: The unmet need for family planning remains a hurdle to reproductive health equity despite, global effortsto improve access, including in Nepal. This study aimed to assess the prevalence of unmet needs for family planning andassociated factors among rural women in Nepal.

Methods: In 2023, a cross-sectional study was conducted among married women of reproductive age in a rural municipalityin Gandaki Province, Nepal. We recruited 310 participants using consecutive sampling. Data were collected through face-to-face interviews using a structured questionnaire developed from previous literature, validated by experts, and pretested.Descriptive analysis was conducted for categorical variables, and multivariate logistic regression analysis was performed toidentify factors associated with unmet needs.

Results: The mean age of the respondents was 28.5 ± 5.75 years (range: 17–45 years), and the mean age at marriagewas 21.07 ± 3.32 years (range: 14–34 years). More than 80% of the respondents reported having good family planningknowledge, with healthcare workers being the primary source of information (74.8%). The unmet need for family planningwas 18.1% (spacing: 16.5%; limiting: 1.6%). The odds of unmet need were higher in Dalit women (AOR 6.66, 95% CI:1.98–22.40) and women without children (AOR 2.78, 95% CI 1.09–7.13). Conversely, women with a basic education orbelow (AOR 0.14, 95% CI: 0.03–0.71) and those with husbands who are engaged in business (AOR 0.32, 95% CI 0.12–0.83)had lower odds.

Conclusion: This study highlights the significant unmet need for family planning among rural women in Nepal, particularlyamong adolescents, Dalit women, and those without children. Therefore, targeted interventions are required to addressthese disparities. Continued efforts should focus on improving family planning access in the study area and similar ruralsettings, although the findings may not be generalizable to the entire country.

Latent Class Analysis of Behavioral and Metabolic Risk Factors Among Patients with Acute Coronary Syndrome

Mon, 27 Apr 2026 08:11:29 +0000

Introduction: The aim of this study was to explore latent classes of risk factors among patients with acute coronary syndrome.

Methods: A cross-sectional study was performed on patients with symptoms of chest pain, unstable angina, or myocardial infarction who had at least one coronary vascular involvement confirmed by angiography. A latent class analysis (LCA) using five categorical risk factors, including metabolic syndrome, physical activity, tobacco use, alcohol, and opium consumption, was conducted on 380 eligible patients. A logistic regression model was used to explore the associations of demographic and clinical variables with latent classes.

Results: The mean age of the patients was 59.05 years (SD= 9.82). A two-class model showed the best fit; Class I (45.1%) was characterized by a high probability of smoking, alcohol, and opium consumption, and Class II was characterized by a high probability of metabolic syndrome (54.9%). There was a significant difference between the two classes in terms of age, sex, job, and educational status. The multiple logistic regression model revealed that age and sex were independent predictors of latent class membership.

Conclusion: This study revealed two distinct latent risk factor patterns among ACS patients emphasizing the need for personalized prevention approaches. Behavioral interventions should be prioritized in younger patients. While, sex-specific metabolic syndrome management strategies should be underscored in older patients

Survival Analysis of Childbirth Using a Mixture Cure Frailty Model

Mon, 27 Apr 2026 08:19:25 +0000

Introduction: Childbirth plays a crucial role in population growth and maternal health. In recent decades, many nations,including Iran, have experienced declining birth rates. Since childbirth is a recurrent event in a parent's life, it is useful toanalyze it through the lens of recurrent event analysis. This methodological framework, commonly employed in biomedicine,allows for a nuanced examination of the relationship between multiple childbirth experiences and the potential for curedsubjects. This study explores childbirth rates in Hamadan province.

Methods: A total of 633 mothers who gave birth to their first child in 2012 at Fatemiyeh Hospital in Hamadan participatedin this retrospective cohort study. Both mixture cure frailty models and simple frailty models were fitted. The analyses wereconducted using the RSTAN package in RStudio version 26.2.4.

Results: In this study, we analyzed the childbearing patterns of couples and found that the majority (60.6%) had twochildren. Additionally, we discovered that 49% of mothers and 55.9% of fathers had education levels below a diploma.The Kaplan-Meier (KM) curves indicated a cure pattern for families with three or more children, revealing that only10.6% of individuals had three children, and a mere 0.8% had four. Furthermore, results from a mixture cure frailty modeldemonstrated that maternal education plays a crucial role in influencing childbirth probabilities.

Conclusion: Based on the findings of this study, we recommend utilizing mixture cure frailty models rather than simplefrailty models when the dataset contains individuals who are cured.

Determinants of MedicationAdherence in Hypertensive Patients: Clinical Evidence from Indonesian Primary Healthcare Settings

Mon, 27 Apr 2026 08:26:58 +0000

Introduction: Adherence to hypertension medication remains a critical challenge in healthcare management, particularly in resource-limited settings. This study investigated the determinants of medication adherence among patients with hypertension in Indonesian primary healthcare settings.

Methods: A cross-sectional study involving 96 hypertensive patients selected through systematic random sampling was conducted at the Public Health Center of Tenggilis, Surabaya. Data were collected via validated questionnaires, including the Morisky Medication Adherence Scale-8 (MMAS-8), and analyzed via multivariate logistic regression.

Results: Among the 96 hypertensive patients included in this study, the majority were aged 40–49 years (30.2%), with a male predominance (67.7%). Most participants had a senior high school education (57.3%) and were employed as civil servants (30.2%). Only 52.1% of patients reported consistent medication adherence, with financial barriers and knowledge gaps identified as the primary challenges. Multivariate logistic regression analysis revealed that regular medical control (odds ratio [OR] = 1.963, 95% CI 1.214-3.181; p = 0.006) and alternative diagnostic methods (OR = 2.326, 95% CI 1.532- 3.538, p<0.001) were significantly associated with better medication adherence. Adherence to doctors' advice (OR = 1.699, 95% CI 1.128–2.559, p = 0.012), the ability to manage medication costs (OR = 1.518, 95% CI 1.012–2.278, p = 0.044), and routine treatment management (OR = 1.825, 95% CI 1.219–2.736, p = 0.004) were identified as key predictors of positive medication adherence.

Conclusion: Medication adherence in patients with hypertension is influenced by multiple factors, including diagnostic approach, healthcare access, cost management, and routine treatment compliance. These findings emphasize the need for comprehensive interventions that address both clinical and socioeconomic barriers to improve hypertension management in primary healthcare settings.

Estimation of Volume Under Receiver Operating Characteristic Surface and Asymptotic Variance for Diagnostic Classifier Following Log-Normal Distribution

G Kumarapandiyan, T. S. Sahana — Mon, 27 Apr 2026 08:31:09 +0000

Introduction: Clinical diagnosis highlights the essential need to assess biomarker performance for effective diseasescreening and diagnosis. The Receiver Operating Characteristic (ROC) curve serves as a fundamental tool for assessingand interpreting biomarker effectiveness. Numerous models and techniques have been developed to analyze biomarkersin binary classification settings (Non-Diseased vs. Diseased). This research article seeks to expand the binary classificationframework to a three-class scenario, incorporating Diseased, Suspicious, and Non-Diseased categories under a Log-Normaldistribution.

Methods: It introduces a three-class Log-Normal ROC model based on a Parametric approach, deriving metrics such asVolume Under the ROC Surface (VUS) and Asymptotic Variance, as well as an alternative Non-Parametric approach. Themodel was validated using simulated data generated for the underlying distribution, and a real-life dataset was used to fitthe VUS and ROC curves.

Results: The simulation study was conducted using four sets with varying parameters. In the fourth set, the Non-ParametricVUS (0.9966) exceeded the Parametric VUS (0.8058), though the difference was smaller compared to the other sets. Thelow Standard Error (SE) (0.0472) across all sets indicates high precision in the estimates. Additionally, for the real-life (Themultiple sclerosis (ms) disease) dataset the VUS value is 0.6782 which gives moderate fit of the model.

Conclusion: In this study, we derived the asymptotic variance and VUS for the Log-Normal distribution using simulateddata with varying parameters. The analysis compares diagnostic performance across parameter sets, highlighting thesuperiority of Non-Parametric VUS over Parametric VUS. Set 4 demonstrated the highest reliability with the lowest standarderror (SE = 0.0472). The real-life MS dataset provided a moderate fit to the proposed model

Prevalence of Obesity, Overweight and other Cardiovascular Risk Factors Among Iranian Military Personnel in 2022

Yousef Alimohamadi, Mojtaba Sepandi, Esmaeil Samadipour, Sima Afrashteh — Mon, 27 Apr 2026 08:38:50 +0000

Introduction: The incidence and prevalence of cardiovascular disease (CVD) have increased in Iran, considering the importance of documenting and generating information about the risk of CVD in the military community, the current study aimed to measure the prevalence of risk CVD factors as well as predict the 10-year risk of CVD among the Iranian military personnel. The FRS items include age, gender, total cholesterol, high density lipoprotein cholesterol (HDL-C), systolic blood pressure, status of diabetes and smoking.

Methods: This cross-sectional study was conducted on 1025 male military personnel in 2022. For comparative analysis, ANOVA or t-test, as well as the Chi-square (or Fisher's exact) test was used. All statistical analyses were conducted using SPSS 22 software. The statistical significance level was set at 0.05.

Results: The prevalence of hypertension was 2.3 % and increased with age. The prevalence of overweight and obesity increased with age and was 54.7% as well as 14.1%, respectively, in those 40- 45 years of age. Diabetes affected 6.2% of the oldest group and 8.2% of participants aged 40–45 years. TC was increased in one-third of understudied cases. The percentage of abnormal LDL-C was 57.5%. These results were accompanied by increased TG in 34.6%, low HDL-C in 36.4%, and FPG >100 mg/dl in 13.2% of subjects. Out of a total of 608 participants over 30 years of old, a low FRS (<10%) was calculated for 571 (93.9%), the others, were classified as moderate (5.6%) and high (0.5%) risk. The prevalence of hypertension among the high and moderate FRS risk group was higher than low-risk group older persons have a higher 10- year CVD risk level (p < 0.001). The level of, blood pressure, FPG, LDL, TC, and TG, in the high-risk group, was significantly higher than in the two other groups (P=0.001).

Conclusion: Although a high proportion of military personnel had a low risk of CVD in the next 10 years, the high prevalence of overweight and other risk factors such as LDL level needs special attention.

Analyzing Cardiovascular Disease Risk Factors Using Generalized Logistic Logic Regression: A Retrospective Study

Mon, 27 Apr 2026 08:43:24 +0000

Introduction: Cardiovascular disease (CVD) is a general term that refers to diseases of the heart or blood vessels. Logic regression is a machine learning method that is commonly used when the number of predictor variables is high, and it can account for interaction effects between predictor variables. As CVD can be influenced by multiple factors, this study was conducted to identify variables related to CVD and predict the occurrence of CVD using generalized logistic logic regression.

Methods: The present study is a retrospective study utilizing data from phase one of the MASHAD study. The analysis was performed on the information of 7,385 individuals. Generalized logistic logic regression analysis was performed using the “LogicReg” package in R software.

Results: Out of the 7385 individuals included in this study, 235 (3.2%) were diagnosed with CVD, while 7150 (96.8%) did not have CVD. Of the variables examined, age, anxiety, depression, metabolic syndrome, and family history were significant as main effects, and an interaction between smoking status and education had a significant effect.

Conclusion: Based on the findings of this study, it can be tentatively concluded that for CVD, the existence of interaction effects among the mentioned risk factors may not be a significant concern. In other words, the primary effects of each variable may be more important, as these variables appear to play a role in CVD independently of each other.

Algorithm-Level Data-Guided Correction for Class Imbalance in Biological Machine Learning Predictions: Protein Interactions as a Case

Ebrahim Barzegari, Parviz Abdolmaleki — Mon, 27 Apr 2026 08:46:32 +0000

Introduction: In real-world biomedical applications of data mining, machine learning and artificial intelligence, there are situations where the widespread problem of class imbalance cannot be addressed by data-level methods such as over- or under-sampling. Correct and efficient use of algorithm-level methods, on the other hand, needs paying heed to data structure and content. This study aims to devise and examine simple methods for addressing the imbalanced class distribution issue in predicting the protein-protein interaction (PPI) sites in membrane proteins as a biomedical case experiment.

Methods: Using an adopted dataset of membrane protein complexes and a retrieved validation set, a class-weighted random forests (CWRF) classifier model was built for predicting interfacial residues from positional frequencies and an evolutionary index.

Results: Among several class weighting methods, a data imbalance-emulating weighting method for the CWRF model achieved an area under the receiver operating characteristics curve (AUC) of 0.815 (95% CI: 0.805-0.823) in the independent test prediction and 0.802 (95% CI: 0.794-0.809) in the prediction for the external validation set, which outperformed previous similar studies. A case prediction confirmed the practical utility of this method.

Conclusion: The proposed approach implies potential applications in other fields of biomedicine and beyond. It also highlights the role of algorithm-data interplay in addressing the class imbalance.