2023 CiteScore: 0.8
pISSN: 2383-4196
eISSN: 2383-420X
Editor-in-Chief:
Hojjat Zeraati, PhD.
Current Issue
Vol 11 No 3 (2025): .
Articles
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Introduction: The unmet need for family planning remains a hurdle to reproductive health equity despite, global efforts
to improve access, including in Nepal. This study aimed to assess the prevalence of unmet needs for family planning and
associated factors among rural women in Nepal.
Methods: In 2023, a cross-sectional study was conducted among married women of reproductive age in a rural municipality
in 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 to
identify 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 marriage
was 21.07 ± 3.32 years (range: 14–34 years). More than 80% of the respondents reported having good family planning
knowledge, with healthcare workers being the primary source of information (74.8%). The unmet need for family planning
was 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 or
below (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, particularly
among adolescents, Dalit women, and those without children. Therefore, targeted interventions are required to address
these disparities. Continued efforts should focus on improving family planning access in the study area and similar rural
settings, although the findings may not be generalizable to the entire country. -
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. -
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 to
analyze 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 cured
subjects. 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 participated
in this retrospective cohort study. Both mixture cure frailty models and simple frailty models were fitted. The analyses were
conducted 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 two
children. 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 only
10.6% of individuals had three children, and a mere 0.8% had four. Furthermore, results from a mixture cure frailty model
demonstrated 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 simple
frailty models when the dataset contains individuals who are cured. -
Ronald Pratama Adiwinoto
, Saptono Putro , Tamam Jauhar , Kellyn Tricia Zenjaya , Dinnara Nelya Rindayu , Lidya Prillyarista Herlambang ; I Made Dwi Mertha Adnyana (Co-Corresponding Author)
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. -
Introduction: Clinical diagnosis highlights the essential need to assess biomarker performance for effective disease
screening and diagnosis. The Receiver Operating Characteristic (ROC) curve serves as a fundamental tool for assessing
and interpreting biomarker effectiveness. Numerous models and techniques have been developed to analyze biomarkers
in binary classification settings (Non-Diseased vs. Diseased). This research article seeks to expand the binary classification
framework to a three-class scenario, incorporating Diseased, Suspicious, and Non-Diseased categories under a Log-Normal
distribution.
Methods: It introduces a three-class Log-Normal ROC model based on a Parametric approach, deriving metrics such as
Volume Under the ROC Surface (VUS) and Asymptotic Variance, as well as an alternative Non-Parametric approach. The
model was validated using simulated data generated for the underlying distribution, and a real-life dataset was used to fit
the VUS and ROC curves.
Results: The simulation study was conducted using four sets with varying parameters. In the fourth set, the Non-Parametric
VUS (0.9966) exceeded the Parametric VUS (0.8058), though the difference was smaller compared to the other sets. The
low Standard Error (SE) (0.0472) across all sets indicates high precision in the estimates. Additionally, for the real-life (The
multiple 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 simulated
data with varying parameters. The analysis compares diagnostic performance across parameter sets, highlighting the
superiority of Non-Parametric VUS over Parametric VUS. Set 4 demonstrated the highest reliability with the lowest standard
error (SE = 0.0472). The real-life MS dataset provided a moderate fit to the proposed model. -
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. -
mohammadAli kianfard (Co-Corresponding Author); Habibollah Esmaily , Mahbubeh Jahani , Majid Ghayour-Mobarhan
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. -
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
