Journal of Biostatistics and Epidemiology

Prevalence of Metabolic Syndrome in Healthcare Workers: A Cross-sectional study using baseline data of the SUMS Employees Health Cohort Study (SUMS EHCS) in South of Iran

Fariba Moradi Ardekani — 2026-10-07

Background: Shift work, a characteristic of healthcare workers' jobs, is recognized as a contributing factor to various health conditions, particularly metabolic syndrome. However, the risk of this medical condition is still understudied among shift workers in the healthcare sector. This study investigated metabolic syndrome and its components in the "Shiraz University of Medical Sciences Employees Health Cohort Study" (SUMS EHCS), a branch of the PERSIAN cohort study in the south of Iran.

Methods: Baseline data from 5,903 participants in SUMS EHCS were included. The ATP III criteria were used for diagnosing metabolic syndrome.

Results: The prevalence of metabolic syndrome was 23.50%, with abdominal obesity identified as the most common component (50.5%). The presence of underlying diseases was significantly associated with the diagnosis of metabolic syndrome in both univariable (prevalence ratio (PR) = 1.93 [95% confidence interval (CI): 1.74, 2.14]; P < 0.001) and multivariable analyses (adjusted PR (aPR) = 1.94 [95% CI: 1.75, 2.15]; P < 0.001). No significant association was found between modifiable lifestyle factors and metabolic syndrome (P > 0.05).

Conclusion: The similar prevalence of metabolic syndrome in healthcare workers compared to the general population highlights the need for similar preventive and curative health strategies, particularly focusing on underlying medical conditions to improve the metabolic health of healthcare workers.

Multilevel Survival Modelling of Neonatal Mortality under Some Prognostic Factors in Uttar Pradesh

Shalini Jaiswal — 2026-10-07

Introduction: Neonatal Mortality, a critical indicator of country’s socio-economic and healthcare status, remains a significant global concern. While India has made significant progress in reducing child mortality over the past decades, further improvements are needed in reducing newborn mortality. The traditional survival models like cox proportional hazard model assume independence among individual, whereas multilevel survival models integrate hierarchical structures, provide more accurate insights into mortality trends and determinants. This study identifies significant prognostic determinants of neonatal mortality in Uttar Pradesh, India, using data from NFHS-V (2019-2021) and explore regional and community-level effects with the multilevel parametric models.

Methods: The multilevel mixed-effects Weibull and exponential parametric survival models were applied, incorporating both individual and community-level factors. Primary Sampling Units (PSUs) and districts were treated as hierarchical levels. Akaike Information Criteria (AIC) was used to determine the best fitted model.

Result: This study highlighted the complex, multi-level determinants of neonatal mortality in Uttar Pradesh. The multilevel Weibull mixed-effects model provided a better fit compared to individual-level and Exponential models. The results determine the significant impact of social inequalities, the crucial role of maternal education, place of delivery and the importance of newborn birth weight and size in determining neonatal outcomes.

Conclusion: The study recommends that efforts to reduce neonatal mortality should address not only individual-level risk factors but also community-level disparities in healthcare access and quality.

Analyzing Postpartum Amenorrhea Duration: Estimating Weibull Distribution Parameters with EM Algorithm Using Current Status Data

SACHIN KUMAR — 2026-10-07

Introduction: The duration of post-partum amenorrhea (PPA), a crucial aspect of reproductive health, re- mains a significant factor in family planning and maternal well-being. Understanding the distribution of this period provides valuable insights into fertility patterns and informs contraceptive strategies. However, this duration often involves current status data, presenting challenges in accurate estimation and analysis.
This study aims to employ statistical modeling, specifically utilizing the Weibull distribution and the EM algorithm, to estimate parameters related to PPA duration. The primary objective is to develop a robust methodology for parameter estimation within current status data.
Methods: The research employs the Weibull distribution, known for its applicability in current status data analyses, as a framework for modeling PPA duration. Leveraging the EM algorithm, the study develops an approach to estimate the Weibull distribution parameters from the current status data. This methodology focuses on overcoming the challenges posed by interval-censored observations, providing a more accurate understanding of the duration.
Results: The application of the EM algorithm to estimate Weibull distribution parameters yields promising results. The methodology successfully addresses the complexities of current status data, offering estimates that enhance the
understanding of PPA duration. The results highlight the efficacy of the proposed approach in handling such nuanced datasets.
Conclusion: This study underscores the significance of statistical modeling techniques, particularly the Weibull distribution coupled with the EM algorithm, in estimating parameters for PPA duration analysis. The successful application of this methodology emphasizes its potential for furthering the understanding of fertility patterns and aiding in informed decision making concerning reproductive health strategies.

Robust inference to parameter estimates in the zero-inflated generalized Poisson: The risk factors affecting the fertility rate

Eghbal Zandkarimi Zandkarimi — 2026-10-07

Introduction: Fertility data frequently exhibit excess zeros, overdispersion, and within-cluster correlation, rendering conventional count models inadequate.
Methods: We propose a multilevel zero-inflated generalized Poisson (ZIGP) model based on the Robust Expectation Solution (RES) algorithm. The model comprises two components: (i) a logistic component to model the probability of structural zeros and (ii) a generalized Poisson component for count responses. Random intercepts at the city and cluster levels account for the hierarchical data structure. All algorithms were implemented by the authors through original programming in R (version 4.3.1), without reliance on pre-existing packages, ensuring flexibility and transparency. Robust estimation employs Huber’s ψ-function and Mallows-type weights to mitigate sensitivity to contamination and outliers.
Results: Simulation studies across various contamination scenarios demonstrated that the robust multilevel ZIGP model yields more stable parameter estimates, with approximately 45% lower bias and 38% lower mean squared error compared to conventional estimators. Model fit criteria (AIC and BIC, unitless) confirmed the superior performance of the proposed model.
Conclusion: The robust multilevel ZIGP model provides a practical and reliable framework for analyzing clustered count data with excess zeros, particularly under contamination. The original R implementation ensures reproducibility and adaptability for biostatistical and epidemiological applications. Application to real fertility data from Sistan and Baluchestan Province, Iran, showed significant zero-inflation and overdispersion, and identified age at marriage, education, and income as factors associated with fertility.

Anemia Prevalence and its Associated Risk Factors using Ten East African Countries: A Recent data from Demographic and Health Survey

Denekew Bitew Belay — 2026-10-07

Background: Anemia is a condition characterized by a deficiency of hemoglobin in the blood and is a significant risk factor for poor health and nutrition in children. The main aim of this study was to assess the prevalence and contributing factors of anemia in children under five in Eastern Africa.

Method: The research was conducted between 2010 and 2023 across ten East African countries. To identify potential factors associated with anemia, a multilevel logistic regression model was utilized, with adjusted odds ratios at a 95% confidence interval.

Results: The prevalence of anemia in Eastern Africa was 54.26%, with the highest in Tanzania (70.32%) and the lowest in Rwanda (36.64%). A multilevel multivariable logistic regression model revealed that; months age, female, stunted, underweight, number of under-five children, birth order, having fever, having diarrhea, vitamin A supplementation, primary educated mother, anemic mother, toilet facility, middle wealth index, a mother had occupation and high community poverty were significantly associated with anemia in Eastern Africa.

Conclusions: The prevalence of anemia was still high in the region. The stakeholder should design and scale up comprehensive nutrition interventions, which may represent a potential consideration to reduce the burden of anemia. Moreover, interventions like improving household wealth index and nutritional education activities are important to decrease the prevalence of anemia.