This study introduces the multilevel zero-inflated generalized Poisson (MZIGP) model for modeling zero-inflated count data with over- and under-dispersion. While maximum likelihood estimation (MLE) is typically used, it is sensitive to outliers and data contamination. Therefore, we propose robust estimation methods using Mallow’s class. In the logistic component, the design matrix is weighted to reduce leverage effects; in the generalized Poisson (GP) component, a general class of M-estimators is applied where the effects of both covariates and responses are bounded. Simulation results demonstrate that the robust expectation-solution (RES) method outperforms the EM algorithm in the presence of outliers and varying levels of zero-inflation. A real-data application using the 2015 IrMIDH fertility data set is also provided.

Background:-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 dura- tion often involves current status data, presenting challenges in accurate estimation and analysis.

Objective:-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.

Methodology:-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.

Result:-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.

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.

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.

Context

Migrant women face multiple vulnerabilities during their journey and in host countries, particularly regarding access to sexual and reproductive health services. In Morocco, migrant women are exposed to gender-based violence due to administrative irregularities, economic precariousness, and social exclusion. This violence negatively impacts their health and limits their access to essential care.

Methodes

A qualitative study was conducted in July 2024 in Rabat and Casablanca, two regions with a high concentration of migrants. Focus group discussions were carried out with 48 participants, including women and men from sub-Saharan African countries. Participants were selected based on their experience with health services and their willingness to share their experiences. The data were manually transcribed and analysed thematically.

Results

The study revealed that migrant women experience physical, sexual, and psychological violence both in private and public spaces. Barriers to accessing care include a lack of information, language difficulties, social stigma, discrimination, and administrative obstacles. Despite these challenges, the presence of non-governmental organisations, awareness campaigns, and supportive healthcare professionals were identified as facilitating factors for accessing health services.

Conclusion

Gender-based violence remains a significant barrier to accessing health services for migrant women in Morocco. Improving access requires better health information, multilingual support, and anti-discrimination measures to ensure equitable care for all women, regardless of their migration status.

Background: An unreliable energy supply in rural and remote healthcare facilities leads to poor quality of health-services affecting health-outcomes. Energy supplied to healthcare facilities has a significant impact especially on maternal-child-health and emergency care. The present study aims to explore the impact of integrating solar energy in rural and remote healthcare facilities on maternal and child health outcomes along with emergency care. This study also explores the applicability of Harmonised Health Facility Assessment (HHFA) framework on healthcare outcomes in LMICs.

Methods: The steps in this process were conducted according to the PRISMA (Preferred-Reporting-Items-for-Systematic-reviews-and-Meta-Analysis) guidelines. The PubMed, Science-Direct, the Cochrane library, and secondary reference were searched and the grey literature was also retrieved through research and policy-reports. A total of 144 studies were identified, of which 131 were excluded being not in scope of present study. Further, 4 studies were extracted using grey literature, and finally, a total of 17 studies including 5 studies for meta-analysis were considered as per inclusion criteria across LMIC’s.

Results: Meta-analysis comparing the conventional sources of energy to solar powered oxygen supply for child healthcare indicates the presence of substantial heterogeneity and variation in risk difference estimates across the studies (N=33224; RD=0.03; 95%CI 0.01, 0.05; p<0.001) depicted through forest and funnel plots. The Meta-regression analysis indicates a positive impact of duration on the effect size indicating the factors responsible for heterogeneity (regression-coefficient r=0.612; 95%CI 0.27, 0.93;  p<0.005) emphasizing on the need for solar-powered-oxygen supply over the conventional sources. Eager’s Regression indicates no publication bias (p=0.79). 

Conclusion: The study underscores the transformative impact of integrating solar-energy in rural and remote healthcare facilities, findings advocate adoption of climate-resilient technologies - decentralised solar energy solutions as a policy imperative to ensure equitable healthcare services.

In area level Small area estimation (SAE) ( Fay–Herriot and extension of it in spatial structure) sampling variances  are assumed to be known. That is a powerful assumption and may be quite restrictive in some applications. The main in this field is the estimation of sampling variances for the small area parameters, which is necessary for obtaining reliable estimates and for evaluating the precision of the estimates. The main objective of this article is to review some of the commonly used sampling variance estimation methods in area level of SAE. In the context of small area estimation, sampling variance estimation methods at the area level can be broadly classified into six categories: Direct Sampling Variance Estimator, Design-Based Variance Estimator, Generalized Variance Function (GVF), Model-based, Empirical Bayes and Extended methods. According to the reviewed studies, no method can be reported as the best method for estimating the sampling variance in all conditions. Because each method needs different information, and on the other hand, simulation studies are needed to compare the methods.

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.