Introduction: 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. Methods: The main objective of this article is to review some of the commonly used sampling variance estimation methods in area level of SAE. Information used to write this paper was collected from the sources of Google scholar, Scopus, MEDLINE and Web of Science and Hand searches of the references of retrieved literature. Results: In the context of small area estimation, sampling variance estimation methods at the area level can be broadly classified into six ctegories: Direct Sampling Variance Estimator, Design-Based Variance Estimator, Generalized Variance Function, Model-based, Empirical Bayes and Extended methods. Conclusion: 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.
Introduction: 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. Methods: 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 included 48 participants, mainly from Côte d'Ivoire (60%), with an average age of 30 (range: 18-49). Most participants (92%) had irregular migration status, with an average duration of stay in Morocco of 2.5 years. Only 6% had medical coverage and 33% reported experiencing gender-based violence, mainly among women. Barriers to accessing care include a lack of information, language difficulties, social stigma, discrimination, and administrative obstacles. Despite these challenges, access was facilitated by support from NGOs such as Caritas, Médecins du Monde, and the Moroccan Association for the Fight Against AIDS (ALCS), as well as awareness campaigns focused on family planning, HIV/STI prevention, and gender-based violence. 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.
Introduction: 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: This cross-sectional study utilized baseline data from 5,903 participants in the SUMS EHCS. The ATP III criteria were applied to diagnose metabolic syndrome, which requires the evated triglycerides, low HDL cholesterol, high blood pressure, and elevated fasting plasma glucose. Statistical analyses were conducted using Poisson regression models and STATA software. 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.
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 decisionmaking concerning reproductive health strategies.
Introduction: 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-childhealth and emergency care. The present study aims to explore the impact of integrating solar energy in rural and remote healthcare settings on maternal and child health outcomes along with Emergency Obstetric care (EmOC) using HarmonisedHealth-Facility-Assessment (HHFA) framework. The HHFA framework measures service-availability, readiness, quality-ofcare and management in healthcare. Methods: The steps in this process were conducted according to the PRISMA (Preferred-Reporting-Items-for-Systematicreviews-and-Meta-Analysis) guidelines. The PubMed, Science-Direct, the Cochrane Library were searched including secondary references and the grey literature was 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: The present Systematic review showed improved Maternal and child health-outcomes adhering with the HHFA framework due to solar-based interventions with improved vaccine-storage-management, lighting, and ventilation, resulting in reduced infant and maternal mortality. The 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. Conclusion: The study underscores the transformative impact of integrating solar-energy in rural and remote healthcarefacilities, advocating adoption of climate-resilient technologies in the form of decentralised solar energy solutions as a policy imperative to ensure equitable healthcare-services.
Introduction: Anemia, defined as a low level of hemoglobin in the blood, is a sign of inadequate nutrition as well as poor health in children. It affects about 269 million (39.8%) children under the age of five worldwide and it 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 among children aged 6–59 months in Eastern Africa. Methods: 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 used. 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 factors like child age in months 12-23 (AOR = 0.623; 95% CI: 0.581-0.669), female (AOR = 0.914; 95% CI: 0.880-0.949), stunted (AOR =1.289; 95% CI: 1.234-1.347), underweight (AOR = 1.232; 95% CI: 1.163-1.306), two under-five children (AOR=1.068; 95% CI: 1.023- 1.116), 2-3 birth order (AOR=1.096; 95% CI:1.034-1.162), having fever (AOR=1.410; 95% CI: 1.343-1.481), having diarrhea (AOR=1.066; 95% CI: 1.009-1.125), vitamin A supplementation (AOR =0.899; 95% CI: 0.863-0.937), primary educated mother (AOR=0.819; 95% CI: 0.775-0.865), anemic mother (AOR =1.844; 95% CI: 1.768-1.923), toilet facility (AOR = 0.879; 95% CI = 0.835-0.926), middle wealth index (AOR =0.876; 95% CI: 0.830-0.925), mother had occupation (AOR=0.930;95%CI: 0.892-0.971) and high community poverty (AOR = 1.047; 95% CI: 1.006-1.089) were significantly associated with anemia in Eastern Africa. Conclusion: The prevalence of anemia among children aged 6–59 months was still high in the region, indicating a continuing public health concern. Child age, sex, nutritional status (stunting and underweight), recent illness (feverand diarrhea), maternal education, anemia status, and occupation, household wealth, toilet facilities, vitamin A supplementation, and community poverty levels have statistically significant association with anemia. These findings underscore the urgent need for comprehensive interventions targeting both child nutrition and broader socio-economic and environmental determinants to effectively reduce the burden of anemia in the region.
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.
Introduction: Neonatal mortality rate is a critical indicator of a nation's healthcare system and socio-economic development. While standard survival models assume independence among observations this assumption is inadmissible for hierarchical data structures, limiting their applicability. To address this limitation, this study employed parametric multilevel mixedeffects survival models that explicitly account for clustering and unobserved heterogeneity. Methods: This study employed unit level data from National Family Health Survey (NFHS-5), 2019-2021 for Uttar Pradesh, which employs a stratified multistage sampling design. Multilevel mixed-effects survival models were fitted (Weibull and exponential distributions) with random intercepts at the Primary Sampling Unit (PSU) and district levels to account for hierarchical clustering. Model fit was assessed using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), while Likelihood Ratio Tests (LRT) compared nested specifications. The Intra-Class Correlation (ICC) was computed to quantify unobserved heterogeneity attributable to clustering at the district and PSU levels. Results: The Multilevel Weibull Mixed-effects model provided the best fit, revealing that unobserved heterogeneity was predominantly clustered at the PSU level (variance: 1.238) rather than the district level (0.023). Advanced maternal age (45–49 years) was a critical risk factor (HR = 2.943, 95% CI: 1.729–4.713), as was the use of Smokey cooking fuels (HR = 1.304, 95% CI: 1.014–1.677). Neonates not weighed at birth faced a significantly higher mortality risk (HR = 1.612, 95% CI: 1.287–2.019) compared to those with normal birth weight. Maternal education, religion, and place of delivery were also identified as significant determinants of 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.
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.
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.
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: 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.
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 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
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.
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: Antenatal care (ANC) is essential for improving maternal and newborn health by enabling early detectionand treatment of potential complications. In 2016, the World Health Organization (WHO) increased the recommendednumber of ANC visits from four to eight, aiming to enhance maternal health outcomes. This study explores the experiencesof pregnant women in Jordanian refugee camps and examines the perceived impact of the revised WHO ANC schedule. Methods: A mixed-methods study was conducted at Zarqa health centers in Jordan from May 28, 2023, to July 26, 2023.Data were collected through structured interviews with 46 female patients (pregnant, postpartum, or trying to conceive),focus group discussions (FGDs) with six participants, and semi-structured interviews with six healthcare providers.Quantitative data were gathered using structured questionnaires, while qualitative data were obtained through FGDs andprovider interviews. Results: Participants unanimously agreed that eight ANC visits are essential for optimal maternal and fetal health.Approximately 90% expressed a need for clearer communication from healthcare providers during appointments. Despitedemonstrating strong self-awareness about when to seek medical attention, participants highlighted key barriers toANC access, including transportation challenges, childcare responsibilities, and long waiting times. Healthcare providersacknowledged these barriers and emphasized the need for improved patient communication and resource allocation.Overall, participants reported general satisfaction with the services provided at refugee health clinics. Conclusion: Optimizing ANC access in refugee settings requires a multifaceted approach that addresses communicationgaps, logistical challenges, and systemic healthcare barriers to ensure equitable maternal health outcomes
Introduction: The impact of seawater on freshwater systems is well known. However, its role in the transmission of human diseases has not been sufficiently studied. Marine vessels entering tropical countries annually discharge thousands of tons of wastewater into water bodies. Although most vessels are equipped with wastewater treatment plants (WWTPs), the lack of regulations governing parasitological control creates significant risks of contamination of water bodies with pathogens causing parasitic diseases. Methods: Between 2006 and 2011, 489 wastewater samples from WWTP-treated vessels arriving at Black Sea ports in Ukraine from parasitic disease-endemic tropical regions were collected. The samples were analyzed for the presence of tropical helminths and for compliance with the “State Sanitary Rules and Norms for the Discharge of Waste, Oil, Ballast Water and Garbage from Ships into Water Bodies” (July 9, 1997, No. 199). Sampling was conducted in accordance with the guidelines of the U.S. Environmental Protection Agency (U.S. EPA Guidance for Sampling and Analysis of Sludge for POTW Facilities, EPA/833/B-89/100). Wastewater analysis was carried out according to the "Standard Methods for the Examination of Water and Wastewater" (APHA, 1995), ecological standards, and technologies of the U.S. EPA — Control of Pathogens and Vector Attraction in Sewage Sludge, as well as the U.S. EPA guidance on the sampling and analysis of POTW sludge. Results: The study results showed that 36.2% (95% CI: 36.1%–36.3%) of the wastewater samples did not meet bacteriological standards, 39.9% (95% CI: 39.8%–40.0%) did not meet chemical standards, and 32.5% (95% CI: 32.4%– 32.6%) of the wastewater samples were contaminated with parasite eggs and cysts. Conclusion: It has been demonstrated for the first time that the WWTPs of marine vessels arriving from tropical regions, which do not ensure the deworming of wastewater, pose a potential health risk to populations living in coastal areas
Introduction: The use of fMRI imaging in medical science has led to the diagnosis of diseases at the very first stages before the disease get advancedwhich plays a significant role in some diseases such as Alzheimer's. Extracting useful information from these images is the first step in the initial diagnosis of the disease that the accuracy in extracting as much of this information as possible contributes significantly to the initial diagnosis. Increases the speed of processing and estimation accuracy which was done in the present study using a multi-purpose method. While in recent studies, simpler methods with a limited number of features were used. Methods: The information of 140 patients with Alzheimer's disease was obtained, and the stable multipurpose feature extraction method was used to extract the information. In this way, two-level wavelet, modeling of wavelet coefficients, normalization method and feature selection are applied. Results: The results obtained from the examination of 285 features in five categories showed that some of the information contained in the features overlapped and lacked useful information. In addition, dimensionality and noise reduction using the PCA algorithm showed that about 41% of the relevant features are outliers or missing information. Conclusion: In general, increasing the speed of processing and estimation accuracy which was done in the present study using a multi-purpose method. While in recent studies, simpler methods with a limited number of features were used.
Introduction: Family planning is a vital aspect of reproductive health, encompassing contraceptive use, pregnancy, and prevention of sexually transmitted infections (STI). Among Palestine refugees in Jordan, particularly those utilizing services by The United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), maternal mortality rates and contraceptive use highlight the urgent need to address gaps in family planning service utilization. Sociocultural factors and systemic barriers remain determinants of contraceptive use and family planning outcomes in this population. Methods: A mixed-methods study was conducted from June 5–7, 2023, at a single UNRWA health center in Marka camp located in Amman, Jordan. The study involved structured interviews with 57 female participants, focus group discussions (FGDs) among participants, and semi-structured interviews with healthcare providers at Marka health center. Quantitative data collection encompassed sociodemographic factors, perceptions of family planning services, and sociocultural determinants influencing contraceptive use. Categorical variable findings were analyzed by frequency and proportions. Subsequently, an exploratory descriptive approach was taken to gather qualitative data via FGD and semi-structured interviews. Transcripts from FGDs and interviews were coded and synthesized by thematic analysis to identify salient determinants of family planning knowledge, perceived barriers and self-efficacy to find access to family planning services. Results: Major findings reported include: (1) Internet and health care professionals are cited as primary sources of knowledge regarding family planning with the most common reason for choosing the preferred contraception being professional advice (22.2%), (2) Sociocultural factors significantly shaped decisions, with a majority of FGD participants citing spousal preferences as the key determinant, and (3) Perceptions of UNRWA’s services were largely positive, with 98.2% rating them excellent or good. However, logistical challenges such as long wait times and transportation costs were frequently reported as barriers. These challenges did not seem to diminish the overall satisfaction for patients, but did hinder utilization of services. Conclusion: While UNRWA’s family planning services are well-regarded, persistent barriers such as sociocultural constraints, limited knowledge, and service accessibility require targeted interventions. Fostering supportive sociocultural environments and improving logistical factors like wait times can enhance service uptake. Future research should explore long-term impacts of family planning initiatives and expand the scope to include other UNRWA health centers to inform inclusive and effective health policies pertaining to reproductive health in this low-income setting.
