Cardiovascular disease prevalence and associated factors in a low-resource setting: A multilevel analysis from Somalia's first demographic health survey.

BACKGROUND: Cardiovascular diseases (CVDs) are a leading cause of death globally, with low- and middle-income countries disproportionately affected. Somalia, a nation grappling with persistent humanitarian crises and a rising burden of non-communicable diseases, has limited data on CVD prevalence and its risk factors. This study, the first of its kind in Somalia, leverages data from the inaugural nationally representative demographic health survey to examine the prevalence and associated factors of CVD among Somali adults. METHODS: A cross-sectional analysis was conducted using data from the 2020 Somali Demographic Health Survey, encompassing 5062 participants aged 35 years and older. Multilevel logistic regression was utilized to explore associations between individual-level and community-level factors and CVD prevalence. Individual-level factors included age, sex, marital status, education, wealth, smoking, and khat chewing. Community-level factors encompassed region and type of residence. RESULTS: The prevalence of CVD in Somalia was found to be 7.1%. Community-level factors were significantly associated with CVD prevalence. Residents of Sool region exhibited higher odds of CVD, while those in Banadir region had lower odds. Nomadic communities demonstrated lower CVD risk compared to urban areas. School attendance was marginally associated with CVD risk. After controlling for other factors, sex of the household head remained significant, with females having slightly lower odds of CVD. CONCLUSION: This study underscores the critical influence of community-level factors, particularly region and type of residence, on CVD prevalence in Somalia. The findings highlight the need for targeted interventions that address geographic disparities and promote healthy lifestyles within communities. Future research should delve deeper into the underlying mechanisms of these associations and explore the impact of individual-level factors like school attendance and sex on CVD risk.

Prevalence and determinants of home delivery among pregnant women in Somaliland: Insights from SLDHS 2020 data.

OBJECTIVE: This cross-sectional study aimed to identify the determinants of home deliveries among women in Somaliland, with the objective of informing targeted interventions to improve maternal and child health outcomes. DESIGN: A cross-sectional study design was employed, utilizing data from a nationally representative sample of 3250 women in Somaliland. A multivariate logistic regression analysis was conducted to examine the factors influencing the likelihood of home delivery. SITE: The study was conducted in Somaliland, a region where home delivery remains prevalent. PARTICIPANTS: The study included 3250 women of reproductive age in Somaliland who had given birth. INTERVENTIONS: No specific interventions were administered as part of this study. The focus was on understanding the factors associated with home deliveries. MAIN MEASUREMENTS: Sociodemographic, economic, and regional factors were examined as potential determinants of home deliveries. Education levels of women and their husbands, maternal age at first marriage and first birth, and household wealth were among the main measurements analyzed. RESULTS: The analysis revealed that higher education levels were associated with a decreased likelihood of home delivery. Women with secondary (OR: 0.42, 95% CI: 0.32-0.55) or higher (OR: 0.21, 95% CI: 0.12-0.37) education were less likely to deliver at home than those with no education. Similarly, women whose husbands had a secondary (OR: 0.55, 95% CI: 0.41-0.73) or higher (OR: 0.43, 95% CI: 0.28-0.66) education were less likely to deliver at home. Increased maternal age at first marriage (OR: 1.04, 95% CI: 1.02-1.06) and first birth (OR: 1.03, 95% CI: 1.01-1.05) were significant predictors of home delivery. Lower household wealth was also associated with a higher likelihood of home delivery. Significant regional variations were observed, with certain regions showing higher rates of home deliveries compared to others. CONCLUSIONS: The findings highlight the importance of targeted interventions to address sociodemographic and regional disparities in the utilization of institutional delivery services in Somaliland. Strategies should focus on improving access to and quality of maternal healthcare services, empowering women's decision-making, and engaging men to address gender norms within households.

Prune belly syndrome in a neonate with severe bilateral hydronephrosis: A rare case report from Somalia.

INTRODUCTION AND IMPORTANCE: Prune belly syndrome (PBS), also known as Eagle-Barret syndrome, is a rare congenital disorder characterized by abdominal wall muscle underdevelopment, urinary system abnormalities, and cryptorchidism. This case report presents the clinical features, diagnosis, and management of PBS in a newborn. This is the first case report of prune belly syndrome in Somalia. The condition is estimated to occur in approximately 1 in 30,000 to 1 in 50,000 live births, making it a relatively uncommon presentation. Recognizing and managing this syndrome is crucial, as it can lead to significant morbidity and mortality if not addressed promptly. CASE PRESENTATION: A term baby delivered without complications developed respiratory distress, jaundice, and urinary retention shortly after birth. Physical examination revealed abdominal distension, fluid in the abdomen, and bilateral undescended testes. Laboratory tests showed elevated bilirubin levels and abnormal blood counts. Ultrasound findings demonstrated bilateral hydroureteronephrosis and underdeveloped abdominal wall muscles. The limited resources and infrastructure in the healthcare setting in Somalia posed challenges in providing comprehensive care for this neonate. CLINICAL DISCUSSION: PBS is a rare congenital syndrome with a higher prevalence in males. Its exact cause is not fully understood, but genetic factors may play a role. The management of PBS in resource-limited settings can be particularly challenging. The differential diagnosis included sepsis, neonatal jaundice, and posterior urethral valves. The key interventions included supportive care, such as maintaining fluid and electrolyte balance, treating infections, and addressing any urinary tract abnormalities. The limited access to specialized pediatric urology services and advanced diagnostic tools, such as magnetic resonance imaging (MRI), hindered the ability to fully characterize the extent of the urinary tract abnormalities and plan definitive surgical interventions. CONCLUSION: Despite the constraints of the resource-limited setting, the supportive care and management strategies implemented led to an improvement in the baby's condition. This case highlights the importance of recognizing and managing Prune Belly Syndrome, even in environments with limited healthcare resources. Continued efforts to improve diagnostic capabilities and access to specialized care are crucial for optimizing the outcomes of patients with this rare and complex congenital disorder.

Prevalance and determinants of hypertension among adults in Somalia using Somalia demographic health survey data, SDHS 2020.

BACKGROUND: Hypertension, a major global health concern, continues to affect millions of adults worldwide, leading to significant morbidity and mortality. The burden of hypertension is particularly pronounced in low- and middle-income countries, where limited healthcare resources and infrastructure pose unique challenges for effective prevention and management. Despite the pressing need for accurate data on hypertension prevalence and determinants, there is a dearth of research focusing on the Somali population. In this study, we aim to fill this critical knowledge gap by analyzing the latest available data from the Somalia Demographic Health Survey (SDHS) conducted in 2020. METHOD: The SDHS 2020 employed a three-stage stratified cluster sampling design to collect data from 48,796 individuals. Hypertension was measured through survey questions related to chronic diseases. Explanatory variables included age, sex, region, residence, tobacco use, wealth index, marital status, and body mass index (BMI). Statistical analysis involved univariate and multivariable logistic regression. RESULTS: The prevalence of hypertension in Somalia was found to be 2.11%, lower than previous studies in Hargeisa and the SLHDS 2020 report. Females had a higher prevalence than males, and urban areas exhibited higher rates compared to rural areas. Age, BMI, region, and wealth index were identified as significant factors associated with hypertension. DISCUSSION: The study's findings contribute to the understanding of hypertension in the Somali population and can inform public health interventions. The study benefits from its representative sample, multivariate analysis, and comparison with earlier studies. However, limitations include the cross-sectional design, limited information on lifestyle behaviors, and lack of data on treatment and control of hypertension. CONCLUSION: Hypertension is a public health concern in Somalia, and targeted interventions are needed to reduce its burden. Addressing factors such as age, gender, urban residence, BMI, and wealth index can contribute to hypertension management and control strategies. Future research should consider longitudinal studies and incorporate additional factors for a comprehensive assessment of hypertension etiology and management.

A new unit distribution: properties, estimation, and regression analysis.

This research commences a unit statistical model named power new power function distribution, exhibiting a thorough analysis of its complementary properties. We investigate the advantages of the new model, and some fundamental distributional properties are derived. The study aims to improve insight and application by presenting quantitative and qualitative perceptions. To estimate the three unknown parameters of the model, we carefully examine various methods: the maximum likelihood, least squares, weighted least squares, Anderson-Darling, and Cramér-von Mises. Through a Monte Carlo simulation experiment, we quantitatively evaluate the effectiveness of these estimation methods, extending a robust evaluation framework. A unique part of this research lies in developing a novel regressive analysis based on the proposed distribution. The application of this analysis reveals new viewpoints and improves the benefit of the model in practical situations. As the emphasis of the study is primarily on practical applications, the viability of the proposed model is assessed through the analysis of real datasets sourced from diverse fields.

Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia.

Extensive research has been conducted on poverty in developing countries using conventional regression analysis, which has limited prediction capability. This study aims to address this gap by applying advanced machine learning (ML) methods to predict poverty in Somalia. Utilizing data from the first-ever 2020 Somalia Demographic and Health Survey (SDHS), a cross-sectional study design is considered. ML methods, including random forest (RF), decision tree (DT), support vector machine (SVM), and logistic regression, are tested and applied using R software version 4.1.2, while conventional methods are analyzed using STATA version 17. Evaluation metrics, such as confusion matrix, accuracy, precision, sensitivity, specificity, recall, F1 score, and area under the receiver operating characteristic (AUROC), are employed to assess the performance of predictive models. The prevalence of poverty in Somalia is notable, with approximately seven out of ten Somalis living in poverty, making it one of the highest rates in the region. Among nomadic pastoralists, agro-pastoralists, and internally displaced persons (IDPs), the poverty average stands at 69%, while urban areas have a lower poverty rate of 60%. The accuracy of prediction ranged between 67.21% and 98.36% for the advanced ML methods, with the RF model demonstrating the best performance. The results reveal geographical region, household size, respondent age group, husband employment status, age of household head, and place of residence as the top six predictors of poverty in Somalia. The findings highlight the potential of ML methods to predict poverty and uncover hidden information that traditional statistical methods cannot detect, with the RF model identified as the best classifier for predicting poverty in Somalia.

A novel extension of half-logistic distribution with statistical inference, estimation and applications.

In the present study, we develop and investigate the odd Frechet Half-Logistic (OFHL) distribution that was developed by incorporating the half-logistic and odd Frechet-G family. The OFHL model has very adaptable probability functions: decreasing, increasing, bathtub and inverted U shapes are shown for the hazard rate functions, illustrating the model's capacity for flexibility. A comprehensive account of the mathematical and statistical properties of the proposed model is presented. In estimation viewpoint, six distinct estimation methodologies are used to estimate the unknown parameters of the OFHL model. Furthermore, an extensive Monte Carlo simulation analysis is used to evaluate the effectiveness of these estimators. Finally, two applications to real data are used to demonstrate the versatility of the suggested method, and the comparison is made with the half-logistic and some of its well-known extensions. The actual implementation shows that the suggested model performs better than competing models.

A novel generalized Weibull Poisson G class of continuous probabilistic distributions with some copulas, properties and applications to real-life datasets.

The current study introduces and examines copula-coupled probability distributions. It explains their mathematical features and shows how they work with real datasets. Researchers, statisticians, and practitioners can use this study's findings to build models that capture complex multivariate data interactions for informed decision-making. The versatility of compound G families of continuous probability models allows them to mimic a wide range of events. These incidents can range from system failure duration to transaction losses to annual accident rates. Due to their versatility, compound families of continuous probability distributions are advantageous. They can simulate many events, even some not well represented by other probability distributions. Additionally, these compound families are easy to use. These compound families can also show random variable interdependencies. This work focuses on the construction and analysis of the novel generalized Weibull Poisson-G family. Combining the zero-truncated-Poisson G family and the generalized Weibull G family creates the compound G family. This family's statistics are mathematically analysed. This study uses Clayton, Archimedean-Ali-Mikhail-Haq, Renyi's entropy, Farlie, Gumbel, Morgenstern, and their modified variations spanning four minor types to design new bivariate type G families. The single-parameter Lomax model is highlighted. Two practical examples demonstrate the importance of the new family.

Fuzzy Stress-Strength Model and Mean Remaining Strength for Lindley Distribution: Estimation and Application in Cancer of Benign Endocrine.

This paper is interested in the Bayesian and non-Bayesian estimation of the stress-strength model and the mean remaining strength when there is fuzziness for stress and strength random variables having Lindley's distribution with different parameters. A fuzzy is defined as a function of the difference between stress and strength variables. In the context of Bayesian estimation, two approximate algorithms are used importance sampling algorithm and the Monte Carlo Markov chain algorithm. For non-Bayesian estimation, maximum likelihood estimation and maximum product of spacing method are used. The Monte Carlo simulation study is performed to compare between different estimators for our proposed models using statistical criteria. Finally, to show the ability of our proposed models in real life, real medical application is introduced.

The Gull Alpha Power Lomax distributions: Properties, simulation, and applications to modeling COVID-19 mortality rates.

The Gull Alpha Power Lomax distribution is a new extension of the Lomax distribution that we developed in this paper (GAPL). The proposed distribution's appropriateness stems from its usefulness to model both monotonic and non-monotonic hazard rate functions, which are widely used in reliability engineering and survival analysis. In addition to their special cases, many statistical features were determined. The maximum likelihood method is used to estimate the model's unknown parameters. Furthermore, the proposed distribution's usefulness is demonstrated using two medical data sets dealing with COVID-19 patients' mortality rates, as well as extensive simulated data applied to assess the performance of the estimators of the proposed distribution.

Analysis of Covid-19 data using discrete Marshall-Olkinin Length Biased Exponential: Bayesian and frequentist approach.

The paper presents a novel statistical approach for analyzing the daily coronavirus case and fatality statistics. The survival discretization method was used to generate a two-parameter discrete distribution. The resulting distribution is referred to as the "Discrete Marshall-Olkin Length Biased Exponential (DMOLBE) distribution". Because of the varied forms of its probability mass and failure rate functions, the DMOLBE distribution is adaptable. We calculated the mean and variance, skewness, kurtosis, dispersion index, hazard and survival functions, and second failure rate function for the suggested distribution. The DI index demonstrates that the proposed model can represent both over-dispersed and under-dispersed data sets. We estimated the parameters of the DMOLBE distribution. The behavior of ML estimates is checked via a comprehensive simulation study. The behavior of Bayesian estimates is checked by generating 10,000 iterations of Markov chain Monte Carlo techniques, plotting the trace, and checking the proposed distribution. From simulation studies, it was observed that the bias and mean square error decreased with an increase in sample size. To show the importance and flexibility of DMOLBE distribution using two data sets about deaths due to coronavirus in China and Pakistan are analyzed. The DMOLBE distribution provides a better fit than some important discrete models namely the discrete Burr-XII, discrete Bilal, discrete Burr-Hatke, discrete Rayleigh distribution, and Poisson distributions. We conclude that the new proposed distribution works well in analyzing these data sets. The data sets used in the paper was collected from 2020 year.

Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application.

This paper provides a novel model that is more relevant than the well-known conventional distributions, which stand for the two-parameter distribution of the lifetime modified Kies Topp-Leone (MKTL) model. Compared to the current distributions, the most recent one gives an unusually varied collection of probability functions. The density and hazard rate functions exhibit features, demonstrating that the model is flexible to several kinds of data. Multiple statistical characteristics have been obtained. To estimate the parameters of the MKTL model, we employed various estimation techniques, including maximum likelihood estimators (MLEs) and the Bayesian estimation approach. We compared the traditional reliability function model to the fuzzy reliability function model within the reliability analysis framework. A complete Monte Carlo simulation analysis is conducted to determine the precision of these estimators. The suggested model outperforms competing models in real-world applications and may be chosen as an enhanced model for building a statistical model for the COVID-19 data and other data sets with similar features.

A Flexible Bayesian Parametric Proportional Hazard Model: Simulation and Applications to Right-Censored Healthcare Data.

Survival analysis is a collection of statistical techniques which examine the time it takes for an event to occur, and it is one of the most important fields in biomedical sciences and other variety of scientific disciplines. Furthermore, the computational rapid advancements in recent decades have advocated the application of Bayesian techniques in this field, giving a powerful and flexible alternative to the classical inference. The aim of this study is to consider the Bayesian inference for the generalized log-logistic proportional hazard model with applications to right-censored healthcare data sets. We assume an independent gamma prior for the baseline hazard parameters and a normal prior is placed on the regression coefficients. We then obtain the exact form of the joint posterior distribution of the regression coefficients and distributional parameters. The Bayesian estimates of the parameters of the proposed model are obtained using the Markov chain Monte Carlo (McMC) simulation technique. All computations are performed in Bayesian analysis using Gibbs sampling (BUGS) syntax that can be run with Just Another Gibbs Sampling (JAGS) from the R software. A detailed simulation study was used to assess the performance of the proposed parametric proportional hazard model. Two real-survival data problems in the healthcare are analyzed for illustration of the proposed model and for model comparison. Furthermore, the convergence diagnostic tests are presented and analyzed. Finally, our research found that the proposed parametric proportional hazard model performs well and could be beneficial in analyzing various types of survival data.

Modelling the COVID-19 Mortality Rate with a New Versatile Modification of the Log-Logistic Distribution.

The goal of this paper is to develop an optimal statistical model to analyze COVID-19 data in order to model and analyze the COVID-19 mortality rates in Somalia. Combining the log-logistic distribution and the tangent function yields the flexible extension log-logistic tangent (LLT) distribution, a new two-parameter distribution. This new distribution has a number of excellent statistical and mathematical properties, including a simple failure rate function, reliability function, and cumulative distribution function. Maximum likelihood estimation (MLE) is used to estimate the unknown parameters of the proposed distribution. A numerical and visual result of the Monte Carlo simulation is obtained to evaluate the use of the MLE method. In addition, the LLT model is compared to the well-known two-parameter, three-parameter, and four-parameter competitors. Gompertz, log-logistic, kappa, exponentiated log-logistic, Marshall-Olkin log-logistic, Kumaraswamy log-logistic, and beta log-logistic are among the competing models. Different goodness-of-fit measures are used to determine whether the LLT distribution is more useful than the competing models in COVID-19 data of mortality rate analysis.

Stress-Strength Reliability for Exponentiated Inverted Weibull Distribution with Application on Breaking of Jute Fiber and Carbon Fibers.

For the first time and by using an entire sample, we discussed the estimation of the unknown parameters θ 1, θ 2, and ß and the system of stress-strength reliability R=P(Y < X) for exponentiated inverted Weibull (EIW) distributions with an equivalent scale parameter supported eight methods. We will use maximum likelihood method, maximum product of spacing estimation (MPSE), minimum spacing absolute-log distance estimation (MSALDE), least square estimation (LSE), weighted least square estimation (WLSE), method of Cramér-von Mises estimation (CME), and Anderson-Darling estimation (ADE) when X and Y are two independent a scaled exponentiated inverted Weibull (EIW) distribution. Percentile bootstrap and bias-corrected percentile bootstrap confidence intervals are introduced. To pick the better method of estimation, we used the Monte Carlo simulation study for comparing the efficiency of the various estimators suggested using mean square error and interval length criterion. From cases of samples, we discovered that the results of the maximum product of spacing method are more competitive than those of the other methods. A two real-life data sets are represented demonstrating how the applicability of the methodologies proposed in real phenomena.

Bayesian and Classical Inference for the Generalized Log-Logistic Distribution with Applications to Survival Data.

The generalized log-logistic distribution is especially useful for modelling survival data with variable hazard rate shapes because it extends the log-logistic distribution by adding an extra parameter to the classical distribution, resulting in greater flexibility in analyzing and modelling various data types. We derive the fundamental mathematical and statistical properties of the proposed distribution in this paper. Many well-known lifetime special submodels are included in the proposed distribution, including the Weibull, log-logistic, exponential, and Burr XII distributions. The maximum likelihood method was used to estimate the unknown parameters of the proposed distribution, and a Monte Carlo simulation study was run to assess the estimators' performance. This distribution is significant because it can model both monotone and nonmonotone hazard rate functions, which are quite common in survival and reliability data analysis. Furthermore, the proposed distribution's flexibility and usefulness are demonstrated in a real-world data set and compared to its submodels, the Weibull, log-logistic, and Burr XII distributions, as well as other three-parameter parametric survival distributions, such as the exponentiated Weibull distribution, the three-parameter log-normal distribution, the three-parameter (or the shifted) log-logistic distribution, the three-parameter gamma distribution, and an exponentiated Weibull distribution. The proposed distribution is plausible, according to the goodness-of-fit, log-likelihood, and information criterion values. Finally, for the data set, Bayesian inference and Gibb's sampling performance are used to compute the approximate Bayes estimates as well as the highest posterior density credible intervals, and the convergence diagnostic techniques based on Markov chain Monte Carlo techniques were used.

Influence Diagnostic Methods in the Poisson Regression Model with the Liu Estimator.

There is a long history of interest in modeling Poisson regression in different fields of study. The focus of this work is on handling the issues that occur after modeling the count data. For the prediction and analysis of count data, it is valuable to study the factors that influence the performance of the model and the decision based on the analysis of that model. In regression analysis, multicollinearity and influential observations separately and jointly affect the model estimation and inferences. In this article, we focused on multicollinearity and influential observations simultaneously. To evaluate the reliability and quality of regression estimates and to overcome the problems in model fitting, we proposed new diagnostic methods based on Sherman-Morrison Woodbury (SMW) theorem to detect the influential observations using approximate deletion formulas for the Poisson regression model with the Liu estimator. A Monte Carlo method is done for the assessment of the proposed diagnostic methods. Real data are also considered for the evaluation of the proposed methods. Results show the superiority of the proposed diagnostic methods in detecting unusual observations in the presence of multicollinearity compared to the traditional maximum likelihood estimation method.