Background:High maternal mortality in sub-Saharan Africa has been linked to inadequate medical care for pregnant women due to limited health facility delivery utilization. In Somalia, many births still take place at home, most often without a skilled birth attendant. This study aimed to identify the prevalence and determinants of facility delivery among pregnant women in Somalia using data from the 2020 Somalia Demographic and Health Survey (SDHS). Methods:This cross-sectional study analyzed SDHS 2020 data for 18,561 women aged 15-49 years. Descriptive statistics summarized participant characteristics and delivery location. Binary logistic regression was used to identify determinants of facility delivery. Variables with p < 0.05 were considered statistically significant, and adjusted odds ratios (AORs) with 95% confidence intervals (CIs) were reported. Results:The prevalence of facility delivery was 20.8%, indicating high utilization of home delivery. Several factors were significantly associated with increased odds of facility delivery. Women in the highest wealth had over five times higher odds of delivering at a health facility (AOR = 5.63; 95% CI: 4.56-6.97). Women who attended ANC had four times higher odds of facility delivery (AOR = 4.42; 95% CI: 3.99-4.89). Women who perceived the distance between their home and the nearest health facility as a barrier had 75% lower odds of facility delivery (AOR = 0.25; 95% CI: 0.23-0.27). Additionally, nomadic residence, high parity (≥5 children), and early marriage were significantly associated with lower likelihood of facility-based delivery. Conclusion:Facility delivery remains low in Somalia. Wealth status, ANC attendance, and perceived distance to health facilities were key determinants of facility delivery, alongside residence type, parity, and early marriage. Improving ANC coverage, enhancing birth preparedness education, and increasing access to skilled birth attendants are vital steps to reduce home delivery.
Introduction:Childhood anaemia remains a major public health problem in Ghana, with marked regional and socioeconomic disparities. Conventional regression may not fully capture complex, non-linear relationships among biological, maternal, and household factors. We used supervised machine learning to predict anaemia among children aged 6-59 months using nationally representative survey data. Methods:We analysed the 2022 Ghana Demographic and Health Survey, including de facto children aged 6-59 months with valid haemoglobin and complete covariates (weighted N = 3,382). Anaemia was defined as altitude-adjusted haemoglobin <11.0 g/dL. Twenty-one predictors were included. Data were split into training (80%) and testing (20%) sets using stratified sampling. Six models (logistic regression, decision tree, random forest, gradient boosting, support vector machine, and artificial neural network) were tuned via grid search with 10-fold cross-validation. Results:The weighted prevalence of childhood anaemia was 48.95% (n = 1,655). Gradient boosting showed the best overall discrimination (AUC = 0.72; F1 = 68.99%; accuracy = 66.27%). Support vector machine and logistic regression achieved the highest sensitivity (recall = 72.73% and 71.74%). Random forest showed overfitting (100% training accuracy; test accuracy = 65.23%). Decision tree and neural network performed poorly (AUC = 0.57 and 0.63). Key predictors across models and SHAP were child age, malaria status, maternal anaemia, region, and household wealth (with feature rankings varying by algorithm). Conclusion:Machine learning models achieved moderate predictive performance for childhood anaemia in Ghana. Gradient boosting provided the strongest discrimination, while support vector machine and logistic regression offered higher sensitivity for screening. However, these sensitivities imply that approximately 28-30% of anaemic children may be missed, which should be considered when applying these models in public health screening. Identified determinants support targeted, malaria-integrated nutrition and maternal-child interventions in high-risk groups.
Health Information Systems (HIS) are vital for the collection, storage, analysis, and dissemination of health data to support effective decision-making and resource management at all levels of healthcare. However, despite their importance, many healthcare professionals do not effectively use routine health information, leading to missed opportunities for data-driven decisions and weakened health system performance. This study aimed to assess the utilization of routine health information and its determinants among healthcare professionals in public health facilities in the Banadir Region, Somalia. A facility-based cross-sectional study was conducted from March to June 2024 across 32 public health facilities in the Banadir Region, Somalia. A total of 405 healthcare professionals were selected using a two-stage cluster sampling technique. Data were collected using self-administered questionnaires and observational checklists developed based on the PRISM framework. The questionnaire included items assessing factors influencing RHIS use, such as the availability of training, standardization of indicators, level of supervision, and attitudes toward data utilization. Data were entered and cleaned in Excel and analyzed using SPSS version 27. Bivariable and multivariable binary logistic regression analyses were performed, with variables showing p < 0.05 in the multivariate analysis considered statistically significant. In this study, 68.1
Breeding male birds face high energetic demands due to simultaneous investment in reproduction and feather moult, yet the metabolic consequences of parasitic infection during this period are poorly understood. To address this gap, we focused on non-moulting and actively moulting breeding adult male village weavers (Ploceus cucullatus) to investigate how microfilariae infection affects host biochemical energy status and overall condition. Using plasma glucose, triglycerides, β-hydroxybutyrate, and body mass adjusted for structural size as integrative markers, we examined how infection influences energy allocation and imposes physiological costs during this critical life-history stage. Specifically, we aimed to: (i) determine whether microfilariae infection and active moult influence short-term energy availability by examining plasma glucose concentrations, and whether absolute body mass modulates the effect of infection; and (ii) evaluate the combined and independent effects of infection and moult on lipid and ketone metabolism, while incorporating absolute body mass and size-corrected body condition index (BCI) to assess overall energetic reserves and physiological trade-offs. A total of 128 breeding males were trapped and screened for microfilariae and moult status. Our results indicate infected birds that are actively moulting experienced higher β-hydroxybutyrate, lower glucose and reduced BCI, when compared with the non-infected birds that were non-moulting. On the other hand, non-infected male birds that were also non-moulting maintained higher triglyceride levels. Our regression analyses indicate both infection and moult independently increased ketone concentrations and decreased triglycerides (P < 0.05), with no significant interaction for most markers. However, for β-hydroxybutyrate, the interaction may approach significance (P = 0.08), which suggest a marginal tendency toward non-additive effects. These results highlight a ‘double burden,’ where concurrent parasitism and moult constrain energy allocation, shifting metabolism from carbohydrates toward lipid catabolism. This study may provide mechanistic insight into how microfilariae infection amplifies energetic costs during high-demand life-history stages in breeding male village weavers.
BackgroundData quality encompasses completeness, accuracy, integrity, timeliness, and confidentiality. In many low- and middle-income countries, including Somalia, data from RHIS are often poor, limiting their usefulness for public health actions. This study assessed the quality of RHIS data and associated factors among public health facilities in the Banadir region, Somalia.MethodsA facility-based cross-sectional study was conducted from October to December 2024 across 36 public health facilities using a multistage sampling approach. Data were collected through document reviews, interviews, and observations using PRISM-based standardized tools. Data were analyzed in SPSS version 27 after checking logistic regression assumptions. Data quality was assessed by the dimensions of accuracy (≥80%), completeness (≥85%), and timeliness (≥85%). Bivariable and multivariable logistic regression analyses identified associated factors.ResultsA total of 398 healthcare workers (59.5% female) participated, yielding a 98% response rate. Overall, good-quality data were observed in 65.3% of departments. Departments in health centers were 2.7 times more likely to report good-quality data than hospitals. Feedback, refresher training, and user-friendly reporting formats were significantly associated with better data quality.ConclusionData quality across the three dimensions was scored at (65.3%). Strengthening supervision, feedback, and context-specific training can improve data reporting and management.
