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1,000 reports · page 99 of 100

Peer-reviewed articleOpenAlex9 Sept 2026

Zero-dose childhood vaccination and associated factors in Ethiopia: A systematic review and meta-analysis

Background Globally, 14 million children are zero-dose, with Ethiopia accounting for approximately 5% of that burden. This study aims to provide national estimates of the pooled prevalence and factors associated with zero-dose vaccination among Ethiopian children. Methods A systematic review and meta-analysis were conducted following PRISMA guidelines using multiple databases and grey literature. Observational studies published from 2019 to March 2026 were included. A total of 14 studies met the inclusion criteria. Quality was assessed using the Joanna Briggs Institute criteria. A random-effects model was used to pool prevalence and adjusted odds ratios (AORs) using STATA. Heterogeneity was evaluated with I 2 statistics, and publication bias was assessed using funnel plots and Egger's test. Results The pooled prevalence of zero-dose vaccination was 22.29% (95% CI: 16.37%–28.20%). Significant associated factors included: children of uneducated mothers (AOR: 1.61, 95% CI: 1.28–1.95), Single mothers (AOR: 2.19, 95% CI: 1.55–2.84), less than four ANC visits (AOR: 1.58, 95% CI: 1.22–1.94), no PNC visit (AOR: 2.04, 95% CI: 1.41–2.67), low household wealth index (AOR: 2.25, 95% CI: 1.69–2.81), rural residence (AOR: 2.02, 95% CI: 1.08–2.96), poor maternal knowledge of vaccine-preventable diseases (AOR: 2.79, 95% CI: 1.74–3.84), and home delivery (AOR: 3.91, 95% CI: 1.81–6.01). Conclusion Approximately one in five Ethiopian children is zero-dose, representing a major public health burden. Key determinants include low maternal education, marital status, limited use of Antenatal and postnatal care services, low household wealth, rural residence, home delivery, and poor maternal knowledge. Strengthening maternal health services, community-based health education, and expanding outreach and mobile vaccination programs are essential to reduce this burden.

Peer-reviewed articleOpenAlex11 Sept 2026

Climate-informed surveillance for climate-sensitive infectious diseases in Somalia: early warning systems, policy gaps, and preparedness priorities

Climate variability is reshaping infectious disease risk through ecological, environmental, social, and health-system pathways. However, its operational implications remain insufficiently defined in fragile and conflict-affected settings. Somalia is a critical case because recurrent droughts and floods intersect with displacement, food insecurity, weak water and sanitation infrastructure, disrupted immunisation, livestock dependence, and constrained health-system capacity. This narrative review synthesises evidence on climate-sensitive infectious disease risks in Somalia, examines existing surveillance and early warning systems, identifies policy and implementation gaps, and proposes priorities for a climate-informed surveillance-to-action model for infectious disease control. The review distinguishes directly climate-sensitive infections, including cholera, malaria, dengue, and Rift Valley fever, from indirectly climate-amplified vaccine-preventable diseases, including measles, diphtheria, and polio. While Somalia has important surveillance and early warning assets, including EWARN, IDSRS/DHIS2, FEWS NET, FSNAU intelligence, WASH monitoring, livestock signals, and community-based reporting, these streams remain insufficiently integrated into district-level risk assessment and pre-financed preparedness action. Therefore, the central challenge is not the absence of warning signals but the failure to convert them into timely, disease-specific, and accountable responses. We propose a surveillance-to-action framework based on multisource signal detection, integrated district risk assessment, disease-specific trigger decisions, early preparedness actions, and feedback, accountability, and learning. Closing Somalia's warning-to-action gap requires pre-agreed disease-specific triggers, One Health coordination, flexible anticipatory financing, strengthened district surveillance capacity, and equity-centred outreach to internally displaced persons, pastoralist and nomadic communities, zero-dose children, malnourished populations, and hard-to-reach areas. Climate-informed surveillance should be understood not merely as better data collection but as an anticipatory public health function designed to act before climate shocks become infectious disease emergencies.

Abdikadir Ahmed Hassan, Liibaan Abdulahi Sudi, Mohamed Daud Mohamed et al.

SurvivalSORead at source

Peer-reviewed articleOpenAlex2 Sept 2026

Explainable machine learning for predicting preterm birth and associated factors in Sub Saharan Africa

Preterm birth remains a leading contributor to neonatal mortality and long-term morbidity worldwide, with Sub-Saharan Africa bearing a disproportionate share of the burden. Structural inequities, limited maternal healthcare access, and socioeconomic vulnerabilities are associated with higher model-predicted probability of preterm birth. Despite its significance, large-scale predictive modeling of preterm birth in this region remains limited. This study aimed to develop robust machine-learning–based prediction models and identify key factors associated with preterm birth using recent multi-country Demographic and Health Survey (DHS) data from (2019–2024). A pooled cross-sectional analysis was conducted using nationally representative DHS data from eleven Sub-Saharan African countries, including 411,863 women aged 15–49 years. Four supervised machine learning algorithms were trained and tested using an 80/20 train–test split. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). The Random Forest model demonstrated the highest predictive performance for preterm birth, achieving an accuracy of 90.20% and ROC-AUC of 96.92%, outperforming the Decision Tree (accuracy of 89.2% and ROC-AUC of 92.19%), and other classifiers. The use of SMOTE for class imbalance handling and evaluation with precision–recall metrics improved model robustness in identifying minority class outcomes. SHAP-based feature importance analysis identified key predictors of the outcome,including low maternal educational attainment,unmarried marital status, lack of media exposure, lower household wealth index, and advanced maternal age (≥ 35 years). Additional important predictors were substance use,rural residence,employed women,anemia, abnormal maternal body mass index, insufficient antenatal care attendance (< 4 visits), and short birth interval. Overall, all these factors were associated with an increased predicted probability of preterm birth. Machine learning models, particularly ensemble methods, demonstrated strong predictive performance for preterm birth in Sub-Saharan Africa. Random Forest performed best, highlighting the effectiveness of ensemble learning in handling complex and imbalanced datasets. Key socio-demographic and maternal health factors were consistently associated with preterm birth in a predictive (non-causal) framework. The integration of explainable artificial intelligence enhances model transparency and supports interpretation of predictive patterns in maternal health data. Findings should be interpreted as predictive associations rather than causal relationships, and caution is required when generalizing across heterogeneous country contexts.

Makda Fekadie Tewelgne, Tirualem Zeleke Yehuala, Mekuriaw Nibret Aweke et al.

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Peer-reviewed articleOpenAlex3 Sept 2026

Effect of malted iron-rich beans flour inclusion on nutritional and sensory properties of broken rice-based complementary food: a community-based experimental study

Abstract Background Iron and zinc deficiencies are a major public health concern among children aged 6-24 months in Northern Uganda, particularly in the Acholi sub-region. These deficiencies are largely linked to complementary foods prepared from inadequately processed local ingredients with high anti-nutritional factors, which impair micronutrient absorption. This study investigated the effect of malting on broken rice-iron-rich beans composite flour, focusing on sensory properties, nutrient composition, bioavailability, and anti-nutritional content. Methods Nine formulations (four malted, four unmalted, and one control) were analyzed. Sensory evaluations were conducted with 124 mothers of children aged 6-24 months. Proximate composition, micronutrient content, and anti-nutritional factors were determined in the laboratory. Data were analyzed using two-way ANOVA at p ≤ 0.05. Results For children aged 6-11 months, the most preferred formulation was composite F (30% unmalted beans: 70% rice), while composite C (40% malted beans: 60% rice) was most acceptable for 12-24 months. Composite A (30% malted beans: 70% rice) and G (40% unmalted beans: 60% rice) recorded moisture contents of 2.3% and 3.0%, respectively, with G being significantly higher (p ≤ 0.05). Composite E (30% unmalted beans: 70% rice) showed the highest water absorption capacity, while malting reduced oil absorption capacity and bulk density. Ash content was higher in malted samples (A = 1.45%, C = 2.28%) than unmalted (E = 1.52%, G = 1.76%). Carbohydrate levels were consistent but highest in the control (100% rice = 91.71%). Protein digestibility was higher in malted samples (A = 33.16%, C = 12.35%), while zinc bioavailability was highest in C (4.14%). Malting increased zinc, calcium, iron, magnesium, potassium, and fiber, and reduced phytate and trypsin inhibitors, though polyphenols and tannins slightly increased. Conclusions Malting improved nutritional quality, functional properties, and acceptability of complementary foods. Composite A is suitable for 6-11 months, and composite C for 12-24 months. Promoting iron-rich bean composites through caregiver education and early childhood feeding programs could enhance nutrient intake, support growth, and prevent micronutrient deficiencies.

Christopher Atibo, Sunday Mark Oyet, Christopher Muggaga et al.

SurvivalPractitionerUGRead at source

Peer-reviewed articleOpenAlex4 Sept 2026

Effect of Pigeon Pea and Sorghum Flour Supplementation on Protein Content, In Vitro Protein Digestibility, and Selected Storage Quality Indicators of Millet Flour Blends

Protein-energy malnutrition (PEM) remains a significant public health issue among children in many parts of Kenya. This study aimed to utilize locally available sorghum, pearl millet, and pigeon peas to develop a protein-rich complementary flour blend that could contribute to efforts to improve the nutritional quality of complementary foods for young children in Tharaka Nithi County, Kenya. The study employed a nested experimental design with pigeon pea level (0, 15, 30, 45, and 60% of the total flour blend) as the main factor and sorghum level (0, 20, and 40% of the remaining flour after pigeon pea addition) nested within each pigeon pea level to evaluate protein content and in vitro protein digestibility of flour blends. An additional factor, storage period (days), was included to assess selected storage quality indicators of the millet-based flour blends during storage. Formulated flour blends were then analysed for protein content, protein digestibility, yeast and mold counts, titratable acidity, and pH. The results revealed that the addition of pigeon peas significantly (p < 0.001) increased the protein content and the digestibility of the flour blends. The average relative humidity and temperature during storage were 55.5% and 25.2 °C, respectively. The addition of pigeon peas to the blends significantly (p < 0.002) increased the titratable acidity of the composite flour blends. The initial mean yeast and mold count was 2.439 ± 0.147 Log CFU/g, which was below the East African Community recommended safety limit for millet flour (4 Log CFU/g). The yeast and mold count slightly increased during storage in the first 16 days, followed by a significant decrease, reaching 1.052 Log CFU/g by day 45. These findings demonstrate the potential of pigeon peas, sorghum, and millet as locally available ingredients for the development of nutritionally improved complementary foods.

Catherine Nzisa Mwangangi, Jackin Njagi Nanua, Willy Kiboi et al.

SurvivalKERead at source

Peer-reviewed articleOpenAlex4 Sept 2026

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

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.

Denekew Bitew Belay, Nigussie Adam Birhan, Tilahun Yemanu Birhan et al.

SurvivalTZRWRead at source

Peer-reviewed articleOpenAlex6 Sept 2026

Multilevel Modelling of the Factors Associated with Stunting among Under-five Children in Tanzania: A Bayesian Approach

Introduction: In Tanzania, prevalence exceeds 30% and is unevenly distributed across geographical areas. The observed distribution may reflect unobserved contextual factors; however, little literature applies Bayesian multilevel generalised linear mixed (B-MGLM) models, which can account for them. This study aimed to model the determinants of stunting, controlling for contextual factors at both the region and enumeration area levels. Methods: This was a secondary data analysis using four recent Tanzania Demographic and Health Surveys (TDHS) datasets. We studied 7,492, 6,806, 8,929, and 4,797 under-five children in TDHS 2004/5, 2010, 2015/16, and 2022, respectively. We used B-MGLM models to evaluate the determinants of stunting. We used the deviance information criterion to compare the models. Results: About 41.4%, 34.1%, and 28.96% of under-five children were stunted in 2010, 2015/16, and 2022, respectively. Controlling for other factors in the model, a unit increase in MPI increased the odds of stunting by 10% [AOR = 1.1; 95% CI: 1.1, 1.2], while a unit increase in WAMI reduced the odds of stunting by 64% [AOR=0.36; 95% CI: 0.3, 0.4] in 2015/16 and 2010, respectively. Other interesting results were that children with multiple births had 17% [AOR=0.83; 95% CI: 0.7, 0.9] lower odds and 68% [AOR=1.68; 95% CI: 1.5, 1.9] higher odds of stunting in 2004/5 and 2010, respectively. Conclusion: Stunting is associated with individual and contextual factors. Critical efforts should focus on the availability of items that form WAMI and MPI, with greater emphasis on elements that form WAMI because of its consistent negative relationship with stunting across the two surveys. Nutrition education should target working mothers and households with food insecurity.

Edwin Musheiguza, Tukae Mbegalo, Justine N. Mbukwa

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Peer-reviewed articleOpenAlex7 Sept 2026

Relative burden of neglected tropical diseases among school-age children in rural and urban slum settings in Eastern Ethiopia

Abstract Introduction: Despite the determination to succeed in eliminating neglected tropical diseases, these have been increasing within certain endemicities, particularly in depriving region. Objective: This study aimed to assess the relative burden of neglected tropical diseases in rural and urban slum settings in Eastern Ethiopia. Methods: A comparative cross-sectional study was conducted from February 25 to April 30, 2024 with a multistage random sampling technique used. A survey tool with the integration of the spectrum was employed. Data were entered using Epi-Data version 3.1 and analyzed using STATA 16. Descriptive statistics and multivariable logistic regression analysis was used to identify the risk factors and the level for statistical significance was declared at p-value < 0.05 with 95 % CI. Result: The response rate was 93 % and the mean age of house heads was 31.6 (Range; 18. 63) with 5.7 average family size. The overall prevalence of NTDs among study participants was 22.6 % (95% CI; 20.2, 25.2); urban slum it was 15 % (95% CI; 12.2, 18.4) while it was 29.8 % (95% CI; 26, 33.9) in rural. Though there is no statistically significant difference, the relative prevalence of NTDs in rural to urban was two. The multivariable analysis provided that the risk factors associated with NTDs were government employment (AOR: 12.5 (1.17, 132.4)), good income (AOR: 0.13 (.03, 0.57)), Child playing with soil (AOR: 5.8 (1.52, 22.0)); child hand washing at critical times ( AOR: 11.2 (1.7, 74.9)), presence of flies in the child faces (AOR: 20.3 (4.7, 88.6)) and type of drinking water sources (AOR: 0.11 (0.02, 0.73)). Conclusion: the burden of NTDs was moderate in the study area dominated by STH, Trachoma, and scabies. Socio economic and WASH factors were found to be associated with NTDs. Collaborated efforts among stakeholders are crucial to prevent and control NTDs.

Getachew Kabew Mekonnen, Fitsum Weldegebreal, Assefa Desalew et al.

SurvivalETRead at source

Every report we find, newest first.

How to use the reports

Zero-dose childhood vaccination and associated factors in Ethiopia: A systematic review and meta-analysis

Climate-informed surveillance for climate-sensitive infectious diseases in Somalia: early warning systems, policy gaps, and preparedness priorities

Childcare for Informal-Sector Workers: Delivery Models, Access, and Welfare

Explainable machine learning for predicting preterm birth and associated factors in Sub Saharan Africa

Loss of salt iodization harmed child survival and academic achievement in Ethiopia

Effect of malted iron-rich beans flour inclusion on nutritional and sensory properties of broken rice-based complementary food: a community-based experimental study

Effect of Pigeon Pea and Sorghum Flour Supplementation on Protein Content, In Vitro Protein Digestibility, and Selected Storage Quality Indicators of Millet Flour Blends

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

Multilevel Modelling of the Factors Associated with Stunting among Under-five Children in Tanzania: A Bayesian Approach

Relative burden of neglected tropical diseases among school-age children in rural and urban slum settings in Eastern Ethiopia

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