To assess the time to first optimal glycaemic control and its predictors among adult patients with type 1 and type 2 diabetes at the University of Gondar Comprehensive Specialized Hospital in Ethiopia.

A retrospective cohort study.

University of Gondar Comprehensive Specialized Hospital, northwest, Ethiopia.

We recruited 423 adult diabetic patients who were diagnosed between 1 January 2018 and 30 December 2022 at the University of Gondar Comprehensive Specialized Hospital.

The primary outcome was the time from diagnosis to the achievement of the first optimal glycaemic control, measured in months. A Cox proportional hazards regression model was fitted to identify predictors of time to first optimal glycaemic control. Data were collected with KoboToolbox from patient medical charts and exported to Stata V.17. The log-rank test was used to determine the survival difference between subgroups of participants.

Median time to first optimal glycaemic control was 10.6 months. Among 423 adult diabetic patients, 301 (71.16%) achieved the first optimal glycaemic control during the study period. Age category (middle age (adjusted HR (AHR)=0.56, 95% CI 0.41 to 0.76), older age (AHR=0.52, 95% CI 0.33 to 0.82)), comorbidity (AHR=0.52, 95% CI 0.35 to 0.76), therapeutic inertia (AHR=0.20, 95% CI 0.13 to 0.30) and medication non-compliance (AHR=0.49, 95% CI 0.27 to 0.89) were significant predictors of time to optimal glycaemic control.

The median time to first optimal glycaemic control was prolonged. Diabetic care should focus on controlling the identified predictors to achieve optimal glycaemic control early after diagnosis.

This study assessed husbands’ knowledge of neonatal danger signs in Dessie City, Northeast Ethiopia, focusing on fathers of infants born within the preceding 6 months (2023).

Community-based cross-sectional study.

Dessie City, Northeast Ethiopia.

We systematically selected 613 husbands of postpartum women (sampling period: December 15, 2022,–January 15, 2023).

Data were collected via structured questionnaires, entered into EpiData (v4.6) and analysed using SPSS (v26). Binary logistic regression identified factors associated with knowledge; statistical significance was set at p

Among the 613 respondents, slightly over half (53%, n=325) demonstrated good knowledge of neonatal danger signs. Several factors were significantly associated with higher knowledge levels. Husbands residing in urban areas were nearly seven times more likely to have good knowledge compared with their rural counterparts (adjusted OR (AOR)=6.93; 95% CI, 3.23 to 14.90). Educational attainment also played a critical role: those with primary education or higher had 6.44 times higher odds of good knowledge than those with no formal schooling (95% CI, 1.83 to 22.61). Parity was another predictor, with fathers of 2–4 children showing markedly greater knowledge (AOR=10.39; 95% CI, 4.68 to 23.05) than those with only one child. Most notably, receiving information from health professionals had the strongest association—respondents who accessed such guidance were 11 times more likely to be knowledgeable (AOR=11.05; 95% CI, 5.46 to 22.36).

Nearly half of the participants lacked adequate knowledge. Thus, integrating targeted health education into maternal and child health programmes could improve awareness and neonatal outcomes.

Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to funding limitations. The recent suspension of the US Agency for International Development funding exacerbates these issues, highlighting the need for accurate birth forecasting to guide policy and resource allocation. This study applied time-series and advanced machine-learning models to forecast future birth trends in Ethiopia.

Secondary data from the Ethiopian Demographic and Health Survey from 2000 to 2019 were used. After data preprocessing steps, including data conversion, filtering, aggregation and transformation, stationarity was checked using the Augmented Dickey-Fuller (ADF) test. Time-series decomposition was then performed, followed by time-series splitting. Seven forecasting models, including Autoregressive Integrated Moving Average, Prophet, Generalised Linear Models with Elastic Net Regularisation (GLMNET), Random Forest and Prophet-XGBoost, were built and compared. The models’ performance was evaluated using key metrics such as root mean square error (RMSE), mean absolute error (MAE) and R-squared value.

GLMNET emerged as the best model, explaining 77% of the variance with an RMSE of 119.01. Prophet-XGBoost performed reasonably well but struggled to capture the full complexity of the data, with a lower R-squared value of 0.32 and an RMSE of 146.87. Forecasts were made for both average monthly births and average births per woman over a 10-year horizon (2025–2034). The forecast for average monthly births indicated a gradual decline over the projection period. Meanwhile, the average births per woman showed an increasing trend but fluctuated over time, influenced by demographic shifts such as changes in fertility preferences, age structure and migration patterns.

This study demonstrates the effectiveness of combining time-series models and machine learning, with GLMNET and Prophet XGBoost emerging as the most effective. While average monthly births are expected to decline due to demographic transitions and migration, the average births per woman will remain high, reflecting persistent fertility preferences within certain subpopulations. These findings underscore the need for policies addressing both population trends and sociocultural factors.

Healthy dietary choices have an important role in preventing chronic diseases such as cardiovascular disease (CVD). Increasing evidence suggests micronutrient intake (essential minerals and vitamins) is associated with abdominal aortic calcification (AAC), which is an advanced marker of CVD. However, the existing reports seem inconsistent. Some studies reported micronutrients are associated with a lower risk of AAC, while others have reported an increased risk. Therefore, this systematic review and meta-analysis sought to summarise the available evidence on the association of dietary micronutrient intake on AAC.

A comprehensive systematic search of the PubMed/MEDLINE, EMBASE, Web of Science and Google Scholar databases from their inception up to September 1, 2024, will be conducted. All clinical studies that report eligible exposure/s (dietary micronutrient intake) and outcome/s (presence/severity of AAC) will be included, and this systematic review and meta-analysis protocol will be reported following the revised Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guidelines. The risk of bias for observational studies will be assessed using the Newcastle-Ottawa Scale and publication bias will be evaluated through visual inspection of funnel plots and the Egger’s and Begg’s regression tests. The Der Simonian and Laird random-effects model meta-analysis will be calculated to provide pooled results, and the weighted risk ratio with their 95% confidence intervals will be presented.

The results will be disseminated through publishing in a peer-reviewed journal and public presentations at relevant local, national and international conferences, workshops and symposiums. Ethical approval is not required as this is a systematic review of publicly available data.

PROSPERO registration number

CRD42024575902