To assess the association between the maternal continuum of healthcare and child immunisation in East Africa using propensity score matching (PSM).

Cross-sectional study using Demographic and Health Survey data.

This study was conducted in East African countries.

This study included a weighted sample of 13 488 women with children aged 12–23 months.

Child immunisation was the outcome variable of this study.

The PSM estimates indicate that the average treatment effect on the treated for complete child immunisation was 0.0583, meaning that children of mothers who received a complete maternal continuum of care had a 5.83% higher probability of being fully immunised compared with children of mothers with incomplete care. Expressed relative to the treated group’s mean, this corresponds to a 7.48% increase. Additionally, our results indicated that the population average treatment effect was 0.0629. This means that, on average, a complete continuum of maternal healthcare increases the probability of full child immunisation by approximately 6.29% across the entire population.

The study highlights that children whose mothers receive comprehensive maternal healthcare are more likely to complete their childhood immunisations. This finding underscores the need to integrate immunisation services into maternal healthcare programmes to enhance vaccination coverage and promote better child health. To maximise this connection, improving access to maternal healthcare, especially in underserved regions, is crucial, along with ensuring that immunisation is a regular part of maternal care.

To develop and validate a risk prediction model for preterm premature rupture of membranes (PPROM) to enable early identification of at-risk women and support clinical decision-making in North Wollo Zone, Ethiopia.

A hospital-based retrospective cross-sectional study.

Six public hospitals in the North Wollo Zone, Northern Ethiopia.

A total of 1098 pregnant women were included in the study using systematic random sampling.

Occurrence of PPROM.

Data were collected between 20 November 2023 and 20 March 2024, using structured interviews and medical record reviews. A risk prediction model was developed using Least Absolute Shrinkage and Selection Operator and logistic regression. Model performance was assessed through area under the curve (AUC), calibration plots and the Hosmer-Lemeshow test. Internal validation was conducted via bootstrap resampling. A simplified risk score was created to categorise women into high-risk and low-risk groups, and its clinical utility was evaluated using decision curve analysis.

Among the 1098 participants (100% response rate), the mean age was 21.54 years (IQR: 18–26), with 57.2% aged 20–34 years. The prevalence of PPROM was 10.75% (95% CI 9.01% to 12.77%). Seven significant predictors were identified: maternal age

PPROM remains a significant obstetric complication in the study area. The validated risk prediction model showed moderate to good performance and can be used to support early screening and risk-based management in antenatal care (ANC). Integrating the tool into routine ANC services, along with health education and management of modifiable risk factors, may help reduce PPROM-related adverse outcomes. Further external validation is recommended.

This study aims to assess parents’ knowledge and attitude towards the human papillomavirus (HPV) vaccination of their daughters and the associated factors in Debre Tabor town, northwest Ethiopia.

A community-based cross-sectional study.

Debre Tabor town, Northwest Ethiopia.

A total of 702 participants were included in the study, with a response rate of 98.2%. Three out of the six kebeles in the town were randomly selected, and participants within the selected kebeles were recruited through a cluster sampling technique. An interviewer-administered structured questionnaire was used to collect data from 15 December 2021 to 15 February 2022.

Parents’ level of knowledge and attitude towards the HPV vaccination of their daughters, and the associated factors.

In the study, parents’ knowledge and attitude towards HPV vaccination were found to be 46.4% (95% CI 42.7% to 50.1%) and 61.5% (95% CI 58.0% to 65.2%), respectively. Parents with a higher level of education (adjusted OR (AOR)=2.27; 95% CI 1.39 to 3.69), media exposure (AOR=3.36; 95% CI 1.21 to 9.33) and a good attitude towards the HPV vaccine (AOR=8.81; 95% CI 5.78 to 13.44) were significantly associated factors that affect parents’ level of knowledge. Positive subjective norms (AOR=1.53; 95% CI 1.01 to 2.31) and perceived behavioural control towards the HPV vaccine (AOR=3.48; 95% CI 2.37 to 5.10) had statistically significant associations with parents’ attitude.

In this study, more than half of parents had poor knowledge of the HPV and its vaccination, while the majority of the participants showed a favourable attitude to the vaccine. Educational attainment, media exposure and a positive attitude were significantly associated with parental knowledge, and parents’ attitude was positively influenced by subjective norms and perceived behavioural control. This suggests a need to increase the parents’ level of awareness through educational interventions, particularly via media and community engagement. To improve the acceptance and uptake of the HPV vaccination, it is important to address negative attitudes and common misconceptions among parents on the safety, efficacy and necessity of the vaccine for their daughters.

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.