Conectar
by Tadesse Tarik Tamir, Berhan Tekeba, Alebachew Ferede Zegeye, Deresse Abebe Gebrehana, Mulugeta Wassie, Gebreeyesus Abera Zeleke, Enyew Getaneh Mekonen
IntroductionSolitary childbirth—giving birth without any form of assistance—remains a serious global public health issue, especially in low-resource settings. It is associated with preventable maternal complications such as hemorrhage and sepsis, and poses significant risks to newborns, including birth asphyxia, infection, and early neonatal death. In Ethiopia, where many births occur outside health facilities, understanding the spatial and socio-demographic patterns of solitary childbirth is vital for informing targeted interventions to improve maternal and child health outcomes. This study aims to identify and map the spatial distribution of solitary childbirth across Ethiopia and to analyze its determinants using data from the 2019 national Interim Demographic and Health Survey.
MethodWe analyzed data from the 2019 Interim Ethiopian Demographic and Health Survey to determine the spatial distribution and factors of solitary birth in Ethiopia. A total weighted sample of 3,884 women was included in the analysis. Spatial analysis was used to determine the regional distribution of solitary birth, and multilevel logistic regression was employed to identify its determinants. ArcGIS 10.8 was used for spatial analysis, and Stata 17 was used for multilevel analysis. The fixed effect was analyzed by determining the adjusted odds ratio with a 95% confidence interval.
ResultThe prevalence of solitary childbirths in Ethiopia was 12.73%, with a 95% confidence interval spanning from 11.71% to 13.81%. The western and southern parts of Oromia, all of Benishangul-Gumuz, most parts of the SNNPR, and the west of Amhara regions were hotspot areas for solitary birth. Having no formal education, not attending ANC visits, and residing in pastoral regions were significantly associated with higher odds of solitary birth in Ethiopia.
CocnlusionA notable proportion of women are experiencing childbirth alone, which highlights a significant aspect of maternal health in the country, reflecting both the challenges and improvements in childbirth practices. The distribution of solitary births exhibited spatial clustering with its hotspot areas located in western and southern parts of Oromia, all of Benishangul-Gumuz, most parts of the SNNPR, and west of Amhara regions. Lack of education, not having an ANC visit, and being a resident of pastoral regions were significant determinants of solitary birth. The implementation of maternal and child health strategies in Ethiopia could benefit from considering the hotspot areas and determinants of solitary birth.
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.
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