Intimate Partner Violence (IPV) is the most prevalent form of violence against women globally and is more prevalent than rape or other violent attacks by strangers. Different observational studies have established a strong positive association between alcohol use and intimate partner violence. Even though there are a lot of studies that show the association between partner alcohol use and intimate partner violence limited studies were conducted that show the direct causative relations of partner alcohol use and IPV among reproductive-age women in East Africa. Therefore, this study aimed to determine the effect of partner alcohol use on intimate partner violence in East Africa’s recent Demographic and Health Survey (DHS) data with Propensity Score Matching (PSM). Community-based cross-sectional study design with a propensity score matching was used from the East African countries’ DHS data. A total of the weighted sample size of 72,554 reproductive-age women was used for this study. Propensity score matching analysis was conducted to determine the causal relation between partner alcohol use and intimate partner violence. Intimate partner violence was the outcome variable and partner alcohol use was the treatment variable. Propensity score matching was carried out through Stata software by using psmatch2 of the logit-based model. The assumption of common support was verified and achieved. Mantel-Haenszel boundaries have been used to investigate the possibility of hidden bias in the outcome. The prevalence of partner alcohol use and intimate partner violence from East African countries was 37.94 with a CI of (37.58%, 38.29%) and 41.45% with a CI (41.09%, 41.80%) respectively. Partner alcohol use contributed to a 2.78% increase in intimate partner violence according to the estimated average treatment on treated values in the treated and control groups were 59.41% and 31.51%, respectively. Ultimately, it was found that among all research participants, the average effect on the population as a whole was 25.33%. We conclude that partner alcohol use has a direct cause for intimate partner violence. Therefore, controlling partner alcohol consumption can reduce the burden of intimate partner violence.

Background: The maternal continuum of care (CoC) is a cost-effective approach to mitigate preventable maternal and neonatal deaths. Women in developing countries, including Tanzania, face an increased vulnerability to significant dropout rates from maternal CoC, and addressing dropout from the continuum remains a persistent public health challenge.Method: This study used the 2022 Tanzania Demographic and Health Survey (TDHS). A total weighted sample of 5,172 women who gave birth in the past 5 years and had first antenatal care (ANC) were included in this study. Multilevel binary logistic regression analyses were used to examine factors associated with dropout from the 3 components of maternal CoC (i.e., ANC, institutional delivery, and postnatal care (PNC)).Results: The vast majority, 83.86% (95% confidence interval (CI): 82.83%, 84.83%), of women reported dropout from the maternal CoC. The odds of dropout from the CoC was 36% (AOR = 0.64, (95% CI: 0.41, 0.98)) lower among married women compared to their divorced counterparts. Women who belonged to the richer wealth index reported a 39% (AOR = 0.61, (95% CI: 0.39, 0.95)) reduction in the odds of dropout, while those belonged to the richest wealth index demonstrated a 49% (AOR = 0.51, (95% CI: 0.31, 0.82)) reduction. The odds of dropout from CoC was 37% (AOR = 0.63, (95% CI: 0.45,0.87)) lower among women who reported the use of internet in the past 12 months compared to those who had no prior exposure to the internet. Geographical location emerged as a significant factor, with women residing in the Northern region and Southern Highland Zone, respectively, experiencing a 44% (AOR = 0.56, 95% CI: 0.35–0.89) and 58% (AOR = 0.42, 95% CI: 0.26–0.68) lower odds of dropout compared to their counterparts in the central zone.Conclusion: The dropout rate from the maternity CoC in Tanzania was high. The findings contribute to our understanding of the complex dynamics surrounding maternity care continuity and underscore the need for targeted interventions, considering factors such as marital status, socioeconomic status, internet usage, and geographical location.

Objectives This study aimed to employ machine learning algorithms to predict the factors contributing to zero-dose children in Tanzania, using the most recent nationally representative data. Design Cross-sectional study. Setting This study was conducted in Tanzania and used the most recent 2022 Tanzania Demographic and Health Survey, accessed from http://www.dhsprogram.com. Participants A total of 2120 children aged 12–23 months were included in this study. Outcome measure Seven classification algorithms were used in this study: logistic regression, decision tree classifier, random forest classifier (RF), support vector machine, K-nearest neighbour, XGBoost (XGB) and Naive Bayes. The dataset was randomly divided into training and testing sets, with 80% allocated for training and 20% for testing. After training the models, the testing data were used to evaluate their performance. This evaluation measured the models’ ability to generalise to unseen data using performance metrics such as accuracy, precision, recall, F1-score and AUC. Results Approximately 7.45% of children (95% CI 6.73%, 8.65%) were categorised as zero-dose children. The RF classifier achieved the highest performance metrics among the evaluated algorithms, with accuracy=0.95, precision=0.94, recall=0.96, F1 score=0.95 and AUC=0.99, making it the most effective supervised machine learning method for predicting zero-dose children in Tanzania. Maternal unemployment had the most significant positive impact (+0.060) on predicting zero-dose children. Lack of maternal education was the second most significant positive factor (+0.048), indicating that mothers without formal education are more likely to have zero-dose children. Small family size was the third most influential factor, with a positive effect (+0.040) on predicting zero-dose children in Tanzania. Conclusions The RF classifier emerged as the top-performing model for predicting children in Tanzania who have not received any vaccinations. This comprehensive approach enabled the accurate identification of zero-dose children, highlighting the effectiveness of machine learning in enhancing public health initiatives and optimising vaccination strategies. Using this algorithm can enhance health outcomes and reduce the prevalence of vaccine-preventable diseases in Tanzania.

Abortion is a critical health issue that leads to numerous complications, maternal deaths, and significant financial burdens on women, families, and healthcare systems. Studies have identified factors associated with abortion using traditional statistical analysis methods; however, no previous research has utilized machine learning to predict abortion in Ethiopia or identify its predictive factors. Machine learning is more effective and offers a better solution as it can capture complex and non-linear relationships in the data, leading to improved prediction accuracy compared to traditional regression models. Therefore, this study employed machine learning algorithms to predict abortion in Ethiopia and identify its predictors using nationally representative data. This study used the recent 2016 Ethiopian Demographic and Health Survey and included a sample of 14,931 women of reproductive age (15–49 years). This study used 7 machine learning algorithms for the classification of abortion. The dataset was randomly split into training and testing sets, with 80% allocated for training and 20% for testing. To evaluate the performance of each predictive model, we used a range of metrics such as accuracy, precision, recall, F1-score, and area under the curve (AUC). In this study, SHapley Additive Explanations (SHAP) values were used to measure the influence of each feature on the model’s predictions. In the current study, 7 machine learning algorithm (i.e. logistic regression, decision tree classifier, random forest classifier, support vector machine, K neighbor classifier, XGBoost, and Nave bayes) were applied. The random forest classifier model were the best predictive models with the accuracy of 0.91 and AUC of 0.97. Moreover, the XGBoost was the 2nd best-performing algorithm with 0.87 accuracy and 0.94 AUC. According to the SHAP beeswarm and bar plots, younger age was identified as the strongest predictor of abortion, with a mean SHAP value of + 0.060. The second most impactful factor was having a younger husband, contributing a mean SHAP value of + 0.050 to abortion prediction in Ethiopia. Additionally, giving birth for the first time before the age of 18 ranked third, with a mean SHAP value of + 0.052. This study underscores the value of integrating machine learning into public health research and practice. Future work should focus on refining these models with larger and more diverse datasets, as well as exploring their applicability in other contexts and regions to further global maternal health initiatives. By harnessing machine learning techniques, healthcare providers can better classify abortion risks in reproductive-age women in Ethiopia. This knowledge can inform targeted interventions, enhance reproductive health services, and ultimately improve maternal health outcomes.

Postpartum depression is a non-psychotic depressive illness that affects women who have recently given birth. Symptoms of the illness include thoughts of suicide, low self-worth, guilt about not being able to care for their newborn, blaming themselves thoughts, and nervousness. Despite there being many studies conducted at the hospital level there are limited studies conducted at the national level to determine the individual and community level factors. Depression during childbirth can have several detrimental effects, including increased risk of complication and decreased satisfaction with birth, decreased trust in medical facilities, and decreased women's participation in maternity and newborn healthcare units. Therefore, this study amid to reveal the prevalence of postpartum depression and associated factors at individual and community levels among women who give birth in Mozambique's recent data Demographic and Health Survey (DHS). Multilevel logistic regression analysis was carried out from the recent Demographic and Health Survey data. A total of 5,468 sample sizes of Mozambique DHS data were used for this secondary data analysis. At individual and community level factors were assessed to determine the burden of postpartum depression at a p-value of 0.05 with a 95 % CI with AOR. The prevalence of postpartum depression among women who give birth from the recent Mozambique DHS data was 24.60 % with a CI of 23.47 %–25.75 %. No education [AOD = 1.37: 95 % CI; (1.26, 1.68)] at individual level and [AOR = 3.14:95 % CI; (2.39, 5.18)] at the community level, widowed/divorced [AOR = 1.73: 95%CI; (1.14, 3.61)], and No occupation [AOR = 1.35:95 % CI; (1.19, 1.64)] were the associated variables with postpartum depression. Nearly three women out of ten women who give birth develop postpartum depression. Low education status, widowed/divorced, and no occupation were the associated factors with postpartum depression. Therefore, enhancing education, creating job opportunities, and keeping married will reduce the burden of postpartum depression. This study indicates maternal depression post-delivery should be given attention by the National Health Office and other police makers. Educational level should be improved to have a continuous and sharp change in maternal depression after delivery.

Background According to the WHO, obstetric fistula (OBF) is an abnormal connection between the genital tract and the urinary tract that occurs as the result of obstetric trauma, typically from prolonged obstructed labour. In 2018, globally, 50 000 and 100 000 cases of OBF are reported each year. The core of activities focused on reducing fistulas depends on a review of the disorder’s knowledge and the features of women at risk of having a lack of understanding. The effect of community-level factors on awareness of OBF was not yet known in Nepal. Therefore, we aimed to investigate the community-level and individual-level factors of awareness of OBF among childbearing-aged women in Nepal. Methods The 2022 Nepal Demographic and Health Survey data were used for this study. It included 14 845 childbearing-aged women. Because of the clustering effects of Demographic and Health Survey data and the binary nature of the outcome variable, a multilevel binary logistic regression model was applied. An adjusted OR (AOR) with a 95% CI was reported to declare the statistical significance. In addition, the model that had the lowest deviance was the one that best fit the data. Results The overall prevalence of awareness of OBF among childbearing women in Nepal was 35.9% (95% CI 35.1%, 36.7%). Educational status (women who attended secondary education (AOR=1.65; 95% CI 1.41, 3.03) and higher education (AOR=4.29; 95% CI 1.14, 36.70)), currently working status (AOR=1.85; 95% CI 1.04, 3.30), birth history (AOR=2.23; 95% CI 1.48, 4.10), media exposure (AOR=1.54; 95% CI 1.07, 3.09) and women’s age from 30 to 39 (AOR=3.38; 95% CI 1.35, 8.93) and 40 to 49 years old (AOR=4.68; 95% CI 1.60, 13.67) at the individual level, as well as urban residence (AOR=1.53; 95% CI 1.99, 2.87) and high community-level media exposure (AOR=2.05; 95% CI 1.67, 2.64) at the community level were statistically significant factors with awareness of OBF. Conclusion Our study revealed that awareness of OBF among childbearing-aged women in Nepal was low (35.9%). The findings of this study will assist policymakers and public health programmers in understanding the magnitude of OBF awareness and the contributory factors. In addition, it will be useful to increasing awareness of OBF in the communities and promoting primary prevention approaches through education and motivation efforts.