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: Knowledge of the fertility period aids women in refraining and engaging in sexual intercourse to avoid and to get pregnant, respectively. The effect of community-level factors on knowledge of the fertility period was not yet known in Kenya. Therefore, we aimed to investigate the community- and individual-level determinants of knowledge of fertility period among women of childbearing age in Kenya. Methods: The 2022 Kenyan Demography and Health Survey data was used for the current study. This study included 16,901 women of reproductive age. To account for the clustering effects of DHS data and the binary nature of the outcome variable, a multilevel binary logistic regression model was applied. An adjusted odds ratio with a 95% confidence interval 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 knowledge of the fertility period among Kenyan women was 38.1% (95%CI = 37.3, 38.9). Women’s age, women’s education status, heard FP, contraceptive use, media exposure, and distance from health facility significant individual factors while place of residence, and community-level education, were all of factors were found to be strongly associated with knowledge of fertility period. Conclusion: As per the findings of our study, Knowledge of the fertility period among reproductive women was low in Kenya. In the era of increasing refusal of hormone-based family planning, fertility-awareness-based family planning methods may be an option. Promoting the correct fertility period through education and media outreach may be helpful strategies for enhancing fertility decision-making.

Low birth weight is the main cause of death, illness, and disability in infancy and childhood and has a lasting effect on adult healing here are several studies that indicate the correlation between low birth weight and anemia using an observational study based on the Demographic and Health Survey (DHS) but there are limited studies to show the direct effect of anemia on low birth weight. Therefore, we used the propensity score-matching (PSM) analysis to estimate the actual impact of maternal anemia on low birth weight babies. The study aims to reveal the effect of maternal anemia on low birth weight babies among reproductive-age women using propensity score matching analysis from the Demographic and Health Survey in Sub-Saharan Africa. Pregnancy-related maternal nutritional deficiencies impact the fetus’s development, which in turn affects the newborn’s birth weight. The initiative to conduct this study is that most of the time mothers suffer from anemia and at the same time they deliver low birth weight children. LBW newborns had a 20-fold increased risk of morbidity and death compared to normal. Despite the burden of maternal anemia on low birth weight the direct relation with controlling other factors to show their direct cause and effect is not well studied. A community-based cross-sectional study was conducted based on the DHS data. We used the recent DHS data of 27 Sub-Saharan African countries. A total of 54,904 women with at least one birth in the last five years were considered. To estimate the impact of anemia on low birth weight, we used PSM analysis with a logit model using psmatch2 ate STATA command to find the average treatment effect on the population (ATE), treated and untreated. The quality of matching was assessed statistically and graphically. Sensitivity analysis was conducted to test the robustness of the PSM estimates using the Mantel- Haenszel test statistic. The prevalence of anemia in sub-Saharan Africa was 41.62%. The prevalence rate of low birth weight was 20.1%. In the PSM analysis, the difference in ATE of anemia on low birth weight was 13.0%. The difference in ATE among the treated group was 2.44% increased risk of low birth weight. The ATE in the treated group had a 12.1% increased risk of low birth weight. The estimates were insensitive to hidden bias and had a good quality of matching. Anemia has a strong effect on low birth weight after matching treated and control groups of observed variables. Reducing maternal anemia for improved birth outcomes or increasing fetal birth weight requires treating anemia before and during the early stages of pregnancy, as well as providing regular nutrition instruction and counseling during routine ANC follow-up. The involvement of stakeholders in the treatment of anemia is warranted to decrease the rate of LBW and its late consequences.

Background: Prematurity is the leading cause of neonatal morbidity and mortality, specifically in low-resource settings. The majority of prematurity can be prevented if early interventions are implemented for high-risk pregnancies. Developing a prognosis risk score for preterm birth based on easily available predictors could support health professionals as a simple clinical tool in their decision-making. Therefore, the study aims to develop and validate a prognosis risk score model for preterm birth among pregnant women who had antenatal care visit at Debre Markos Comprehensive and Specialized Hospital, Ethiopia.Methods: A retrospective follow-up study was conducted among a total of 1,132 pregnant women. Client charts were selected using a simple random sampling technique. Data were extracted using structured checklist prepared in the Kobo Toolbox application and exported to STATA version 14 and R version 4.2.2 for data management and analysis. Stepwise backward multivariable analysis was done. A simplified risk prediction model was developed based on a binary logistic model, and the model’s performance was assessed by discrimination power and calibration. The internal validity of the model was evaluated by bootstrapping. Decision Curve Analysis was used to determine the clinical impact of the model.Result: The incidence of preterm birth was 10.9%. The developed risk score model comprised of six predictors that remained in the reduced multivariable logistic regression, including age < 20, late initiation of antenatal care, unplanned pregnancy, recent pregnancy complications, hemoglobin < 11 mg/dl, and multiparty, for a total score of 17. The discriminatory power of the model was 0.931, and the calibration test was p > 0.05. The optimal cut-off for classifying risks as low or high was 4. At this cut point, the sensitivity, specificity and accuracy is 91.0%, 82.1%, and 83.1%, respectively. It was internally validated and has an optimism of 0.003. The model was found to have clinical benefit.Conclusion: The developed risk-score has excellent discrimination performance and clinical benefit. It can be used in the clinical settings by healthcare providers for early detection, timely decision making, and improving care quality.

Background: Birth weight is a crucial factor linked to a newborn’s survival and can also affect their future health, growth, and development. Earlier, researchers focused on exploring maternal and fetal factors contributing to low birth weight. However, in recent years, there has been a shift toward effectively predicting low birth weight by utilizing a combination of variables. This study aims to develop and validate a nomogram for predicting low birth weight in Ethiopia.Methods: A retrospective follow-up study was conducted, and a total of 1,120 pregnant women were included. Client charts were selected using a simple random sampling technique. Data were extracted using a structured checklist prepared on the KoboToolbox (Cambridge, Massachusetts in the United States) and exported to STATA version 14 (Computing Resource Center in California) and R version 4.2.2 (University of Auckland, New Zealand) for data management and analysis. A nomogram was developed based on a binary logistic model, and its performance was assessed by discrimination power and calibration. Internal validation was performed using bootstrapping. To evaluate the clinical impact, decision curve analysis was applied.Results: The nomogram included gestational age, hemoglobin, primigravida, unplanned pregnancy, and preeclampsia. The AUROC of the predicted nomogram was 84.3%, and internal validation was 80.1%. The calibration plot indicated that the nomogram was well calibrated. The model was found to have clinical benefit.Conclusion: The nomogram demonstrates strong discrimination performance and can predict low birth weight clinically. As a result, it can be used in clinical practice, which will help clinicians in making quick and personalized predictions simply and rapidly, enabling the early identification and medical intervention. For broader applicability, the nomogram must be externally validated.

Background: When a pregnant woman experiences unusual circumstances during a vaginal delivery, an unplanned cesarean section may be necessary to save her life. It requires knowledge and quick assessment of the risky situation to decide to perform an unplanned cesarean section, which only occurs in specific obstetric situations. This study aimed to develop and validate a risk prediction model for unplanned cesarean sections among laboring women in Ethiopia. Method: A retrospective follow-up study was conducted. The data were extracted using a structured checklist. Analysis was done using STATA version 14 and R version 4.2.2 software. Logistic regression was fitted to determine predictors of unplanned cesarean sections. Significant variables were then used to develop a risk prediction model. Performance was assessed using Area Under the Receiver Operating Curve (AUROC) and calibration plot. Internal validation was performed using the bootstrap technique. The clinical benefit of the model was assessed using decision curve analysis. Result: A total of 1,000 laboring women participated in this study; 28.5% were delivered by unplanned cesarean section. Parity, amniotic fluid status, gestational age, prolonged labor, the onset of labor, amount of amniotic fluid, previous mode of delivery, and abruption remained in the reduced multivariable logistic regression and were used to develop a prediction risk score with a total score of 9. The AUROC was 0.82. The optimal cut-off point for risk categorization as low and high was 6, with a sensitivity (85.2%), specificity (90.1%), and accuracy (73.9%). After internal validation, the optimism coefficient was 0.0089. The model was found to have clinical benefits. Conclusion: To objectively measure the risk of an unplanned Caesarean section, a risk score model based on measurable maternal and fetal attributes has been developed. The score is simple, easy to use, and repeatable in clinical practice.