ObjectiveTo develop and validate a risk prediction model for the prediction of preterm birth using maternal characteristics.DesignThis was a retrospective follow-up study. Data were coded and entered into EpiData, V.3.02, and were analysed using R statistical programming language V.4.0.4 for further processing and analysis. Bivariable logistic regression was used to identify the relationship between each predictor and preterm birth. Variables with p≤0.25 from the bivariable analysis were entered into a backward stepwise multivariable logistic regression model, and significant variables (p<0.05) were retained in the multivariable model. Model accuracy and goodness of fit were assessed by computing the area under the receiver operating characteristic curve (discrimination) and calibration plot (calibration), respectively.Setting and participantsThis retrospective study was conducted among 1260 pregnant women who did prenatal care and finally delivered at Felege Hiwot Comprehensive Specialised Hospital, Bahir Dar city, north-west Ethiopia, from 30 January 2019 to 30 January 2021.ResultsResidence, gravidity, haemoglobin <11 mg/dL, early rupture of membranes, antepartum haemorrhage and pregnancy-induced hypertension remained in the final multivariable prediction model. The area under the curve of the model was 0.816 (95% CI 0.779 to 0.856).ConclusionThis study showed the possibility of predicting preterm birth using maternal characteristics during pregnancy. Thus, use of this model could help identify pregnant women at a higher risk of having a preterm birth to be linked to a centre.

Objective: Emergency obstetric and newborn care services treat 70–80% of maternal deaths. This study aimed to assess satisfaction with comprehensive emergency obstetric and newborn care (CEmONC) services and associated factors among clients in the University of Gondar Specialized Hospital. Methods: Institution-based cross-sectional study was conducted on 404 participants using a systematic random sampling method. The study was conducted from March 5 to May 5, 2020, using interviewer-administered structured questionnaires. Binary logistic regression was used to find the association between independent variables and client satisfaction. The level of statistical significance was declared at a p value less than 0.05. Results: The overall clients’ satisfaction with CEmONC services was 65.1% (95% confidence interval (CI): 60.9–69.8). Clients’ satisfaction was affected by women who had antenatal care (ANC) of three visits (adjusted odds ratio (AOR): 6.5; 95%, CI: 2.04–20.8), women waited less than 15 min (AOR: 4.15, 95% CI: 1.9–9.06), mothers stayed ⩽1 day (AOR: 0.28, 95% CI: 0.09–0.9) and 2–3 days (AOR: 0.98, 95% CI: 0.1–0.69), obtaining a welcoming environment (AOR: 4.6, 95% CI: 2.15–9.88), and getting providers explanation of examinations (AOR: 3.3, 95% CI: 1.97–5.52). Conclusion: The observed clients’ satisfaction with CEmONC services was suboptimal. Having ANC of three visits, waiting less than 15 min, duration of stay, obtaining a welcoming environment, and an explanation of providers’ examination were the identified factors of client’s satisfaction. Therefore, hospital managers and health professionals should work on the identified factors to increase the client’s satisfaction with these services.

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Background. The average duration of recovery from COVID-19 and influencing factors, which would help inform optimal control strategies, remain unclear. Moreover, studies regarding this issue are limited in Ethiopia, and no region-wise studies were conducted. Hence, this study aimed to investigate the median recovery time from COVID-19, and its predictors among patients admitted to Amhara regional state COVID-19 treatment centers, Ethiopia. Methods. A facility-based retrospective follow-up study was conducted at Amhara regional state COVID-19 treatment centers from 13 March 2020 through 30 March 2021. Data were entered using EpiData version 3.1, and STATA version 14 was used for analysis. A Kaplan–Meier curve was used to estimate survival time, and the Cox regression model was fitted to identify independent predictors. P value with 95% CI for the hazard ratio was used for testing the significance at alpha 0.05. Results. Six hundred twenty-two cases followed, and 540 observations developed an event at the end of the follow-up. The median time to recovery was 11 days with an interquartile range of 9–14 days. Most of the patients were recovered from COVID-19 between days seven and fourteen. In the first six days of admission, only 4.2% of cases had recovered, but by day 14, 73.8% had recovered. Patients without comorbid illness/s were faster to recover than their counterparts (AHR = 1.44 : 95% CI: 1.10, 1.91) and those who have signs and symptoms on admission (AHR = 0.42 : 95% CI: 0.30, 0.60) and old-aged (AHR = 0.988; 95% CI: 0.982, 0.994) took longer to recover. Conclusion. In conclusion, a relatively short median recovery time was found in this study. Significant predictors for delayed recovery from COVID-19 were older age, presence of symptoms at admission, and having at least one comorbid condition. These factors should be placed under consideration while developing a strategy for quarantining and treating COVID-19 patients.

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