Background The highest neonatal mortality is in Sub-Saharan Africa, where neonatal sepsis accounts for approximately 50%. At Pemba Island, Tanzania, we examined the use of prophylactic antibiotics in neonates and related it to WHO guidelines and compared clinical signs of infection with the use of antibiotic treatment; furthermore, we aimed to investigate all use of antibiotic treatment in the neonatal period. Method This prospective observational cohort study was performed from 1 January 2022 to 15 April 2022 at a district hospital on Pemba Island, Tanzania. Women admitted in early established or active labour, and their neonates, were eligible for inclusion. We used questionnaires for mother and health worker and examined the neonates 2 h after birth. Follow-up was made at discharge or at 18 h of life, and days 7 and 28. Results We included 209 women and their 214 neonates. The neonatal mortality was 5 of 214 (23 per 1000 live births). According to WHO guidelines 29 (13.6%) had ≥ 1 risk factor for infection. Of these, three (10.3%) received prophylactic antibiotic treatment; only one (3.4%) received the correct antibiotic drug recommended in guidelines. Thirty-nine (18.2%) neonates had ≥ 1 clinical indicator of infection and 19 (48.7%) of these received antibiotic treatment. A total of 30 (14.0%) neonates received antibiotics during the study period. Twenty-three (76.7%) were treated with peroral antibiotics. Conclusion Adherence to WHO guidelines for prophylactic antibiotic treatment to prevent neonatal infection was low. Further, only half of neonates with clinical signs of infection received antibiotics.
Background More than 2 million third-trimester stillbirths occur yearly, most of them in low- and middle-income countries. Data on stillbirths in these countries are rarely collected systematically. This study investigated the stillbirth rate and risk factors associated with stillbirth in four district hospitals in Pemba Island, Tanzania. Methods A prospective cohort study was completed between the 13th of September and the 29th of November 2019. All singleton births were eligible for inclusion. Events and history during pregnancy and indicators for adherence to guidelines were analysed in a logistic regression model that identified odds ratios [OR] with a 95% confidence interval [95% CI]. Results A stillbirth rate of 22 per 1000 total births in the cohort was identified; 35.5% were intrapartum stillbirths (total number of stillbirths in the cohort, n = 31). Risk factors for stillbirth were breech or cephalic malpresentation (OR 17.67, CI 7.5-41.64), decreased or no foetal movements (OR 2.6, CI 1.13–5.98), caesarean section [CS] (OR 5.19, CI 2.32–11.62), previous CS (OR 2.63, CI 1.05–6.59), preeclampsia (OR 21.54, CI 5.28–87.8), premature rupture of membranes or rupture of membranes 18 h before birth (OR 2.5, CI 1.06–5.94) and meconium stained amniotic fluid (OR 12.03, CI 5.23–27.67). Blood pressure was not routinely measured, and 25% of women with stillbirths with no registered foetal heart rate [FHR] at admission underwent CS. Conclusions The stillbirth rate in this cohort was 22 per 1000 total births and did not fulfil the Every Newborn Action Plan’s goal of 12 stillbirths per 1000 total births in 2030. Awareness of risk factors associated with stillbirth, preventive interventions and improved adherence to clinical guidelines during labour, and hence improved quality of care, are needed to decrease the stillbirth rate in resource-limited settings.
Background: Babies born early and/or small for gestational age in Low- and Middle-income countries (LMIC) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound is not available in these settings, gestational age (GA) is estimated using newborn assessment, LMP recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasound-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMIC settings.Methods: This study uses data from AMANHI-ACT prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasound estimated GA and birth weight are available and metabolite screening data in a subset of 1318 newborn are available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RSME) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed.Results: Overall model estimated GA, had MAE of 5.8 days (95%CI 5.6-6.3), which was similar to performance in SGA, MAE 6.3 days (95%CI 5.6-7.0). GA was correctly estimated to within 1 week for 70.9% (95%CI 67.9-73.7). For preterm birth classification, AUC in ROC analysis was 92.6% (95%CI 87.5-96.1; p<0.001). This model performed better than Iowa regression, AUC Difference 2.8% (95%CI 0.9-11.8%; p=0.021).Conclusions: Machine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMIC settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation.
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