Does neonatal resuscitation associated with neonatal death in low-resource settings? A follow-up study.

BACKGROUND: The newborn period is the most vulnerable phase for a child's survival, with around half of all under-five deaths worldwide occurring during this time. Despite existing policies and measures, Ethiopia ranks among the top 10 African countries in terms of newborn mortality. In spite of many studies being carried out in the country, the incidence and predictors of neonatal mortality in the Pastoralist and agro-pastoralist parts of the country's southern still remain unidentified. Therefore, this study aimed to identify the predictors of neonatal mortality in selected public Hospitals in southern Ethiopia. MATERIALS AND METHODS: An institution-based retrospective cohort study was conducted among 568 neonates admitted to the neonatal intensive care unit at Bule Hora University teaching Hospital and Yabelo General Hospital, Southern Ethiopia from 1 January 2020-31 December 2021. A simple random sampling technique was used to select records of neonates. Data entry was performed using Epidata version 3.1 and the analysis was performed using STATA version 14.1 Kaplan Meir curve and Log-rank test were used to estimate the survival time and compare survival curves between variables. Hazard Ratios with 95% CI were computed and all the predictors associated with the outcome variable at p-value 0.05 in the multivariable cox proportional hazards analysis were declared as a significant predictor of neonatal death. RESULTS: Out of 565 neonates enrolled, 54(9.56%) neonates died at the end of the follow-up period. The overall incidence rate of death was 17.29 (95% CI: 13.24, 22.57) per 1000 neonatal days with a restricted mean follow-up period of 20 days. Of all deaths, 64.15% of neonates died within the first week of life. In the multivariable cox-proportional hazard model, neonatal age < 7 days (AHR: 9.17, 95% CI: (4.17, 20.13), place of delivery (AHR: 2.48, 95% CI: (1.38, 4.47), Initiation of breastfeeding after 1 h of birth (AHR: 6.46, 95% CI: (2.24, 18.59), neonates' body temperature <36.5 °C (AHR: 2.14, 95% CI: (1.19, 3.83), and resuscitated neonates (AHR: 2.15, 95% CI: (1.20, 3.82) were independent predictors of neonatal death. CONCLUSION: In the research setting, the incidence of neonatal death was high, especially during the first week of life. The study found that neonatal age < 7 days, place of delivery, Initiation of breastfeeding after 1 h of birth, neonates' body temperature <36.5 °C, and resuscitated neonates were predictors of neonatal death. To improve newborn survival, significant neonatal problems, improved resuscitation, and other relevant factors should be addressed.

Shotgun Metagenomics-Guided Prediction Reveals the Metal Tolerance and Antibiotic Resistance of Microbes in Poly-Extreme Environments in the Danakil Depression, Afar Region.

The occurrence and spread of antibiotic resistance genes (ARGs) in environmental microorganisms, particularly in poly-extremophilic bacteria, remain underexplored and have received limited attention. This study aims to investigate the prevalence of ARGs and metal resistance genes (MRGs) in shotgun metagenome sequences obtained from water and salt crust samples collected from Lake Afdera and the Assale salt plain in the Danakil Depression, northern Ethiopia. Potential ARGs were characterized by the comprehensive antibiotic research database (CARD), while MRGs were identified by using BacMetScan V.1.0. A total of 81 ARGs and 39 MRGs were identified at the sampling sites. We found a copA resistance gene for copper and the ß-lactam encoding resistance genes were the most abundant the MRG and ARG in the study area. The abundance of MRGs is positively correlated with mercury (Hg) concentration, highlighting the importance of Hg in the selection of MRGs. Significant correlations also exist between heavy metals, Zn and Cd, and ARGs, which suggests that MRGs and ARGs can be co-selected in the environment contaminated by heavy metals. A network analysis revealed that MRGs formed a complex network with ARGs, primarily associated with ß-lactams, aminoglycosides, and tetracyclines. This suggests potential co-selection mechanisms, posing concerns for both public health and ecological balance.