Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Data challenge_Neonatal sepsis at Kawempe Hospital Uganda

Domaine:

healthcare

Type de record:

dataset
Créateur:
KII
Éditeur:
Viv
Hôte:avatar
Neonatal mortality is a significant challenge in sub-Saharan Africa, particularly in Uganda, where infections contribute substantially to high mortality rates. At Kawempe National Referral Hospital in Kampala, the management of neonatal sepsis is complicated by limited resources, resulting in infrequent blood cultures and a lack of local antimicrobial resistance (AMR) data. This project aims to leverage the AMR Vivli data to address these gaps by identifying the most common microorganisms affecting neonates at the hospital and analyzing their resistance and susceptibility patterns. Our research will directly impact patient outcomes by providing clinicians with critical information on the pathogens responsible for neonatal sepsis and their resistance profiles. This data-driven approach will enable more accurate and effective treatment decisions, potentially reducing mortality and morbidity rates among newborns. Additionally, understanding local AMR patterns will help in developing targeted interventions and antibiotic stewardship programs, ensuring that antibiotics are used judiciously and effectively, thus preserving their efficacy. By identifying prevalent pathogens and their resistance trends, our study will also inform public health practices. The insights gained can be used to design and implement better infection control measures and surveillance systems, not only within the hospital but also at a regional and national level. This will enhance the overall capacity to monitor and respond to AMR threats, contributing to stronger health systems. Ultimately, our research aims to bridge the data gap in neonatal AMR surveillance in Uganda, providing a foundation for improved clinical practice, better patient outcomes, and enhanced public health strategies. The findings will be disseminated to key stakeholders, including healthcare providers, policymakers, and public health officials, ensuring that the knowledge gained is translated into actionable strategies to combat AMR and improve neonatal health in Uganda and global heath at large.

Visit

doi.orgsearchamr.vivli.org

Tags

Antimicrobial Resistance

Similaires

The 2024 Pediatric Sepsis Challenge: Predicting In-Hospital Mortality in Children With Suspected Sepsis in UgandaThe role of the season at admission in neonatal sepsis: a retrospective chart review of a 1-year data at University of Gondar comprehensive specialized hospitalRisk Factors Associated with Neonatal Sepsis: A Case Study at a Specialist Hospital in Ghanamjc2244/Uganda-Sepsis-Response-Signature-Analysesmjc2244/Uganda-Proteomic-Sepsis-Signature-Analysesmjc2244/Uganda-Sepsis-Endotype-Derivation-and-Validation

The 2024 Pediatric Sepsis Challenge: Predicting In-Hospital Mortality in Children With Suspected Sepsis in Uganda

The aim of this “Technical Note” is to inform the pediatric critical care data research community ab

The role of the season at admission in neonatal sepsis: a retrospective chart review of a 1-year data at University of Gondar comprehensive specialized hospital

Abstract Objective Neonatal sepsis is a global p

Risk Factors Associated with Neonatal Sepsis: A Case Study at a Specialist Hospital in Ghana

Worldwide, neonatal sepsis accounts for an estimated 26% of under-five deaths, with sub-Saharan Afri

mjc2244/Uganda-Sepsis-Response-Signature-Analyses

R code for Uganda Sepsis Response Signature Analyses

mjc2244/Uganda-Proteomic-Sepsis-Signature-Analyses

R code for derivation, validation, and characterization of proteomic sepsis signatures in Uganda and

mjc2244/Uganda-Sepsis-Endotype-Derivation-and-Validation

R code for derivation, validation, differential expression and enrichment analyses of transcriptomic