A Global Health Research Checklist for clinicians.

Global health research has become a priority in most international medical projects. However, it is a difficult endeavor, especially for a busy clinician. Navigating the ethics, methods, and local partnerships is essential yet daunting.To date, there are no guidelines published to help clinicians initiate and complete successful global health research projects. This Global Health Research Checklist was developed to be used by clinicians or other health professionals for developing, implementing, and completing a successful research project in an international and often low-resource setting. It consists of five sections: Objective, Methodology, Institutional Review Board and Ethics, Culture and partnerships, and Logistics. We used individual experiences and published literature to develop and emphasize the key concepts. The checklist was trialed in two workshops and adjusted based on participants\u27 feedback

A Global Health Research Checklist for clinicians

Abstract Global health research has become a priority in most international medical projects. However, it is a difficult endeavor, especially for a busy clinician. Navigating the ethics, methods, and local partnerships is essential yet daunting. To date, there are no guidelines published to help clinicians initiate and complete successful global health research projects. This Global Health Research Checklist was developed to be used by clinicians or other health professionals for developing, implementing, and completing a successful research project in an international and often low-resource setting. It consists of five sections: Objective, Methodology, Institutional Review Board and Ethics, Culture and partnerships, and Logistics. We used individual experiences and published literature to develop and emphasize the key concepts. The checklist was trialed in two workshops and adjusted based on participants’ feedback

Predicting severe pneumonia in the emergency department: a global study of the Pediatric Emergency Research Networks (PERN)—study protocol

Introduction Pneumonia is a frequent and costly cause of emergency department (ED) visits and hospitalisations in children. There are no evidence-based, validated tools to assist physicians in management and disposition decisions for children presenting to the ED with community-acquired pneumonia (CAP). The objective of this study is to develop a clinical prediction model to accurately stratify children with CAP who are at risk for low, moderate and severe disease across a global network of EDs.Methods and analysis This study is a prospective cohort study enrolling up to 4700 children with CAP at EDs at ~80 member sites of the Pediatric Emergency Research Networks (PERN; https://pern-global.com/). We will include children aged 3 months to <14 years with a clinical diagnosis of CAP. We will exclude children with hospital admissions within 7 days prior to the study visit, hospital-acquired pneumonias or chronic complex conditions. Clinical, laboratory and imaging data from the ED visit and hospitalisations within 7 days will be collected. A follow-up telephone or text survey will be completed 7–14 days after the visit. The primary outcome is a three-tier composite of disease severity. Ordinal logistic regression, assuming a partial proportional odds specification, and recursive partitioning will be used to develop the risk stratification models.Ethics and dissemination This study will result in a clinical prediction model to accurately identify risk of severe disease on presentation to the ED. Ethics approval was obtained for all sites included in the study. Cincinnati Children’s Hospital Institutional Review Board (IRB) serves as the central IRB for most US sites. Informed consent will be obtained from all participants. Results will be disseminated through international conferences and peer-reviewed publications. This study overcomes limitations of prior pneumonia severity scores by allowing for broad generalisability of findings, which can be actively implemented after model development and validation