Ebbs, Daniel;Denish, Olanya;Bongomin, Felix ;Chandna, Arjun;Haseefa, Fathima;Canarie, Michael;Cappello, Michael2025-08-272025-08-272025-06-26Ebbs, D., Denish, O., Bongomin, F., Chandna, A., Haseefa, F., Canarie, M., & Cappello, M. (2025). Community health workers identify children requiring health center admission in Northern Uganda: prehospital risk prediction using vital signs and advanced point-of-care tests. Global Health Action, 18(1). https://doi.org/10.1080/16549716.2025.2519704ISSN 1654-9716, 1654-9880EISSN 1654-9880https://nru.uncst.go.ug/handle/123456789/12029Over five million children die annually from preventable and treatable illnesses. Most of these deaths occur in sub-Saharan Africa, predominantly in socioeconomically deprived regions. With nearly half of pediatric mortality occurring at the community level, serious illnesses must be detected early in the prehospital setting. The purpose of this 18-month, prospective, observational pilot study was to introduce the first use of the proinflammatory biomarker, CRP, in the prehospital setting to community health workers and to develop a prehospital predictive model to identify sick children requiring health center admission.BACKGROUNDOver five million children die annually from preventable and treatable illnesses. Most of these deaths occur in sub-Saharan Africa, predominantly in socioeconomically deprived regions. With nearly half of pediatric mortality occurring at the community level, serious illnesses must be detected early in the prehospital setting. The purpose of this 18-month, prospective, observational pilot study was to introduce the first use of the proinflammatory biomarker, CRP, in the prehospital setting to community health workers and to develop a prehospital predictive model to identify sick children requiring health center admission.We recruited 636 children presenting to one of four community health worker teams who completed a prehospital evaluation and referred each child to the closest health center. The primary outcome for this study was admission at the health center for more than 24 h. We used logistic regression to quantify the area under the receiver operating characteristic curve (AUC).METHODSWe recruited 636 children presenting to one of four community health worker teams who completed a prehospital evaluation and referred each child to the closest health center. The primary outcome for this study was admission at the health center for more than 24 h. We used logistic regression to quantify the area under the receiver operating characteristic curve (AUC).We found poor discrimination of danger signs and CRP, with AUCs of 0.55 (95% CI 0.52-0.57) and 0.52 (95% CI 0.47-0.57), respectively. A model comprising vital signs demonstrated superior discrimination, with an AUC of 0.66 (95% CI 0.62-0.71), which improved with the addition of danger signs (AUC 0.69; 95% CI 0.64-0.73), and when restricted to children who tested negative for malaria (n = 327; AUC 0.71; 95% CI 0.65-0.77).RESULTSWe found poor discrimination of danger signs and CRP, with AUCs of 0.55 (95% CI 0.52-0.57) and 0.52 (95% CI 0.47-0.57), respectively. A model comprising vital signs demonstrated superior discrimination, with an AUC of 0.66 (95% CI 0.62-0.71), which improved with the addition of danger signs (AUC 0.69; 95% CI 0.64-0.73), and when restricted to children who tested negative for malaria (n = 327; AUC 0.71; 95% CI 0.65-0.77).We demonstrate that performing advanced point-of-care tests is feasible in resource-limited community settings and present one of the first prehospital prediction models developed with community health workers.CONCLUSIONSWe demonstrate that performing advanced point-of-care tests is feasible in resource-limited community settings and present one of the first prehospital prediction models developed with community health workers. MEDLINE - AcademicenArticle