Malaria is a major cause of death in children under five years old in low- and middle-income countries such as Malawi. Accurate diagnosis and management of malaria can help reduce the global burden of childhood morbidity and mortality. Trained healthcare workers in rural health centers manage malaria with limited supplies of malarial diagnostic tests and drugs for treatment. A clinical decision support system that integrates predictive models to provide an accurate prediction of malaria based on clinical features could aid healthcare workers in the judicious use of testing and treatment. We developed Bayesian network (BN) models to predict the probability of malaria from clinical features and an illustrative decision tree to model the decision to use or not use a malaria rapid diagnostic test (mRDT).
children under five years
Malaria remains a major public health problem in Indonesian Papua, with children under five years of age being the most affected group. Haematological changes, such as cytopenia that occur during malaria infection have been suggested as potential predictors and can aid in the diagnosis of malaria. This study aimed to assess the haematological alterations associated with malaria infection in children presenting with signs and symptoms of malaria.
Malaria remains a major cause of morbidity and mortality in Africa, particularly in children under five years of age. Availability of effective anti-malarial drug treatment is a cornerstone for malaria control and eventual malaria elimination. Artemisinin-based combination therapy (ACT) is worldwide the first-line treatment for uncomplicated falciparum malaria, but the ACT drugs are starting to fail in Southeast Asia because of drug resistance.
Malaria is still a major cause of morbidity and mortality among children aged <5 y (U5s). This study assessed individual, household and community risk factors for malaria in Nigerian U5s.