Perception and Experience of Childhood Malaria Management among Mothers of Under-five Children in Osogbo Osun State, Nigeria

Malaria remains a major health challenge in Nigeria despite efforts at reducing its prevalence. Previous studies on malaria focused mainly on the biomedical aspects with little attention given to the social characteristics influencing malaria management among mothers as primary caregivers of under-five children. This study, therefore, investigated perception and experience of childhood malaria management among mothers of under-five children in Osogbo Metropolis classified in literature as area with high childhood malaria prevalence. The Health Belief Model was adopted as theoretical framework, while the cross-sectional survey research design was employed using both quantitative and qualitative methods. The study was conducted among selected mothers of under-five children using a multi-stage sampling procedure. Cochrane’s formula was used to determine the sample size of 561 respondents used. A structured questionnaire was administered on mothers to elicit information on socio-demographic characteristics, perceptions and experience of childhood malaria. Twelve focus group discussions were conducted with mothers whose under-five children had malaria in the six weeks preceding the study. Quantitative data were analysed using descriptive statistics and Chi-square at 0.05 level of significance. Qualitative data were content analysed. The age the mothers was 41.00 ± 7.2years. About 98.0% of the mothers perceived malaria as treatable, 54.0% of mothers perceived fever as major symptom of malaria, 58.3% said mosquito bite was the cause of malaria, while 65.6% stated that Insecticide Treated Net was the most effective method of malaria prevention. There were significant associations between knowledge of malaria prevention and income (χ2 = 57.00), and between knowledge of consequences of malaria and education (X2 = 50.55). Misconceptions still surround perception of malaria management among mothers of under-five children. More enlightenment efforts are needed to dispel fallacies mitigating against malaria management.

Bayesian network models with decision tree analysis for management of childhood malaria in Malawi

Background 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). Methods We developed two BN models to predict malaria from a dataset of outpatient encounters of children in Malawi. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method. The performance of the BN models was compared to other statistical models on a range of performance metrics at multiple thresholds. We developed a decision tree that integrates predictions with the costs of mRDT and a course of recommended treatment. Results The manually created BN model achieved an area under the ROC curve (AUC) equal to 0.60 which was statistically significantly higher than the other models. At the optimal threshold for classification, the manual BN model had sensitivity and specificity of 0.74 and 0.42 respectively, and the automated BN model had sensitivity and specificity of 0.45 and 0.68 respectively. The balanced accuracy values were similar across all the models. Sensitivity analysis of the decision tree showed that for values of probability of malaria below 0.04 and above 0.40, the preferred decision that minimizes expected costs is not to perform mRDT. Conclusion In resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support clinical decision making.

Bayesian Network Models with Decision Tree Analysis for Management of Childhood Malaria in Malawi

Background: 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).Methods: We developed two BN models to predict malaria from a dataset of outpatient encounters of children in Malawi. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method. The performance of the BN models was compared to other statistical models on a range of performance metrics at multiple thresholds. We developed a decision tree that integrates predictions with the costs of mRDT and a course of recommended treatment. Results: The manually created BN model achieved an area under the ROC curve (AUC) equal to 0.60 that was statistically significantly higher than the other models. The BN models had high sensitivity and specificity values (0.74 and 0.42 respectively for the manual BN model, 0.45 and 0.68 respectively for the automated BN model) at the optimal threshold for classification. The balanced accuracy values were similar across all the models. Sensitivity analysis of the decision tree showed that at low (below 0.04) and high (above 0.40) probabilities of malaria in a child, the preferred decision that minimizes expected costs is not to perform mRDT.Conclusion: In resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support such clinical decision making.