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A cross-sectional study for algorithm in diagnosing simple uncomplicated malaria in children in health facilities
1Department of Community Medicine, University of Abuja Teaching Hospital, Gwagwalada, Abuja, Nigeria.
Insights
A new malaria diagnosis algorithm using fever and vomiting in children (0-13 years) shows promise for areas lacking lab support. This simple diagnostic tool aims to improve malaria case management and reduce the disease burden.
Area of Science:
- Pediatrics
- Infectious Diseases
- Public Health
Background:
- Accurate malaria diagnosis in children is crucial for effective treatment, especially in resource-limited settings.
- Clinical signs and symptoms are often used for presumptive diagnosis, but their accuracy varies.
- Developing a reliable diagnostic algorithm based on readily available clinical information is essential.
Purpose of the Study:
- To determine an effective algorithm for diagnosing uncomplicated malaria in children aged 0-13 years.
- To identify a combination of presenting signs and symptoms that accurately predicts parasitaemia.
- To develop a practical diagnostic tool for use in peripheral health facilities.
Main Methods:
- A study involving 800 children (400 with presumptive malaria, 400 controls) in Abuja, Nigeria.
- Data collection using a validated questionnaire on presenting signs and symptoms.
- Giemsa-stained thick blood films for parasitaemia confirmation and logistic regression analysis for algorithm development.
Main Results:
- Fever, rigor, vomiting, and pallor were statistically associated with malaria parasitaemia.
- Individually, these symptoms had low sensitivity and/or specificity.
- An algorithm combining fever and vomiting demonstrated the highest sensitivity (56.2%), specificity (76.4%), and positive predictive value (60.0%).
Conclusions:
- Fever and vomiting in children, in the absence of other causes, can guide antimalarial treatment in resource-limited areas.
- The developed algorithm (fever + vomiting) is a promising tool for malaria diagnosis in peripheral health facilities.
- Further field-testing is recommended to validate and potentially adopt this algorithm for widespread use.
Aims And Objectives:
The objective of this study was to determine an algorithm for malaria diagnosis using presenting signs and symptoms of children (aged 0-13 years) with uncomplicated malaria in Gwagwalada Area Council of Abuja, Nigeria.
Materials And Methods:
A validated questionnaire was used to obtain relevant data from 400 children diagnosed presumptively of simple malaria by clinicians and 400 other children of similar sex and age considered as not having malaria. Giemsa-stained thick blood films were used to determine parasitaemia. Data obtained was analysed using Epi-Info version 3.3.2.
Results:
Thirty-eight per cent of children with presumptive diagnosis of malaria had parasitaemia. Fever, rigor, vomiting, jaundice, pallor and spleen enlargement had significant statistical relationship with parasitaemia on bivariate analysis, but only fever (p=0.00), rigor (p=0.00), vomiting (p=0.00), and pallor (p=0.00) maintained the relationship when subjected to logistic regression analysis. But these symptoms individually had low sensitivity and/or specificity. Candidate algorithms (combinations of symptoms) were then successively subjected to bivariate, logistic and validity analyses. Fever with vomiting gave the highest sensitivity (56.2%), specificity (76.4%) and PPV (60.0%) and were therefore adopted as the algorithm of choice.
Conclusion And Recommendations:
Children presenting with fever and vomiting without any other obvious cause in health facilities without laboratory support in the research area should receive antimalarial treatment, to help reduce the malaria scourge. This algorithm should be field-tested and if found reliable should be adopted to ease the problem of malaria diagnosis in peripheral health facilities.
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