Profiling malaria infection among under-five children in the Democratic Republic of Congo
Open Access
- 6 May 2021
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 16 (5), e0250550
- https://doi.org/10.1371/journal.pone.0250550
Abstract
In 2018, Malaria accounted for 38% of the overall morbidity and 36% of the overall mortality in the Democratic Republic of Congo (DRC). This study aimed to identify malaria socioeconomic predictors among children aged 6–59 months in DRC and to describe a socioeconomic profile of the most-at-risk children aged 6–59 months for malaria infection. This study used data from the 2013 DRC Demographic and Health Survey. The sample included 8,547 children aged 6–59 months who were tested for malaria by microscopy. Malaria infection status, the dependent variable, is a dummy variable characterized as a positive or negative test. The independent variables were child’s sex, age, and living arrangement; mother’s education; household’s socioeconomic variables; province of residence; and type of place of residence. Statistical analyses used the chi-square automatic interaction detector (CHAID) model and logistic regression. Of the 8,547 children included in the sample, 25% had malaria infection. Four variables—child’s age, mother’s education, province, and wealth index—were statistically associated with the prevalence of malaria infection in bivariate analysis and multivariate analysis (CHAID and logistic regression). The prevalence of malaria infection increases with child’s age and decreases significantly with mother’s education and the household wealth index. These findings suggest that the prevalence of malaria infection is driven by interactions among environmental factors, socioeconomic characteristics, and probably differences in the implementation of malaria programs across the country. The effect of mother’s education on malaria infection was only significant among under-five children living in Ituri, Kasaï-Central, Haut-Uele, Lomami, Nord-Ubangi, and Maniema provinces, and the effect of wealth index was significant in Mai-Ndombe, Tshopo, and Haut-Katanga provinces. Findings from this study could be used for targeting malaria interventions in DRC. Although malaria infection is common across the country, the prevalence of children at high risk for malaria infection varies by province and other background characteristics, including age, mother’s education, wealth index, and place of residence. In light of these findings, designing provincial and multisectoral interventions could be an effective strategy to achieve zero malaria infection in DRC.This publication has 29 references indexed in Scilit:
- Prompt access to effective malaria treatment among children under five in sub-Saharan Africa: a multi-country analysis of national household survey dataMalaria Journal, 2015
- Malaria eradication and elimination: views on how to translate a vision into realityBMC Medicine, 2015
- Assessment of Malawian Mothers’ Malaria Knowledge, Healthcare Preferences and Timeliness of Seeking Fever Treatments for Children Under FiveInternational Journal of Environmental Research and Public Health, 2015
- Identifying HIV most-at-risk groups in Malawi for targeted interventions. A classification tree modelBMJ Open, 2013
- Factors associated with malaria parasitaemia, malnutrition, and anaemia among HIV-exposed and unexposed Ugandan infants: a cross-sectional surveyMalaria Journal, 2012
- Applying CHAID for logistic regression diagnostics and classification accuracy improvementJournal of Targeting, Measurement and Analysis for Marketing, 2010
- Major reduction of malaria morbidity with combined vitamin A and zinc supplementation in young children in Burkina Faso: a randomized double blind trialNutrition Journal, 2008
- Using classification trees to assess low birth weight outcomesArtificial Intelligence in Medicine, 2006
- The burden of malaria mortality among African children in the year 2000International Journal of Epidemiology, 2006
- An Exploratory Technique for Investigating Large Quantities of Categorical DataJournal of the Royal Statistical Society Series C: Applied Statistics, 1980