Individual and Contextual Factors Associated with Malaria among Children 6–59 Months in Nigeria: A Multilevel Mixed Effect Logistic Model Approach

Abstract

Background/Purpose: Over the last two decades, malaria has remained a major public health concern worldwide, especially in developing countries leading to high morbidity and mortality among children. Nigeria is the world most burdened malaria endemic nation, contributing more than a quarter of global malaria cases. This study determined the prevalence of malaria among children at 6–59 months in Nigeria, and the effects of individual and contextual factors. Methods: This study utilized data from 2018 Nigeria Demographic and Health Survey (NDHS) involving a weighted sample size of 10,185 children who were tested for malaria using rapid diagnostic test (RDT). Given the hierarchical structure of the data set, such that children at Level-1 were nested in communities at Level-2, and nested in states and Federal Capital Territory (FCT) at Level-3, multilevel mixed effect logistic regression models were used for the analysis. Results: The proportion of children 6–59 months of age in Nigeria that had malaria fever positive as assessed by RDTs was 35.5% (3418/10,185), (CI: 33.9–37.1). Kebbi State had 77.7%, (CI: 70.2–83.5), which was the highest proportion of 6–59 months who were malaria positive, next in line was Katsina State with 55.5%, (CI: 47.7–63.1). The Federal Capital Territory (FCT), Abuja had the proportion of 29.6%, (CI: 21.6–39.0), malaria positive children of 6–59 months of age. Children between the age of 48 and 59 months were 2.68 times more likely to have malaria fever than children of ages 6–11 months (AOR = 2.68, 95% CI: 2.03–3.54). In addition, children from the rural area (AOR = 2.12, 95% CI: 1.75–2.57), were more likely to suffer from malaria infection compared to children from urban area. Conclusion: The study identified some individual and contextual predictors of malaria among children in Nigeria. These factors identified in this study are potential areas that need to be considered for policy designs and implementations toward control and total elimination of malaria-related morbidity and mortality among children in Nigeria.

Malaria profile and socioeconomic predictors among under-five children: an analysis of 11 sub-Saharan African countries

“…For instance, a negative association was found between malaria infection and mothers’ education as malaria prevalence was higher among children whose mothers did not attend school (61.5%) compared to those whose mothers had secondary or higher education (11.3%). This finding agrees with previous studies that reported a similar association between maternal education and malaria prevalence among children [ 16 , 30 , 31 ]. Low educational status among mothers may facilitate poor knowledge of malaria prevention, risk factors, and symptoms leading to higher malaria prevalence among their children [ 9 , 16 ].…”

“…The logistic regression model in this study revealed that six out of the nine included independent variables (under-five child’s age, maternal education, female of household head, household wealth index, place of residence, and African region where mother–child pair lives) had statistically significant associations with malaria prevalence among under-five children assessed. This finding is similar to those documented by previous studies conducted in the SSA region [ 1 , 11 , 16 , 30 ].…”

“…Low educational status among mothers may facilitate poor knowledge of malaria prevention, risk factors, and symptoms leading to higher malaria prevalence among their children [ 9 , 16 ]. Additionally, such poor knowledge may translate to delay in seeking healthcare and facilitate late detection of malaria infection among under-five children and increased risk of complications [ 1 , 30 ]. In contrast, mothers with higher levels of education are likely to have better agency in preventing malaria among their children and seeking healthcare services early [ 31 ].…”

Multilevel analysis of individual- and community-level determinants of birth certification of children under-5 years in Nigeria: evidence from a household survey

“…ICC com is used to evaluate whether the community factors are more relevant in explaining the variation in the outcome variable and establish the need for the multilevel analysis [ 38 , 40 ]. The higher the ICC, the larger the contribution of the community factors in explaining the variation, and an ICC of less than 5% at the null model suggests that hierarchical modelling may not be necessary [ 42 ]. The PCV, which indicates the additional effects of the included variables (individual- and community-level factors), is calculated as [PCV = ], where V e is the variance in the null model and V m is the variance in the successive models.…”

The Effect of Psychological Burden on Dyslipidemia Moderated by Greenness: A Nationwide Study from China

“…A likelihood ratio test was performed after each multilevel logistic regression to determine whether two-level model was more appropriate than individual-level model [ 57 , 58 ]. In addition, Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) were utilized to check fitness of different models [ 59 ].…”