Multilevel modelling of the risk of malaria among children aged under five years in Nigeria

Abstract

Background 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. Methods Data from the Nigerian Malaria Health Indicator Survey 2015 were pooled for analyses. This comprised a national survey of 329 clusters. Children aged 6–59 mo who were tested for malaria using microscopy were retained. Multilevel logit model accounting for sampling design was used to assess individual, household and community factors associated with malaria parasitaemia. Results A total of 5742 children were assessed for malaria parasitaemia with an overall prevalence of 27% (95% CI 26 to 28%). Plasmodium falciparum constituted 98% of the Plasmodium species. There was no significant difference in parasitaemia between older children and those aged ≤12 mo. In adjusted analyses, rural living, northwest region, a household size of >7, dependence on river and rainwater as primary water source were associated with higher odds of parasitaemia, while higher wealth index, all U5s who slept under a bed net and dependence on packaged water were associated with lower odds of parasitaemia. Conclusion Despite sustained investment in malaria control and prevention, a quarter of the overall study population of U5s have malaria. Across the six geopolitical zones, the highest burden was in children living in the poorest rural households.

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

“…Effective control of malaria in Nigeria will require strategies that identify areas and characteristics of people that are highly vulnerable to malaria infections leading to the development of plans and the implementation of policies to reach them. This study complements the findings in previous studies to show potential effects of contextual variables at both cluster and state levels [ 2 , 11 , 18 ]. Therefore, this study is aimed at establishing the prevalence of malaria across the states and federal capital territory, and to examine the individual- and contextual-level predictors of malaria fever among children 6–59 months of age in Nigeria.…”

“…In this study, three categories were derived, such that poor and poorest groups were collapsed into poor, while rich and richest were collapsed into rich categories [ 41 , 45 ]; household had mosquito bed net; household number size measures the number of people that stay in a household, and was originally a scale variable, but for the purpose of this analysis, the variable was classified into four categories in line with average typical family sizes of four to five members in Nigeria [ 11 ]; number of bedrooms in household, though in the survey the number of rooms available for sleeping in a household were given in scale values with less than 10% indicated having five or more rooms for sleep. A typical building in Nigeria has either one-, two-, three-, or four-bedrooms, hence in this analysis, we categorized the variables into five groups; number of children under-5 year in household were also treated as categories as in previous studies [ 2 , 45 ]; improved source of drinking water, toilet facilities, floor materials, roofing materials, and wall materials. These materials are either natural, rudimentary, or finished.…”

“…The past twenty years and up till now, malaria has persisted as a major global public health concern [ 1 , 2 ], with over 300 million cases reported in 2018 [ 3 ] and has remained a leading cause of morbidity and mortality. Low and medium-income countries (LMIC), especially in Sub-Sharan Africa (SSA), contribute to more than 80% of the global malaria burden [ 4 , 5 ].…”

Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

“…In the Philippines, ARIMA (2,1,0) was found to be the appropriate model to predict malaria incidence using weekly data [ 43 ]. Similar studies were carried out in Ghana, Afghanistan, Nigeria, Zambia, and India using monthly data with comparable results [ 7 , 43 , 44 , 45 , 46 ].…”

“…This study result may be over- or underestimated since there are generally underreported cases, especially from places lacking health centers, while some cases patients may be diagnosed more than once a year, be self-medicated, or use traditional healers [ 23 ]. The weekly malaria missing data was 13%, and missing data proportion is directly related to the quality of statistical inference, although there is no established cutoff regarding an acceptable percentage of missing data for valid statistical inferences [ 44 ]. One advantage of using the ARIMA approach is the relative simplicity and stability of the model in predicting malaria cases in a context where political unrest and poor resources lead to a lack of detailed data [ 7 ].…”