The analysis of risk factors associated with multimorbidity of anaemia, malaria, and malnutrition among children aged 6- 59 months in Nigeria

Abstract

In the last ten years multimorbidity in children under the age of five years has becoming an emerging health issue in developing countries. The absence of a proper understanding of the causes, risk factors, and prevention of these new health disorders (multimorbidity) in children is a significant cause for concern, if the sustainable development goal 3 of ensuring healthy lives and the promotion of well-being for all especially in the associated aim of ending preventable deaths of new-borns and children must be achieved by 2030. In the past, most studies conducted in Nigeria and some other least developed nations of the world focused on these multiple diseases by employing conventional analytical techniques to examine them separately as distinct disease entities. But the study of multimorbidity of anaemia, malaria, and malnutrition has not been done, especially in children. This study aims to investigate the multiple overlaps in the impact of individual and contextual variables on the prevalence of multimorbidity among children aged 6 to 59 months in Nigeria. The study used two nationally representative cross-sectional surveys, the 2018 Nigeria Demographic and Health Survey and the 2018 National Human Development Report. A series of multilevel mixed effect ordered logistic regression models were used to investigate the associations between child/parent/household variables (at level 1), community-related variables (at level 2) and area-related variables (at level 3), and the multimorbidity outcome (no disease, one disease only, two or more diseases). The interaction effects between child's sex, age, and household wealth quintiles and the outcome while accounting for some covariates in the model were also investigated. The result shows that 48.3% (4,917/10,184) of the sample of children aged 6-59 months cohabit with two or more of the disease outcomes. Child's sex, age, maternal education, mother’s anaemia status. household wealth quintiles, the proportion of community wealth status, states with high human development index, region, and place of residence, were among the significant predictors of MAMM (p<0.05). There was a significant interaction effect between a child's age and wealth status when some other covariates were accounted for. The prevalence of MAMM observed in the sample is large, with almost half of the children living with two or more of the diseases at the time of the survey and several potentially modifiable risk factors have been identified. If suitable actions are not urgently taken, Nigeria’s ability to actualise the SDG 3 will be in grave danger. Therefore, possible actions to ameliorate this problem include developing and implementing a suitable policy that will pave the way for integrated care models to be developed.

The Intersection of a Child’s Demographics and Household Socioeconomic Status in the Multimorbidity of Malaria, Anaemia, and Malnutrition among Children Aged 6–59 Months in Nigeria

“…Multimorbidity of anemia, malaria, and malnutrition (MAMM) is a condition in which an individual has two or more of these health outcomes, and is becoming an emergent public health concern in sub-Saharan African countries. A recent study conducted in Nigeria found that one in every two children aged 6-59 months has two or more MAMMs [1]. Similarly, other findings showed that 68.1%, 35.5%, and 43.6% of children aged 6-59 months in Nigeria are anemic [2], malaria positive [3], and malnourished [4], respectively.…”

“…Meanwhile, in Nigeria, the independent associations of a child's sex, age, and household socioeconomic disparities with a child's independent health and multiple health outcomes have been established in the literature [1,28]. However, the effects of the intersection of these factors have not been reported in the literature.…”

“…This study involved fitting three-level mixed-effects ordinal logistic regression models, and testing the violation of the proportional assumption is difficult and not straightforward, especially when employing brant and omodel tests in Stata, following a mixed-effect ordered logistic regression. However, a 'naïve' approach (explained elsewhere [1]) was applied and it was found that there was no violation in the test.…”