Key Determinants of Anemia among Youngsters under Five Years in Senegal, Malawi, and Angola

Abstract

Anemia is characterized as a condition where there is a deficient number of hematocrit, hemoglobin, or red cells in the human body. This condition affects most youngsters under five years old and pregnant women. The fundamental goal of this paper is to investigate anemia, recognize its determinants, and propose critical proposals to achieve 2030 Sustainable Development Goal with a focus on Senegal, Malawi, and Angola. This research utilized 2016 nationally representative information from Senegal, Malawi, and Angola, which involved collecting data on the demographic and health of the populaces. The Demographic and Health Survey information from Senegal, Malawi, and Angola was then merged to create a pooled sample. This statistical technique enables to generalize and compare the results. A generalized linear mixed model was utilized to decide the factors correlated with anemia among youngsters under five years in Senegal, Malawi, and Angola. The analysis was performed in SPSS and SAS software. A generalized linear mixed model results showed that, compared to youngsters aged less than 12 months, youngsters in the age interval 13–23, 24–35, 36–47, and 48–59 months are more likely to be affected by anemia (OR = 1.419, 2.282, 3.174 and 4.874 respectively). In this study, seven factors were included in the final model. However, only five were found to be significant in explaining anemia at the 5% level of significance. The generalized linear mixed model identified youngster’s age, gender, mother’s level of schooling, wealth status, and nutritional status as determinants of anemia among youngsters under five years in Senegal, Malawi, and Angola.

Multinomial logistic regression analysis of the determinants of anaemia severity among children aged 6-59 months in Ghana: New evidence from the 2019 Malaria Indicator Survey

“…Anaemic children from the poorest, poorer and middle household wealth index had a high probability of being moderately anaemic compared to children from the richest households. This nding is supported by the ndings of other studies in sub-Saharan Africa [16,22,25,[34][35][36][49][50][51][52][53][54]. The predominant explanation is that richer households can afford good quality healthcare services, live under proper sanitary conditions, consume more nutritious foods and are food secured.…”

“…Other studies conducted within the African region found similar anaemia levels among children aged 6-59 months using nationally representative data [18, [34][35][36].…”

“…People who migrate to urban areas often settle in densely populated areas, and poor socioeconomic status and sanitation and limited access to quality health care make them vulnerable to many health problems. However, a number of studies have found contrary evidence where the severity of anaemia was found among children living in rural areas compared to urban areas within the Africa, North America and Asia regions [16,18,21,22,25,28,35,48].…”

Association of women's empowerment with anaemia and haemoglobin concentration in children in sub‐Saharan Africa: A multilevel analysis

“…Based on a review of the literature (Khulu & Ramroop, 2020 ; Moschovis et al, 2018 ), information available in the DHS data set and the use of a directed acyclic graph, we considered the following as potential confounders: year of the survey, wealth index quintile, place of residence (urban, rural), living with a partner (yes, no) and woman's age (continuous). Conceptually, potential confounders were associated with both women's empowerment and anaemia but not on the causal pathway between these two variables.…”

Copula Geo-Additive Modeling of Anaemia and Malnutrition among Children under Five Years in Angola, Senegal, and Malawi

“…Anaemia in Malawi, Senegal, and Angola was revealed to be associated with household wealth status, the mother’s level of schooling, and the age of the child, whereas, malnutrition was found to be associated with the type of residence, mother’s level of schooling, household wealth status, and child’s age [ 23 , 24 ].…”