Determinants of malaria risk among Under-five children in Nigeria: Results from 2021 Nigeria malaria indicator cross-sectional survey

Abstract

Background Malaria is one of the global disease burdens especially in Africa with Nigeria having the highest prevalence of the disease. Children under five are vulnerable to the risk of malaria spread. This research aimed at identifying the determinants of malaria spread among the under-5 children in Nigeria. Methods This study used 2021NMIS data. The NMIS was implemented by the National Malaria Elimination Programme (NMEP) and data was collected between 12 October to 4 December 2021. This study included 3678 children and data cleaning and analysis were done using STATA version 17 software. Results Child’s age in months of 13–23 (AOR = 2.97; 95% CI = 1.62–5.45, p-value: 0.00), 24–35 months (AOR = 2.64; 95% CI = 1.43–4.88, p-value: 0.002),36–47 months (AOR = 2.18; 95% CI = 1.17–4.08, p-value: 0.015) and months of 48–59(AOR = 2.82; 95% CI = 1.53–5.23, p-value: 0.001), households headed by females (AOR = 0.71; 95% CI = 0.54–0.95, p-value: 0.019),households with all children slept in mosquito nets last night (AOR = 2.43; 95% CI = 1.39–4.21, p-value: 0.002), some children slept in the mosquito bed net (AOR = 2.83; 95% CI = 1.50–5.35, p-value: 0.001) and households with no mosquito bed nets (AOR = 2.18; 95% CI = 1.22–3.88, p-value: 0.008),mothers who agreed to have heard or seen malaria messages in the last 6 months (AOR = 1.32; 95% CI = 1.62–1.74, p-value: 0.000),respondents with medium level of awareness of malaria prevention messages had 2.35 odds (AOR = 2.35; 95% CI = 1.62–3.4, p-value: 0.000), Children from North East (AOR = 0.7; 95% CI = 0.54–0.9, p-value: 0.005), South-South (AOR = 0.65; 95% CI = 0.5–0.85, p-value: 0.002) and South West (AOR = 0.52; 95% CI = 0.37–0.73, p-value: 0.000) were the determinants of malaria spread in Nigeria Conclusion Maternal education and Regional disparities in malaria risk need to be addressed through in-depth assessments, identifying specific factors contributing to varying risk levels across different regions. Based on these insights, region-specific strategies and resource allocation can tackle unique challenges faced by each area.