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

doi:10.21203/rs.3.rs-4612950/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-4612950/v1

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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.

Malaria spread

Under-five children

Determinants of malaria spread

Malaria spread

NMIS data

Nigeria

Malaria has remained one of the leading global health burdens, especially in tropical climatic regions. According to the World Health Organization 2022 report, sub-Saharan Africa accounts for 94% of global malaria cases and 95% of deaths [1]. Approximately 80% of all malaria deaths are recorded in children under the age of five in the region[1], and Nigeria accounts for approximately 27% of the world's malaria cases, making it the country with the largest malaria burden. There is a year-round threat of transmission across the entire nation [2]. Children under five years of age are most susceptible to contracting malaria, suffer from severe illness, and die from the disease because their immune systems are still developing and they are not able to produce enough antibodies to fight the Plasmodium parasites that cause it[3, 4]. It has been reported that at least one child under five dies of malaria in Africa every 75 seconds [5]. The impact of malaria on child health and survival in Nigeria is profound. It significantly negatively impacts a child's development, resulting in stunted growth, diminished cognitive function, and more detrimental educational attainment [6]. Numerous risk factors, such as the country's climate, the location and behaviour of malaria vectors, socioeconomic determinants, and the effectiveness of current control strategies, contribute to Nigeria's high malaria burden [7]. Unfortunately, there is no clear prevalence rate for all risk factors for malaria transmission among Nigerian children under the age of five. The complex interaction of several factors influences malaria transmission risk and prevention, and the prevalence can vary across different regions, population groups, healthcare systems, and periods [8, 9]. However, certain significant risk factors and their possible prevalence in Nigeria can be examined based on general data and investigations. These include poverty and low socioeconomic status: Approximately 40% of Nigeria's population lives below the national poverty line [10]. Poor households frequently lack access to preventive measures such as insecticide-treated nets and are more vulnerable due to insufficient living conditions [11]. Inadequate housing and overcrowding are common living circumstances for poor people, increasing the risk of malaria. Dwellings that are hastily constructed or composed of widely accessible materials may allow mosquitoes to enter more easily than well-crafted houses with screened windows, hence boosting vector interaction [12].

Environmental factors also affect the rate of transmission, intensity, seasonality, and geographical spread of malaria, coupled with the vector, host, and parasite that comprise the malaria system [13]. Nigerian seasonal variations and the prevalence of malaria can be partly attributed to changes in the country's climate and weather. According to studies conducted in southern Nigeria, malaria is more prevalent during the rainy season, although it occurs year round [14, 15]. Another study revealed that there are more severe childhood malaria infections, with a prevalence of 77% of cases occurring in the wet season in Nigeria [16]. Another risk factor for the incidence of malaria in children is low maternal education. In Nigeria, only approximately 36% of adult women have completed at least secondary school [17]; hence, maternal education is statistically related to less awareness of malaria prevention and care-seeking behaviors [18].

Furthermore, rural residence has also been implicated as a risk factor for malaria infections in the region. By 2022, 46.48% of Nigerians were estimated to be living in rural areas [19]. The risk of malaria is typically greater in rural areas because of factors such as the presence of habitats where vectors breed and the difficulty in accessing treatments and medical care [20]. According to other studies, comorbidities and poor nutritional status are risk factors for malaria in children younger than five years [21]. Nigeria has the second highest incidence of stunted children worldwide, with a nationwide prevalence rate of 32% of children under the age of 5. Approximately two million children in Nigeria suffer from severe acute malnutrition (SAM) [5]. Other comorbidities that are risk factors for malaria infection and its related complications include anaemia. Its prevalence in children under 5 years of age is reported to be 71% [22, 23].

Several malaria control measures have been implemented in Nigeria according to the recommendations of the World Health Organization (WHO). These include the dissemination of insecticide-treated nets (ITNs), indoor residual spraying (IRS), prompt diagnosis through the use of rapid diagnostic tests (RDTs) and microscopy, and the use of artemisinin-based combination therapy (ACT) to treat mild cases of malaria[24]. Campaigns to provide ITN have been a vital component of Nigeria's attempts to control malaria since 2009 [25]. However, there are still gaps in the implementation and utilization of these measures by the WHO and governments for their populations. Consistent reports of nonutilization of LLIN were found, especially among susceptible populations such as pregnant women and children under five years of age [24]. Despite major investments and interventions for malaria control and prevention in Nigeria, progress has been slow and disproportionate. The Nigeria Malaria Indicator Survey (NMIS) is a nationally representative household survey that collects critical information on malaria incidence, distribution, and determinants in the country [26]. This article aims to use the most current NMIS 2021 data to study the prevalence and determinants of malaria risk among children under five years of age and to inform targeted strategies for the control and elimination of malaria in the region.

Study setting and design

Nigeria is a Federal Republic located in the western part of sub-Saharan Africa. It is neighbored by Benin to the west, Chad and Cameroon to the east, Niger to the north and the Gulf of Guinea of the Atlantic Ocean to the south. Nigeria is estimated to occupy an area of 923,769 square kilometres, and it is the most populous country in Africa. The country is composed of 36 geopolitical regions, which include 36 states and one federal capital territory where the capital city Abuja is located. The country is divided into North Central, Northeast, Northwest, Southeast, South and Southwest Regions, each with several states.

Nigeria experiences a tropical climate that is favourable for mosquitoes that spread malaria. The risk of malaria transmission exists throughout the country throughout the year. Nigeria had the highest burden of malaria globally in 2021, accounting for nearly 27% of the global malaria burden. There were an estimated 68 million malaria cases and 194,000 deaths due to the disease in 2021.

The 2021 NMIS was conducted between 12 October and 4 December 2021. The exercise was initially planned for 2021 but postponed due to the COVID-19 pandemic in the country. This was done to provide current estimates of demographic and health indicators related to malaria to enable policymakers and programme managers to evaluate and design programmes and strategies to improve the health of the population of Nigeria. To achieve this goal, the 2021 NMIS collected a broad spectrum of information ranging from information on vector control interventions such as mosquito nets to information on malaria knowledge, practices and behaviour. [27]

The NMIS was implemented by the National Malaria Elimination Programme (NMEP) of the Federal Ministry of Health (FMoH) in collaboration with the National Population Commission (NPC) and the National Bureau of Statistics (NBS). Technical support for the NMIS was received from ICF Macro. The NMIS used a frame from the 2023 Population and Housing Census (PHC) of the Federal Republic of Nigeria.

Sampling and Participants

The 2021 Nigeria Malaria Indicator Survey (NMIS) is a national survey designed and implemented as a follow-up to the 2010 and 2015 Nigeria Malaria Indicator Surveys.

The country was stratified into 73 sampling strata, which were obtained by dividing the 36 states and the federal capital area of Lagos (which is 100% urban) into urban and rural areas. The 2021 NMIS adopted a two-stage stratified sampling technique for data collection. During the first stage, 568 enumeration areas (195 from urban areas and 373 from rural areas) were selected with a probability proportional to the EA size. The EA size was the number of households residing in the enumeration area. The design ensured that the sizes were representative of each state in Nigeria. [27].A listing of households was then compiled for each of the 568 selected EAs between 26 August and 18 September 2021. GPS coordinates were captured during the process of listing the 2021 NMIS sample clusters. The list of households served as the sampling frame for the selection of households in the second stage. During the second stage of the selection process, 25 households were selected in each EA using systematic random sampling.

Data collection

The NMIS 2021 data collection utilized a computer-assisted personal interviewing (CAPI) mode. The fieldwork was carried out between 12 October and 4 December 2021, during which time a total of 37 field teams, each comprising a supervisor, 2 female interviewers, one biomarker specialist, and one nurse, collected the data. The teams were closely supervised by state coordinators, zonal biomarker representatives, quality control officers, and national monitors[27].

The trained personnel visited the selected households to interview and capture the required information using different questionnaires. Three questionnaires were used, namely, a household questionnaire, a biomarker questionnaire, and a woman questionnaire. The household questionnaire captured basic information such as each member's biodata, characteristics of the household’s dwelling unit such as water, toilet facilities, nature of the house floor, wall and roof, ownership of assets, and ownership and use of mosquito nets. The Woman’s Questionnaire collected an array of information from all eligible women in the sample. Biomarker questionnaires were used to record the results of malaria and anaemia testing in children under five years of age.The prevalence of determinants of malaria spread among children under five years of age was determined using two test procedures: microscopy and rapid diagnostic test (RDT). RDT results were available after 15 minutes and recorded as either positive or negative, with faint test lines considered positive. RDT results were verbally and in written form provided to the child’s parent or guardian and were recorded on the Biomarker Questionnaire. [27]

The second test procedure involved preparing thick and thin blood smears in the field. Each blood smear slide was given a barcode label, with a duplicate affixed to the Biomarker Questionnaire. Blood smears were examined to determine the presence or absence of Plasmodium parasites and to determine parasite density.

Variable definitions and data analysis.

The outcome/dependent variable of the study was Malaria status, which was derived from a question that asked the mothers of the children whether they were told by health workers at any health facility that their children had malaria; the question was binary (yes or no). The predictors of the study were the child’s age in years (12 and below, 13–23, 24–35, 36-47.48-59), the sex of the head of household (male or female), the age of the child who slept under a mosquito bed during the previous night (no, all children, some children, no net in the household), heard/seen malaria messages in the last 6 months (yes or no), the mother’s level of education (primary, secondary+), the wealth index (poorest, poorer, middle, richer, richest), the malaria prevention message awareness level (low level, medium level, high level) and region (north central, northeast, northwest, southeast, south‒south, southwest). Data cleaning and analysis were performed using STATA version 17 software.

The study included 3678 children. A descriptive analysis was performed to obtain the frequencies and percentages of the sociodemographic characteristics and determinants of malaria risk among the children. Among the participants, 760 (20.97%) were aged 24–35 months, 736 (20.30%) were aged 13–23 and 36–47 months, and 671 (18.51%) were aged younger than 12 months. Compared with their female counterparts, 1913 (52.01%) were male, 1765 (47.99%) were male, and 3417 (92.90%) were headed by males, while 261 (7.10%) were headed by females. The number of households with all children who slept under mosquito bed nets last night was 1383 (37.90%), and the number of households with only some children who slept under mosquito bed nets was 531 (14.55%).

During the survey, it was found that most households had 7 or more members (1500, 40.78%), while those with 0–4 household members had the least (757, 20.58%), and households with 1–2 under5 children had the highest frequency (2363, 64.25%).

There were 2128 (57.86%) respondents who had never seen or heard about malaria in the last 6 months, and 1550 (42.14%) respondents had seen or heard about malaria. Most households had mothers with no education, 1735 (47.7%), and most of the households were poorer, with 852 (23.16%). Most of the household members received drinking water from piped water (2564, 69.71%), and most respondents had a low level of awareness of malaria prevention measures (3523, 95.79%). Most of the respondents were from the northwestern region of Nigeria (1460, 39.70%), while the southwestern region had the lowest number of respondents (229, 6.23), as shown in Table 1 below.

Table 1

Sociodemographic characteristics and determinants of malaria spread among children under age 5
Study Characteristic	Malaria status
	Frequencies (N = 3678)	Percentage (%)
	(n/N)
The child’s age in months
12 and below	671/3625	18.51
13–23	736/3625	20.30
24–35	760/3625	20.97
36–47	736/3625	20.30
48–59	722/3625	19.92
Sex of child
male	1913/3678	52.01
Female	1765/3678	47.99
Sex of Head of Household
Male	3417/3678	92.90
Female	261/3678	7.10
Children under 5 slept under mosquito bed net last night
No	607/3649	16.63
All children	1383/3649	37.90
Some children	531/3649	14.55
No net in the household	1128/3649	30.91
Number of household members
0–4	757/3678	20.58
5–7	1421/3678	38.64
7 and above	1500/3678	40.78
Number of under5 children
0	29/3678	0.79
1–2	2363/3678	64.25
2+	1286/3678	34.96
Heard/seen malaria messages in the last 6 months
Yes	1550/3678	42.14
No	2128/3678	57.86
Mothers education level
No education	1735/3678	47.17
Primary	570/3678	15.50
Secondary+	1373/3678	37.33
Wealth Index
Poorest	816/3678	22.19
poorer	852/3678	23.16
middle	747/3678	20.31
richer	692/3678	18.81
richest	571/3678	15.52
Source of drinking water
Piped water	2564/3678	69.71
Tube well water	1082/3678	29.42
Dug well(open/protected)	10/3678	0.27
Surface from spring	22/3678	0.60
Malaria prevention messages awareness
Low level	3523/3678	95.79
Medium level	140/3678	3.81
High level	15/3678	0.41
Region
Northcentral	474/3678	12.89
North East	643/3678	17.48
North West	1460/3678	39.70
South East	387/3678	10.52
South South	485/3678	13.19
South West	229/3678	6.23

As shown in Table 2, a univariate analysis was performed to determine the associations between the study characteristics and the malaria status of the children. The corresponding frequencies, chi-square values, and p values were recorded. The following study characteristics were significantly related to malaria status: a p value of 0.000 for moths, a p value of 0.006 for the sex of the household, a p value of 0.006 for children under 5 who slept under a mosquito bed net last night, a p value of 0.000 for Heard/seen malaria messages in the last 6 months, a p value of 0.000 for the sex of the household, a p value of 0.033 for the source of drinking water, awareness of the malaria prevention messages (p-0.000) and region (p-0.000). The rest of the study variables were not significantly associated with malaria status according to univariate analysis.

Table 2

Univariate association between study characteristics and malaria status among children under age 5 in Nigeria
Study Characteristics	Frequency n/N	X²	P value
The child’s age in months		24.6309	0.000
12 and below	253/671
13–23	327/736
24–35	365/760
36–47	329/736
48–59	361/722
Sex of the child		0.1562	0.693
Male	873/1913
Female	794/165
Sex of Head of Household		7.5463	0.006
Male	1570/3417
Female	97/261
Children under 5 slept under mosquito bed net last night		22.6886	0.000
No	235/607
All children	674/1383
Some children	260/531
No net in the household	484/1128
Number of household members		1.1172	0.572
0–4	331/757
5–7	645/1421
7 and above	691/1500
Number of under5 children		0.1722	0.917
0	13/29
1–2	1077/2363
2+	577/1286
Heard/seen malaria messages in the last 6 months		46.3434	0.000
No	863/2128
Yes	804/1550
Mothers level of education		7.2479	0.064
No education	786/1735
primary	284/570
Secondary+	597/1373
Wealth index		5.1025	0.277
Poorest	369/816
Poorer	390/852
Middle	358/747
Richest	291/692
Richest	259/571
Source of drinking water		8.7076	0.033
Piped water	1138/2564
Tube wall water	516/1082
Dug water(open/protected)	7/10
Surface from spring	6/22
Malaria prevention messages awareness		25.8760	0.000
Low level	1566/3523
Medium level	92/140
High level	9/15
Region		37.9140	0.000
Northcentral	231/474
North East	268/643
North West	707/1460
South East	198/387
South South	183/485
South West	80/229

The study variables that were significant in univariate analysis were fit into a multivariate logistic regression model at a 95% confidence interval to determine the level of association with malaria status among children under five years of age. After adjusting the model, the corresponding odds ratios, p values, and confidence intervals were recorded.

According to Table 3, ages 13 to 23 months (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 48–59 months (AOR = 2.82; 95% CI = 1.53–5.23, p value: 0.001) were significantly associated with malaria status. Households headed by females had 0.71 lower odds of malaria risk (AOR = 0.71; 95% CI = 0.54–0.95, p value: 0.019) than households headed by males. 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) were significantly associated with malaria risk among children. There were 1.5 times more 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) than mothers who did not see or hear any messages in the last 6 months. Respondents with a 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) greater odds than those with low and high levels of malaria prevention message awareness. Children from the 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 Southwest (AOR = 0.52; 95% CI = 0.37–0.73, p value: 0.000) Regions had 0.7 odds, 0.65 odds and 0.52 odds, respectively, of having a lower risk of malaria than did children from the Northwest and Southeast Regions

Table 3

Determinants of malaria risk among children under 5 years of age in Nigeria
Study Characteristics	AOR	P value	95% Confidence Interval
The child’s age in months
12 and below	1
13–23	2.970602	0.000^***	1.618142–5.453465
24–35	2.642013	0.002^***	1.429599–4.882647
36–47	2.182358	0.015^**	1.166998–4.081144
48–59	2.8206	0.001^***	1.52522–5.216156
Sex of Head of Household
Male	1
Female	0.7117474	0.019^**	0.5352002–0.9465325
Children under 5 slept under mosquito bed net last night
No	1
All children	2.416467	0.002^***	1.38604–4.212944
Some children	2.830334	0.001^***	1.498138–5.347164
No net in the household	2.179499	0.008^***	1.223802–3.881524
Heard/seen malaria messages in the last 6 months
No	1
Yes	1.51618	0.000^***	1.320904–1.740324
Malaria prevention messages awareness
Low level	1
Medium level	2.347381	0.000^***	1.619348–3.402727
High level	2.418791	0.102	0.8380563–6.981094
Region
Northcentral	1
North East	0.6966831	0.005^***	0.5421665–0.8952367
North West	0.93355	0.539	0.7497552-1.1624
South East	1.138804	0.360	0.8622922–1.503985
South South	0.6491517	0.002^***	0.495546–0.8503709
South West	0.5195713	0.000^***	0.3694864–0.7306205

Note: ***p < 0.01, ** p < 0.05, * p < 0.1, OR: odds ratios

The findings of this study reveal numerous significant variables and risk factors related to malaria incidence among Nigerian children under the age of five. The child's age appeared to be a key predictor, with children aged 13–59 months having a greater risk of malaria than those aged under 12 months. This finding is consistent with earlier studies indicating that children under five years of age have lower susceptibility to malaria infection [3, 4]. This finding is consistent with evidence showing that young children's immune systems are still developing and that they are less able to produce a potent immune response against the Plasmodium parasites that cause malaria infections [28]. More research is necessary to determine the specific immunological and developmental variables that underlie age-related risk patterns. However, there is no evidence indicating that as children age, their immunity to malaria decreases, increasing their susceptibility to the disease.

Household factors also play an important role. Interestingly, homes with a female head have been related to decreased odds of malaria risk compared with those with a male head, possibly due to several factors contributing to this observation, including better health-seeking behaviors and adherence to preventive measures by female parents, such as net usage or preferential allocation of resources favouring child health by female caregivers. This finding warrants further investigation to understand the underlying sociocultural and behavioral factors, such as gender norms, decision-making dynamics, and care practices, within households to elucidate the mechanisms underlying this observation.

The use of insecticide-treated mosquito nets (ITNs) proved to be an effective preventive approach, as homes with all or some children sleeping under ITNs had lower malaria rates than those without nets. The present investigation confirms the well-known benefit of ITNs in lowering malaria transmission [24, 25]. ITNs have the potential to be an effective malaria control technique, particularly in Africa, where nighttime indoor feeding is the primary malaria vector[29]. When mosquitoes attempt to bite, the nets create a chemical and physical barrier that stops them and kills them with insecticide. According to one study, compared with no nets, ITNs can lower overall child mortality by 77%[30]. ITNs are safe for use as a personal protection technique during pregnancy, and women should begin using them as early as feasible. They can also be utilized during pregnancy and postpartum for both mothers and children [31]. ITNs can contain a variety of insecticides, including pyrethroid-piperonyl butoxide (PBO) nets and pyrethroid-pyriproxyfen nets. PBO improves the efficacy of several pesticides by blocking metabolic enzymes in the mosquitos that detoxify them. Pyrethroid-pyriproxyfen nets combine a pyrethroid with an insect growth regulator (IGR), which inhibits mosquito growth and reproduction [32]. This finding reinforces ITNs as a crucial malaria prevention tool and emphasizes the significance of maintaining widespread availability and supporting consistent net utilization, especially in high-burden regions [33]. However, the fact that roughly one-third of households reported in this study did not have mosquito nets highlights ongoing gaps in intervention coverage that must be addressed.Maternal education surfaced as an important socioeconomic predictor, with children of mothers who had secondary or higher education experiencing lower malaria risk.

This highlights the significance of health education initiatives and the empowerment of women through education in malaria prevention and control efforts [17, 18]. This is likely due to the multidimensional benefits of female education, which include improved health literacy, the adoption of beneficial preventative behaviors, greater household resources, and empowerment in care-seeking decisions [34]. Integrating malaria education into school curricula and promoting girls' access to quality education could have far-reaching impacts on malaria control.

Exposure to malaria messaging initiatives in the previous six months was also associated with decreased childhood malaria risk. This emphasizes the significance of persistent, evidence-based communication efforts to promote awareness, dispel misunderstandings, and motivate beneficial behaviors associated with prevention, early detection, and timely treatment seeking [35]. The effectiveness of such efforts could be further increased by customizing messaging content and distribution methods to appeal to particular target audiences.

Regional variation in malaria risk was evident, with children from the Northeast, South‒South, and Southwest Regions having lower odds than those from the Northwest and Southeast Regions. This could be attributable to differences in socioeconomic disparities between locations, differences in intervention coverage, rural‒urban dynamics affecting healthcare access, and ecological variances that favour vector proliferation across regions [13, 20]. The country's differences in humidity, vegetation, and altitude also favour the reproductive stages of the parasite in mosquitoes and make it easier for them to fly [36]. The development of localized, context-appropriate interventions requires a granular, region-specific study of these potential contributing factors. Addressing these factors through targeted interventions and policies is essential for reducing the substantial malaria burden in this vulnerable population.

This study highlighted important factors influencing malaria risk among children under the age of five in Nigeria by using the most recent nationally representative NMIS 2021 data. The findings underscore the multifactorial nature of malaria transmission, involving child age, household characteristics, maternal education, exposure to health education awareness, and regional variations. Addressing these factors through targeted interventions and policies is essential for reducing the substantial malaria burden in this vulnerable population. Reducing the risk and prevalence of malaria among children under five years of age, particularly those aged 13–59 months who are more vulnerable, is a critical priority. A multipronged strategy encompassing targeted interventions, community engagement, regional adaptation, multisectoral collaboration, and continued research is proposed. A key focus is enhancing the distribution and utilization of insecticide-treated mosquito nets (ITNs) in households with young children. This involves community mobilization and behavior change communication campaigns to promote consistent net usage and malaria prevention practices. Maternal education and awareness programs are crucial, equipping mothers and caregivers with knowledge about malaria prevention, early symptom recognition, and timely treatment seeking. Comprehensive education initiatives can empower families to take proactive measures.

Fostering multisectoral collaboration between the health sector, educational institutions, community organizations, and other stakeholders is essential for an integrated approach to malaria control and prevention. Coordinated efforts can amplify impact and reach. Finally, further research is vital to explore the underlying sociocultural, behavioural, environmental, and climatic factors influencing malaria transmission dynamics and risk. Continuous learning can inform more effective interventions and adaptive strategies over time.

2021NMIS Nigeria Malaria Indicator Survey 2021

ITNs Insecticide-treated nets

RDTs Rapid diagnostic tests

ACT Artemisinin-based combination therapy

NMEP National Malaria Elimination Programme

FMoH Federal Ministry of Health

NPC National Population Commission

NBS National Bureau of Statistics

PHC Population and Housing Census

WHO World Health Organization

Ethical approval and considerations

There is ethical approval required for this study as it utilized secondary data. The authors obtained permission to use the secondary data from the DHS Program websitehttps://www.dhsprogram.com/data/available-datasets.cfm.The Demographic and Health Survey also ensured that all participants provided informed verbal consent to participate in the study, and for minors, their parents or guardians consented on their behalf.

Consent for publication

All authors consent to publish the findings of this research.

Availability of data and materials

The data used in this study can be accessed online through the DHS website upon request (https://dhsprogram.com/).

Competing interest

All the authors declare no competing interests.

Funding

There was no funding for this study.

Author contributions

II Conceptualized the research; II and SN wrote the methodology, analysed the data, and presented and interpreted the results; II and AM wrote the introduction and discussion; JMA, HO and SCA reviewed the manuscript; and II compiled the final draft of the manuscript. All authors reviewed and approved the final draft of the manuscript.

Acknowledgement

The authors acknowledge the National Malaria Elimination Programme of the Federal Ministry of Health in collaboration with the National Population Commission and the National Bureau of Statistics for implementing the 2021 Nigerian Malaria Indicator Survey, the United States Agency for International Development for Funding and the DHS programme for granting us access to the data.

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

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