Determinants of malaria spread among under-five children in Nigeria: results from a 2021 Nigerian malaria indicator cross-sectional survey

Abstract

Background

Malaria is a global disease burden, especially in Africa, with Nigeria having the highest prevalence of malaria, with approximately 50% affecting children. Children under five years of age are vulnerable to the risk of malaria spread. This research aimed to identify the determinants of malaria spread among children under the age of 5 in Nigeria.

Methods

This study used national malaria indicator survey data from 2021 (2021NMIS). The NMIS was implemented by the National Malaria Elimination Programme (NMEP), and data were collected between 12 October and 4 December 2021. This study included 3678 children, and data cleaning and analysis were performed via STATA version 17 software.

Results

There was a positive association between Child’s age in months of 13–23 (AOR = 2.97; 95% CI = 1.62–5.45), 24–35 months (AOR = 2.64; 95% CI = 1.43–4.88),36–47 months (AOR = 2.18; 95% CI = 1.17–4.08) and months of 48–59(AOR = 2.82; 95% CI = 1.53–5.23), households headed by females (AOR = 0.71; 95% CI = 0.54–0.95),households with all children slept in mosquito nets last night (AOR = 2.43; 95% CI = 1.39–4.21), some children slept in the mosquito bed net (AOR = 2.83; 95% CI = 1.50–5.35) and households with no mosquito bed nets (AOR = 2.18; 95% CI = 1.22–3.88),mothers who agreed to have heard or seen malaria messages in the last 6 months (AOR = 1.32; 95% CI = 1.62–1.74),respondents with medium level of awareness of malaria prevention messages had (AOR = 2.35; 95% CI = 1.62–3.4), Children from North East (AOR = 0.7; 95% CI = 0.54–0.9), South-South (AOR = 0.65; 95% CI = 0.5–0.85) and South West (AOR = 0.52; 95% CI = 0.37–0.73) and malaria status of children under five years of age in Nigeria.

Conclusion

The government and other concerned malaria prevention organizations should emphasize maternal education programs that are vital for malaria prevention, early symptom recognition, and timely treatment, empowering families to take proactive measures. Collaboration among health, education, and community organizations is also crucial for integrated malaria control and prevention.

Background of the study

Malaria remains a significant global health issue, with 249 million cases and 608,000 deaths reported worldwide [1]. According to the World Health Organization 2022 report, sub-Saharan Africa accounts for 94% of global malaria cases and 95% of all malaria deaths [1]. Approximately 80% of all malaria deaths are recorded in children under the age of five in the region [1]; Nigeria accounts for approximately 27% of the world’s malaria cases, making it the country with the largest malaria burden [2]. Children under five years of age are most susceptible to malaria because of their developing immune system and inability to produce sufficient antibodies to fight Plasmodium parasites [3, 4]. At least one child under five dies of malaria in Africa every 75 s [5]. The impact of malaria on child health and survival in Nigeria is profound. It significantly 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 behavior of malaria vectors, socioeconomic determinants, and the effectiveness of current control strategies, contribute to Nigeria’s high malaria burden [7]. The prevalence of malaria transmission among Nigerian children under five years of age is unclear because of the complex interaction of factors affecting risk and prevention across regions, population groups, healthcare systems, and periods [8, 9]. Nigeria faces significant risk factors, including poverty and low socioeconomic status, with 40% of the population living below the national poverty line [10]. Poor households are more vulnerable to malaria due to inadequate living conditions, a lack of preventive measures such as insecticide-treated nets, and overcrowding, which can increase vector interactions and allow mosquitoes to enter easily [11]. Environmental factors also affect the rate of transmission, intensity, seasonality, and geographical spread of malaria, coupled with the vector, host, and parasite that constitute the malaria system [12]. The prevalence of malaria in Nigeria is influenced by climate and weather changes, with the rainy season being more prevalent in southern Nigeria, despite its year-round presence [13, 14]. In Nigeria, 77% of severe childhood malaria infections occur during wet seasons [15]. Furthermore, low maternal education and a lack of secondary school completion are risk factors for malaria spread in children [16, 17]. Rural residence in Nigeria is also a significant risk factor for malaria infections, with 46.48% of the population living in rural areas by 2022 [18]. Malaria risk is greater in rural areas than in rural areas because of vector breeding habitats and challenges in accessing treatments and medical care [19]. In other studies, comorbidities such as respiratory and digestive infections [20] 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 globally, with 32% of children under 5 stunted and approximately two million suffering from severe acute malnutrition [5]. Anaemia is another comorbidity related to malaria infection and its complications, with a 71% prevalence in children under 5 years of age [22, 23]. Nigeria has implemented malaria control measures since 2009, including insecticide-treated nets (ITNs), indoor residual spraying, rapid diagnostic tests, and artemisinin-based combination therapy (ACT), following WHO recommendations [24, 25]. The WHO and governments are still failing to effectively implement and utilize LLIN measures, particularly among vulnerable populations such as pregnant women and children under five years of age [24]. Nigeria’s malaria control efforts, despite significant investments, have been slow and disproportionately affected [26]. This study aimed to use the most current Nigerian Malaria Indicator Survey (NMIS) 2021 data to study the prevalence and determinants of malaria risk among children under five years of age and provide evidence-based policymaking strategies for malaria control, identify gaps, understand socioeconomic and environmental determinants, and monitor progress towards national and international targets.

Methods and materials

Study setting

Nigeria is a Federal Republic located in the western part of sub-Saharan Africa. It is neighboured 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 comprises 36 geopolitical regions, including 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 [25, 27]. There were an estimated 68 million malaria cases and 194,000 deaths due to the disease in 2021 [25, 27].

Study design

The 2021 NMIS has a cross-sectional study design and was designed 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.

Study 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 [27]. The study included children under five years of age from the sampled households.

Sample size and sampling procedure

The sample size of this study was 3678 children under five years of age. 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. In 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 [27]. 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 via systematic random sampling [27].

Data collection

The NMIS 2021 data collection utilized computer-assisted personal interviewing (CAPI) mode. The fieldwork was carried out between 12 October and 4 December 2021. During that time, a total of 37 field teams, each comprising a supervisor, 2 female interviewers, one biomarker specialist, and one nurse, collected the data.

The enumerators were trained and equipped with the knowledge and skills of data collection, and they then visited the selected households to interview and capture the required information via different questionnaires. Three questionnaires were used, namely, a household questionnaire, a woman questionnaire and a biomarker questionnaire, which were derived from the standard DHS questionnaire edited to fit the requirements of the study [27]. 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 about children under 5 years of age 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 through rapid diagnostic tests and microscopy.

Laboratory diagnosis of malaria

The presence or absence of Plasmodium parasites in the blood of children under five years of age was determined via two test procedures: microscopy and rapid diagnostic tests (RDTs). RDT results were available after 15 min and recorded as either positive or negative, with faint test lines considered positive [27]. RDT results were verbally and in written form provided to the child’s parent or guardian and were recorded on the Biomarker Questionnaire, and the children who were found to be positive were started on antimalarial drugs and given free insecticide-treated mosquito nets [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 via oil immersion in high-power fields on thick films, and the parasite density was determined by tallying the parasites against white blood cells (WBCs) until 500 parasites or 1000 white blood cells were detected, resulting in parasites per microliter of blood.

Variable definitions

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 response was binary (yes or no), and the predictors of the study were the child’s age in months (12 and below, 13–23, 24–35, 36-47.48-59), the sex of the head of household (male or female), the number 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 analysis

The study included 3678 children. The variables were identified and weighted by dividing by 1,000,000 to account for biases and nonresponsiveness, which was accomplished via STATA’s SVY command. A descriptive analysis was performed to obtain the frequencies and percentages of the sociodemographic characteristics and determinants of malaria spread among the children. Bivariate chi-square tests and multivariate binary logistic regression tests were used to assess the determinants of malaria spread. After bivariate analysis, only the significant study variables were fit into a multivariate logistic regression model to determine the level of association with malaria status among children under five years of age. Cofounding variables such as the source of drinking water were removed from the final model irrespective of their significance in the bivariate model. Data cleaning and analysis were performed via STATA version 17 software, and the significance level was set at p < 0.05, with a 95% confidence interval.

Results

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 children, 1765 (47.99%) were female children, 3417 (92.90%) of the households were headed by males, and 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 531 (14.55%) households had only some children who slept under mosquito bed nets.

During the survey, most households had 7 or more members, 1500 (40.78%), while those with 0–4 household members had the lowest frequency, 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 not seen or heard about malaria messages in the last 6 months and 1550 (42.14%) respondents who had seen or heard malaria messages. Most households had mothers with no education, 1735 (47.7%), and most were poorer, with 852 (23.16%) compared with the richest households, with 571 (15.51%), and most respondents had a low level of awareness of malaria prevention measures 3523 (95.79%). Most respondents were from the northwestern region of Nigeria (1460, 39.70%), while the southwestern region had the lowest number of respondents (229, 6.23%) (Table 1).

Table 1.

Sociodemographic characteristics of children under five years of age in Nigeria

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

Bivariate analysis of determinants of malaria spread among children under five years of age in Nigeria

The bivariate associations between the study characteristics and the malaria status of the children are shown in Table 2. The corresponding frequencies, chi-square values, and values were recorded. There was a positive association between malaria status and the child’s age in months, sex of the household, number of children under 5 who slept under a mosquito bed net last night, Heard/seen malaria messages in the last 6 months, sex of the household, source of drinking water, awareness of malaria prevention messages and region. The remaining study variables were not significantly associated with malaria status according to univariate analysis.

Table 2.

Bivariate associations between study characteristics and malaria status among children under age 5 in Nigeria

Study Characteristics	Frequency (%)	X2	P value
The child’s age in months		24.6309	0.000
12 and below	253(37.70)
13–23	327(44.43)
24–35	365(48.03)
36–47	329(44.70)
48–59	361(50)
Sex of the child		0.1562	0.693
Male	873(45.64)
Female	794(68.15)
Sex of Head of Household		7.5463	0.006
Male	1570(45.95)
Female	97(37.16)
Children under 5 slept under mosquito bed net last night		22.6886	0.000
No	235(38.72)
All children	674(48.73)
Some children	26048.96)
No net in the household	484(42.91)
Number of household members		1.1172	0.572
0–4	331(43.73)
5–7	645(45.39)
7 and above	691(46.07)
Number of under5 children		0.1722	0.917
0	13(44.83)
1–2	1077(45.57)
2+	577(44.87)
Heard/seen malaria messages in the last 6 months		46.3434	0.000
No	863(40.55)
Yes	804(51.87)
Mothers level of education		7.2479	0.064
No education	786(45.30)
primary	284(49.83)
Secondary+	597(43.48)
Wealth index		5.1025	0.277
Poorest	369(45.22)
Poorer	390(45.77)
Middle	358(47.73)
Richest	291(42.05)
Richest	259(45.36)
Source of drinking water		8.7076	0.033
Piped water	1138(44.38)
Tube wall water	516(47.69)
Dug water(open/protected)	7(70.00)
Surface from spring	6(27.27)
Malaria prevention messages awareness		25.8760	0.000
Low level	1566(44.45)
Medium level	92(65.71)
High level	9(60.00)
Region		37.9140	0.000
Northcentral	231(48.73)
North East	268(41.68)
North West	707(48.42)
South East	198(51.16)
South South	183(37.81)
South West	80(34.93)

Determinants of malaria spread among children under five years of age in Nigeria

After adjusting the model, the corresponding odds ratios, p values, and confidence intervals were recorded.

According to Table 3, ages 13–23 months (AOR = 2.97; 95% CI = 1.62–5.45), 24–35 months (AOR = 2.64; 95% CI = 1.43–4.88), 36–47 months (AOR = 2.18; 95% CI = 1.17–4.08) and 48–59 months (AOR = 2.82; 95% CI = 1.53–5.23) were significantly associated with malaria status. Households with all children slept in mosquito nets last night (AOR = 2.43; 95% CI = 1.39–4.21), some children slept in the mosquito bed net (AOR = 2.83; 95% CI = 1.50–5.35) and households with no mosquito bed nets (AOR = 2.18; 95% CI = 1.22–3.88) 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) 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) greater odds than those with low and high levels of malaria prevention message awareness. Households headed by females had 0.71 lower odds of malaria risk (AOR = 0.71; 95% CI = 0.54–0.95) than did households headed by males. Children from Northeast China (AOR = 0.7; 95% CI = 0.54–0.9), South China (AOR = 0.65; 95% CI = 0.5–0.85) and Southwest China (AOR = 0.52; 95% CI = 0.37–0.73) had 0.7 odds, 0.65 odds and 0.52 odds, respectively, of having a lower risk of malaria than did children from Northwest China and Southeast China.

Table 3.

Multivariate analysis of determinants of malaria spread among children under 5 years of age in Nigeria

Study Characteristics	Malaria status N = 3678 YES NO		AOR	P value	95% Confidence Interval
The child’s age in months
12 and below	253	418	1
13–23	327	409	2.97	0.000	1.62–5.45
24–35	365	395	2.64	0.002	1.43–4.88
36–47	329	407	2.18	0.015	1.17–4.08
48–59	361	361	2.82	0.001	1.53–5.23
Sex of Head of Household
Male	1570	1847	1
Female	97	164	0.71	0.019	0.54–0.95
Children under 5 slept under mosquito bed net last night
No	235	372	1
All children	674	709	2.42	0.002	1.39–4.21
Some children	260	271	2.83	0.001	1.50–5.35
No net in the household	484	644	2.18	0.008	1.22–3.88
Heard/seen malaria messages in the last 6 months
No	863	1265	1
Yes	804	746	1.52	0.000	1.32–1.74
Malaria prevention messages awareness
Low level	1566	1957	1
Medium level	92	48	2.35	0.000	1.62–3.40
High level	9	6	2.42	0.102	0.84–6.98
Region
Northcentral	231	243	1
North East	268	375	0.70	0.005	0.54–0.90
North West	707	753	0.93	0.539	0.75–1.16
South East	198	189	1.14	0.360	0.86–1.50
South South	183	302	0.65	0.002	0.50–0.85
South West	80	149	0.52	0.000	0.37–0.73

Discussion

This study utilized NMIS 2021 data to analyse malaria risk among children under five years of age, propose evidence-based policy strategies for malaria control, and track progress toward national and international targets. The findings of this study revealed 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 may be related to the strong immunity against most common infant infections caused by colostrum (first breast milk) during the first months of breastfeeding after childbirth [28]. However, no evidence indicates that their immunity to malaria decreases as children age, increasing their susceptibility to the disease. Household factors, including female heads, have been linked to decreased malaria risk in homes with male heads, possibly due to better health-seeking behaviours and adherence to preventive measures by female parents. However, this contradicts the study by [29], which reported a greater percentage of malaria-positive test results for under5-year-old children in female-headed homes than in male-headed homes [29]. The use of insecticide-treated mosquito nets (ITNs) has proven 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 [30]. 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% [31]. 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 [32]. 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 [33]. This finding reinforces ITNs as crucial malaria prevention tools and emphasizes the importance of maintaining widespread availability and supporting consistent net utilization, especially in high-burden regions [34]. However, the fact that approximately one-third of the households reported in this study did not have mosquito nets highlights ongoing gaps in intervention coverage that must be addressed.

Maternal education has been reported to significantly reduce malaria risk in children, emphasizing the importance of health education initiatives and women’s empowerment in malaria prevention and control efforts [16, 17]. 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 [35]. In Nigeria, higher education levels of mothers are responsible for the existence of female-headed homes in urban areas, better household wealth, and hence better health-seeking behaviors [29]. Hence, 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 importance of persistent, evidence-based communication efforts to promote awareness, dispel misunderstandings, and motivate beneficial behaviors associated with prevention, early detection, and timely treatment seeking [36]. 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 [12, 19]. A 2021 study revealed that Northeast China had the lowest ITN distribution (0.196 ± 0.071), whereas South China had the highest distribution (0.309 ± 0.075). Compared with urban areas, rural regions had better ITN coverage (0.281 ± 0.074) (0.240 ± 0.096, p < 0.05) [37]. Another study conducted in Nigeria revealed that poor individuals have a far greater risk of malaria than rich individuals do. People who earn less than N300 per day are less likely to believe that malaria is a preventable illness and experience significantly more episodes of malaria per month [38, 39].

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 [40]. A study performed in southwestern states of Nigeria revealed that states with higher rainfall amounts reported greater malaria incidences than did their neighboring states with lower rain amounts [41]. 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.

Conclusion and recommendations

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. Targeted interventions and policies are crucial for reducing the malaria burden in vulnerable populations, especially children under five years of age. 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 behaviour change communication campaigns to promote consistent net usage and malaria prevention practices. Maternal education programs are vital for malaria prevention, early symptom recognition, and timely treatment, empowering families to take proactive measures. Collaboration among health, education, and community organizations is crucial for integrated malaria control and prevention. Further research is needed to understand the sociocultural, behavioural, environmental, and climatic factors influencing malaria incidence in children under 5 years of age in Nigeria.

Abbreviations

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

Author contributions

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

Funding

There was no funding for this study.

Data availability

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

Declarations

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

Not applicable.

Competing interests

The authors declare no competing interests.