Malaria remains a major global public health problem. In 2022, there were an estimated 249 million malaria cases and more than 600,000 deaths worldwide, with about half of the global population living in areas at risk of malaria transmission across 91 countries and territories [1]. The burden is disproportionately concentrated in Africa. In 2023, the WHO African Region accounted for 94% of global malaria cases and 95% of malaria deaths, with children under five years old representing about 76% of these deaths [2]. Despite extensive control efforts, malaria continues to cause significant illness and death, particularly among young children and pregnant women in sub-Saharan Africa, including Liberia. To address this, the World Health Assembly (WHA) adopted the Global Technical Strategy for Malaria (2016–2030) in 2015, aiming to reduce malaria incidence and mortality by at least 90% by 2030 [3]. However, progress began to slow by 2017, prompting the launch of the High Burden to High Impact (HBHI) initiative in 2018, which prioritized intensified control efforts in countries bearing the greatest malaria burden [4]. Malaria remains highly endemic in Liberia despite global efforts and national, disproportionately affecting pregnant women and children under five years old due to their lower immunity [5]. It accounts for 34% of outpatient visits, 47% of inpatient admissions, and 23% of hospital deaths [6]. To address challenge, The country’s National Malaria Control Program (NMCP) is currently implementing its fourth National Strategic Plan (2021–2025), which seeks to reduce malaria morbidity and mortality by 75% by 2025, in alignment with the WHO’s global strategy [7]. In Liberia, the major vectors causing malaria are the Anopheles gambiae and the Anopheles funestus, whose transmission is persistent throughout the year but high during the rainy season when mosquito breeding is increased [8]. Malaria transmission levels vary between coastal and inland areas because of differences in environmental, socioeconomic, and infrastructural conditions. Coastal region of Liberia including Montserrado, Magibi, Maryland, Cape Mount, Grand Bassa, Grand Kru, River Cess, and Sinoe counties have high humidity, frequent rainfall, and extensive wetlands, providing ideal mosquito breeding sites [9]. In contrast, inland areas such as Lofa, Bong, and Nimba counties have higher elevations, drier seasons, and seasonal climatic variations, which influence malaria transmission intensity [9]. While malaria transmission remains steady in coastal areas, inland regions often experience seasonal fluctuations due to changes in temperature and rainfall. Malaria transmission is significantly affected by climatic variables, particularly temperature, precipitation, and humidity. These factors create suitable conditions for developing malaria vectors and parasites [10, 11, 12, 13, 14]. Socioeconomic factors such as poverty is a major determinant of malaria risk, affecting access to preventive tools, healthcare services, and treatment adherence [15, 16]. Despite the malaria control strategies in Liberia that prioritizes protecting young children and pregnant women through proven interventions such as ITNs, chemoprevention, vaccination, case management and behavior change, there are still challenges in reaching the needed population [7, 6, 17, 18, 19]. The 2022 Liberia Malaria Indicator Survey (LMIS) reported a Malaria prevalence that ranges from 1% in Greater Monrovia to 19% in South Eastern B. and malaria prevalence of 10% among children aged 6–59 months, indicating ongoing transmission within the population. Additionally, in 2022, Liberia reported 480,614 cases, highlighting the ongoing burden of the disease [6, 20]. However, most studies on Liberia report only national averages, offering little insight into regional differences, particularly between coastal and inland counties [6, 21]. Moreover, previous studies in Liberia and across sub-Saharan Africa shown that inequalities in healthcare access, socioeconomic status, and uptake of preventive measures contribute significantly to variations in malaria burden between regions. However, there is limited research that specifically examines whether these disparities are more pronounced between coastal and inland settings within Liberia [22–23, 15, 16]. This study addresses these gaps by comparing these geographical areas and analyzing how socioeconomic, and behavioral factors influence malaria transmission and burden in Liberia. Findings from this research can inform targeted interventions that address region-specific risk factors, ultimately improving malaria prevention and control policies and outcomes in Liberia. Accordingly, this study seeks investigates the socioeconomic, and behavioral factors that drive geographic disparities in malaria burden among children under five between coastal and inland counties of Liberia.

This study included all children who were of the de facto children population, tested for malaria, and value record during LMIS. The study excluded all children who were not part of the de facto population, not tested for malaria and those whose values were not recorded.

Based on the aim of the study, a secondary data analysis was done using the Liberia Malaria Indicators Survey 2022 dataset. The LMIS was a nationally representative cross-sectional survey conducted every five years in Liberia. The 2022 LMIS was conducted from October 4, 2022 to December 12, 2022. The 2022 Liberia Malaria Indicator Survey was implemented by the Liberia National Malaria Control Program of the Ministry of Health (MoH) in collaboration with the Liberia Institute of Statistics and Geo-Information Services (LISGIS). The primary objective of the 2022 LMIS was to provide current information for policymakers, planners, researchers, and program managers. The survey also provides population-based prevalence estimates for anaemia and malaria among children aged 6–59 months. Standardized household cluster sampling methods were applied [6]. Accordingly, this study utilised quantitative research methods to provide answers the research questions.

The 2022 LMIS followed a two-stage sample design. The first stage involved the selection of sampling points (clusters) consisting of enumeration areas (EAs) delineated for the Liberia’s 2008 National Population and Housing Census (2008 PHC) which served as the Sampling frame. A total of 150 clusters were randomly selected using probability proportional to size. of these clusters, 70 were in urban areas and 80 in rural areas [6]. In the second stage, 30 households per cluster were systematically selected, resulting in a total sample size of 4,500 households. 4500 households were selected for the survey but 40306 were enumerated [6].

Finally, a total of 2864 children under five were selected for malaria testing which the study used as sample size. This Study used a weighted sample size of 2,189 children under five (5) years (aged 6–59 months) and their caregivers.

This study used Liberia Malaria Indicators Survey 2022 data that was accessed from the official database of the Demographic and Health Surveys (DHS) Program [25], the 2022 LMIS was conducted from October 4, 2022 to December 12, 2022.

All women age 15 to 49 who were either regular inhabitants of the selected households or guests who remained in the family the night before the survey were eligible to be interviewed with the parents or guardians consent all children age 659 months were tested for anemia and for malaria infection. The 2022 LMIS employed three questionnaires: the biomarker questionnaire, the women's questionnaire, and the household questionnaire. Questions concerning the 2021 mass insecticide-treated net (ITN) distribution campaign, the acceptability of the new malaria vaccine, and mass drug administration of seasonal malaria chemoprevention for children were among the topics covered in the questionnaires, which were based on the DHS programs model questionnaires and modified to reflect the population and health issues pertinent to Liberia [6].

The explanatory variables used in this study were grouped into four categories as socioeconomic, demographic, behavioral, and geographic in view of literature and conceptual framework [16, 15, 10, 11, 27, 28]. Most of the variables used in this study are constructed according to standard DHS definitions [26].

selected factor, type of place of residence, this variable was kept in its original form, which

was as either urban or rural.

Malaria continues to pose a major public health problem across Sub-Saharan Africa (SSA), including Liberia [2]. This study contributes to an in-depth understanding of malaria prevalence and associated factors (socioeconomic, demographic, and behavioral) that drive geographic disparities in malaria burden among children under five between coastal and inland counties of Liberia.

Nationally, the malarias prevalence for children age 6–59 months classified as having malaria according to microscopy and who were of the de facto children population was reported as 10.2%. This indicates progress in the malaria control efforts by the NMCP in supporting Liberia’s goal of a 75% reduction in malaria burden by 2025, while contributing to the global 2030 targets [7, 3]. Though the overall malaria prevalence is low but malaria remains a major burden in Liberia [2].

The findings from this study indicate that malaria distribution is spatially uneven, and the national average conceals notable county-level hotspots. Both coastal and inland regions showed significant hotspot counties (Sinoe, Maryland, Grand Bassa, River Gee, Grand Kru, Lofa, and Bong). Spatial heterogeneity remains a defining feature of malaria risk in Sub-Sahara Africa, as national and subnational surveys repeatedly demonstrate that some localities, often rural or ecologically favorable zones experience much higher malaria prevalence than others.

In this research, the observed pattern of higher malaria prevalence in inland (12.3%) compared to coastal (8.7%) regions confirms this variability. It specifically indicates that children living in coastal counties are considerably less affected by malaria than those in inland areas. This pattern aligns with observations made in several studies, even though not all research explicitly distinguishes coastal and inland zones [22, 23, 28, 29, 30, 31].

Liberia’s climate is predominantly equatorial, featuring three main topographic zones. The first is a low-lying coastal belt, roughly 40 kilometers wide, made up of tidal creeks, shallow lagoons, and mangrove swamps. Inland lies the second belt of undulating hills rising to between 60 and 150 meters (200–500 feet). The third, and largest, belt comprises a series of low mountains and plateaus, marked by abrupt elevation changes and relatively sparse forest cover [32].

Differences in malaria transmission between Liberia’s coastal and inland areas arise from variations in environmental, socioeconomic, and infrastructural factors. The coastal counties like Montserrado, Margibi, Maryland, Grand Cape Mount, Grand Bassa, Grand Kru, River Cess, and Sinoe experience high humidity, frequent rainfall, and widespread wetlands, all of which provide ideal breeding habitats for mosquitoes [9]. In contrast, inland regions such as Lofa, Bong, and Nimba have higher altitudes, longer dry periods, and more pronounced seasonal climate variations, resulting in differences in transmission intensity [9]. While malaria transmission in coastal regions tends to persist throughout the year, inland areas often experience more seasonal fluctuations influenced by rainfall and temperature.

The finding that certain counties remain malaria hotspots indicates a significant threat to malaria control progress in Liberia and highlights the urgent need for targeted intervention measures [21].

Logistic regression results revealed that children from poorer households were more likely to be infected with malaria than those from wealthier families, with the highest risk observed among the poorest households. This outcome is consistent with research conducted in Liberia, Tanzania, Nigeria, and Kenya [21, 22, 28, 29, 31], emphasizing the critical influence of socioeconomic inequality on malaria burden. Families with limited financial means typically face reduced access to health services [16] and preventive measures [15].

Child age also played a notable role in malaria risk. Younger infants aged six to eight months often retain partial maternal immunity, while older under-five children tend to experience higher infection rates. Evidence from Liberia, Tanzania, Nigeria, and Uganda [21, 28, 29, 30] shows a similar trend, where older children (48–59 months) face a greater risk of malaria infection. This pattern likely reflects the protection younger infants receive from maternal antibodies transferred during pregnancy and breastfeeding [28].

Anaemia status was another strong determinant, showing a statistically significant link with malaria prevalence among under-five children. Findings from Nigeria and Tanzania support this relationship [28, 29]. Anaemic children were found to be more prone to malaria infection compared with non-anaemic peers, underscoring the interdependence between both conditions. Preventing malaria, therefore, also contributes to reducing anaemia rates among young children since the two conditions share common underlying causes.

Similarly, the analysis revealed that children residing in urban areas had lower malaria prevalence compared to their rural counterparts, a finding that agrees with previous research [28, 29]. This disparity is likely related to factors such as greater mosquito exposure near breeding sites, poor housing, limited economic resources, and inadequate access to healthcare or preventive tools in rural communities [15, 16].

The logistic regression analysis also showed that ITN use and care-seeking for fever were negatively associated with malaria prevalence among under-five children in Liberia. This is consistent with extensive evidence from Sub-Saharan Africa, which confirms the protective effect of insecticide-treated nets (ITNs) and timely medical care against malaria morbidity and mortality [2, 33, 34]. Children who did not sleep under any mosquito net had considerably higher odds of malaria infection compared with those who slept under treated ITNs, echoing findings from earlier studies. ITNs serve both as a physical shield and a chemical deterrent, reducing mosquito-human interaction and thereby lowering transmission [2]. Studies have also reaffirmed ITN use as a key protective factor, whereas non-use increases risk [29, 31]. However, their effectiveness depends on the net’s condition, consistent nightly use, and correct installation. A properly maintained ITN can reduce malaria episodes among under-five children by as much as 50% [2].

Care-seeking for fever also exhibited a negative association with malaria prevalence, suggesting that children whose caregivers sought early medical attention were more likely to receive testing and prompt treatment, preventing severe disease and continued transmission. Delays in seeking treatment contribute to ongoing infections and sustained transmission [16]. Research in Liberia, Nigeria, and Kenya also demonstrates that timely fever care, along with access to rapid diagnostic testing (RDT) and artemisinin-based combination therapy (ACT), substantially reduces malaria cases [29, 31]. These findings emphasize the need to strengthen healthcare availability, community awareness, and affordability of care, especially in rural and resource-limited areas.

This study revealed that malaria among children under five is heterogenous across Liberia. Significant hotspot counties were identified in both coastal and inland regions, including Sinoe, Maryland, Grand Bassa, River Gee, Grand Kru, Lofa, and Bong. The main determinants of malaria prevalence among under-five children were ITN usage, household wealth index, and care-seeking behavior for fever, all of which showed significant influence.

This study, through the two-proportion Z-test confirmed that inland counties had significantly higher under-five malaria prevalence compared to coastal counties, indicating substantial geographical disparities. The logistic regression analysis further linked malaria infection to anemia status, child age, ITN use, household wealth, and rural residence.

Based on the study’s findings, Liberia’s malaria profile showed intense spatial heterogeneity, some coastal and inland counties such as Sinoe, Maryland, Grand Bassa, River Gee, Grand Kru, Lofa, and Bong have much higher under‑five prevalence.

To reduce malaria among children and achieve the global 2030 targets of elimination, policy must shift from uniform national approaches to targeted subnational responses, and interventions must be targeted to these hotspots based on the key risk factors identified. This underscores the need for prioritizing protecting young children and pregnant women through proven interventions like ITNs, chemoprevention, vaccination, case management and behavior change in line with WHO guidance.