Background
Malaria, a mosquito-borne disease continues to be a major public health concern in Africa with longstanding infections leading to significant morbidity, and mortality especially among children under 5 years [
1]. By 2021, approximately 234 million malaria cases, and 593,000 deaths occurred in Africa [
2], imposing a heavy burden on human societies, negatively impacting community welfare, and constraining socio-economic development [
3]. Some malaria related deaths in Africa have also been attributed to the COVID-19 disruptions, which significantly affected health care delivery systems, while constraining malaria control funding, including the distribution of insecticide-treated bed nets (ITNs), indoor-residual spraying (IRS), and treatment [
4,
5].
In sub-Saharan Africa (SSA), malaria transmission is mediated by complex interactions between humans, and infected mosquitoes, exacerbated by the favourable physical environments for mosquito survival, and breeding, opportunities for human exposure to mosquito bites, poor healthcare systems, inadequate malaria control interventions [
1,
6,
7], as well as land use and land cover changes [
8]. Malaria infections can even be more devastating among the structurally disadvantaged populations (i.e. refugees, internally displaced, and asylum-seekers) who live in confined settlements characterized by poor sanitation, poor housing infrastructure, limited access to health care services, inadequate malaria vector control, and economic deprivation [
9,
10]. Considering the complexity of malaria transmission dynamics, modelling the determinants of malaria presents numerous challenges in regards to inclusion of uncertainties, non-linearity, and dynamism [
11]. It is thus paramount to apply integrated robust models that consider malaria transmission dynamics, to guide pre-emptive policies, and targeted actions for malaria control, and optimal use of resources in the refugee settlements of Uganda, and other refugee hosting countries in Africa.
In most malaria studies conducted in SSA, logistic regression models have been widely used by different scholars to analyse malaria risk factors. For instance, a recent systematic review by Obasohan and colleagues focusing on the period between January 1990 and December 2020 [
6], revealed that logistic regression models have been extensively utilized to identify statistically significant malaria risk factors including the nature of housing materials, household wealth status, possession of ITNs, mother’s level of education, environmental resources, drinking water sources and sanitary conditions. In refugee geographical settings, researchers have also used logistic regressions to examine malaria risk factors. For-example, a study conducted in Tongogara refugee camp in Zimbabwe used a logistic regression model, and revealed that housing structures, outdoor activities, and wearing clothes that do not cover the whole body, increased the risk of contracting malaria [
12]. Another study conducted in Kiryandongo refugee camp in Uganda also utilized a logistic regression model, and concluded that
Plasmodium falciparum and intestinal parasitic co-infection was associated with malaria and anaemia [
13]. A recent study focusing on all the refugee settlements in Uganda also used a logistic regression model, and revealed that the use of pit latrines, open water sources, lack of ITNs, inadequate knowledge on malaria causes, and prevention, were the key drivers of malaria infections among children under-five [
14].
Although these, and recent studies provide valuable insights on malaria risk factors in refugee settlements, they have potential limitations. First, the logistic regression models employed in these studies were used to measure the statistical significance of each determinant of malaria infections with respect to probabilities (
P-value < 0.01; < 0.05), without any form of importance ranking to inform malaria control efforts in refugee settlements. Second, logistic regression models have been observed to struggle with restrictive expressiveness, and predictive performance, and sometimes multiplicative interpretation of their generated results is difficult [
15]. Third, multiple factors influencing the risk for malaria infections do not act in isolation, but rather in an aggregated format [
11]. Fourth, logistic regression models were unable to represent conceptual reasoning [
16], or complex interactions [
15] among the malaria risk factors that were uncertain, stochastic, nonlinear, and multidimensional. Finally, in these studies, the inclusion criteria (
P < 0.20) that was used to include variables in multivariable logistic regression, left out some key malaria risk determinants.
In response to the limitations of existing research, this study provides an alternative knowledge-based Bayesian belief network (BBN) modelling approach to holistically analyse, predict, and rank the determinants of malaria infections among children under-5 years in the refugee settlements of Uganda. Among others, the BBN is a key integrated modelling approach [
17]. Increasingly, BBNs are becoming popular, because of their probabilistic abilities to model uncertainties, and complex environmental domains [
18]. A BBN model has several advantages over logistic regression models. BBNs are: (1) highly transparent; (2) flexible in modelling causal relationships; (3) capable of integrating information from various sources (i.e. experimental data, historical data, and expert opinion), and (4) have the potential to explicitly handle uncertainties, and missing data [
18,
19]. Because of their versatility, BBNs have been widely used in prediction, data analysis, updating, diagnosis, optimization, deviation detection, and decision-making based on available information [
20]. Despite their increasing application in related malaria studies [
21‐
24], BBNs have not been used to study malaria risk factors in refugee settlements of Uganda, and elsewhere.
Thus, a BBN model was developed and utilized data from the 2018–2019 Uganda Malaria Indicator Survey (UMIS), which is the first national wide malaria survey in Uganda to include households, and people in the refugee settlements [
25]. Specifically, this study aimed to: (1) develop a novel, and effective knowledge-based BBN model illustrating the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda; (2) predict, and rank the risk factors for malaria infections among children under-five in refugee settlements of Uganda. The study’s contribution to the growing body of literature on malaria is twofold. First, this study contributes to the methodological literature on the comprehensive, and holistic assessment of malaria risk factors using BBN technique in refugee settlements. Second, unlike in the previous studies which focused on eliciting statistical significance of the malaria risk factors, this study ranks the risk factors to inform malaria control interventions efforts in refugee settlements. Ranking, and prioritizing malaria risk factors are crucial for allocating resources to targeted malaria control interventions when operating within a context of limited resources.
Discussion
Despite extensive research on malaria, the disease remains a major health challenge in many countries of SSA attributed to various socio-economic, and environmental factors. The household level risk factors of malaria infections are known to be complex, stochastic, nonlinear, multidimensional, and do not act in isolation [
21]. In refugee settlements, these determinants are also linked to a range of closely related factors including poverty, low levels of education, low access to basic social services, inadequacy of some public policies, racism, sexism, and economic deprivation [
9,
46]. Thus, integrated models [
17] that consider all these factors are urgently required to enable decision-makers, and stakeholders to draw appropriate conclusions in malaria control interventions in refugee settlements. The recent attempt to model household level determinants of malaria infections in refugee settlements in Uganda [
14] was based on a logistic regression model which is not able to fully capture dependencies, uncertainties, complex interactions and ranking of the various malaria risk factors to inform and direct policy interventions on malaria control [
11]. The same logistics regression models have been widely used to determine the significant malaria risk factors in many countries of SSA [
6,
11,
47].
Here for the first time, a knowledge-based BBN modelling approach has been presented as a potential method to clarify the holistic understanding of the complex interactions among the risk factors of malaria infections among children under 5 years in refugee settlements in Uganda, and quantify the impact of various malaria risk factors. Although a BBN modelling technique has been used to model household factors influencing the risk of malaria among children under 5 years in SSA [
21], this is the first study to use a BBN approach focusing on refugee settlements, which are unique given the fact that there are inhabited by structurally disadvantaged populations [
46]. Moreover, these structurally disadvantaged populations characterized by racism, sexism, and economic deprivation [
46] can lead to further geographical distribution of parasites (i.e. introducing new parasite strains in new locations), cause re-emergence or re-infections as well as lead to multiple, and co-infections with various populations of malaria parasites [
14].
Basing on the BBN classification categories (i.e. alpha, beta and gamma) proposed by Marcot et al. [
29], the developed BBN model can be considered as a gamma-level model or final application model containing well tested, calibrated, validated, and updated state beliefs with reliable, and accurate probabilistic results which can further be used to inform policy in malaria control programmes in refugee settlements of Uganda. The graphical representation of the model with summarized results in a visually attractive and easy-to-analyse format can be used as part of decision analysis tool in malaria interventions in refugee settlements. The explicit recognition of uncertainty by the developed BBN model can help decision-makers to identify the risks associated with different malaria intervention strategies.
In this study, the risk factors of malaria infections among children under 5 years in refugee settlements in Uganda were ranked in their order of importance (Fig.
5). This is a major advantage of a BBN-modelling structure over traditional statistical models [
18,
28]. In Table
2, the predications and estimates provided indicate specific areas which need interventions. The top ranked 10 determinants (i.e., age of child, main roof, wall and floor materials, whether children sleep under ITNs, type of toilet facility used, walk time distance to water sources, type of cooking fuel used, drinking water sources and household wealth) had a higher probability of contributing to malaria burden in refugee settlements. Although lack of ITNs, and IRS, age of household head, sex of household head, mother’s level of education, lack of knowledge on the causes and prevention of malaria have been associated with malaria infections among children under 5 years in SSA as shown in the recent review study [
1], in this study, there are not among the 10 ranked determinants in refugee settlements of Uganda. This is because refugee settlements are occupied by structurally disadvantaged populations [
46] coming from diverse social-cultural and economic backgrounds which in turn may have varying impact on malaria infections among children.
The top 10 ranked determinants (Fig.
5) are crucial in enhancing the mosquito survival, biting and feeding, parasite development, and breeding [
1,
44]. The vulnerability of refugee children to malaria infections is dependent on parents’ personal behaviours, gender roles, physical and environmental factors, social-cultural aspects, and access rights [
9]. Ranking and prioritizing risk factors of malaria infections in refugee settlements rather than providing their statistical significance is an important component because, it helps to allocate resources to malaria control interventions within the constraint of limited humanitarian funding [
48]. Moreover, ranking and prioritization of malaria risk factors is crucial to provide targeted interventions, since the health services in malaria-endemic countries have had to re-allocate funding and resources towards COVID-19 containment efforts [
4].
Conclusion
Targeted interventions, and resource allocation are essential for effective malaria control in refugee settlements in Uganda, with predictive integrated models providing important information for decision-making. A BBN model can be used for accurate malaria prediction, and ranking of malaria risk factors. The developed BBN model has an accuracy rate of 91.11% of predicting 48.1% positive, and 51.9% negative malaria cases correctly among children under 5 years in refugee settlements of Uganda. Unlike in the previous studies that focused on the statistical significance of malaria risk factors, the sensitivity analysis results in this study identified, and ranked the malaria risk factors which is an excellent approach to inform policy recommendations on strategic malaria control interventions. The top ranked risk factors of malaria infections included: (1) age of child, (2) roof materials (i.e. thatch roofs), (3) wall materials (i.e., cardboard walls, plastered walls, poles with mud, and thatch wall), (4) whether children slept under ITNs, (5) type of toilet facility used (i.e., no toilet facility, pit latrines with slabs, and VIPs), (6) walk time distance to water sources (i.e., between 0 and 10 min), (7) type of cooking fuel used (i.e., charcoal), (8) drinking water sources (i.e., open water sources, and piped water on premises), and (9) household wealth status (i.e., poor). These results can aid in the identification of priority measures to reduce mosquito density, survival, breeding, mosquito biting rates and human vector contact in refugee settlements. Future studies can focus on the development of a GIS-BBN model that can take into account the Global Positioning System datasets of the 2018–2019 UMIS, and other spatio-temporal environmental, and climate data to disclose interesting features of the malaria transmission hotspots. Risk mapping will captivate the spatial-regional malaria dimension of risk factors in refugee settlements of Uganda within a context of climate change.
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