1 Introduction

Over the years, social epidemiology has evolved certain measurement parameters in the study of disease as regards the human population. Some of these measurement tools include rate, ratio, prevalence, incidence, among others. However, the focus of this study was confined to incidence as one of the major tools in epidemiology. Contextually, disease incidence is defined as the number of fresh cases that happen within a particular time frame in a certain population, particularly within a year [1]. Malaria incidence, therefore, could be described as the number of newly infected cases of malaria per one thousand people at risk in a certain population, usually within a year. When applied to this study, the newly infected cases of malaria were considered among people in 28 African countries spanning the years 2000–2019. Being mostly a tropical disease, cases of malaria and the costs of treatment are likely to be higher during the rainy season [2].

Globally, malaria has been acknowledged as a public health concern with catastrophic consequences that cut across many spheres of human existence [3]. Conservatively, it has been suggested that at least 300 million malaria-induced health attacks take place annually in the continent of Africa. Also, it was noted that about 40% of children's clinically reported febrile sickness was underscored by malarial infection [4]. This is in addition to pregnancy-related anaemia and the baby's low birth weight being underpinned by the issue of malaria. So, attack from malaria is not only dangerous to the pregnant woman but also threatening to the survival chances of the child.

In terms of the macroeconomic burden of malaria, the weight on resource-deficit countries is quite enormous and overwhelming. The disease constitutes a burden on investment in labour empowerment and productivity [5]. A group of scholars has established an unhealthy relationship between malaria incidence and economic growth [6]. In Uganda, for example, increasing malaria cases were associated with a declining economic growth quantified in Dollar terms [7]. Also, 12% of Nigeria's Gross Domestic Product [GDP] was lost at a point due to malaria treatment [8]. At the household level, the average expenditure for treating an out-patient malaria case was put at $12.57, and it was almost double when it has to do with an in-patient [9]. This is in addition to losing workdays due to malaria illness, as revealed by a group of scholars [10]. On average, a malaria episode cost about 10 days lost, which translated to an average of over N40,000 per episode. Besides, over 20–30% of malaria-related hospital admissions and 30–50% of out-patient consultations in Africa have been documented [11]. Similarly, in an agrarian society, malaria incidence was noted to impact negatively not only on health status but also farm productivity and income due to incapacitation resulting from sick days [12].

Malaria is one of the most common ailments threatening the lives of over 200 million individuals each year [13]. The major cause of severe malaria is Plasmodium falciparum, which may be deadly and result in neuro-cognitive sequelae. Six species of malaria parasites inflicting humans have been identified, namely: Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale wallikeri, Plasmodium ovale curtisi, Plasmodium malariae, and Plasmodium knowlesi. These species undergo 10 or more morphological states and replicate from single to 10,000 + cells, varying in total population from one to many more than 106 organisms. Only a small number of these morphological stages lead to clinical disease in the human host, and the large majority of all malaria-infected patients in the world produce few symptoms and sometimes no symptoms in humans [14].

Although there have been some notable achievements in reducing the worldwide impact of malaria, it continues to pose a significant health challenge on a global scale. In 2018 alone, there were 228 million reported cases of malaria, with 2–4 million classified as severe cases, and sadly, 405,000 resulted in death. In 2020 alone, 241 million cases and 627,000 deaths due to malaria were estimated worldwide [15]. Furthermore, it has been documented that an estimated 98% of global malaria-related deaths occurred in sub-Saharan Africa [16]. Unfortunately, the majority of these fatalities occurred among children living in sub-Saharan Africa [17]. Malaria control has stagnated in recent years, with increased morbidity and mortality, especially in highly endemic countries in Africa. The main interventions, such as insecticide-treated bed nets and indoor residual spraying, are facing challenges due to vector resistance. Artemisinin-based Combination Therapy [ACT] has been effective, but artemisinin-resistant parasites have emerged in South-East Asia and possibly in Africa. Novel therapeutic approaches, including new antimalarial drugs like KAF-156 and triple ACTs, are being explored. The development of an effective malaria vaccine has been challenging, with a vaccine showing limited efficacy and waning protection. Therefore, identifying healthcare system funding in relation to malaria prevention and control remains an active area of research [18].

The scourge of malaria has multiple devastating effects on any society where it is endemic. Regions where malaria is most prevalent tend to perform poorly in terms of human development indicators. The distribution of per capita gross domestic product worldwide reveals a clear association between malaria and poverty, with countries affected by malaria experiencing lower rates of economic advancement. Malaria hinders development through various means, such as impacting fertility, population growth, saving and investment, worker productivity, absenteeism, premature death, and medical expenses [19]. For example, malaria morbidity results in a substantive loss in GDP in Uganda. The high burden of malaria leads to decreased long-term economic growth and works against poverty eradication efforts and the socioeconomic development of the country [7].

Malaria is a serious public health problem, particularly in developing countries, with certain factors associated with its incidence and prevalence. In Indonesia, research [20] has shown that socio-demographic, environmental, economic, cultural, and behavioural factors determine the frequency, severity, and outcome of malaria infection. Additionally, some environmental and ecological factors like rainfall and temperature have been found to increase the risk of malarial transmission and spread [21]. While at one point, the incidence of the disease reduced worldwide, since 2014, the rate of decline has been inconsistent and uneven in some regions [22]. In 2017, according to the latest World Malaria Report, 219 million malaria cases were reported. Despite this achievement, the African region continues to account for about 92% of malaria cases and deaths worldwide [22]. In 2017, the African continent alone accounted for 80% of all malaria cases worldwide, with the highest percentage in Nigeria [25%], followed by the Democratic Republic of the Congo [11%], Mozambique (5%), Ghana, Burkina Faso, Niger, and Uganda (4%), Mali, United Republic of Tanzania, Cameroon, and Rwanda [3%], and 2% for Guinea, Angola, Benin, and Malawi. With a focus on Nigeria, a study found that the incidence of malaria was higher in the Northern part of the country than in the Southern region [21]. Similarly, the incidence of the disease was significantly higher in the North-Central region than in the rest of the country. Malaria incidence rate in rural residential areas was significantly higher than in urban settings.

This situation suggests that environmental and geographical factors are associated with malaria incidence. Since malaria is recognized as a liability to any economy, some scholars emphasised the importance of including it in preventive and control agendas geared towards sustainable development [5]. However, specific factors that influence malaria control strategies, include donor agendas, costs, effectiveness of interventions, health and environmental impacts, financial sustainability, and vector resistance to insecticides, among others [23]. Against this backdrop, this study aims to assess the impact of domestic government general health expenditure [DGGHE], out-of-pocket health expenditure [OOPHE], external health expenditure, and the proportion of people using basic sanitation services on malaria incidence in 28 selected African countries over a 19-year period [2000–2019]. The specific objectives are to assess the impact of DGGHE on Malaria Incidence [MI], estimate the impact of OOPHE on malaria incidence, examine the impact of external health expenditure on malaria incidence, and estimate the impact of people using at least basic sanitation services on malaria incidence in the selected countries.

A study in Sub-Saharan Africa from 1995 to 2011 examined the effectiveness of health expenditure in 43 nations. It found that while health expenditure had a higher impact on proximate targets like immunization, malaria, HIV/AIDS, and nutrition, its effect on ultimate goals such as life expectancy and infant and child mortality was lower. The authors suggested that public health expenditure would be more effective if public service delivery improves, along with more female education and inclusive healthcare systems [24]. In Nigeria, the impact of public and private health expenditure on malaria cases during 1990–2014 was investigated. Government health expenditure significantly and negatively influenced malaria cases in the short run, while private health expenditure had a negative and insignificant impact. In the long run, the relationship was positive but insignificant. Other factors like literacy rate and per capita income were also found to influence malaria cases in Nigeria [25].

Another study examined the impact of public health expenditure on health outcomes in Africa, focusing on infant, maternal, malaria, and HIV/AIDS mortalities in Ghana and Nigeria. The findings revealed low public health expenditure in both countries, with Ghana showing a negative and insignificant relationship, while Nigeria indicated a positive relationship [7]. Additionally, it was found that government health expenditure had a positive impact on malaria prevalence [26]. A study in Indian states found that public health care expenditure adversely affected infant and child mortality rates and malaria cases [27]. A study specifically focused on Nigeria from 1990 to 2013 showed that effective government health expenditure significantly reduced malaria deaths in the country [28].

Out-of-pocket health expenditure, the direct payments made by individuals for healthcare services, is a significant factor impacting malaria incidence. High out-of-pocket expenses act as a barrier to accessing prevention and treatment measures, leading to delayed diagnosis and inadequate treatment. This contributes to increased malaria transmission and higher incidence rates. Financial constraints can hinder the implementation and effectiveness of preventive measures like insecticide-treated bed nets and antimalarial medication distribution programs, perpetuating the cycle of malaria transmission. Out-of-pocket health expenditure can also impede timely and accurate malaria diagnosis, leading to higher morbidity and mortality rates. Additionally, it exacerbates healthcare access inequalities, disproportionately affecting vulnerable populations [29,30,31,32,33,34].

External health expenditure, provided by international organizations and donors, has had a significant impact on reducing malaria cases and deaths in Africa. The Roll Back Malaria Partnership, supported by external funding, has been instrumental in coordinating malaria control efforts, leading to a substantial decline in malaria prevalence. However, challenges include sustainability of funding, coordination issues, and potential neglect of overall health system strengthening. The impact of external health expenditure can also be influenced by contextual factors such as political stability, governance, and socioeconomic conditions [35,36,37]. Poor sanitation conditions, unclean surroundings, and inadequate drainage systems contribute to the prevalence of malaria in rural areas. Improved drinking water and sanitation conditions have been identified as significant risk factors for malaria infection in sub-Saharan Africa. Enhancing water and sanitation infrastructure is suggested as a potential long-term intervention for malaria prevention [12, 38,39,40].

1.1 Theoretical framework

The study was anchored on "Health Production Function" [HPF] model. The model explains the impact of government health expenditure and other factors on malaria incidence. It considers health inputs, such as healthcare spending and access to sanitation service, and their relationship to health outcomes, including malaria incidence. It posits that increased government health expenditure leads to more resources for malaria prevention and control, reducing incidence. The model also incorporates out-of-pocket health expenditure and external health expenditure as inputs affecting malaria incidence. Additionally, it considers the impact of basic sanitation services on malaria incidence, suggesting that improved access to sanitation reduces malaria transmission [40]. By analysing the relationships between economic inputs and malaria incidence, including healthcare systems, infrastructure, and relevant factors, researchers can better understand the broader context of malaria control and prevention efforts.

2 Methods

2.1 Model specification

In terms of econometric engagement, the model was fashioned after selected studies [42,43,44,45,46] in the literature.

The functional model is stated below:


General panel linear model

$$Y_{it} = \alpha_i + \beta_1 X_{1it} + \beta_2 X_{2it} + e_{it}$$
(1)
$$MI = F \, \left( {lnDGGHE, lnOOPHE, lnEHE, lnPBSS} \right)$$
(2)

Econometric model:

$$lnMI_{it} = \alpha + \beta_{1\ln } DGGHE_{it} + \beta_{2\ln } OOPHE_{it} + \beta_{3\ln } EHE_{it} + \beta_{4 \, \ln } PBSS_{it} + e_{it}$$
(3)

where β1–β4 = A priori estimated expectations of parameters. lnMI = logarithm of Malaria Incidence [dependent variable]. lnDGGHE = logarithm of Domestic Government Health Expenditure [explanatory variable]. lnOOPHE = logarithm of Out-Of-Pocket Health Expenditure [explanatory variable]. lnEHE = logarithm of External Health Expenditure [explanatory variable]. lnPBSS = logarithm of People with at least Basic Sanitation Service [explanatory variable]. α = Constant. e = Error term. i = countries. t = Period.


Note: All the variables were in their natural log form to correct the problem of heteroskedasticity as well as the non-normality distribution of the data challenge.

The aim of this study was to investigate the association between certain independent variables and malaria incidence [MI] in 28 African countries over a 19-year period from 2000 to 2019. The authors used a retrospective study design with panel data obtained from the World Bank's World Development Indicators [WDI] annual reports and accounts. The selected countries included Nigeria, The Gambia, Ghana, Benin, Senegal, South Africa, Kenya, Tanzania, Rwanda, Zambia, Madagascar, Angola, Chad, Republic of Congo, Algeria, Botswana, Namibia, Mozambique, Malawi, Burkina Faso, Sierra Leone, Togo, Ethiopia, Mauritania, Uganda, Central African Republic, Democratic Republic of Congo, and Gabon. The countries were chosen based on data availability for the study variables and adherence to diagnostic test requirements.

To analyse the data, the authors employed the robust fixed effect estimator and multiple regressions technique. The study examined the impacts of Domestic Government Health Expenditure, Out-Of-Pocket Health Expenditure, External Health Expenditure, and People with at least Basic Sanitation Service on Malaria Incidence [MI] in the selected countries.

Before conducting the multiple regression analysis, the authors carried out several tests to ensure the validity of their results. The correlation matrix was used to check for multicollinearity among the independent variables, and the Variance Inflation Factor [VIF] test was applied to assess the presence of multicollinearity as suggested in the literature [41]. The Breusch Pagan/Cook Weisberg test was used to check whether the residual variance in the regression model was homoskedastic or heteroskedastic. The normality distribution of the central mean around the variables was assessed using the Shapiro–Wilk normality test.

Additionally, the authors used the Hausman specification test to determine the appropriate estimator for the analysis in line with a previous study [42], and the outcome indicated that the robust fixed effect estimator was suitable. The hypotheses were tested at a 5% significance level, and the data analysis was carried out using STATA 13.0 version software.

3 Results

The analysis of the results presented in this section was based on the panel data collected from the World Bank’s annual reports and accounts on Human Development Indicators covering 28 African countries. The results of the data analysis are presented as follows: The descriptive statistics indicated an average malaria incidence of 242.6 cases per 1000 population at risk in the 28 selected African countries over a 19-year period [2000–2019]. Also, the maximum malaria incidence was put at 724.6. Meanwhile, domestic government health expenditure [% of current health expenditure], Out-Of-Pocket Health Expenditure [% of current health expenditure], External Health Expenditure [% of current health expenditure] and People with at least Basic Sanitation Service [% of population] averaged 33.6%, 35.8%, 21.4% and 30.0%, respectively. Within the same period, the respective maximums domestic government general health expenditure, out-of-pocket health expenditure, external health expenditure and people with access to at least basic sanitation service were 78.6%, 77.8%, 79.9% and 87.6%. Moreover, the minimums DGGHE, OOPE, EHE and PBSS amounted to 3.9%, 2.9%, 0.1% and 3.4% accordingly (see Table 1).

Table 1 Descriptive statistics
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3.1 Test for multicollinearity using pairwise correlation matrix

As shown in Table 2, the results of pairwise correlation coefficients of all variables included in this study suggested that a moderate negative relationship of − 0.48 existed between DGGHE and malaria incidence. Meanwhile, except PBSS that showed a moderately negative correlation of − 0.54, OOPHE and EHE were weakly positively correlated with MI as reflected in the coefficients of 0.37 and 0.27 respectively. Also, OOPHE and DGGHE showed a moderate negative correlational relationship as indicated by − 0.55. Similarly, there was a moderate negative relationship between EHE and DGGHE as suggested by the correlation coefficient of − 0.46. The coefficient of 0.57 implied a moderate positive correlation between PBSS and DGGHE. Moreover, EHE has a weak negative relationship with OOPHE as indicated by − 0.34. The relationship between PBSS and OOPHE was a negatively moderate one as reflected in the coefficient of − 0.41 while that of PBSS and EHE was a weak negative one as implied by − 0.28 coefficients. Since the relationships that existed between the variables were within the parameter recommended by some scholars [41], it could be deduced that the assumption of linear regression regarding the issue of multicollinearity has not been violated.

Table 2 Correlation matrix test for multicollinearity
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3.2 Diagnostic test for heteroskedasticity

A further test was conducted to ascertain whether the residual variance of any variable in the model was constant or not using the Breusch Pagan/Cook-Weisberg approach. In other words, heteroskedastic. The a priori hypothesis of the model being heteroskedastic was accepted as deduced from Table 3. This was attributed to the chi2 [1] value of 458.59 with a corresponding probability value of 0.0000 being statistically significant at 1% alpha level [p-value < 0.05]. Since the probability value was less than 1%, it suggested the problematic issue of heteroskedasticity in the model and this consequently failed to meet the basic requirement assumption of the linear regression that emphasises homoskedasticity [i.e., no constant variances]. At this point, it became necessary for the authors to apply the robust fixed effect regression to estimate the model with a view of correcting the heteroskedasticity challenge.

Table 3 Breusch–Pagan/Cook–Weisberg test for heteroscedasticity
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3.3 Normality distribution and model specification test results

The test for normality distribution of the data across the variables was done using Shapiro–Wilk technique. Since the central means of the variables had their Prob < Z values less than 0.05 level of significance, it implied a violation of another assumption of the general linear regression requiring normal distribution of series. Therefore, it has necessitated the use of the robust regression technique to rectify the pitfall. To determine the appropriate estimator to be used, the Hausman specification test was applied due to the nature of the panel data. The result of the Hausman test suggested the robust fixed effect estimator as suitable for the study as depicted in the chi2 value of 458.59 and the significant Prob. Chi2 of 0.0000.

Relying on the F-Statistic probability value of 0.00 in Table 4, it could be deduced that the model was fit and admissible for decision making. Based on the given regression results, the findings in relation to the impact of different factors on Malaria Incidence [MI] in the selected countries are interpreted thus: The coefficient for DGGHE is 0.0454708, indicating a positive relationship with MI. However, the coefficient is not statistically significant [p-value = 0.604], suggesting that the impact of DGGHE on MI is not statistically reliable. The coefficient for OOPHE is 0.2839394, implying a positive relationship with MI. Additionally, the coefficient is statistically significant [p-value = 0.009], indicating that an increase in OOPHE is associated with a higher MI in the selected countries.

Table 4 Effect of health care expenditure and access to sanitation services on malaria incidence using robust fixed effect regression
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The coefficient for EHE is − 0.1452344, indicating a negative relationship with MI. Moreover, the coefficient is statistically significant [p-value = 0.012], suggesting that an increase in EHE is associated with a lower MI in the selected countries. The coefficient for PBSS is − 0.3854207, suggesting a negative relationship with MI. Additionally, the coefficient is statistically significant [p-value = 0.000], indicating that an increase in the usage of basic sanitation services is associated with a lower MI in the selected countries.

The lack of statistical significance for DGGHE suggests that while there was a positive relationship between government health expenditure and MI, this relationship may vary widely across different regions or countries. This could be attributed to differences in healthcare system effectiveness, governance, or the allocation of health funds. A possible theoretical explanation could be that increased government spending on health might not necessarily result in an immediate reduction of malaria incidence, as other contextual factors such as healthcare infrastructure and public health programmes play crucial roles. The statistically significant positive relationship between OOPHE and MI underscores the critical role financial barriers play in malaria prevention and control. This finding aligns with the theoretical concept that out-of-pocket health expenses can deter individuals from accessing necessary prevention measures and treatment. The study suggests that addressing financial barriers should be a priority in malaria control strategies.

The statistically significant negative relationship between EHE and MI posits that external funding can be highly effective in reducing malaria incidence. However, sustainability and coordination of these funds are crucial to ensure their long-term impact. Contextually, the presence of strong partnerships and well-coordinated programmes may enhance the effectiveness of external funding in malaria control. Similarly, the negative relationship between basic sanitation services and MI, coupled with statistical significance, is consistent with the understanding that improving sanitation and environmental conditions can lead to a reduction in malaria transmission. This highlights the importance of integrated public health approaches, addressing not only healthcare but also environmental factors in malaria control efforts.

4 Discussion

In light of the previous studies, the authors observed a lack of statistical significance for the coefficient related to Domestic Government General Health Expenditure (DGGHE) in its impact on Malaria Incidence (MI). It is worth noting that a separate study found that health expenditure had a more substantial effect on immediate targets such as immunisation, malaria, HIV/AIDS, and nutrition but a weaker influence on long-term goals like life expectancy and child mortality [24]. This observation aligns with the current finding. However, the lack of statistical significance in the present study suggests that the impact of DGGHE on MI is not statistically reliable, which was a point not directly addressed by Ssozi and Amlani's study [24]. In addition, another study found that government health expenditure had a significant negative influence on short-term malaria cases but an insignificant positive relationship in the long run [25]. In contrast to the present finding of a positive relationship between DGGHE and MI, the current study did not reveal a significant negative impact in the short term.

Moreover, a study conducted in Ghana found a negative and statistically insignificant relationship between public health expenditure and health outcomes, whereas in Nigeria, a positive relationship was observed [47]. This partially aligns with the current finding, as it implies that the impact of DGGHE on MI may vary among countries, with Nigeria potentially exhibiting a positive relationship. However, a couple of authors discovered that government health expenditure had a positive impact on malaria prevalence, which is consistent with the current finding [27]. Nonetheless, a study conducted in India did not directly correlate with the current finding of a positive but statistically insignificant relationship between DGGHE and MI [27]. It is worth mentioning that the literature has emphasised the importance of effectively utilising government health expenditure, which plays a significant role in reducing malaria deaths [28]. While this study did not focus on the incidence of malaria, it underscores the significance of efficient spending and may provide context for the lack of statistical significance in the current finding.

To summarise, the present finding of a positive coefficient for DGGHE, with no statistical significance in relation to MI, aligns with some previous studies [47] that found either a positive but insignificant or a negative and insignificant relationship. However, it contradicts a study [26] that found a positive impact, and it does not directly address the adverse effects found in another study [27]. The importance of effective utilisation of health expenditure, as highlighted in the previous study [28], could provide further context for interpreting the lack of statistical significance in the current finding.

The current study also discovered a positive relationship between out-of-pocket health expenditure [OOPHE] and MI in the selected countries. An increase in OOPHE was associated with a higher MI, and this relationship is statistically significant. This finding is consistent with previous research. The World Health Organization [WHO] acknowledges the impact of OOPHE on MI, stating that it is a significant factor that can affect the incidence and management of the disease. The literature consistently demonstrated a strong association between OOPHE and MI. High out-of-pocket expenses act as a barrier to accessing appropriate prevention and treatment measures, leading to delayed diagnosis and inadequate treatment. Several studies have highlighted how OOPHE hinders the implementation and effectiveness of preventive measures such as insecticide-treated bed nets [ITNs], indoor residual spraying [IRS], and antimalarial medication distribution programs. Financial constraints prevent individuals from affording these interventions, increasing their likelihood of contracting malaria. This finding aligns with a study that emphasised that low uptake of preventive measures due to financial constraints undermines community-wide protection and perpetuates the cycle of malaria transmission [32]. Additionally, the impact of OOPHE on timely and accurate malaria diagnosis is discussed in the literature. Individuals facing financial constraints may opt for cheaper or inaccurate diagnostic methods or delay seeking diagnosis altogether, leading to delayed treatment initiation and higher morbidity and mortality rates [33]. Another study highlighted how OOPHE exacerbated existing healthcare access inequalities, particularly among vulnerable populations. Resource-challenged households, rural areas, and marginalised communities face higher financial burdens when seeking malaria-related healthcare services. This perpetuates health disparities and socioeconomic inequities [34]. The positive relationship between OOPHE and malaria incidence, as well as the associated barriers to accessing prevention, treatment, and timely diagnosis, are consistent with the existing literature. These findings underscore the importance of addressing financial barriers and promoting equitable access to healthcare services in order to effectively control and manage malaria.

The finding that an increase in External Health Expenditure (EHE) is associated with a lower MI in selected countries aligns with previous studies on the impact of external health expenditure on malaria control and prevention in Africa. Several studies and reports have highlighted the positive impact of external health expenditure on reducing malaria cases and deaths across the continent. For example, some authors concluded from their study that increased external funding resulted in a substantial reduction in malaria prevalence, with a significant decline in the number of clinical cases and deaths between 2000 and 2015 [13]. The RBM partnership, a global initiative supported by external health expenditure, has also played a crucial role in coordinating and implementing malaria control efforts. The RBM partnership's World Malaria Reports provide comprehensive data on malaria incidence, interventions, and funding across countries, demonstrating the positive impact of external health expenditure on reducing malaria burden in Africa. These reports highlight the contribution of external health expenditure in facilitating key interventions such as ITNs, IRS, and effective antimalarial treatments, which have led to a reduction in malaria transmission and improved health outcomes.

A group of authors found a significant association between ITN ownership and reduced malaria prevalence [4]. This supports the notion that external health expenditure has been effective in implementing interventions and reducing malaria incidence. However, it is important to acknowledge that there are challenges and limitations associated with external health expenditure in malaria control. One major concern is the sustainability of funding, as reliance on external funds can create uncertainty in long-term financing, potentially hindering the continuity of malaria control programmes. Additionally, the coordination and alignment of various funding sources can be complex, leading to inefficiencies and fragmented interventions. It is also crucial to ensure that investments are made to strengthen overall healthcare infrastructure and capacity-building, as neglecting health system strengthening may limit the long-term impact and sustainability of malaria control efforts. Contextual factors such as political stability, governance, and socio-economic conditions can also influence the impact of external health expenditure on malaria control.

The finding that an increase in the usage of basic sanitation services is associated with a lower MI is in consonance with the existing literature. Previous research found that poor sanitary conditions were a major factor responsible for high malaria incidence in rural households [12]. This aligns with the finding that an increase in basic sanitation services, which improves sanitary conditions, is associated with a lower MI. A study in rural areas of Nigeria equally identified bushy surroundings and unclean drainage systems as contributors to the prevalence of malaria [38]. Although their study focused on different aspects of sanitation, it supports the notion that poor sanitation conditions are associated with increased malaria incidence. Improving basic sanitation services would address these issues and potentially reduce malaria transmission. Some authors implicated poor environmental sanitation and housing conditions in increasing the burden of the malaria parasite in Cameroon [39]. This finding supports the idea that improving sanitation, including basic sanitation services, can play a significant role in reducing malaria incidence.

A recent study examined the relationship between improved water and sanitation conditions and the risk of malaria infection in sub-Saharan Africa. The researchers highlighted the importance of water and sanitation conditions as significant risk factors for malaria infection among children [40]. The findings reinforce the significance of basic sanitation services in reducing malaria incidence. Overall, the finding that an increase in basic sanitation services is associated with a lower MI aligns with the existing literature, which consistently demonstrates the role of poor sanitation conditions in contributing to malaria transmission.

4.1 Limitations of the study

While the current study offers valuable insights into the relationship between various types of health expenditure and malaria incidence, it is important to acknowledge its limitations. These limitations include the following: Firstly, the study relied on aggregated secondary data, which could obscure important regional or local variations. Variations in health expenditure and malaria incidence at the micro-level might not be fully captured in the analysis. Secondly, the findings of this study are specific to the selected countries or regions under investigation. The relationship between health expenditure and malaria incidence could differ in other settings or countries with distinct healthcare systems, socio-economic conditions, or disease epidemiology. Therefore, caution should be exercised when applying these findings to other contexts. Thirdly, the study could not have accounted for potential time lags or non-linear relationships between health expenditure and malaria incidence. Understanding the temporal aspects of this relationship could provide deeper insights. Fourthly, the study did not have fully consider contextual factors that could influence the relationship between health expenditure and malaria incidence. Factors such as political stability, governance, healthcare infrastructure, and cultural practices could interact with health expenditure and affect malaria outcomes. Finally, despite these limitations, it is worth noting that the study's findings align with previous research and offer valuable insights into the relationship between health expenditure, sanitation services, and malaria incidence. These insights have practical implications for policy-makers and practitioners engaged in malaria control and prevention efforts.

5 Conclusion

The study has been able to highlight the intricate and multifaceted nature of factors influencing Malaria Incidence (MI) in Africa. While the significance of some variables, such as Domestic Government General Health Expenditure (DGGHE), remains inconclusive and subject to contextual variations across countries, it is evident that out-of-pocket health expenditure (OOPHE) poses a significant barrier to effective malaria prevention, treatment, and diagnosis. Additionally, the well-established role of External Health Expenditure (EHE) in reducing MI is encouraging, but the sustainability and systemic strengthening of such funding sources must be carefully considered. Also, the positive relationship between basic sanitation services and lower MI underscores the importance of addressing environmental factors in malaria control efforts. The study underscores the need for a comprehensive and multifaceted approach to combat malaria in Africa. This approach should not only focus on increased health expenditure but also prioritise measures to reduce financial barriers, ensure sustainable external funding, and improve sanitation and environmental conditions. Effectively addressing malaria in Africa necessitates a holistic strategy that takes into account the various factors at play and implements them in a coordinated and sustainable manner.

5.1 Policy recommendations

The study offers valuable insights and practical policy recommendations for various agencies and stakeholders engaged in combatting malaria incidence in the selected African countries. Here are the key implications based on the findings:

While the study suggests that increasing Domestic Government General Health Expenditure (DGGHE) may not have a statistically significant impact on reducing MI, it remains crucial to maintain or enhance investment in general government health expenditure. A well-funded healthcare system is vital to provide comprehensive health services, not only for malaria but for the overall well-being of the population.

Given the positive and statistically significant relationship between out-of-pocket health expenditure and malaria incidence, policy-makers and health agencies in African countries should prioritise efforts to reduce the reliance on out-of-pocket health expenditure. This can be achieved by implementing health insurance schemes or social protection measures to alleviate financial burdens on individuals seeking malaria treatment. Ensuring affordable and accessible healthcare services is imperative.

The negative and statistically significant coefficient for external health expenditure suggests that governments of African countries should actively encourage and support increased external health expenditure directed towards malaria prevention, control, and treatment. International organisations, donor agencies, and governments should continue investing in initiatives that aim to reduce the burden of malaria. This could include funding research, awareness campaigns, and improving access to healthcare facilities.

The study further recommended that governments of African countries, in partnership with their respective private sectors, prioritise the importance of basic sanitation services in malaria prevention programmes. Investments should be made in improving sanitation infrastructure, promoting proper waste management, and ensuring access to clean water sources. Public health campaigns should educate communities about the significance of maintaining good sanitation practices.

Overall, the study suggests that addressing malaria incidence necessitates a multifaceted approach. This approach should encompass not only increasing health expenditure but also reducing out-of-pocket expenses, increasing external funding, and improving basic sanitation services. These should inform policy-makers, health agencies, international organisations, and other stakeholders involved in formulating and implementing strategies to combat malaria in the selected countries. Collaborative efforts that span the healthcare sector, financial institutions, and the broader community are essential to effectively reduce malaria incidence and its associated burdens. By implementing the aforementioned recommendations, stakeholders can contribute to a comprehensive and integrated strategy that addresses the complexities of malaria control in African countries, ultimately leading to improved health outcomes and reduced malaria incidence.