Malaria remains a significant public health challenge in sub-Saharan Africa with an estimated 233 million cases and 580,000 deaths in 2022[1]. Even though considerable progress has been made in malaria control over the past two decades, the disease continues to pose a substantial health and socioeconomic burden in high-transmission countries[2]. Understanding the complex interplay of factors influencing malaria transmission and developing targeted interventions are crucial for achieving global malaria elimination goals.

Tanzania has long been a high-burden country for malaria, with transmission occurring in over 90% of the country[3]. Between 2000 and 2015, Tanzania saw a substantial decline in malaria prevalence, from 29% to 14% among minors[4, 5]. However, the declines have not been observed everywhere and resulting in an interesting malaria epidemiological diversity. Malaria transmission is persistently intense in the Lake Zone (upper northwest) and coastal belt (east and south), with prevalence ranging from 20 to more than 40 percent. The Central Plateau of the country experiences seasonal malaria transmission, with prevalence between 5% and 20%. The Northern highlands have a pattern of low or seasonal transmission (less than three months per year) and a prevalence of less than 5 percent[6, 7]. The underlying drivers for these regional variations remain unclear.

Due to the diversity that has been reported to exist at the level of the household and the individual, it is necessary to expand beyond the sub-national level stratification by investigating the underlying dynamics of malaria risk factors at a high resolution to enhance evidence-based decision making and resource allocation for more effective malaria control strategies based on the established. Emerging challenges in Tanzania's malaria landscape also raise significant attention on the infection outcomes, including the labor lost, treatment cost, as well as health disparities among different regions and populations[8–11]. These challenges necessitate a deeper understanding of the socioeconomic factors influencing malaria infection and the development of tailored interventions to address persistent and emerging threats.

The relationship between malaria infection and socioeconomic factors has been the subject of extensive research. A systematic review by Degarege, et al. [12] found that individuals living in poor-quality houses, with lower education levels, and engaged in farming occupations had significantly higher odds of Plasmodium infection. Taylor, et al. [13] also applied analyses to show disparities in insecticide-treated net ownership across socioeconomic strata. Additionally, Sumari et al. [14]found significant differences in malaria knowledge, attitude, and practices among primary school children in Tanzania, highlighting the need for health education equity. Tusting, et al. [15]also demonstrated through a systematic review that socioeconomic development can be an effective intervention against malaria, emphasizing the importance of addressing broader determinants of health. In the Tanzania context, Dickinson, et al. [16] found the association between poverty and malaria infection, evidencing that a lower socioeconomic condition would lead to negative consequences in malaria prevention, control, and treatment. Additionally, original research by Somi, et al. [17]found that family income acted as a significant barrier to malaria treatment, and families with lower socioeconomic status suffer a higher share of spending for malaria treatment. This body of work has significantly contributed to our understanding of malaria epidemiology and control, while also revealing important gaps in knowledge.

Even though the relationship between socioeconomic status and malaria is well recognized, and socioeconomic equity has been widely described, the relationship between socioeconomic equity and malaria infection has not been specifically researched. This insufficiency of evidence and consideration limits approaches for further malaria control on a global scale. More context-focused research is necessary to address the latest malaria control challenges of transmission from a social equity perspective, particularly in high-transmission settings like Tanzania.

To address these gaps and gain a more comprehensive understanding of socioeconomic disparities in malaria outcomes, the use of measures of health equity, such as the Slope Index of Inequality (SII) and Relative Index of Inequality (RII), offers several advantages. Unlike simple comparisons between extreme groups, SII and RII take into account the entire socioeconomic distribution, providing a more complete picture of the equity condition across the population of different classes[18]. While SII provides an absolute measure of inequality (the difference between the hypothetically most and least advantaged individuals), RII offers a relative measure (the ratio between these extremes)[19]. These indices are relative measures that account for differences in the size of socioeconomic groups, allowing for valid comparisons across different populations or periods [20]. SII and RII are also sensitive to changes in the distribution of health outcomes across socioeconomic strata, making them valuable tools for monitoring progress in reducing health inequities over time [21]. This comprehensive approach is particularly important in the Tanzanian context, where diverse socioeconomic landscapes and varying malaria transmission intensities across regions necessitate tools that can capture subtle gradients in malaria risk.

Additionally, employing SII and RII in malaria research in Tanzania can provide valuable insights and contribute to more effective, equity-focused malaria control strategies. These indices can reveal inequities that might be missed by cruder measures, informing more targeted interventions [22]. As Tanzania strives to achieve its malaria elimination goals, tracking progress in reducing socioeconomic disparities in malaria outcomes becomes crucial. These indices can also help elucidate the complex relationships between malaria and various socioeconomic factors, supporting an intersectoral approach to malaria control that addresses broader determinants of health [23].

Furthermore, SII and RII facilitate valid comparisons of malaria-related inequities across different regions of Tanzania, helping to identify areas where disparities are most pronounced and informing the allocation of resources [24]. This comprehensive approach to measuring and analyzing health inequalities is crucial for developing evidence-based, equity-focused strategies to accelerate progress towards malaria elimination in Tanzania.

In light of these considerations, this study aims to address two key research questions:

By employing SII and RII analyses, this research seeks to provide a novelly nuanced understanding of the socioeconomic disparities in malaria outcomes across different regions of Tanzania. The findings will contribute to the development of targeted, equity-focused interventions and inform policy decisions aimed at reducing the burden of malaria in the region.

This was a secondary data analysis study from the cross-sectional household survey of China-Tanzania Demonstration Project on Malaria Control conducted from July to September 2019 in three districts of Kilwa, Rufiji, and Kibiti in southeastern coastal Tanzania [25, 26]. Description of study area and objectives has been described in other peer-reviewed publications [27, 28], where a 1,7-mRCTR malaria surveillance and control project was implemented. This study utilized the dataset from the project, which covered 185,000 people in the intervention areas and 40,000 in the control areas by deploying a stratified sampling approach, first randomly selecting wards, villages, and households in program areas sequentially, and then identifying participants within these households. The household surveys conducted in the project were developed following the structure of the Malaria Indicator Survey Tool [29] customized to the context of the study area. These survey aimed at collecting comprehensive data on various aspects, including asset ownership, which could help derive the socio-economic conditions of households, knowledge and adherence to malaria preventative measures, healthcare expenditures, utilization of medical services, and travel history. Additionally, blood samples were collected from participants with their consent for malaria infection diagnosis. Demographic details of the participants are in Table 1.

As the data collected for socioeconomic factors are qualified, we used Primary Component Analysis (PCA) to quantify the participants’ socioeconomic conditions and transform them into continuous variables. The PCA output is presented in Appendix 1. The system measured the participant’s socioeconomic condition from three perspectives: 1. Household Infrastructure; 2. Ownership and Property, and 3. Social Potential. The inclusion of the perspectives was with the consideration of the household’s current status, property ownership of items indicating wealth situation, as well as the household’s socioeconomic sustainability in maintenance and promotion. The method of variable inclusion and exclusion was adjusted from studies by Filmer and Pritchett [30] and Schellenberg et al. [31], where tangible belongings (item ownerships) and intangible features (occupation, education, etc.) were selected, as these variables presented significant associations with socioeconomic status. The PCA weighting methods for socioeconomic status have been applied by studies by Vyas and Kumaranayake [32], Houweling et al. [33], and McKenzie [34] in rural areas in Brazil and Ethiopia, where it was proven to be valuable in practical guidance with a reference range of proportion of variance value from 12% to 27% and eigenvalue from 2.2 to 4. The PCA output in this study showed a proportion of variance of 12.56% and an eigenvalue of 6.404, with acceptance for adoption and representativeness capturing the participant’s socioeconomic facts [35].

By applying PAC, each participant was given a “Score” to estimate their socioeconomic condition according to the weighting criteria (see Appendix 1). A higher score refers to a higher socioeconomic condition with better household construction, decoration, richer ownership of assets, and more socioeconomic potential for higher social classes. Unidentified socioeconomic characteristics and missing values were assigned as “other” in their following categories.

Dependent variables related to malaria infection outcomes were selected as 1. mRDTs blood test for malaria infection (binary: positive/negative), 2. Total cost for malaria infection treatment (continuous, Tanzania Shilling), and 3. Total days absent from work or education caused by malaria infection (continuous, day). Variable distribution is as Table 2.

There was no missing value in blood test diagnosis. For missing values shown in total cost, as the total cost was summed by: a. clinical registration fee, b. clinical consultation fee, c. medicine purchase fee, d. clinical infection test fee, and e. clinical admission fee. Missing value occurred in the subcategory and was replaced by the mean value of the subcategory; for those with negative malaria blood test outcome, missing value for total cost was replaced by zero. Missing values presented in days absent were replaced by mean value; for those with negative malaria blood test outcome, missing value was replaced by zero.

The data analysis was performed via R (version 4.3.3). Based on the socioeconomic score (continuous) quantified by the PCA method, logistic regression was first applied to learn the socioeconomic status’s impact on the malaria infection diagnosis outcomes. Linear regression was then performed to estimate the impact of the socioeconomic scores on the total cost and work/education days absent caused by the infection. Both logistic regression and linear regression were performed four times, respectively, for 1. The whole studied area, 2. Rufiji area, 3. Kilwa area, and 4. Rufiji area.

The SII and RII were adopted to equity conditions of malaria infection outcomes (infection rate, treatment spent, and absent days) across different areas. Both approaches are essential for assessing the magnitude of health disparities within a population. By using SII and RII, it is possible to compare disparities across various populations for outcomes related to disease infection and treatment. Adjusted from linear regression, SII can capture absolute change of health outcomes across different population groups, while RII can capture the relative rate of health outcomes over different population groups. According to the equation [36–38]:

Where refers to the average health condition (outcome) of the socioeconomic population group , refers to the population size of the group , referring to the Relative Rank of the population group , and , referring to the average health condition (outcome) of the whole socioeconomic population[36–38].

Transformed into the case of analysis:

It requires a series of pre-defined variables as follows:

Both SII and RII analyses were also performed four times for one whole studied area and three mentioned districts. To interpret SII result, a result near zero means a better equal condition; vice versa. A RII result near to one means a better equal condition; vice versa.

Based on the comprehensive analysis of malaria infection outcomes across the studied areas, Rufiji district consistently emerges as the district with the most equitable condition for malaria infection risk, malaria treatment cost, and days absent from education or work due to malaria infection. Firstly, Rufiji showed the strongest risk reduction per socioeconomic score increase (0.35% per score). Rufiji district also demonstrated the smallest variance in infection risk across different socioeconomic levels, as evidenced by its lowest SII (-12.62%) and RII (1.28) values for malaria infection risk. Secondly, Rufiji exhibited the weakest change in treatment cost per socioeconomic score increase (-1.57 Tanzania Shilling), the smallest gap between socioeconomic extremes (SII of -837.769), and the most limited relative disparity between the richest and the poorest in terms of treatment cost (RII of 0.26). Thirdly, the district showed the least impact of socioeconomic status on days off from work or education, with the lowest regression coefficient (-0.0008), highest SII value (-0.46), and lowest RII value (0.19). These results collectively suggest that Rufiji maintains an equitable distribution of malaria-related outcomes across socioeconomic strata, with the most effective infection-protection effect by socioeconomic improvements, most limited infection treatment cost (money and time) by socioeconomic changes, and the smallest poor-rich differences across the social classes for infection risk, treatment cost, and days off. This pattern of equitable health outcomes has been observed in other public health interventions [42].

The equitable outcomes in Rufiji suggested by its lower SII/RII values were possibly linked to documented interventions. Firstly, malaria control project by Khatib, et al. [43] implemented the distribution of insecticide-treated nets and indoor residual spraying across socioeconomic groups. The distribution of the equipment with sufficient coverage of different socioeconomic groups led to a decreased inequity of family spend for malaria prevention and a lowered risk for malaria infection [44]. Secondly, the 1,7-mRCTR malaria programs conducted in Rufiji by Mlacha, et al. [28] focused on the local health service accessibility by mobilizing local clinics and health workers to provide prompt diagnosis and treatment in less than one day. The implementation of providing accessible health services in developing regions has been identified as a crucial factor in reducing health inequities [45]. Thirdly, community education engagement also played a significant role in Rufiji's equity for malaria infection outcomes. Education program conducted in Rufiji by Mosha, et al. [46] for local female and children's health led to a higher blood and pregnant awareness, ending up with further focus on malaria prevention and treatment for women and children with lower socioeconomic conditions.

The examination of malaria treatment costs reveals parallel equity patterns to infection risk, with variations in financial burdens across socioeconomic groups. Districts with more equitable cost distributions likely benefit from better healthcare infrastructure, subsidized treatment programs, or improved access to facilities, which alleviate financial strain on lower-income populations. In contrast, areas with higher cost disparities may reflect barriers such as distant healthcare facilities, out-of-pocket expenses, or limited subsidy programs. These observations are consistent with studies highlighting the economic burden of malaria on low-income households, particularly in areas with limited healthcare access [47]. The community-based malaria control programs, including health education and health workforce integration that reach across socioeconomic strata, have been witnessed with feasibility to transform in other regional settings[25, 26, 48]. With Rufiji’s reference, further malaria control implementations, accessibility construction, local health education, as well as community engagement could be applied to improve equity issues regarding malaria infection in other malaria endemic regions.

As previous academic efforts evidenced that Rufiji area experienced more malaria control programs with lower malaria health burden than other two regions [4, 43, 49], and Rufiji place presented the equitable condition for malaria infection outcomes in this study, we are hinted that whether there would be a statistical significance of relationship between “malaria burden” and “infection equity conditions”. Theoretically, for a region with a lower malaria health burden, there would be better infection outcome equity conditions. This hypothesis may also be generalized to other health issues. Sufficient studies were found focusing on equity conditions, while limited academic work was found focusing on learning the causation between health equity and disease burdens from a public health view [39, 41, 50]. Further academic devotion and research output of this aspect are highly essential. Evidence for the hinted hypothesis may provide critically important guidelines for malaria control and social justice for policymaking and public health implementations. Importantly, to address these inequities, policies should prioritize expanding access to affordable or free treatment and improving healthcare infrastructure in underserved areas [51].

The observed variations in equity across the districts suggest that successful strategies in the most equitable areas could serve as models for others. Policymakers should investigate factors such as healthcare access, community health initiatives, or socioeconomic support systems that contribute to equitable outcomes. Targeted interventions, including equitable distribution of preventive resources, subsidized treatments, and health education, are essential to reducing disparities in less equitable districts. Addressing structural determinants, such as poverty and healthcare accessibility, will be critical to achieving health equity in malaria control [54].

This study is constrained by its focus on three districts, which may not fully represent broader regional or national trends. The cross-sectional design of our study captures a snapshot in time and cannot establish long-term relationships between socioeconomic factors and malaria outcomes. Longitudinal studies could provide deeper insights into the causal relationships between socioeconomic status and malaria outcomes, informing more effective policy strategies [55].

Additionally, the PCA presented a proportion of variance of 12.56%. Although this proportion stands with practical value for statistics and estimation, it is not adequately high to completely represent the socioeconomic factor’s influence on malaria infection outcomes and equity conditions [32–34, 56]. Further malaria control projects with more specific socioeconomic-focused survey design, as well as more comprehensive statistical model design, may help the PCA with better practical values. Furthermore, some variables, such as time absent from work or education, relied on self-reporting, which may be subject to recall bias, which may have affected the precision of our estimates [56]. Further cohort study based on continuous surveillance and follow-up focusing on the participants’ socioeconomic status and malaria infection equity may provide a comprehensive vision for exact measurement.

Unmeasured confounders may influence malaria outcomes beyond the socioeconomic factors assessed in our study. For example, behavioral patterns, seasonal variations in malaria transmission, and differences in healthcare quality were not included in our analysis. These factors, potentially linked to socioeconomic conditions, could affect malaria susceptibility but were not accounted for in our study [45, 48, 56]. To address these unmeasured confounders, future studies could incorporate detailed behavioral surveys, longitudinal data on seasonal transmission patterns, and standardized assessments of healthcare quality to better quantify their impact on malaria outcomes. Also, the coverage of study areas may not be representative of all of Tanzania or other East African countries. The generalizability of our findings to other regions with different socioeconomic profiles and malaria transmission patterns should be considered with strong caution [57]. These limitations align with challenges noted in similar studies [58]. Future research should explore these factors and evaluate the effectiveness of specific interventions in reducing inequities.

Based on these findings, we propose several recommendations to improve equity in malaria control: implementing targeted interventions for vulnerable socioeconomic groups, particularly in high-inequity areas like Kilwa and Kibiti; improving housing conditions for the poorest households; enhancing malaria education programs across all socioeconomic levels; improving access to affordable malaria diagnosis and treatment services; strengthening community-based malaria control programs; promoting intersectoral collaboration to address broader socioeconomic determinants of malaria risk; implementing regular monitoring of malaria equity indicators using tools; and conducting in-depth studies of areas achieving high equity, like Rufiji, to identify transferable strategies. By addressing these recommendations, Tanzania can work towards more equitable malaria outcomes, contributing to the broader goal of malaria elimination while ensuring that the benefits of control efforts reach all segments of the population.

This study quantifies socioeconomic disparities in malaria-related outcomes across districts in Southeastern Tanzania, highlighting the critical need for targeted public health interventions to address health inequities. Our findings reveal a clear inverse correlation between malaria burden and health equity: districts with lower malaria prevalence exhibit higher socioeconomic health equity. Notably, the Rufiji district demonstrates a successful model, achieving both low malaria burden and high health equity, offering actionable insights for designing interventions in high-burden regions. These results emphasize the necessity of integrating strategies to reduce malaria prevalence with efforts to mitigate socioeconomic disparities, ensuring equitable and effective malaria control policies.