2.1. Overview

Our methodology drew on BCA (Robinson et al., Reference Robinson, Hammitt, Cecchini, Chalkidou, Claxton, Cropper and Eozenou2019) and cost-effectiveness analysis (Wilkinson et al., Reference Wilkinson, Sculpher, Claxton, Revill, Briggs, Cairns and Teerawattananon2016) reference cases. We adopted a societal perspective, presenting combined costs to both the provider (government) and households, and used a microeconomic approach to estimate the intervention’s benefits and costs. First, we conducted a BCA for each country individually. Second, we combined the ten country-specific point estimates to generate an aggregate result across the ten HBHI countries. Third, we performed a simulated probabilistic generalized BCA and probabilistic sensitivity analysis, to model average conditions across the study settings and explore the impact of parameter variation and uncertainty on BCRs.

2.2. Conceptual framework

In addition to poor health outcomes, malaria is also linked to increased school absenteeism, impaired cognitive development and function and, consequently, reduced learning outcomes. These factors limit the ability of SAC to reach their full potential, affecting both individual academic achievement and broader human capital development. The long-term economic consequences can be substantial. To illustrate these dynamics, we developed a conceptual framework (Figure 1), which highlights the interplay between malaria and education, at the individual pupil level. This framework was designed for settings with high malaria burden and is directly applicable to our study population.

2.3. Intervention

Malaria chemoprevention may be delivered to SAC, either as intermittent preventive treatment or as extended-age seasonal malaria chemoprevention (Morlino et al., Reference Morlino, Byrne, Achan, Baraka, Barry, Bousema and Camara2025). Both involve administering full courses of antimalarials at regular intervals, targeting age groups according to local malaria burden. Delivery can occur in communities or at fixed sites like schools. Our analysis modeled chemoprevention delivered to SAC enrolled in government-funded primary schools using a school-based delivery platform.

For the intervention drug, we assumed the use of sulfadoxine–pyrimethamine plus amodiaquine (SPAQ) in all countries, except Uganda and Tanzania. In these two countries, where either programmatic delivery is underway (Tanzania) or a clinical trial is planned (Uganda), we assumed the use of dihydroartemisinin–piperaquine (DP) (National Institute for Medical Research, 2023). We assumed six rounds of treatment for all countries except Tanzania, where the current practice is to administer malaria chemoprevention in SAC three times per year.

2.4. Decision tree model

We used a decision tree model to evaluate the health outcomes, educational outcomes and associated costs of implementing malaria chemoprevention in SAC. The model estimated the costs of implementing malaria chemoprevention in SAC over one year, capturing associated morbidity-related cost savings within that time frame, while mortality risk reduction and educational benefits were projected over the cohort’s lifespan. We applied a 5% discount rate consistently across both the benefit–cost and cost-effectiveness analyses, in line with the Copenhagen Consensus methodology and recent literature suggesting that 5% better reflects low–middle-income country (LMIC) contexts than the 3% rate recommended in the iDSI reference case (Wilkinson et al., Reference Wilkinson, Sculpher, Claxton, Revill, Briggs, Cairns and Teerawattananon2016; Haacker et al., Reference Haacker, Hallett and Atun2019; Copenhagen Consensus Center, 2021).

Our analysis focused on ten African countries designated as HBHI priority countriesFootnote ¹ by the WHO, including Burkina Faso, Cameroon, the Democratic Republic of the Congo, Ghana, Mali, Mozambique, Niger, Nigeria, Tanzania and Uganda (World Health Organization, 2018).

2.5. Data

The population of children in government-funded primary schools in 2024 came from the United Nations Educational, Scientific and Cultural Organization (UNESCO) indicator data of total primary school pupils and percentage enrolled at private schools and using sub-Saharan wide pupil growth rates (UNESCO Institute for Statistics (UIS), 2024). Data on primary school years and the number of primary schools per country were obtained from national sources, varying by country. Where available, country-specific inputs for costs, epidemiological data and educational variables were used. Detailed data sources are provided below and in the parameters table (Supplementary Table S2).

3.1. Intervention cost

We estimated the economic costs for both households and providers (government), categorizing costs into drugs, supplies and materials, human resources, community sensitization, storage, transportation and monitoring and evaluation. Drug costs were sourced from Global Fund reference pricing, with an additional 5% for wastage and 10% for transportation, customs clearance and insurance (Montresor et al., Reference Montresor, Gabrielli, Diarra and Engels2010, 2024a; Global Fund, 2024b). We assumed 90% intervention coverage for children who are enrolled in government-funded primary schools (GiveWell, 2021). For drug administration, we estimated that six teachers per primary school would each dedicate two hours per round (12 hours total) to administering the drugs to pupils. Additionally, we assumed that 50% of parents would attend a two-hour informational session, with time valued in relation to GNI per capita (World Bank, 2025). Alternative approaches, including statutory minimum wages, informal wage rates and household consumption data, were considered but not applied due to concerns regarding comparability between and data availability across the ten countries. Costs for training, community sensitization (including social behavior change campaigns), monitoring and evaluation and administrative personnel were modeled drawing from cost analyses of interventions in Uganda and Kenya (Zulaika et al., Reference Zulaika, Nyothach, van Eijk, Wang, Opollo, Obor and Mason2023; Gonahasa et al., Reference Gonahasa, Nassali, Maiteki-Sebuguzi, Namuganga, Opigo, Nabende and Okiring2024). Across countries, costs varied by the number of pupils, schools and districts per country and by cost of drugs, teacher salaries and value per lost hour (GNI per capita). Consistent with our one-year analytic time frame, we excluded broader education system costs (e.g. teacher salaries or classroom expansion), as these are considered fixed in the short term and unlikely to be affected by the intervention within this period.

3.2. Estimating health outcomes

3.3. Valuing morbidity reductions

Using cost-of-illness methods, we modeled costs for three different categories of morbidity from symptomatic malaria cases/episodes: uncomplicated cases that did not seek care from a formal health provider; uncomplicated cases that sought care from a formal health provider; and severe malaria cases that required hospitalization (Larg & Moss, Reference Larg and Moss2011; Jo, Reference Jo2014).

3.4. Valuing mortality risk reduction

We translated the estimated reduction in deaths into monetary benefits by multiplying deaths averted by the value of a statistical life (VSL). VSL represents a population’s aggregate willingness to pay for small reductions in mortality risk and is empirically derived from individuals’ willingness to trade income for marginal changes in their own risk of death.

Because most primary VSL studies are conducted in high-income settings, we extrapolated VSL estimates to each study country following the reference case (Pradhan & Jamison, Reference Pradhan and Jamison2019; Robinson et al., Reference Robinson, Hammitt, Cecchini, Chalkidou, Claxton, Cropper and Eozenou2019). Study country-specific VSLs were calculated as

$$ VS{L}_c= VS{L}_{US}\times {\left(\frac{GN{I}_c}{GN{I}_{US}}\right)}^{\varepsilon } $$

where VSL_US was the U.S. reference value, GNI_c was a study country-specific GNI per capita, GNI_US was the U.S. GNI per capita, and ε was the income elasticity of VSL. Income elasticity measures the responsiveness of a change in demand (in this case, willingness to pay for reductions in mortality risk) to a change in income; when elasticity is 1, the change is proportionate, and where it is greater than 1, demand is highly responsive to income changes. Thus, when ε ≠ 1, the VSL to income ratio differed across countries.

The base case used the reference case featured specification, applying an income elasticity of 1.5 to extrapolate the U.S. VSL to lower-income settings. This higher elasticity reflects evidence that willingness to pay for mortality risk reductions increases more than proportionally with income, implying lower VSL-to-income ratios in lower-income settings (Robinson et al., Reference Robinson, Hammitt, Cecchini, Chalkidou, Claxton, Cropper and Eozenou2019).

Sensitivity analyses followed the standardized reference case approach, assuming an elasticity of 1 and applying constant VSL-to-income ratios of 100 and 160 times GNI per capita. These ratios reflect values commonly observed in high-income settings and are recommended to facilitate comparison across studies and to explore uncertainty in extrapolated VSL estimates.

Because malaria deaths in our model occurred at approximately age 9, we derived an age-specific VSL using a value per statistical life year (VSLY) approach. VSLY was calculated as

$$VSL{Y}_c=\frac{VS{L}_c}{L{E}_{adult}}$$

where LE_adult represented the undiscounted remaining life expectancy at the average adult age. Age-specific VSL was then calculated as

$$VS{L}_{age9}= VSL{Y}_c\times L{E}_{age9}$$

where LE_age9 represented the remaining life expectancy at age 9 based on country life tables (World Health Organization, 2025). All costs and deaths averted occurred within a single program year. Because VSL represented a present-value measure of mortality risk reduction, no additional intertemporal discounting was applied. Detailed country-specific calculations are provided in the Supplementary Materials (Table S3).

4.1. Education quantity as measured by absenteeism

The base case approach used absenteeism reductions associated with malaria chemoprevention in SAC to estimate gains in earnings. We assumed a linear relationship between attendance and schooling progression, whereby each additional day of school contributes proportionally to overall grade progression. Using estimates of the average number of school days missed due to malaria (uncomplicated case: 5 days; severe case: 10 days) (Korenromp et al., Reference Korenromp, Health, Switzerland, Wong, Dubosse and Muula2021) and modeled estimates of cases averted through malaria chemoprevention in SAC, we calculated the total additional days of schooling gained per child annually. These additional days were then converted into fractions of a school year to estimate total years of schooling gained. To estimate the economic impact, we applied country-specific returns to schooling from the Mincer earnings function – an econometric model that predicts earnings based on education – to translate these gains into increased expected lifetime earnings (Fink et al., Reference Fink, Peet, Danaei, Andrews, McCoy, Sudfeld, Smith Fawzi, Ezzati and Fawzi2016). Earnings were projected across the working lifespan (ages 15–60), discounted to present value and adjusted for expected wage growth.

4.2. Combining education quantity and quality measured by foundational skills in literacy

The second approach translated foundational skills in literacy gains from an intermittent preventive treatment in schoolchildren trial in Malawi (Sixpence et al., Reference Sixpence, Vokhiwa, Kumalakwaanthu, Pitchford, Seydel, Magder, Laufer, Mathanga and Cohee2024) into gains in LAYS and then into lifetime earnings. We calculated Cohen’s d effect size (Baird & Pane, Reference Baird and Pane2019) from observed test score changes and sample size and then converted this into LAYS consistent with the micro-LAYS approach and Human Capital Index standards (Evans & Yuan, Reference Evans and Yuan2019; Angrist et al., Reference Angrist, Evans, Filmer, Glennerster, Rogers and Sabarwal2020). LAYS were then mapped to lifetime earnings gains using country-specific Mincerian returns (Fink et al., Reference Fink, Peet, Danaei, Andrews, McCoy, Sudfeld, Smith Fawzi, Ezzati and Fawzi2016) applied to discounted expected earnings from ages 15–65, adjusting for wage growth.

5.1. Benefit–cost analysis

The modeled benefits of malaria chemoprevention in SAC intervention included direct cost savings to the health system and households from reduced use of outpatient and inpatient services, indirect savings from reduced morbidity and mortality and human capital gains from improved educational outcomes. These benefits were estimated using the outputs from the decision tree model, which provided estimates of malaria cases and deaths averted and educational gains in malaria chemoprevention in the SAC scenario relative to baseline.

We calculated the BCR, which quantifies the economic return per dollar spent, by dividing the net present value of the incremental health and educational benefits by the net present value of the incremental intervention costs. The Copenhagen Consensus “traffic light” rating system, which categorizes the BCR into four categories: excellent (BCR ≥ 20), good (5 ≤ BCR < 20), fair (1 ≤ BCR < 5) and poor (BCR < 1) (Angrist et al., Reference Angrist, Aurino, Patrinos, Psacharopoulos, Vegas, Nordjo and Wong2023a), was applied to aid interpretation of results. Additionally, the net benefits (total benefits minus total costs) are reported, as relying solely on the BCR can obscure the relative magnitude of the effects.

5.2. Sensitivity analysis

To address uncertainty in both model parameters and model structure, we performed several sensitivity analyses. First, in line with suggestions for standardized sensitivity analysis in BCA (Pradhan & Jamison, Reference Pradhan and Jamison2019; Robinson et al., Reference Robinson, Hammitt, Cecchini, Chalkidou, Claxton, Cropper and Eozenou2019), we performed structural sensitivity analyses, testing different model assumptions for alternative discount rates and varying how educational benefits (reductions in absenteeism vs gains in LAYS due to foundational skills in literacy gains) and health benefits (VSL estimation methods) are monetized. Structural sensitivity analyses were performed for the individual country BCRs.

Second, we conducted a probabilistic sensitivity analysis using XLRisk (Vose Software 2021) to model average conditions across the ten HBHI countries and to assess the impact of parameter variation and uncertainty in the results. Input parameters were derived by taking the mean of fixed values across the 10 HBHI countries or fitting probability distributions to pooled data where appropriate (Supplementary Table S4). This approach produced a simulated country representing average conditions across the study settings. Monte Carlo simulations were used to vary all uncertain parameters across plausible distributions, generating a distribution of benefit–cost outcomes that reflected uncertainty and heterogeneity across the ten countries. The reported confidence interval reflects the precision of the simulated mean estimate given the number of iterations, whereas the full range of possible outcomes is represented by the percentile distribution. From this analysis, we derived a benefit–cost acceptability curve and produced a tornado diagram to illustrate the correlation between the BCR and the sampled input values, using the absolute values of these correlations to highlight influential parameters.

5.3. Cost-effectiveness analysis

We modeled cost-effectiveness over a 1-year intervention period, adopting a provider perspective for both intervention costs and cost savings. Mortality risk reduction benefits and educational gains were not included. Results are presented as incremental cost-effectiveness ratios (ICERs) and compared against cost-effectiveness thresholds based on opportunity cost approaches, which better reflect the health gains that could be achieved if resources were allocated to other competing interventions in LMICs (Pichon-Riviere et al., Reference Pichon-Riviere, Drummond, Palacios, Garcia-Marti and Augustovski2023).

5.4. Limitations

6.1. Cost projections

Our model found that the aggregate cost of implementing malaria chemoprevention in SAC for one year in the ten HBHI countries was $422 million, with 13% of total costs incurred by households. The modeled societal annual cost of malaria chemoprevention in SAC delivery ranged from $9 million to $113 million per country (Figure 1, Table 1, Supplementary Table S5). Drug costs were the primary cost driver, accounting for 68–81% of total societal costs in countries using DP, compared to 30–53% in SPAQ-using countries. The cost per dose delivered (excluding drug costs) was similar across countries, ranging from $0.28 in Niger to $0.67 in Tanzania, with the main cost drivers being the mean number of pupils per primary school and number of doses administered (3 vs. 6). These values align with published estimates of the cost per dose delivered (excluding drugs) for school-based deworming programs in Kenya (GiveWell, 2021) and Vietnam (Caesar delos Trinos et al., Reference Caesar delos, Paul, Ng-Nguyen, Coffeng, Clare, Clarke, Traub, Halton, Wiseman, Watts and Nery2023) (Figure 2).

Figure 2.

Annual intervention cost per pupil (societal perspective). Note: All costs in 2023 USD. * Includes storage ** Includes supplies and materials and monitoring and evaluation.

Figure 2. Long description

Line graph showing the cumulative distribution of benefit-cost ratios from a probabilistic sensitivity analysis. The x-axis shows benefit-cost ratios ranging from 0 to approximately 80, while the y-axis shows the cumulative distribution from 0 to 1. The curve rises steeply between ratios of approximately 5 and 20 before plateauing near 1.0. The background is shaded in different colours representing interpretation ranges of the benefit-cost ratio, with most of the distribution lying in the region where benefits exceed costs. The figure demonstrates that the majority of simulations produced positive economic returns.

6.2. Health benefit estimates

Our model estimated that implementing malaria chemoprevention in SAC aggregated across the ten HBHI countries for one year would avert an estimated 9.1 million malaria cases, 20,367 deaths and 389,765 DALYs. The intervention would generate $245.1 million in morbidity-related savings and $5.53 billion in mortality risk protection benefits. The modeled number of malaria cases, deaths and DALYs averted ranged from 256,515 to 2,995,407 cases, 443 to 8,119 deaths and 8,496 to 155,341 DALYs, with the lowest and highest estimates from Mali and Nigeria, respectively (Table 1, Supplementary Table S6). Mortality risk reduction benefits would consistently outweigh morbidity-related benefits by at least tenfold in all countries, with the ratio approaching 30:1 in Tanzania, Nigeria and Niger.

Table 1.

Incremental costs, health and educational benefits and benefit–cost ratios

Table 1. Long description

Table presenting incremental intervention costs, health and educational benefits, and benefit-cost ratios for malaria chemoprevention in ten high-burden African countries: Nigeria, Ghana, Niger, Cameroon, Burkina Faso, Mali, Democratic Republic of the Congo, Mozambique, Tanzania, and Uganda. Costs are presented in millions of 2023 USD. Interventions used either sulfadoxine-pyrimethamine plus amodiaquine (SPAQ) or dihydroartemisinin-piperaquine (DP) with varying dosing schedules. The table reports intervention costs, malaria cases averted, deaths averted, morbidity and mortality cost savings, net health benefits, benefit-cost ratios, school years gained, human capital gains from education, and combined health and education net benefits. Aggregate intervention costs across countries total approximately 423 million USD, while aggregate net benefits including education exceed 5.6 billion USD. Benefit-cost ratios including education range from approximately 3.7 in Uganda to nearly 36 in Nigeria, indicating positive economic returns across all countries. Educational benefits contribute additional human capital gains beyond direct health benefits. The table also applies Copenhagen Consensus traffic-light ratings to classify interventions as excellent, good, or fair investments.

Note: All costs presented in millions of 2023 USD. Educational gains estimated using the absenteeism approach. Green color: “excellent” rating; yellow color: “good” rating; orange: “fair rating” according to the Copenhagen Consensus “traffic light” rating system. DRC: Democratic Republic of the Congo; HBHI: High Burden to High Impact; DP: dihydroartemisinin–piperaquine; SPAQ: sulfadoxine–pyrimethamine + amodiaquine.

6.3. Educational gains

In the ten HBHI countries, our model found that malaria chemoprevention in SAC reduced absenteeism, resulting in an estimated 126,852 additional years of schooling (equivalent to 46.3 million additional school days) and $86.7 million in projected human capital gains (Table 1).

Based on our modeling of gains in foundational skills of literacy, malaria chemoprevention in SAC was estimated to generate 18.5 million LAYS in the aggregated ten HBHI countries, corresponding to $97 billion in human capital gains (Supplementary Table S6). The model projected that for every $100 invested, malaria chemoprevention in SAC could yield 5.03 LAYS. Among 150 education interventions analyzed globally, only four were estimated to deliver more LAYS per $100, one of which is school-based deworming. Programs that deliver three additional LAYS per $100 per child are considered highly cost-effective (Angrist et al., Reference Angrist, Evans, Filmer, Glennerster, Rogers and Sabarwal2020).

6.4. Benefit–cost ratio and net benefits

From a societal perspective, aggregated across the ten HBHI countries and using the absenteeism-based approach, our model estimated that implementing malaria chemoprevention in SAC would generate $5.7 billion in net benefits, resulting in a BCR of 14.28, classified as “good” according to the traffic light rating system. Two countries were predicted to have BCRs in the “excellent” category, equivalent to the top 20th percentile of interventions evaluated by the Copenhagen Consensus. Five countries fell into the “good” category and three into the “fair” category. Modeled country-level societal BCRs ranged from 3.71 in Uganda to 35.70 in Nigeria. The lowest BCR in Uganda’s DP scenario reflected the high costs of delivering DP six times annually, while Nigeria’s substantially higher BCR was driven by a combination of (1) a high VSL due to higher GNI per capita; (2) a higher malaria CFR; and (3) a higher malaria incidence. Across all countries, mortality risk reduction benefits accounted for most modeled benefits. The importance of drug choice is further explored in the sensitivity analysis found in the Supplementary Materials (Table S7) (Figure 3).

Figure 3.

Incremental costs and benefits of malaria chemoprevention in SAC (societal perspective). Note: All costs presented in 2023 USD. Educational gains estimated using the absenteeism approach. A break in the y-axis is included to accommodate Nigeria’s substantially larger benefit values.

Figure 3. Long description

Stacked bar chart comparing intervention costs with estimated health and education benefits across ten countries: Nigeria, Ghana, Niger, Cameroon, Burkina Faso, Mali, Democratic Republic of Congo, Mozambique, Tanzania, and Uganda. Costs are shown as negative values below zero, while benefits are shown as positive stacked values above zero in millions of 2023 USD. Benefits are divided into mortality-related health benefits, morbidity-related health benefits, and education benefits. Education benefits represent the largest share of total benefits in most countries. Nigeria shows the largest absolute benefits and costs. In all countries, total estimated benefits exceed intervention costs.

6.5. Structural sensitivity analysis

Structural sensitivity analysis revealed that our modeled BCR estimates were highly sensitive to the method used to quantify educational gains. BCRs based on foundational skills in literacy improvements were up to 100 times higher than those based solely on reduced absenteeism. This result was not surprising, as literacy-based gains reflect improvements in test scores across the entire classroom cohort, assuming all children received a benefit from malaria chemoprevention in SAC. In contrast, absenteeism-based estimates capture only the individual-level benefit of additional school days for pupils who would otherwise have missed school due to malaria.

Discount rates had a significant impact on our models that estimated literacy-based BCRs but had little effect on absenteeism-based estimates. The choice of VSL method also influenced our models, in some cases doubling the BCRs, though this effect was modest compared to differences driven by the choice of educational outcome metric. Despite these structural variations, all modeled scenarios produced fair or good BCRs, with literacy-based BCRs consistently falling in the “excellent” range across all countries (Table 2).

Table 2.

Structural sensitivity analysis for discount rate, method of valuing mortality-risk reduction benefits and education benefits

Table 2. Long description

Table presenting structural sensitivity analyses of benefit-cost ratios for malaria chemoprevention across ten African countries under alternative assumptions for discount rates, mortality-risk valuation methods, and education benefit estimation methods. Countries are ordered from most to least cost-beneficial. Analyses compare three discount rates: 8%, 5% base case, and 1%. Mortality-risk reduction benefits are valued using value of a statistical life year (VSLY) and three alternative value of statistical life (VSL) sensitivity analyses labelled SA1, SA2, and SA3. Education benefits are estimated using both an absenteeism approach and a foundational literacy learning-adjusted years of schooling (LAYS) approach. Across all scenarios and countries, benefit-cost ratios remain above one, indicating positive economic returns. Ratios are substantially larger when education benefits are estimated using the literacy LAYS method, with some exceeding 1,000 in Ghana and the Democratic Republic of the Congo under lower discount rates. Nigeria consistently shows the highest benefit-cost ratios among countries, while Uganda and Tanzania show the lowest but still strongly positive returns. Results demonstrate that conclusions remain robust across alternative structural assumptions.

Note: ^aBase-case analysis. All costs presented in millions of 2023 USD. Green color: “excellent” rating; yellow color: “good” rating; orange: “fair rating” according to the Copenhagen Consensus “traffic light” rating system.

6.6. Probabilistic sensitivity analysis of benefit–cost ratios

Performing a probabilistic sensitivity analysis with 10,000 iterations based on pooled input distributions, the simulated BCR for malaria chemoprevention in SAC across the ten HBHI countries was 11.00 (95% CI: 10.89–11.11), with a median of 9.94 and a range from 1.02 to 79.12. The relatively narrow CI for the “mean country” BCR reflects the design of this analysis: Pooled inputs were used to simulate average conditions, and uncertainty was varied only around this profile. As a result, the CI represents the precision of the simulated mean estimate, rather than the full spread of possible outcomes. The distribution of BCRs was right-skewed (skewness = 1.44), with a kurtosis of 4.81, reflecting the presence of a small number of simulations with very high BCRs. The benefit–cost acceptability curve illustrates the probability that the intervention is cost-beneficial at varying BCR thresholds (Figure 4). While any BCR above 1 indicates that benefits outweigh costs, decision-makers often compare across competing interventions and may apply higher thresholds to prioritize those with the greatest relative returns. In our analysis, the acceptability curve indicated a >95% probability that malaria chemoprevention in SAC is cost-beneficial at a BCR threshold of 3, increasing to nearly 100% at a threshold of 15. The tornado diagram highlighted the CFR, incidence rate and intervention effect size as the most influential drivers of variation in the BCR, underscoring the importance of accurate parameter estimates and the potential to target programs based on these characteristics (Supplementary Figure 1).

Figure 4.

Benefit–cost acceptability curve using average input parameters across 10 high-burden-to-high-impact countries. Note: The probabilistic sensitivity analysis was conducted on a simulated country profile to explore the uncertainty in model outputs based on average values or distributions derived from the 10 countries included in the analysis. Educational gains estimated using the absenteeism approach. Green color: “excellent” rating; yellow color: “good” rating; orange: “fair” rating; purple: “poor” rating according to the Copenhagen Consensus “traffic light” rating system.

Figure 4. Long description

Stacked bar chart showing the annual cost per pupil per year in 2023 USD for malaria chemoprevention implementation across ten countries: Nigeria, Ghana, Niger, Cameroon, Burkina Faso, Mali, Democratic Republic of Congo, Mozambique, Tanzania, and Uganda. Costs are divided into categories including drugs, human resources, transportation, provider community sensitization, household community sensitization, and other costs. Total annual costs per pupil range from approximately 2 to 12 USD depending on the country. Drug and human resource costs make up the largest share of total costs in most settings, while transportation and sensitization costs contribute smaller proportions. Uganda and Tanzania show among the highest total per-pupil costs.

6.7. Incremental cost-effectiveness ratios

From a provider perspective and using cost-effectiveness thresholds that range from 15 to 36% gross domestic product per capita (reflecting opportunity costs of health spending in LMICs), our models found that malaria chemoprevention in SAC in the ten HBHI countries had an ICER of $944 (range: $297–2,975) per DALY averted and was not cost-effective compared to no chemoprevention in any HBHI country or in the aggregated analysis (Supplementary Table S6) (Pichon-Riviere et al., Reference Pichon-Riviere, Drummond, Palacios, Garcia-Marti and Augustovski2023). However, these ICERs may underestimate the true value of the intervention, as they do not account for potential reductions in malaria transmission to other age groups. The incremental cost per case averted across the ten HBHI countries ($40 per case averted; range: $26–67 per case averted) was higher than that reported for bednets ($7) and IPTi ($12) but lower than seasonal malaria chemoprevention ($145) (Conteh et al., Reference Conteh, Shuford, Agboraw, Kont, Kolaczinski and Patouillard2021).