Evolution of the performance of the malaria control program in Burkina Faso: analysis from 2020 to 2024

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Despite significant progress, its elimination remains a major challenge. The objective of this study was to evaluate the performance of the National Program for Malaria Control (PNLP) from 2020 to 2024. Methods An ecological approach, based on secondary data time series from the PNLP, was used. National data from 2020 to 2024 were analyzed to assess the evolution of coverage, the availability and use of interventions, the incidence of malaria. A projection of the impact up to 2030 was carried out by modeling TBATS (Trigonometric, Box-Cox transformation, ARMA errors, Trend and Seasonal components). The analysis, based on the theory of change, examined the link between the implementation of interventions and their epidemiological impact. Results In 2024, long-lasting insecticide-treated mosquito nets (LLINs) covered 75.27% of pregnant women and 81.02% of children under 5 years old, while seasonal malaria chemoprevention (SMC) reached 94.00%. The use of rapid diagnostic tests (RDTs) and artemisinin-based therapeutic combinations (ACTs) were 98.5% and 97.96%, respectively. These interventions reduced the incidence of malaria from 537.5 to 446.4 cases per 1,000 inhabitants. However, supply chain disruptions and insufficient input use limit the overall impact. Projections estimate the incidence to reach 79.31 cases per 1,000 inhabitants in the first quarter and 187.82 cases in the fourth quarter in 2030. Conclusion Despite significant progress, the elimination of malaria by 2030 remains uncertain. Sustainable supply chain strengthening, improved effective use of interventions and integrated governance supported by multi-sectoral coordination are still essential to transform gains into a sustainable health impact. Performance malaria interventions program malaria Burkina Faso Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Despite the significant progress made in recent decades in the fight against malaria, its elimination remains a major challenge in several African countries such as Burkina Faso. Indeed, in Burkina Faso, the disease remains a major public health problem, particularly affecting pregnant women and children under 5 years old [ 1 ]. The global framework for malaria elimination by 2030, adopted by countries in 2015, is based on three pillars: ensuring universal access, accelerating efforts towards elimination and making malaria surveillance a core intervention. Pillars of support include: fostering an enabling environment, harnessing innovation and developing research [ 2 ]. This framework has enabled some countries to initiate malaria pre-elimination and elimination. Some countries have been recognized as malaria-free, notably Azerbaijan, Tajikistan, and Belize, certified in 2023 by the World Health Organization (WHO)[ 3 ]. In Africa, the gradual deployment of RTS,S/AS01 vaccines[ 4 , 5 ] and R21/Matrix-M [ 5 ] is considered as a hope to significantly reduce the incidence of malaria among children [ 6 ]. However, in high-transmission countries such as Burkina Faso, the burden of malaria remains high. In 2024, the country recorded 10,805,020 cases of malaria, including 563,383 severe cases and 3,523 deaths, representing an incidence of 446.4 cases per 1000 inhabitants[ 7 ]. Children under 5 are the most affected, representing about 63% of all deaths [ 7 , 8 ]. Faced with this situation, Burkina Faso has gradually strengthened its control strategies through the National Program for Malaria Control (PNLP), which in 2022 became the Permanent Secretariat for Malaria Elimination (SP/Palu). [ 8 , 9 ]. Key interventions include the use of long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), seasonal malaria chemoprevention (SMC), intermittent preventive treatment (IPT), artemisinin-based therapeutic combinations (ACT)[ 10 ]. More recently, the country introduced the RTS,S/AS01 vaccine in some pilot high transmission regions [ 6 ]. In theory, improving the coverage, utilization and quality of implementation of interventions should lead to a considerable reduction in malaria incidence and mortality. However, despite the high coverage reported in several key interventions, malaria incidence remains high and tends to stagnate. This gap raises important questions about the actual performance of the program, the effectiveness of implementation, and contextual constraints. [ 11 ]. To date, most national evaluations have focused on a descriptive approach focusing on coverage indicators, use or availability of interventions, without thoroughly analyzing the causal chain linking programmatic inputs to epidemiological outcomes. Few studies have combined a longitudinal time series analysis with an explicit conceptual framework, such as the theory of change, to examine whether current trajectories are consistent with elimination goals. The present study aims to analyze the performance of the main malaria control interventions from 2020 to 2024, in order to examine the links between malaria coverage, availability, use and incidence from a time series analysis of routine data, completed by a prospective modeling until 2030. The use of the theory of change will make it possible to analyze the alignment between the coverage of interventions, the actual use and the evolution of the impact, in order to identify structural bottlenecks and inform the strategic decisions needed to move towards elimination. This study would like to contribute to the country’s recent statement on the successes of the malaria control program to support these progress with evidence and elimination goals. [ 8 , 12 ]. 2. Methods 2.1. Framework of the study In Burkina Faso, malaria surveillance is based on a structured system designed to guide strategic decisions. This system is based on several complementary components, namely: routine epidemiological surveillance for monitoring indicators, sentinel surveillance for early detection of local outbreaks, entomological surveillance to analyze the vector and its resistance, therapeutic monitoring to evaluate the effectiveness of treatments and parasite resistance, as well as community surveillance to detect and treat simple cases at the local level. The data from these monitoring operations are transmitted via the District Health Information System 2 (DHIS2) platform of the Burkina Faso Health Data Warehouse (ENDoS-BF). These data are organized according to a three-level hierarchical structure: The peripheral level (health districts), responsible for primary data collection in health facilities; The intermediate level (Regional Health Directorate), responsible for validation and aggregation; The central level (SP-Palu/DGESS) which centralizes, consolidates and then uses data at the national level. 2.2. Type of study We conducted an ecological study to evaluate the performance of a health program, based on time series. It was carried out from secondary data covering the period from 2020 to 2024. The data collection took place between June and August 2025. 2.3. Data source The data for this study were mainly extracted from the databases of the National Program for Malaria Control, now the Permanent Secretariat for Malaria Elimination (SP/Palu), for the period from 2020 to 2024. They also come from the Health Data Warehouse of Burkina Faso (ENDoS-BF) for the same period, the 2021 Demographic and Health Surveys (EDS) of Burkina Faso, as well as databases from mixed multi-indicator surveys to fight against malaria. 2.4. Data collection Data were extracted in accordance with the classification of indicators from the National Strategic Plan (NSP) for Malaria Control of Burkina Faso. The extraction focused on impact indicators, including effectiveness, incidence, prevalence, mortality and resistance if available. It also focused on impact or outcome indicators, in particular coverage, as well as programme management indicators, including the availability and use of inputs. The data thus extracted were then subjected to verification processes including checking the completeness, internal consistency and plausibility of the values. Inconsistent or missing values were identified through inter-annual comparisons and analysis of temporal trends. Subsequently, the data were cleaned and transformed to harmonize formats, units of measurement and reference periods. Finally, the variables have been grouped according to the logic of the theory of change, in coherence with the structure of the PSN, thus allowing an analysis directly aligned with the causal hypotheses of the program. 2.5. Analytical framework of the study: theory of change The theory of change used in this study is based on the central assumption that if malaria interventions in Burkina Faso are implemented in an integrated manner, with sustainable funding, strengthened governance and active community participation, then the incidence, mortality and lethality of malaria will decrease in a sustainable way. This theory is based on the following components : Inputs : total expenditure on health care, malaria-related costs, per capita annual health expenditure Immediate products (outputs) : availability of (LLINs, SMC, IPT, RDT, and ACT) in health facilities, coverage of interventions (LLINs, SMC, IPT, RDT, and ACT), distance to be covered by the populations to reach health facilities, Intermediate results (outcomes) : Effective use of LLINs, recourse to the RDT in case of fever, appropriate treatment by ACT Impact : incidence of malaria, number of simple malaria cases, number of severe malaria cases, mortality and lethality 2.6. Analysis methods A descriptive analysis was first conducted using Excel 2024 to assess the performance of different malaria control interventions from 2020 to 2024, through indicators of coverage, availability, utilization, incidence, mortality and lethality. In a second step, the Spearman test was performed with Stata 17 to examine the correlation between malaria incidence and coverage, utilization and availability indicators. Given the low number of annual observations (n = 5), these analyses should be interpreted as exploratory. Furthermore, predictive modeling of the TBATS type (Trigonometric, Box-Cox transformation, ARMA errors, Trend and Seasonal components) was carried out using RSstudio to estimate the future evolution of malaria incidence until 2030 (Annex B). This model is also composed of a trend that can be linear or damped, as well as seasonal components modeled using trigonometric functions to represent multiple seasonal patterns or evolutionary over time [ 13 , 14 ]. The model was adjusted automatically, then the optimal specification was selected using the Akaike Information Criterion (AIC). The final model was validated by the analysis of residuals, including autocorrelation, normality and homogeneity of variances. The validated model was then used to generate forecasts of malaria incidence up to 2030, accompanied by 95% confidence intervals. 2.7. Ethical and regulatory considerations The data used in this study come mainly from the National Program for Malaria Control (PNLP) of Burkina Faso. Access to the data was obtained following a formal administrative request addressed to the Permanent Secretariat for Malaria Elimination (SP/Palu), in which were specified the scientific objectives of the study as well as guarantees of confidentiality. Additional information has been extracted from public sources such as WHO reports and national surveys (EDS, MIS). The study relies exclusively on secondary data analysis and does not involve any primary data collection. The protocol was submitted to the Ethics Committee for Health Research (ESRB) of Burkina Faso, which granted an exemption from informed consent. The principles of confidentiality, data security and responsible use have been strictly observed. 3. Results 3.1. Description of the evolution of the indicators Between 2020 and 2023, the Ministry of Health of Burkina Faso invested a total of US$6,052 million in health spending, including US$950 million in malaria control. The ratio of current health expenditure to gross domestic product (GDP) fluctuated between 8% and 8.5%. Per capita health spending increased from 55.8 to 75.5 US dollars, an increase of 35.30%. Between 2021 and 2024, the availability of LLINs in health facilities has increased from 44.5% to 62.1%. Similarly, the availability of sulfadoxine-pyrimethamine (SP) in health facilities increased from 43.2% in 2021 to 61.1% in 2024. Regarding RDTs, their availability in health facilities increased from 61% in 2021 to 70.1% in 2024. As for treatments, the availability of CTA increased from 64.7% in 2021 to 69.8% in 2024, while that of artesunate decreased from 54.6% in 2021 to 44% in 2024. MILDA coverage increased from 69.49% in 2020 to 88.89% in 2021, before stabilizing at 81% in 2024 among pregnant women. Among children aged 0 to 11 months, this coverage increased from 21.3% to 68.2% from 2020 to 2024. Furthermore, CPS coverage among children under 5 years old increased from 94.35% in 2021 to 97.8% in 2024. Finally, the coverage of the TPI experienced a notable increase, going from 55.9% in 2020 to 75.3% in 2024. Regarding the use of LLINs, according to data from the 2021 EDS, 61% of the population was sleeping under an LLIN, namely 67% among children under 5 years old and 71% among pregnant women.[15]. However, the proportion of use of parasitological diagnosis (TDR or microscopy) was almost universal since 2021, exceeding 98% each year. The proportion of use of treatment by CTA increased from 94.76% in 2020 to 98% in 2024. The incidence of malaria decreased from 537.5 cases per 1000 inhabitants in 2021 to 446.4 cases per 1000 inhabitants in 2024, a reduction of 16.95%. The number of malaria-related deaths decreased from 4355 to 3523 from 2021 and 2024, corresponding to a decrease of 19.10%, while the lethality increased from 0.8 to 0.6 over the period from 2020 to 2024. Furthermore, the mortality rate decreased from 20.3 to 14.9 per 100,000 inhabitants from 2021 to 2024. Table I: analysis of performance indicators for the malaria control program in Burkina Faso over the period from 2020 to 2024 Indicators 2020 2021 2022 2023 2024 Total healthcare expenses ({ mathbf{10}} mathbf{6}$) 1199 1390 1662 1801 NA Malaria-related costs ({ mathbf{10}} mathbf{6}$) 189 208 293 260 NA Health expenditure per capita per year ({ mathbf{10}} mathbf{6}$) 55,8 62.2 70.3 75.5 NA Health expenditure as a percentage of GDP 8.0 8.3 8.3 8.5 NA Distance to be covered by populations to reach health facilities (km) 6.2 6.1 6.1 6.0 6.0 Availability of CTAs in health facilities NA 64,7 60,1 62,6 69,8 Availability of artesunate in health facilities NA 54,5 37,6 37,2 44 Availability of LIDNs in health facilities NA 44,5 51,6 56,3 62,1 Availability of SP in health facilities NA 43,2 45,8 52,1 61,1 Availability of RDTs in health facilities NA 61 57,9 64,2 70,1 LLIN coverage among pregnant women (%) 69,49 88,89 82,61 81,34 81,02 LLIN coverage for 0-11 months (%) 21,3 47,8 53,4 61 68,2 Coverage in TPI-CPN (%) 55,85 65,07 68,64 70,4 75,27 CPS coverage (%) NA 94,35 92,08 94,17 97,8 Use of diagnostic tests (TDR or microscopy) (%) 90,3 99,4 99,39 97,5 98,5 CTA usage (%) 94,76 94,87 96,19 96,64 97,96 Number of malaria cases (presumed+confirmed) 11312562 12231086 11656675 10770246 10805020 Number of malaria cases (presumed+confirmed) among those under 5 years old 4886844 4868717 4300948 3545022 3195655 Number of cases of Severe malaria (Confirmed+Suspected) 508610 605504 539488 546628 563383 Number of cases of severe malaria (presumed+confirmed) in those under 5 years old 207113 220885 195805 166863 170160 Confirmed malaria incidence (per 1000 hbts) 484,3 537,5 500,9 447,1 446,4 Incidence of malaria in children under 5 years (per 1000 hbts) 1306,2 1237,6 1070,5 866,7 769,3 Mortality rate attributable to malaria (per 100,000 hbts) 18,7 20,3 19,1 14,8 14,9 Hospital lethality attributable to malaria 0,80 0,70 0,80 0,60 0,6 Number of deaths in the general population 3983 4355 4243 3385 3523 3.2. Analysis of the relationship between incidence, coverage, availability, use as well as the distance traveled to reach health centers The results of this analysis show a negative relationship between malaria incidence and LLIN coverage among children aged 0-11 months (r = -0.7), as well as with CPS (r = -0.5) and TPI (r = -0.7) coverage. However, a positive correlation is observed between malaria incidence and MILDA coverage in pregnant women (ρ=0.7). At the level of use, the combined use of RDTs and ACTs is negatively correlated with malaria incidence (r = -0.7). In contrast, the use of RDTs alone shows a positive correlation with incidence (ρ = 0.6). In terms of availability of inputs, a negative correlation is observed between the incidence of malaria and the availability of LIDNs (r = -1.0), RDRs (r = -0.9) and AOCs (r = -0.3) in health facilities (Table II). However, a positive correlation is observed between the incidence of malaria and the distance that people have to travel to reach health facilities in km (r = 0.6325). Table II: Spearman correlation between incidence and coverage, uses, and availability of malaria control interventions in Burkina Faso from 2020 to 2024 Indicators Incidence of malaria MILDA coverage in pregnant women LLIN coverage among children aged 0-11 months Coverage in TPI Coverage in CPS MILDA use in FEs MILDA use in 0-11months TDR Usage TDR + CTA use Availability of MILDA in FS Availability of TDR in FS CTA availability in FS Distance travelled by people to reach health facilities (km) Incidence of malaria 1.0000 MILDA coverage in pregnant women 0.7000 1.0000 LLIN coverage among children aged 0-11 months -0.7000 0.0000 1.0000 Coverage in TPI -0.7000 0.0000 1.0000 1.0000 Coverage in CPS -0.5000 -0.6000 0.1000 0.1000 1.0000 MILDA use in FEs -0.5000 -0.6000 0.1000 0.1000 1.0000 1.0000 MILDA use in 0-11months -0.9000 -0.9000 0.4000 0.4000 0.7000 0.7000 1.0000 TDR Usage 0.6000 0.9000 0.1000 0.1000 -0.3000 -0.3000 -0.7000 1.0000 TDR + CTA use -0.7000 0.0000 1.0000 1.0000 0.1000 0.1000 0.4000 0.1000 1.0000 Availability of MILDA in FS -1.0000 -0.7000 0.7000 0.7000 0.5000 0.5000 0.9000 -0.6000 0.7000 1.0000 Availability of TDR in FS -0.9000 -0.6000 0.6000 0.6000 0.7000 0.7000 0.8000 -0.5000 0.6000 0.9000 1.0000 CTA availability in FS -0.3000 -0.2000 0.2000 0.2000 0.9000 0.9000 0.4000 0.1000 0.2000 0.3000 0.6000 1.0000 Distance travelled by people to reach health facilities (km) 0.6325 -0.1054 -0.9487 -0.9487 -0.0527 -0.0527 -0.2635 -0.1054 -0.9487 -0.6325 -0.6325 -0.2108 1.0000 3.3. Analysis of projected malaria incidence in 2030 In 2024, the incidence of malaria was 446.4 cases per 1000 inhabitants. Predictive modeling of malaria incidence up to 2030, shows an incidence of 79.31 cases per 1000 inhabitants in the first quarter (CI: [17.60; 141.03]) and 187.82 cases per 1000 inhabitants in the fourth quarter of 2030 (CI: [126.08; 249.55]). 4. Discussion This study evaluated the performance of malaria control interventions in Burkina Faso based on indicators from the National Strategic Plan, using the theory of change as an analytical framework. This approach explored the relationships between incidence, coverage, availability, distance to health facilities and use of malaria interventions. This analysis is carried out in a short time series context, while taking into account the non-linearity of the relationships between variables. 4.1. The evolution of indicators Funding allocated to health and the fight against malaria in Burkina Faso improved significantly between 2020 and 2024. These funds have contributed to the establishment and equipping of health structures, the recruitment and deployment of health personnel within these structures, the supply of essential drugs, as well as capacity building of health workers for better management of malaria. Despite this improvement, the ratio of current health expenditure to GDP remains below the recommendations of the World Health Organization (WHO), which recommends an annual increase of at least 1% in GDP to have a significant impact on the health system.[16,17]. Moreover, these financial efforts have made it possible to reduce the distance traveled by the population to access health facilities. However, the goal of 5 km set by the National Health Development Plan (PNDS) 2021-2030 has not yet been achieved [18]. The analyses also reveal a contrasting situation characterized by high coverage, notably of MILDA, TPI and CPS, as well as high use of TDR and CTA. Despite these performances, the availability of inputs in health facilities remains limited and the incidence remains high. This observation aligns with the statements of Cissoko et al., 2025, which emphasize that high coverage alone is not sufficient to significantly lower the incidence when other indicators do not work optimally [19]. This dissociation between the different levels of the intervention chain highlights a structural fragility in the supply chain, already reported in the studies by Cohen et al., 2012 and Obeagu et al., 2026[20,21]. 4.2. Analysis of the relationship between incidence, coverage, availability, use as well as the distance traveled to reach health centers Spearman’s correlation analyses show a heterogeneous relationship between coverage and incidence. A positive correlation is observed between malaria incidence and distance to health facilities, suggesting that increased remoteness is associated with an increase in the number of cases. This could be explained by the fact that distance promotes delays in consultation, the use of self-medication, less access to preventive strategies as well as the progression of the disease towards severe forms. These results are consistent with the work of Broekhuizen et al., 2021 and Chuma et al., 2010, which show that proximity to health services reduces the risk of malaria and that remoteness constitutes a major obstacle to healthcare use [22,23]. The positive correlation observed between ITN incidence and coverage in pregnant women may seem counterintuitive, but it is explained by the priority targeting of interventions to areas with high transmission. In this context, an increase in coverage may apparently coincide with a high incidence when data are analysed at an aggregate level, without calling into question the intrinsic effectiveness of interventions [24,25]. This correlation therefore does not mean an intrinsic inefficiency of the LLINs, but rather reflects a programmatic response targeted at high-risk areas, combined with persistent contextual factors such as climatic conditions favorable to vector proliferation and the limits of actual use by populations. Conversely, the negative correlation observed for certain components suggests that coverage, when accompanied by proper and sustainable use, can contribute to reducing transmission. This situation confirms the findings of the study by Killeen et al., 2017, according to which coverage in isolation is insufficient to judge the performance of malaria control interventions [26]. Regarding the use of inputs, the positive correlation between RDT use and malaria incidence reflects an intensification of screening in areas and periods of high transmission. This phenomenon is widely described in the literature and reflects an improvement in case detection rather than a real increase in transmission. This observation is consistent with the studies conducted by Hofer et al., 2023 and Bastiaens et al., 2014 [27,28]. On the other hand, the negative correlation observed between ACT use and incidence suggests that rapid and effective clinical management of confirmed cases plays an important role in gradually reducing transmission, when the conditions for access and adherence to the treatment are met. Studies conducted by Habtamu et al., 2025 and Vanheer et al., 2025 have demonstrated that access to ACTs can contribute to the reduction of malaria transmission, promoting a gradual decrease in incidence when conditions for use are met.[29,30]. In terms of availability, the negative correlation between the availability of inputs in health facilities and the impact highlights the importance of the supply chain in the overall performance of interventions. Stock-outs compromise the continuity of care, undermining people’s confidence in the health system and promoting self-treatment or non-recommended treatments, thus reducing the potential impact of interventions that are well covered and used. This observation is consistent with the work of Cohen et al., 2012 and Cissoko et al., 2025, which highlighted a close relationship between stock shortages of antimalarial inputs, the increase in malaria-related morbidity and the decrease in the overall effectiveness of control programs[19,20]. 4.3. Analysis of projected malaria incidence in 2030 The TBATS modeling results of incidence in this study show an incidence greater than 5 cases per 1,000 inhabitants in 2030. This projection indicates that Burkina Faso is unlikely to achieve malaria elimination by 2030. This observation, based on the affirmations of Bhatt et al., 2015 and on the recommendations of the WHO proposes a continuous strengthening of strategies, with emphasis on innovations and resource mobilization[2,18]. 4.4. The appreciation of the theory of change The analysis of the theory of change makes it possible to observe consistency between the resources mobilized, the activities implemented and the observed results. The central hypothesis of this theory is based on the idea that an adequate mobilization of inputs (financing, human resources, medical inputs) allows the implementation of activities (distribution, diagnosis, treatment, awareness). These activities produce immediate effects, including improved coverage and increased use of malaria control interventions. These advances translate into a decrease in incidence and mortality, gradually contributing to the elimination goal. This approach is in line with the evaluation frameworks for complex programs described by de Savigny and Adam. [32], who consider health systems as complex adaptive systems, within which interactions between components strongly influence performance and results [32]. In the context of Burkina Faso, this theory appears relevant conceptually, insofar as the priority interventions are generally aligned with international recommendations and the national epidemiological profile[12]. However, despite the efforts made, the incidence of malaria remains high and mortality remains a concern, reflecting a gap between strategic ambitions and observed results. Breaks are observed in the chain of causality. Logistical constraints limiting the transformation of resources into effective services; territorial inequalities reduce the conversion of services into health effects; finally, high financial dependence on external funding undermines the sustainability of achievements. The studies of Feachem et al., 2019, suggest an integrated approach combining technical interventions, institutional strengthening and community engagement in order to progress towards malaria elimination [33]. Similarly, Walker et al., 2016, show that only a coordinated and sustained intensification of interventions can sustainably reduce transmission [34]. Thus, this theory suggests that if the strategic foundations are solid, their effectiveness will depend on strengthening the health system, securing long-term funding and increased community involvement, essential conditions for transforming current progress into sustainable advances towards elimination. The fight against malaria in Burkina Faso illustrates undeniable advances in intervention coverage, followed by limited progress in interventions related to the availability and use of interventions. To accelerate progress towards elimination, it is essential to consolidate the gains of coverage-related interventions and strengthen the availability and use of these interventions. It is also essential to strengthen coordination between stakeholders to propose new strategies. This observation is in line with the analyses of Fambirai et al., 2025 and Montresor et al., 2020 that highlight the importance of coordinated and participatory multisectoral governance and engagement, as well as increased national leadership for successful malaria control programs[7,15]. 4.5. Study limitations This study presents several limitations that must be taken into account when interpreting the results. The use of secondary data at national level does not allow for highlighting local disparities in malaria transmission. Moreover, the limited number of years analysed limits the statistical robustness of the results and does not allow for establishing strong causal links between interventions and changes in incidence. Finally, indicators of coverage, availability and utilization are based on administrative data that may be subject to reporting bias and overestimation, which could explain the weak or inconsistent relationships observed with malaria incidence. 5. Conclusion This study evaluated the performance of malaria control in Burkina Faso. The theory of change makes it possible to show breaks in the causal chain, particularly at the level of the availability of inputs and their effective use, thus compromising overall performance. Eliminating malaria requires not only maintaining high coverage, but above all sustainably strengthening the supply chain, improving the quality of use of interventions, as well as more integrated governance. In this regard, strengthened multisectoral coordination and the development of new targeted strategies based on the analysis of these results and the projection of the impact for 2030 is important. It can be realized in the form of a co-creation involving all interested parties to consolidate the achievements and accelerate progress towards malaria elimination. Abbreviations SMC: Seasonal Malaria Chemoprevention ACT: Artemisinin-based combination therapies DHIS2: District Health Information System 2 DHS: Demographic and Health Survey ENDoS-BF: Burkina Faso National Health Data Repository ENPS: National Survey on Malaria Indicators IRS : indoor residual spraying IPT : intermittent preventive treatment MILDA: Long-lasting insecticidal nets WHO: World Health Organisation PNLP: National Malaria Control Programme PSN: National Strategic Plan for Malaria Control RDT : Rapid Diagnostic Test SP/Palu: Permanent Secretariat for Malaria Elimination. Declarations Ethical approval and consent to participate This study is based solely on secondary data drawn from institutional documents and scientific publications. It did not involve any direct collection of data from participants or the use of individual data that could identify specific individuals. In this context, and in accordance with the principles generally applied to studies based on documentary sources and aggregated data, approval from an ethics committee was not required. The data used are drawn primarily from reports and databases of Burkina Faso’s National Malaria Control Programme, supplemented by publicly available scientific publications. Consent to publication Not applicable. This study does not contain any individual data or information that could identify participants. Availability of data and materials The data used in this study are primarily derived from secondary data from the National Malaria Control Programme (PNLP), as well as from institutional documents and the scientific literature. The programme and epidemiological data were extracted from Burkina Faso’s national health information system, specifically from the District Health Information System 2 (DHIS2), which is accessible via the National Health Data Repository. The remaining information was obtained from institutional reports produced by the National Malaria Control Programme, as well as from scientific publications available in academic databases such as PubMed, Google Scholar, Springer and African Journals Online. Conflict of interest : The authors declare no conflict of interest. Contribution to authors: All the authors mentioned in this article contributed to the study and the manuscript. Thanks : We thank all colleagues who provided advice and technical support during the writing of this manuscript. Funding : This research did not receive any specific grant from a funding agency. Additional material The additional material of this item is available References OMS. Rapport 2024 sur le paludisme dans le monde: Données & tendances régionales [Internet]. 2024 [cité 4 janv 2025]. https://cdn.who.int/media/docs/default-source/malaria/world-malaria-reports/world-malaria-report-2024-regional-briefing-kit-fre.pdf?sfvrsn=bceac4ae_9&download=true. Consulté le 4 janv 2025 Organisation mondiale de la Santé (OMS). Stratégie technique mondiale de lutte contre le paludisme 2016-2030 [Internet]. Genève: Organisation mondiale de la Santé; 2015 [cité 5 mai 2025]. https://iris.who.int/handle/10665/176720. Consulté le 5 mai 2025 OMS. 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Funded investments contributed to the reduction of malaria morbidity and mortality in children under five: fifteen years retrospective study from 2009 to 2023 in Burkina Faso. Malar J. 2026;25:84. https://doi.org/10.1186/s12936-025-05779-8 Organisation mondiale de la Santé. Les pays doivent investir au moins 1% supplémentaire de leur PIB dans les soins de santé primaires pour éliminer les lacunes flagrantes de la couverture [Internet]. [cité 21 févr 2026]. https://www.who.int/fr/news/item/22-09-2019-countries-must-invest-at-least-1-more-of-gdp-on-primary-health-care-to-eliminate-glaring-coverage-gaps. Consulté le 21 févr 2026 MINISTERE DE LA SANTE DU BURKINA FASO. PLAN NATIONAL DE DÉVELOPPEMENT SANITAIRE (PNDS) 2021-2030. Cissoko M, Sagara I, Guindo A, Maiga M, Dembélé P, Bationo CS, et al. Impact of Control Interventions on Malaria Incidence in the General Population of Mali. J Epidemiol Glob Health. 2025;15:40. https://doi.org/10.1007/s44197-025-00381-2 Cohen JM, Smith DL, Cotter C, Ward A, Yamey G, Sabot OJ, et al. Malaria resurgence: a systematic review and assessment of its causes. Malar J. 2012;11:122. https://doi.org/10.1186/1475-2875-11-122 Obeagu EI, Abdulrahman AO, Anetor JI. Drug stockouts and treatment delays in African health systems: impact on malaria morbidity and mortality. Annals of Medicine and Surgery. 2026;10.1097/MS9.0000000000004641. https://doi.org/10.1097/MS9.0000000000004641 Broekhuizen H, Fehr A, Nieto-Sanchez C, Muela J, Peeters-Grietens K, Smekens T, et al. Costs and barriers faced by households seeking malaria treatment in the Upper River Region, The Gambia. Malar J. 2021;20:368. https://doi.org/10.1186/s12936-021-03898-6 Chuma J, Okungu V, Molyneux C. Barriers to prompt and effective malaria treatment among the poorest population in Kenya. Malar J. 2010;9:144. https://doi.org/10.1186/1475-2875-9-144 Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015;526:207‑11. https://doi.org/10.1038/nature15535 Lim SS, Fullman N, Stokes A, Ravishankar N, Masiye F, Murray CJL, et al. Net Benefits: A Multicountry Analysis of Observational Data Examining Associations between Insecticide-Treated Mosquito Nets and Health Outcomes. PLoS Med. 2011;8:e1001091. https://doi.org/10.1371/journal.pmed.1001091 Killeen GF, Tatarsky A, Diabate A, Chaccour CJ, Marshall JM, Okumu FO, et al. Developing an expanded vector control toolbox for malaria elimination. BMJ Glob Health. 2017;2:e000211. https://doi.org/10.1136/bmjgh-2016-000211 Bastiaens GJH, Bousema T, Leslie T. Scale-up of Malaria Rapid Diagnostic Tests and Artemisinin-Based Combination Therapy: Challenges and Perspectives in Sub-Saharan Africa. PLOS Medicine. 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Public Library of Science; 2025;22:e1004683. https://doi.org/10.1371/journal.pmed.1004683 Bhatt S, Weiss DJ, Cameron E, Bisanzio D, Mappin B, Dalrymple U, et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 2015;526:207‑11. https://doi.org/10.1038/nature15535 De Savigny D, Adam T, Alliance for Health Policy and Systems Research, World Health Organization, éditeurs. Systems thinking for health systems strengthening. Geneva: Alliance for Health Policy and Systems Research ; World Health Organization; 2009. Feachem RGA, Chen I, Akbari O, Bertozzi-Villa A, Bhatt S, Binka F, et al. Malaria eradication within a generation: ambitious, achievable, and necessary. The Lancet. Elsevier; 2019;394:1056‑112. https://doi.org/10.1016/S0140-6736(19)31139-0 Walker PGT, Griffin JT, Ferguson NM, Ghani AC. Estimating the most efficient allocation of interventions to achieve reductions in Plasmodium falciparum malaria burden and transmission in Africa: a modelling study. Lancet Glob Health. 2016;4:e474-484. https://doi.org/10.1016/S2214-109X(16)30073-0 Fambirai T, Chimbari M, Mhindu T. Factors associated with contracting border malaria: A systematic and meta-analysis. PLoS One. 2025;20:e0310063. https://doi.org/10.1371/journal.pone.0310063 Montresor A, Mupfasoni D, Mikhailov A, Mwinzi P, Lucianez A, Jamsheed M, et al. The global progress of soil-transmitted helminthiases control in 2020 and World Health Organization targets for 2030. PLoS Negl Trop Dis. 2020;14:e0008505. https://doi.org/10.1371/journal.pntd.0008505 Additional Declarations No competing interests reported. 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2","display":"","copyAsset":false,"role":"figure","size":92475,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of the coverage of malaria control interventions in Burkina Faso from 2020-2024\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9053026/v1/82ff5532d3951f5ec1295571.png"},{"id":106206400,"identity":"b04a0ba3-81a3-40f2-82a2-99294c89aaa9","added_by":"auto","created_at":"2026-04-06 05:43:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92663,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of the coverage of malaria control interventions in Burkina Faso from 2020-2024\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9053026/v1/e71cffa25410aef8de4edf48.png"},{"id":106403067,"identity":"03fc2d9d-d333-46af-89cd-5570123c0d65","added_by":"auto","created_at":"2026-04-08 09:13:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54137,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of the use of inputs for malaria control in health facilities in Burkina Faso from 2020-2024\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9053026/v1/6e7b51be9761262a18b254ac.png"},{"id":106206402,"identity":"5b4e2482-dc32-4010-8b82-0c0d07dfa5e0","added_by":"auto","created_at":"2026-04-06 05:43:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive modeling curve of malaria incidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndications: \u0026nbsp;: Forecast\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e---- : Confidence interval\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9053026/v1/8aac328058b40a9d8392a618.png"},{"id":106405570,"identity":"593a574c-424b-42f8-8b84-e03a52c25d97","added_by":"auto","created_at":"2026-04-08 09:27:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1627469,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9053026/v1/40c9643d-564c-4b6b-a7ea-a003ed700acb.pdf"},{"id":106206397,"identity":"0bb5453f-5adc-468e-90c3-c692e32b448e","added_by":"auto","created_at":"2026-04-06 05:43:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":445256,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydocument.docx","url":"https://assets-eu.researchsquare.com/files/rs-9053026/v1/ca449b73c6c093f58171b065.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evolution of the performance of the malaria control program in Burkina Faso: analysis from 2020 to 2024","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDespite the significant progress made in recent decades in the fight against malaria, its elimination remains a major challenge in several African countries such as Burkina Faso. Indeed, in Burkina Faso, the disease remains a major public health problem, particularly affecting pregnant women and children under 5 years old [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe global framework for malaria elimination by 2030, adopted by countries in 2015, is based on three pillars: ensuring universal access, accelerating efforts towards elimination and making malaria surveillance a core intervention. Pillars of support include: fostering an enabling environment, harnessing innovation and developing research [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This framework has enabled some countries to initiate malaria pre-elimination and elimination. Some countries have been recognized as malaria-free, notably Azerbaijan, Tajikistan, and Belize, certified in 2023 by the World Health Organization (WHO)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In Africa, the gradual deployment of RTS,S/AS01 vaccines[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and R21/Matrix-M [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] is considered as a hope to significantly reduce the incidence of malaria among children [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, in high-transmission countries such as Burkina Faso, the burden of malaria remains high. In 2024, the country recorded 10,805,020 cases of malaria, including 563,383 severe cases and 3,523 deaths, representing an incidence of 446.4 cases per 1000 inhabitants[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Children under 5 are the most affected, representing about 63% of all deaths [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFaced with this situation, Burkina Faso has gradually strengthened its control strategies through the National Program for Malaria Control (PNLP), which in 2022 became the Permanent Secretariat for Malaria Elimination (SP/Palu). [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Key interventions include the use of long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), seasonal malaria chemoprevention (SMC), intermittent preventive treatment (IPT), artemisinin-based therapeutic combinations (ACT)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. More recently, the country introduced the RTS,S/AS01 vaccine in some pilot high transmission regions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In theory, improving the coverage, utilization and quality of implementation of interventions should lead to a considerable reduction in malaria incidence and mortality. However, despite the high coverage reported in several key interventions, malaria incidence remains high and tends to stagnate. This gap raises important questions about the actual performance of the program, the effectiveness of implementation, and contextual constraints. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo date, most national evaluations have focused on a descriptive approach focusing on coverage indicators, use or availability of interventions, without thoroughly analyzing the causal chain linking programmatic inputs to epidemiological outcomes. Few studies have combined a longitudinal time series analysis with an explicit conceptual framework, such as the theory of change, to examine whether current trajectories are consistent with elimination goals.\u003c/p\u003e \u003cp\u003eThe present study aims to analyze the performance of the main malaria control interventions from 2020 to 2024, in order to examine the links between malaria coverage, availability, use and incidence from a time series analysis of routine data, completed by a prospective modeling until 2030. The use of the theory of change will make it possible to analyze the alignment between the coverage of interventions, the actual use and the evolution of the impact, in order to identify structural bottlenecks and inform the strategic decisions needed to move towards elimination. This study would like to contribute to the country\u0026rsquo;s recent statement on the successes of the malaria control program to support these progress with evidence and elimination goals. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Framework of the study\u003c/h2\u003e \u003cp\u003eIn Burkina Faso, malaria surveillance is based on a structured system designed to guide strategic decisions. This system is based on several complementary components, namely: routine epidemiological surveillance for monitoring indicators, sentinel surveillance for early detection of local outbreaks, entomological surveillance to analyze the vector and its resistance, therapeutic monitoring to evaluate the effectiveness of treatments and parasite resistance, as well as community surveillance to detect and treat simple cases at the local level.\u003c/p\u003e \u003cp\u003eThe data from these monitoring operations are transmitted via the District Health Information System 2 (DHIS2) platform of the Burkina Faso Health Data Warehouse (ENDoS-BF). These data are organized according to a three-level hierarchical structure:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe peripheral level (health districts), responsible for primary data collection in health facilities;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe intermediate level (Regional Health Directorate), responsible for validation and aggregation;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe central level (SP-Palu/DGESS) which centralizes, consolidates and then uses data at the national level.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Type of study\u003c/h2\u003e \u003cp\u003eWe conducted an ecological study to evaluate the performance of a health program, based on time series. It was carried out from secondary data covering the period from 2020 to 2024. The data collection took place between June and August 2025.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data source\u003c/h2\u003e \u003cp\u003eThe data for this study were mainly extracted from the databases of the National Program for Malaria Control, now the Permanent Secretariat for Malaria Elimination (SP/Palu), for the period from 2020 to 2024. They also come from the Health Data Warehouse of Burkina Faso (ENDoS-BF) for the same period, the 2021 Demographic and Health Surveys (EDS) of Burkina Faso, as well as databases from mixed multi-indicator surveys to fight against malaria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data collection\u003c/h2\u003e \u003cp\u003eData were extracted in accordance with the classification of indicators from the National Strategic Plan (NSP) for Malaria Control of Burkina Faso. The extraction focused on impact indicators, including effectiveness, incidence, prevalence, mortality and resistance if available. It also focused on impact or outcome indicators, in particular coverage, as well as programme management indicators, including the availability and use of inputs. The data thus extracted were then subjected to verification processes including checking the completeness, internal consistency and plausibility of the values. Inconsistent or missing values were identified through inter-annual comparisons and analysis of temporal trends. Subsequently, the data were cleaned and transformed to harmonize formats, units of measurement and reference periods. Finally, the variables have been grouped according to the logic of the theory of change, in coherence with the structure of the PSN, thus allowing an analysis directly aligned with the causal hypotheses of the program.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Analytical framework of the study: theory of change\u003c/h2\u003e \u003cp\u003eThe theory of change used in this study is based on the central assumption that if malaria interventions in Burkina Faso are implemented in an integrated manner, with sustainable funding, strengthened governance and active community participation, then the incidence, mortality and lethality of malaria will decrease in a sustainable way. This theory is based on the following components :\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInputs\u003c/b\u003e: total expenditure on health care, malaria-related costs, per capita annual health expenditure\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImmediate products (outputs)\u003c/b\u003e: availability of (LLINs, SMC, IPT, RDT, and ACT) in health facilities, coverage of interventions (LLINs, SMC, IPT, RDT, and ACT), distance to be covered by the populations to reach health facilities,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntermediate results (outcomes)\u003c/b\u003e: Effective use of LLINs, recourse to the RDT in case of fever, appropriate treatment by ACT\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImpact\u003c/b\u003e: incidence of malaria, number of simple malaria cases, number of severe malaria cases, mortality and lethality\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Analysis methods\u003c/h2\u003e \u003cp\u003eA descriptive analysis was first conducted using Excel 2024 to assess the performance of different malaria control interventions from 2020 to 2024, through indicators of coverage, availability, utilization, incidence, mortality and lethality.\u003c/p\u003e \u003cp\u003eIn a second step, the Spearman test was performed with Stata 17 to examine the correlation between malaria incidence and coverage, utilization and availability indicators. Given the low number of annual observations (n\u0026thinsp;=\u0026thinsp;5), these analyses should be interpreted as exploratory.\u003c/p\u003e \u003cp\u003eFurthermore, predictive modeling of the TBATS type (Trigonometric, Box-Cox transformation, ARMA errors, Trend and Seasonal components) was carried out using RSstudio to estimate the future evolution of malaria incidence until 2030 (Annex B). This model is also composed of a trend that can be linear or damped, as well as seasonal components modeled using trigonometric functions to represent multiple seasonal patterns or evolutionary over time [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The model was adjusted automatically, then the optimal specification was selected using the Akaike Information Criterion (AIC). The final model was validated by the analysis of residuals, including autocorrelation, normality and homogeneity of variances. The validated model was then used to generate forecasts of malaria incidence up to 2030, accompanied by 95% confidence intervals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Ethical and regulatory considerations\u003c/h2\u003e \u003cp\u003eThe data used in this study come mainly from the National Program for Malaria Control (PNLP) of Burkina Faso. Access to the data was obtained following a formal administrative request addressed to the Permanent Secretariat for Malaria Elimination (SP/Palu), in which were specified the scientific objectives of the study as well as guarantees of confidentiality. Additional information has been extracted from public sources such as WHO reports and national surveys (EDS, MIS). The study relies exclusively on secondary data analysis and does not involve any primary data collection. The protocol was submitted to the Ethics Committee for Health Research (ESRB) of Burkina Faso, which granted an exemption from informed consent. The principles of confidentiality, data security and responsible use have been strictly observed.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. \u0026nbsp;Description of the evolution of the indicators\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween 2020 and 2023, the Ministry of Health of Burkina Faso invested a total of US$6,052 million in health spending, including US$950 million in malaria control. The ratio of current health expenditure to gross domestic product (GDP) fluctuated between 8% and 8.5%. Per capita health spending increased from 55.8 to 75.5 US dollars, an increase of 35.30%. Between 2021 and 2024, the availability of LLINs in health facilities has increased from 44.5% to 62.1%. Similarly, the availability of sulfadoxine-pyrimethamine (SP) in health facilities increased from 43.2% in 2021 to 61.1% in 2024. Regarding RDTs, their availability in health facilities increased from 61% in 2021 to 70.1% in 2024. \u0026nbsp;As for treatments, the availability of CTA increased from 64.7% in 2021 to 69.8% in 2024, while that of artesunate decreased from 54.6% in 2021 to 44% in 2024.\u003c/p\u003e\n\u003cp\u003eMILDA coverage increased from 69.49% in 2020 to 88.89% in 2021, before stabilizing at 81% in 2024 among pregnant women. Among children aged 0 to 11 months, this coverage increased from 21.3% to 68.2% from 2020 to 2024. Furthermore, CPS coverage among children under 5 years old increased from 94.35% in 2021 to 97.8% in 2024. Finally, the coverage of the TPI experienced a notable increase, going from 55.9% in 2020 to 75.3% in 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding the use of LLINs, according to data from the 2021 EDS, 61% of the population was sleeping under an LLIN, namely 67% among children under 5 years old and 71% among pregnant women.[15]. However, the proportion of use of parasitological diagnosis (TDR or microscopy) was almost universal since 2021, exceeding 98% each year. The proportion of use of treatment by CTA increased from 94.76% in 2020 to 98% in 2024. The incidence of malaria decreased from 537.5 cases per 1000 inhabitants in 2021 to 446.4 cases per 1000 inhabitants in 2024, a reduction of 16.95%. The number of malaria-related deaths decreased from 4355 to 3523 from 2021 and 2024, corresponding to a decrease of 19.10%, while the lethality increased from 0.8 to 0.6 over the period from 2020 to 2024. Furthermore, the mortality rate decreased from 20.3 to 14.9 per 100,000 inhabitants from 2021 to 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable I:\u0026nbsp;\u003c/strong\u003eanalysis of performance indicators for the malaria control program in Burkina Faso over the period from 2020 to 2024\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eTotal healthcare expenses ({ mathbf{10}} \u0026nbsp;mathbf{6}$)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eMalaria-related costs ({ mathbf{10}} \u0026nbsp;mathbf{6}$)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eHealth expenditure per capita per year ({ mathbf{10}} \u0026nbsp;mathbf{6}$)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e55,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e62.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e70.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e75.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eHealth expenditure as a percentage of GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eDistance to be covered by populations to reach health facilities (km)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eAvailability of CTAs in health facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e64,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e60,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e62,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e69,8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eAvailability of artesunate in health facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e54,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e37,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e37,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eAvailability of LIDNs in health facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e44,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e51,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e56,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e62,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eAvailability of SP in health facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e43,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e45,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e52,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e61,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eAvailability of RDTs in health facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e57,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e64,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e70,1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eLLIN coverage among pregnant women (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e69,49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e88,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e82,61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e81,34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e81,02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eLLIN coverage for 0-11 months (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e21,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e47,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e53,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e68,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eCoverage in TPI-CPN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e55,85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e65,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e68,64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e70,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e75,27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eCPS coverage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e94,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e92,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e94,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e97,8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eUse of diagnostic tests (TDR or microscopy) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e90,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e99,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e99,39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e97,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e98,5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eCTA usage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e94,76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e94,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e96,19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e96,64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e97,96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNumber of malaria cases (presumed+confirmed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e11312562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e12231086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e11656675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e10770246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e10805020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNumber of malaria cases (presumed+confirmed) among those under 5 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4886844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4868717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4300948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3545022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3195655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNumber of cases of Severe malaria (Confirmed+Suspected)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e508610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e605504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e539488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e546628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e563383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNumber of cases of severe malaria (presumed+confirmed) in those under 5 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e207113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e220885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e195805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e166863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e170160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eConfirmed malaria incidence (per 1000 hbts) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e484,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e537,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e500,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e447,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e446,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eIncidence of malaria in children under 5 years (per 1000 hbts) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1306,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1237,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1070,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e866,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e769,3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eMortality rate attributable to malaria (per 100,000 hbts)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e18,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e20,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e19,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e14,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e14,9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eHospital lethality attributable to malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0,60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0,6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNumber of deaths in the general population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr clear=\"all\"\u003e\u003c/strong\u003e\u003cstrong\u003e3.2. \u0026nbsp;Analysis of the relationship between incidence, coverage, availability, use as well as the distance traveled to reach health centers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of this analysis show a negative relationship between malaria incidence and LLIN coverage among children aged 0-11 months (r = -0.7), as well as with CPS (r = -0.5) and TPI (r = -0.7) coverage. However, a positive correlation is observed between malaria incidence and MILDA coverage in pregnant women (\u0026rho;=0.7).\u003c/p\u003e\n\u003cp\u003eAt the level of use, the combined use of RDTs and ACTs is negatively correlated with malaria incidence (r = -0.7). In contrast, the use of RDTs alone shows a positive correlation with incidence (\u0026rho; = 0.6).\u003c/p\u003e\n\u003cp\u003eIn terms of availability of inputs, a negative correlation is observed between the incidence of malaria and the availability of LIDNs (r = -1.0), RDRs (r = -0.9) and AOCs (r = -0.3) in health facilities (Table II). However, a positive correlation is observed between the incidence of malaria and the distance that people have to travel to reach health facilities in km (r = 0.6325).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable II:\u0026nbsp;\u003c/strong\u003eSpearman correlation between incidence and coverage, uses, and availability of malaria control interventions in Burkina Faso from 2020 to 2024\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1101\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncidence of malaria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMILDA coverage in pregnant women\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLLIN coverage among children aged 0-11 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoverage in TPI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoverage in CPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMILDA use in FEs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMILDA use in 0-11months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTDR Usage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTDR + CTA use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of MILDA in FS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of TDR in FS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTA availability in FS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance travelled by people to reach health facilities (km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncidence of malaria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMILDA coverage in pregnant women\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLLIN coverage among children aged 0-11 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoverage in TPI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoverage in CPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMILDA use in FEs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMILDA use in 0-11months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTDR Usage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTDR + CTA use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of MILDA in FS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of TDR in FS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.7000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.8000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTA availability in FS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.9000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistance travelled by people to reach health facilities (km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.6325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.1054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.9487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.9487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.0527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e-0.0527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.2635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.1054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.9487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.6325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.6325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.2108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. \u0026nbsp; Analysis of projected malaria incidence in 2030 \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 2024, the incidence of malaria was 446.4 cases per 1000 inhabitants. Predictive modeling of malaria incidence up to 2030, shows an incidence of 79.31 cases per 1000 inhabitants in the first quarter (CI: [17.60; 141.03]) and 187.82 cases per 1000 inhabitants in the fourth quarter of 2030 (CI: [126.08; 249.55]).\u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study evaluated the performance of malaria control interventions in Burkina Faso based on indicators from the National Strategic Plan, using the theory of change as an analytical framework. This approach explored the relationships between incidence, coverage, availability, distance to health facilities and use of malaria interventions. This analysis is carried out in a short time series context, while taking into account the non-linearity of the relationships between variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1. The evolution of indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding allocated to health and the fight against malaria in Burkina Faso improved significantly between 2020 and 2024. These funds have contributed to the establishment and equipping of health structures, the recruitment and deployment of health personnel within these structures, the supply of essential drugs, as well as capacity building of health workers for better management of malaria. Despite this improvement, the ratio of current health expenditure to GDP remains below the recommendations of the World Health Organization (WHO), which recommends an annual increase of at least 1% in GDP to have a significant impact on the health system.[16,17]. Moreover, these financial efforts have made it possible to reduce the distance traveled by the population to access health facilities. However, the goal of 5 km set by the National Health Development Plan (PNDS) 2021-2030 has not yet been achieved [18]. \u003c/p\u003e\n\u003cp\u003eThe analyses also reveal a contrasting situation characterized by high coverage, notably of MILDA, TPI and CPS, as well as high use of TDR and CTA. Despite these performances, the availability of inputs in health facilities remains limited and the incidence remains high. This observation aligns with the statements of Cissoko et al., 2025, which emphasize that high coverage alone is not sufficient to significantly lower the incidence when other indicators do not work optimally [19]. This dissociation between the different levels of the intervention chain highlights a structural fragility in the supply chain, already reported in the studies by Cohen et al., 2012 and Obeagu et al., 2026[20,21]. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. \u003c/strong\u003e \u003cstrong\u003eAnalysis of the relationship between incidence, coverage, availability, use as well as the distance traveled to reach health centers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman’s correlation analyses show a heterogeneous relationship between coverage and incidence. A positive correlation is observed between malaria incidence and distance to health facilities, suggesting that increased remoteness is associated with an increase in the number of cases. This could be explained by the fact that distance promotes delays in consultation, the use of self-medication, less access to preventive strategies as well as the progression of the disease towards severe forms. These results are consistent with the work of Broekhuizen et al., 2021 and Chuma et al., 2010, which show that proximity to health services reduces the risk of malaria and that remoteness constitutes a major obstacle to healthcare use [22,23].\u003c/p\u003e\n\u003cp\u003eThe positive correlation observed between ITN incidence and coverage in pregnant women may seem counterintuitive, but it is explained by the priority targeting of interventions to areas with high transmission. In this context, an increase in coverage may apparently coincide with a high incidence when data are analysed at an aggregate level, without calling into question the intrinsic effectiveness of interventions [24,25]. This correlation therefore does not mean an intrinsic inefficiency of the LLINs, but rather reflects a programmatic response targeted at high-risk areas, combined with persistent contextual factors such as climatic conditions favorable to vector proliferation and the limits of actual use by populations. Conversely, the negative correlation observed for certain components suggests that coverage, when accompanied by proper and sustainable use, can contribute to reducing transmission. This situation confirms the findings of the study by Killeen et al., 2017, according to which coverage in isolation is insufficient to judge the performance of malaria control interventions [26].\u003c/p\u003e\n\u003cp\u003eRegarding the use of inputs, the positive correlation between RDT use and malaria incidence reflects an intensification of screening in areas and periods of high transmission. This phenomenon is widely described in the literature and reflects an improvement in case detection rather than a real increase in transmission. This observation is consistent with the studies conducted by Hofer et al., 2023 and Bastiaens et al., 2014 [27,28]. On the other hand, the negative correlation observed between ACT use and incidence suggests that rapid and effective clinical management of confirmed cases plays an important role in gradually reducing transmission, when the conditions for access and adherence to the treatment are met. Studies conducted by Habtamu et al., 2025 and Vanheer et al., 2025 have demonstrated that access to ACTs can contribute to the reduction of malaria transmission, promoting a gradual decrease in incidence when conditions for use are met.[29,30].\u003c/p\u003e\n\u003cp\u003eIn terms of availability, the negative correlation between the availability of inputs in health facilities and the impact highlights the importance of the supply chain in the overall performance of interventions. Stock-outs compromise the continuity of care, undermining people’s confidence in the health system and promoting self-treatment or non-recommended treatments, thus reducing the potential impact of interventions that are well covered and used. This observation is consistent with the work of Cohen et al., 2012 and Cissoko et al., 2025, which highlighted a close relationship between stock shortages of antimalarial inputs, the increase in malaria-related morbidity and the decrease in the overall effectiveness of control programs[19,20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3. Analysis of projected malaria incidence in 2030 \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TBATS modeling results of incidence in this study show an incidence greater than 5 cases per 1,000 inhabitants in 2030. This projection indicates that Burkina Faso is unlikely to achieve malaria elimination by 2030. This observation, based on the affirmations of Bhatt et al., 2015 and on the recommendations of the WHO proposes a continuous strengthening of strategies, with emphasis on innovations and resource mobilization[2,18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4. The appreciation of the theory of change \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of the theory of change makes it possible to observe consistency between the resources mobilized, the activities implemented and the observed results. The central hypothesis of this theory is based on the idea that an adequate mobilization of inputs (financing, human resources, medical inputs) allows the implementation of activities (distribution, diagnosis, treatment, awareness). These activities produce immediate effects, including improved coverage and increased use of malaria control interventions. These advances translate into a decrease in incidence and mortality, gradually contributing to the elimination goal. This approach is in line with the evaluation frameworks for complex programs described by de Savigny and Adam. [32], who consider health systems as complex adaptive systems, within which interactions between components strongly influence performance and results [32].\u003c/p\u003e\n\u003cp\u003eIn the context of Burkina Faso, this theory appears relevant conceptually, insofar as the priority interventions are generally aligned with international recommendations and the national epidemiological profile[12]. However, despite the efforts made, the incidence of malaria remains high and mortality remains a concern, reflecting a gap between strategic ambitions and observed results. Breaks are observed in the chain of causality. Logistical constraints limiting the transformation of resources into effective services; territorial inequalities reduce the conversion of services into health effects; finally, high financial dependence on external funding undermines the sustainability of achievements. The studies of Feachem et al., 2019, suggest an integrated approach combining technical interventions, institutional strengthening and community engagement in order to progress towards malaria elimination [33]. Similarly, Walker et al., 2016, show that only a coordinated and sustained intensification of interventions can sustainably reduce transmission [34]. Thus, this theory suggests that if the strategic foundations are solid, their effectiveness will depend on strengthening the health system, securing long-term funding and increased community involvement, essential conditions for transforming current progress into sustainable advances towards elimination.\u003c/p\u003e\n\u003cp\u003eThe fight against malaria in Burkina Faso illustrates undeniable advances in intervention coverage, followed by limited progress in interventions related to the availability and use of interventions. To accelerate progress towards elimination, it is essential to consolidate the gains of coverage-related interventions and strengthen the availability and use of these interventions. It is also essential to strengthen coordination between stakeholders to propose new strategies. This observation is in line with the analyses of Fambirai et al., 2025 and Montresor et al., 2020 that highlight the importance of coordinated and participatory multisectoral governance and engagement, as well as increased national leadership for successful malaria control programs[7,15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5. Study limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study presents several limitations that must be taken into account when interpreting the results. The use of secondary data at national level does not allow for highlighting local disparities in malaria transmission. Moreover, the limited number of years analysed limits the statistical robustness of the results and does not allow for establishing strong causal links between interventions and changes in incidence. Finally, indicators of coverage, availability and utilization are based on administrative data that may be subject to reporting bias and overestimation, which could explain the weak or inconsistent relationships observed with malaria incidence.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study evaluated the performance of malaria control in Burkina Faso. The theory of change makes it possible to show breaks in the causal chain, particularly at the level of the availability of inputs and their effective use, thus compromising overall performance. Eliminating malaria requires not only maintaining high coverage, but above all sustainably strengthening the supply chain, improving the quality of use of interventions, as well as more integrated governance. In this regard, strengthened multisectoral coordination and the development of new targeted strategies based on the analysis of these results and the projection of the impact for 2030 is important. It can be realized in the form of a co-creation involving all interested parties to consolidate the achievements and accelerate progress towards malaria elimination.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eSMC:\u0026nbsp;\u003c/strong\u003eSeasonal Malaria Chemoprevention\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACT:\u0026nbsp;\u003c/strong\u003eArtemisinin-based combination therapies\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDHIS2:\u0026nbsp;\u003c/strong\u003eDistrict Health Information System 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDHS:\u0026nbsp;\u003c/strong\u003eDemographic and Health Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eENDoS-BF:\u0026nbsp;\u003c/strong\u003eBurkina Faso National Health Data Repository\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eENPS:\u0026nbsp;\u003c/strong\u003eNational Survey on Malaria Indicators\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIRS\u003c/strong\u003e: indoor residual spraying\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIPT\u003c/strong\u003e: intermittent preventive treatment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMILDA:\u0026nbsp;\u003c/strong\u003eLong-lasting insecticidal nets\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHO:\u0026nbsp;\u003c/strong\u003eWorld Health Organisation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePNLP:\u0026nbsp;\u003c/strong\u003eNational Malaria Control Programme\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePSN:\u0026nbsp;\u003c/strong\u003eNational Strategic Plan for Malaria Control\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRDT\u003c/strong\u003e: Rapid Diagnostic Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSP/Palu:\u0026nbsp;\u003c/strong\u003ePermanent Secretariat for Malaria Elimination.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based solely on secondary data drawn from institutional documents and scientific publications. It did not involve any direct collection of data from participants or the use of individual data that could identify specific individuals. In this context, and in accordance with the principles generally applied to studies based on documentary sources and aggregated data, approval from an ethics committee was not required. The data used are drawn primarily from reports and databases of Burkina Faso’s National Malaria Control Programme, supplemented by publicly available scientific publications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not contain any individual data or information that could identify participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are primarily derived from secondary data from the National Malaria Control Programme (PNLP), as well as from institutional documents and the scientific literature.\u0026nbsp;The programme and epidemiological data were extracted from Burkina Faso’s national health information system, specifically from the District Health Information System 2 (DHIS2), which is accessible via the National Health Data Repository.\u003c/p\u003e\n\u003cp\u003eThe remaining information was obtained from institutional reports produced by the National Malaria Control Programme, as well as from scientific publications available in academic databases such as PubMed, Google Scholar, Springer and African Journals Online.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution to authors:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors mentioned in this article contributed to the study and the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThanks\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWe thank all colleagues who provided advice and technical support during the writing of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from a funding agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe additional material of this item is available\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOMS. 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Consult\u0026eacute; le 20 oct 2025\u003c/li\u003e\n \u003cli\u003eOMS. 18 millions de doses du tout premier vaccin contre le paludisme allou\u0026eacute;es \u0026agrave; 12 pays africains pour la p\u0026eacute;riode 2023-2025 : Gavi, l\u0026rsquo;OMS et l\u0026rsquo;UNICEF [Internet]. 2023 [cit\u0026eacute; 14 juin 2025]. https://www.who.int/news/item/05-07-2023-18-million-doses-of-first-ever-malaria-vaccine-allocated-to-12-african-countries-for-2023-2025--gavi--who-and-unicef. Consult\u0026eacute; le 14 juin 2025\u003c/li\u003e\n \u003cli\u003eDirection g\u0026eacute;n\u0026eacute;rale des \u0026eacute;tudes et des statistiques sectorielles du Minist\u0026egrave;re de la Sant\u0026eacute;. Annuaire statistique 2024 [Internet]. 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Malar J. 2026;25:84. https://doi.org/10.1186/s12936-025-05779-8\u003c/li\u003e\n \u003cli\u003eOrganisation mondiale de la Sant\u0026eacute;. Les pays doivent investir au moins 1% suppl\u0026eacute;mentaire de leur PIB dans les soins de sant\u0026eacute; primaires pour \u0026eacute;liminer les lacunes flagrantes de la couverture [Internet]. [cit\u0026eacute; 21 f\u0026eacute;vr 2026]. https://www.who.int/fr/news/item/22-09-2019-countries-must-invest-at-least-1-more-of-gdp-on-primary-health-care-to-eliminate-glaring-coverage-gaps. Consult\u0026eacute; le 21 f\u0026eacute;vr 2026\u003c/li\u003e\n \u003cli\u003eMINISTERE DE LA SANTE DU BURKINA FASO. PLAN NATIONAL DE D\u0026Eacute;VELOPPEMENT SANITAIRE (PNDS) 2021-2030.\u003c/li\u003e\n \u003cli\u003eCissoko M, Sagara I, Guindo A, Maiga M, Demb\u0026eacute;l\u0026eacute; P, Bationo CS, et al. Impact of Control Interventions on Malaria Incidence in the General Population of Mali. J Epidemiol Glob Health. 2025;15:40. https://doi.org/10.1007/s44197-025-00381-2\u003c/li\u003e\n \u003cli\u003eCohen JM, Smith DL, Cotter C, Ward A, Yamey G, Sabot OJ, et al. Malaria resurgence: a systematic review and assessment of its causes. Malar J. 2012;11:122. https://doi.org/10.1186/1475-2875-11-122\u003c/li\u003e\n \u003cli\u003eObeagu EI, Abdulrahman AO, Anetor JI. Drug stockouts and treatment delays in African health systems: impact on malaria morbidity and mortality. Annals of Medicine and Surgery. 2026;10.1097/MS9.0000000000004641. https://doi.org/10.1097/MS9.0000000000004641\u003c/li\u003e\n \u003cli\u003eBroekhuizen H, Fehr A, Nieto-Sanchez C, Muela J, Peeters-Grietens K, Smekens T, et al. Costs and barriers faced by households seeking malaria treatment in the Upper River Region, The Gambia. Malar J. 2021;20:368. https://doi.org/10.1186/s12936-021-03898-6\u003c/li\u003e\n \u003cli\u003eChuma J, Okungu V, Molyneux C. 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Geneva: Alliance for Health Policy and Systems Research ; World Health Organization; 2009.\u003c/li\u003e\n \u003cli\u003eFeachem RGA, Chen I, Akbari O, Bertozzi-Villa A, Bhatt S, Binka F, et al. Malaria eradication within a generation: ambitious, achievable, and necessary. The Lancet. Elsevier; 2019;394:1056‑112. https://doi.org/10.1016/S0140-6736(19)31139-0\u003c/li\u003e\n \u003cli\u003eWalker PGT, Griffin JT, Ferguson NM, Ghani AC. Estimating the most efficient allocation of interventions to achieve reductions in Plasmodium falciparum malaria burden and transmission in Africa: a modelling study. Lancet Glob Health. 2016;4:e474-484. https://doi.org/10.1016/S2214-109X(16)30073-0\u003c/li\u003e\n \u003cli\u003eFambirai T, Chimbari M, Mhindu T. Factors associated with contracting border malaria: A systematic and meta-analysis. PLoS One. 2025;20:e0310063. https://doi.org/10.1371/journal.pone.0310063\u003c/li\u003e\n \u003cli\u003eMontresor A, Mupfasoni D, Mikhailov A, Mwinzi P, Lucianez A, Jamsheed M, et al. The global progress of soil-transmitted helminthiases control in 2020 and World Health Organization targets for 2030. PLoS Negl Trop Dis. 2020;14:e0008505. https://doi.org/10.1371/journal.pntd.0008505\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-epidemiology-and-global-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Epidemiology and Global Health](https://www.springer.com/journal/44197)","snPcode":"44197","submissionUrl":"https://submission.nature.com/new-submission/44197/3","title":"Journal of Epidemiology and Global Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Performance, malaria, interventions, program, malaria, Burkina Faso ","lastPublishedDoi":"10.21203/rs.3.rs-9053026/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9053026/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eIntroduction:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMalaria remains a public health problem in Burkina Faso, causing harm to the population. Despite significant progress, its elimination remains a major challenge. The objective of this study was to evaluate the performance of the National Program for Malaria Control (PNLP) from 2020 to 2024.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAn ecological approach, based on secondary data time series from the PNLP, was used. National data from 2020 to 2024 were analyzed to assess the evolution of coverage, the availability and use of interventions, the incidence of malaria. A projection of the impact up to 2030 was carried out by modeling TBATS (Trigonometric, Box-Cox transformation, ARMA errors, Trend and Seasonal components). The analysis, based on the theory of change, examined the link between the implementation of interventions and their epidemiological impact.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn 2024, long-lasting insecticide-treated mosquito nets (LLINs) covered 75.27% of pregnant women and 81.02% of children under 5 years old, while seasonal malaria chemoprevention (SMC) reached 94.00%. The use of rapid diagnostic tests (RDTs) and artemisinin-based therapeutic combinations (ACTs) were 98.5% and 97.96%, respectively. These interventions reduced the incidence of malaria from 537.5 to 446.4 cases per 1,000 inhabitants. However, supply chain disruptions and insufficient input use limit the overall impact. Projections estimate the incidence to reach 79.31 cases per 1,000 inhabitants in the first quarter and 187.82 cases in the fourth quarter in 2030.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDespite significant progress, the elimination of malaria by 2030 remains uncertain. Sustainable supply chain strengthening, improved effective use of interventions and integrated governance supported by multi-sectoral coordination are still essential to transform gains into a sustainable health impact.\u003c/p\u003e","manuscriptTitle":"Evolution of the performance of the malaria control program in Burkina Faso: analysis from 2020 to 2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 05:43:50","doi":"10.21203/rs.3.rs-9053026/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-28T06:10:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T08:38:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T18:18:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91378181582241379311157025489154061059","date":"2026-04-07T11:45:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320349422056319419207713205390185296076","date":"2026-04-06T13:31:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221291065003505572636106851549189824381","date":"2026-04-05T06:37:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192822186885831866682505989047484556514","date":"2026-04-03T23:47:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T19:34:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T05:37:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-30T00:39:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Epidemiology and Global Health","date":"2026-03-16T12:21:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-epidemiology-and-global-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Epidemiology and Global Health](https://www.springer.com/journal/44197)","snPcode":"44197","submissionUrl":"https://submission.nature.com/new-submission/44197/3","title":"Journal of Epidemiology and Global Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53048949-17b7-4ab9-9df0-fe14e9ff052a","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T20:53:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 05:43:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9053026","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9053026","identity":"rs-9053026","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}