Impact of seasonal malaria chemoprevention: a plausibility evaluation of routine data from health facilities in three implementing states in Nigeria

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Ikechukwu, Ekechi Okereke, Olabisi Ogunmola, Jennifer Chukwumerije, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6413508/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2025 Read the published version in Malaria Journal → Version 1 posted 9 You are reading this latest preprint version Abstract Background Seasonal malaria chemoprevention (SMC) has been recommended by the World Health Organization since 2012 for children aged 3–59 months in areas where malaria transmission is highly seasonal. By 2024, SMC had been successfully implemented in all 21 eligible states in Nigeria. Given this widespread implementation, there has been increasing interest in understanding the impact of the intervention under programmatic conditions. This study assessed changes in malaria incidence and related epidemiological outcomes among the target population of children in three SMC implementing states in Nigeria. Methods A pre-post study plausibility evaluation design was used for this study. Data from routine health management information systems were extracted from selected health facilities to compare the incidence of parasitologically-confirmed uncomplicated malaria cases and secondary outcomes among children aged 3–59 months within the catchment populations of those health facilities. Mixed-effects, multilevel, negative binomial regression models were employed to estimate the impact of SMC on outcomes of interest between the pre-SMC period (2021) and SMC period (2022). Results Data were collected in 36 health facilities: 12 each in Kogi state, Oyo state, and the Federal Capital Territory. The mean incidence of uncomplicated malaria was 20 cases per 1000 children aged 3–59 months in 2021, and 9 cases per 1000 children in 2022. After accounting for potential confounders, malaria incidence was 50% (95% confidence interval [CI]: 39–60) lower in the SMC period compared with the pre-SMC period (adjusted incidence rate ratio (IRR): 0.50, 95% CI: 0.40–0.61, p < 0.001), with notable variations in the level of reduction across the three study locations. Incidence of all-cause fever per 1000 children was 29% (95% CI: 14–41) lower in 2022 compared with 2021 (adjusted IRR: 0.71, 95% CI: 0.59–0.86, p < 0.001). Observed levels of severe malaria and attributable deaths were too low to measure the impact of SMC on those outcomes. Conclusion The study found significantly lower levels of incidence of uncomplicated malaria following the introduction of SMC. It thus provides evidence on the potential impact of the intervention in real-world settings while underscoring the need for further improvement to and utilisation of routine data to monitor impact in eligible settings. Figures Figure 1 Figure 2 INTRODUCTION Malaria continues to pose a significant public health concern in many sub-Saharan African countries. 1 Globally, there were an estimated 247 million malaria cases in 2021, with the African region accounting for an estimated 95% of all cases. 2,3 Between 2000 and 2019, malaria incidence declined in the region from 368 to 222 per 1000 population at risk. However, an increase to 232 per 1000 was reported 2020 due to the disruptions to health services during the COVID-19 pandemic. 4,5 In Nigeria, it is estimated that malaria is responsible for approximately 60% of outpatient visits and 30% of admissions. 6 The disease also contributes up to 11% of maternal mortality, 25% of infant mortality, and 30% of under-5 mortality. 7,8 The disease overburdens the already-stretched health system and exerts a severe social and economic burden on the country, contributing to substantial productivity losses and hindering economic growth. 7,8 Defined as the intermittent administration of full treatment courses of an antimalarial medicine to children in areas of high seasonal malaria transmission, seasonal malaria chemoprevention (SMC) was first recommended by the WHO as a malaria chemoprevention strategy in eligible settings in 2012. 9,10,11 . In 2013, WHO released an implementation guide to help countries adopt and implement this new intervention. 12 Following pilot studies in seven local government areas (LGAs) in three Sahelian states, Nigeria adopted SMC as a chemoprevention strategy in 2014. By 2020, the intervention had been implemented across all nine states that were targeted for the initial scale-up phase. As part of the High Burden to High Impact approach for malaria control, a subnational stratification exercise enabled the identification of appropriate intervention-mixes for the different epidemiological settings in the country aimed at aiding prioritization for impact. This led to the expansion of SMC to additional states. As of 2024, SMC had been successfully implemented in all 20 eligible states and the Federal Capital Territory, targeting about 20 million children. SMC involves the administration of sulfadoxine-pyrimethamine plus amodiaquine (SPAQ) to children aged 3–59 months for four or five monthly cycles, given in 28-day intervals during the peak transmission season. 13 Evidence suggests that SMC using SPAQ monthly for up to at least 4 monthly cycles during the high malaria transmission season for children less than 5 years of age prevents approximately 73–75% of uncomplicated malaria episodes 14,15, and 62% of malaria parasitemia in children less than 5 years of age. 15 While previous evidence from both randomised and observational studies in different countries across sub-Saharan Africa has shown that SMC is highly effective 11,14,16,17,18,19,20,21 , much of the current evidence on SMC effectiveness is based on studies conducted several years ago. Considering the large-scale delivery of SMC over several years, there has been growing interest in assessing the impact of the intervention when delivered under programmatic conditions using routine health data. Generating up-to-date, real-world evidence on the impact of the programme when delivered at scale is essential for tracking progress, improving the programme’s performance and ensuring accountability to stakeholders and partners. However, obtaining reliable routine health management information system (HMIS) data to estimate the impact of the SMC programme can be challenging, as routine health data sources are known to have data quality challenges. Despite those limitations, existing HMIS data can offer a useful data source for evaluating and understanding the impact of SMC in real world settings. 22 The goal of this study was thus to determine the contributions of SMC using SPAQ to the reduction in malaria disease burden and to provide information to guide decisions on the future of SMC and other malaria prevention and elimination interventions in Nigeria. The specific objectives of the study were to assess changes in the incidence of uncomplicated malaria and secondary epidemiological outcomes, including all-cause fever, severe malaria and malaria-associated deaths at the health facility level between the pre-SMC period and the start of SMC implementation in selected implementing states in Nigeria. METHODS Study design The study adopted a pre-post plausibility evaluation design involving routine HMIS data for the periods before and after the introduction of SMC in the study locations. Study setting This study was conducted in three SMC implementing locations: Oyo, Kogi and the Federal Capital Territory (FCT) in Nigeria ( Fig. 1 ). The FCT is located in the North Central geopolitical region in Nigeria and serve as the country's capital. It is situated within the savannah region with moderate climatic conditions. The peak of the rainy season in FCT typically lasts between July and October each year. Based on the duration of the high transmission season, it requires five monthly SMC cycles, typically starting in June. In partnership with the National Malaria Elimination Programme (NMEP), Malaria Consortium supported the implementation of SMC for the first time in the FCT in 2022. As of 2024, the target population of SMC eligible children in the FCT was about one million. 23 Kogi state is situated in the north-central Nigeria within the tropical Guinean forest–savanna mosaic ecoregion. The peak of the rainy season in Kogi state lasts from April to October every year. Based on malaria transmission seasonality patterns, five monthly SMC cycles are required, typically starting in May or June. In partnership with the NMEP, Malaria Consortium supported the introduction of SMC in Kogi state in 2022. As of 2024, the target population of SMC eligible children in Kogi state was estimated at 1.2 million. 23 Oyo State is situated in the southwestern region of Nigeria and experiences an equatorial climate characterized by both dry and wet seasons, along with relatively high humidity. The dry season spans from November to March, while the wet season occurs from April to October. Due to seasonal patterns of malaria transmission, five monthly cycles of Seasonal Malaria Chemoprevention (SMC) are typically conducted, beginning in May or June. In 2022, Malaria Consortium, in partnership with the NMEP, supported the introduction of SMC in six out of the 33 LGAs in Oyo State. By 2024, the estimated target population of children eligible for SMC in these six LGAs was approximately 310,000. 23 Study population The study compared key malaria outcome measures between the pre-intervention (2021) year and the year when SMC implementation started (2022) in the three selected study locations. The pre-intervention year provides data on the outcome if the intervention had not been implemented, thus serving as the counterfactual. To measure the impact of SMC, key malaria indicators (incidence, severe malaria cases and malaria-related deaths) obtained from routine health facility data in 2021 were compared with data from 2022. In addition, data was collected for children aged 5–10 years to check the trends in this older age group of children to act as a contemporaneous counterfactual group for the study. Thus, the study population consisted of children aged 3–59 months and children aged 5–10 years who attended the selected health facilities for any reason. Sample size determination and sampling procedures The number of health facilities selected was determined using a power calculation based on a mixed-effects negative binomial regression model. This approach accounts for the overdispersion often observed in count data and the clustering of observations within facilities over time. We assumed a baseline malaria incidence of 300 per 1,000 person months and aimed to detect a 50% reduction (to 150 per 1,000 person-months, corresponding to an incidence rate ratio of 0.5) with 80% power at a two-sided 5% significance level (α = 0.05). Each health facility was expected to contribute 24 monthly observations (12 pre-intervention, 12 post-intervention). To account for within-facility correlation, we incorporated an intra-cluster correlation coefficient of 0.2, which yielded a design effect (DE) of 4.6 using the standard formula based on a cluster size of 24 (representing the number of monthly observations per facility). Further adjustments were made to accommodate instances of missing or incomplete monthly health facility data. Based on this, we estimated the required number of independent observations using standard power calculations for a mixed-effects negative binomial regression model and adjusted for clustering. The study sample, consisting of 36 health facilities across the three locations, with facilities each contributing 24 monthly observations over the two evaluation years, provides at least 80% power to detect a statistically significant reduction in malaria incidence based on the assumed baseline incidence and expected effect size. A multi-stage sampling approach was followed, stratified by state and by LGA. First, three LGAs were randomly selected from each state, resulting in a total of nine LGAs across the three states. Based on pre-defined eligibility criteria, health facilities listed on the National Health Management Information System (NHMIS) District Health Information System (DHIS2) instance were identified and listed to create a sampling frame stratified by LGA. The selection criteria for health facilities to be included in the sampling frame for the SMC impact study included: health facilities listed on the NHMIS DHIS2 instance, facilities providing malaria testing services either by microscopy or rapid diagnostic tests (RDT), high volume health facilities with average monthly outpatient attendance of at least 120, availability of data reporting tools in the health facilities as well as health facilities with an average monthly reporting rate of at least 85% on the malaria indicators of interest in the years of interest (2021 and 2022). Health facilities in locations with security challenges were excluded. From the lists of potentially eligible health facilities, four facilities were randomly sampled per LGA, resulting in a total of 36 health facilities across the nine participating LGAs and three states. Where a secondary-level health facility exists and was not randomly picked, at least one is purposefully included in the sample in each selected LGA to provide data on outcomes that occur less at lower-level health facilities, such as data on severe malaria cases and malaria-related deaths. Study outcomes The primary outcome was the monthly count of malaria cases confirmed parasitologically using RDT or microscopy among children aged 3–59 months, reported by each participating health facility. Secondary outcomes included monthly counts of all-cause fever episodes, severe malaria cases and malaria-associated deaths in age-eligible children. Data collection methods, procedures and sources Data collection was done in December 2023 by trained research assistants at the respective health facilities. Data from January to December 2021 and 2022 were retrospectively extracted from the selected health facility registers (NHMIS tools) by direct observation and counting. The approach of directly extracting data from health facility registers enabled more granular age-disaggregation of data, as only data for children under 5 and persons above 5 years are available on the NHMIS DHIS2 instance in Nigeria. With the dichotomous age disaggregation in DHIS2 data, it is impossible to discount cases occurring in non-eligible children aged 0–2 months in the estimation of impact using such data. The direct extraction of data from health facility records was also important to gather other necessary background information as additional data points required for analysis, including occurrences of RDT and microscopy stock-out. Monthly rainfall data were obtained for each LGA for both years of interest. A review of SMC programmatic records and End-of-Round (EoR) survey findings was conducted to obtain estimates of SMC coverage and other key indicators for the 2022 campaign in each LGA. Furthermore, desk reviews of the 2021 malaria indicator survey (MIS) and the 2006 national population census data conducted by the National Population Commission (NPC) were conducted to obtain other necessary background information such as coverage of other malaria control interventions like insecticide-treated bet nets in participating LGAs in 2021 and 2022. The review also facilitated an understanding of important contextual factors and potential confounders such as information on the occurrence of floods, droughts, insecurity or industrial strike actions by health workers which could influence the outcomes of interest. The data collection tool was adapted based on content- and data-focused piloting and field testing conducted in one health facility that was not selected for the study in the FCT. This enabled the team to flag any sensitive areas, determine survey duration and check for any aspect prone to missingness. The final version of the data collection tool was a structured questionnaire with logic rules, mandatory response and skip patterns for quality assurance. The questionnaire has sections on geographical characteristics, including catchment population; characteristics of health facility, including monthly outpatient attendance and occurrences of stock-out of RDT and microscopy supplies; and monthly fever cases, RDT and microscopy testing, RDT and microscopy-confirmed malaria cases, severe cases and mortality counts. It was preloaded on the SurveyCTO platform and used to extract data electronically from the records of participating health facilities. Android global positioning system (GPS) devices were used to collect GPS coordinates of all health facilities visited during fieldwork. Data were reviewed each day during data collection and spot-checked by state field supervisors before submission to a password-protected central database. All data sent to the central database were encrypted and daily quality assurance checks were carried out on them using Stata® version 16. 35 HMIS data submitted to the National DHIS2 for the period of the study were compared with data collected by the team of research assistants for completeness, consistency and accuracy as an additional data quality assurance measure. Where there were inconsistent reports, facility-based records were re-verified and assumed to be more accurate if inconsistency persisted upon re-verification. Data cleaning was done by verifying and querying all HMIS data uploaded on SurveyCTO for outliers and missing data. Data analysis The unit of analysis was monthly observations of participating health facilities, with each facility-month treated as an observation within the analytical dataset. Descriptive and summary statistics were computed for each indicator, with categorical data summarised as frequencies and relative percentages, while count and continuous data were summarised as means and standard deviations. Mean counts and incidence of parasitologically-confirmed uncomplicated malaria cases and secondary outcomes (per 1000 children aged 3–59 months) were computed for each period (2021 and 2022) for each state and all three states combined. To estimate the impact of SMC, we employed mixed-effects multilevel negative binomial regression models with random intercepts at the LGA and state levels. This analytical approach was chosen due to the hierarchical structure of the data, where observations were nested within health facilities, which were themselves nested within LGAs and states, reflecting the multilevel nature of healthcare delivery and intervention implementation. The decision to use negative binomial regression was based on the presence of overdispersion in the count data, where the variance exceeded the mean, making traditional Poisson regression unsuitable. Consequently, we adopted a three-level random-intercept model framework: health facilities constituted level 1, nested within LGAs (level 2), which were further nested within states (level 3). This hierarchical structure allowed us to account for unobserved heterogeneity at multiple levels, acknowledging that variations in malaria incidence could be influenced by contextual factors specific to each LGA or state. Models were adjusted for a range of time-varying factors and potential confounders to mitigate bias. These included population growth, health-seeking behaviour (operationalised as monthly out-patient attendance per 1,000 children aged 3–59 months), malaria testing rates, and environmental variables such as monthly rainfall and seasonality, captured by including the month of the year as a covariate. Such adjustments are critical in ecological studies to differentiate between changes attributable to the intervention and those due to external influences. For the impact evaluation, we focused on data from July to December for each of the study years—specifically comparing July to December 2021 (pre-SMC period) with July to December 2022 (SMC implementation period). This comparison was necessitated by differences in the commencement of the first SMC cycle in 2022, which began in June for Oyo and Kogi states and July for the FCT. By harmonising the evaluation period to July to December 2022, we ensured consistency across the three states, thereby enhancing the comparability of findings. Impact was quantified as incidence rate ratios (IRRs), accompanied by their 95% confidence intervals (95% CIs), providing a measure of relative change in malaria incidence due to the SMC intervention. Model fit and validity were assessed using the Akaike Information Criterion, which facilitated the comparison of competing models by balancing goodness-of-fit with model complexity. Given the frequency of zero values in reported monthly incidence data—a common issue in malaria surveillance datasets—we conducted sensitivity analyses using zero-inflated negative binomial regression models. This approach allowed us to account for excess zeros, potentially due to underreporting or true absence of cases, thus providing a more robust estimation of intervention impact. To further strengthen causal inference, a counterfactual analysis was conducted on incidence data of SMC-ineligible children (aged 5–10 years) in the selected study locations. This age-group served as a natural control, to support the interpretation of findings as to whether observed changes in incidence may be attributable to the intervention rather than other contemporaneous factors. All statistical analyses were performed using Stata® version 16. 24 Statistical significance was set at p-values < 0.05. Ethical considerations Research ethical approval for the study was obtained from the National Health Research Ethics Committee (NHREC) (REF: NHREC/01/01/2007-05/06/2023). In addition, state-specific research ethical approval was obtained from each study state, except for FCT where the research ethical approval from NHREC sufficed. Institutional permissions, including from the state malaria elimination programmes were also secured from relevant authorities in each study state. In the interest of confidentiality and privacy, aggregate rather than individual patient level data were used for the study across all study locations. RESULTS Characteristics of participating health facilities A total of 36 health facilities contributed data to this study, of which 12 were sampled in Kogi state, 12 in Oyo state, and 12 in the FCT. The majority (86.1%) of the facilities provided services at the primary level, while the rest (13.9%) were secondary-level facilities. Outpatient attendance among children under 5 was higher in 2022 compared to 2021 in all states, particularly in FCT. Malaria testing rates varied, with RDT usage being lowest in FCT and higher in Kogi and Oyo, while microscopy testing was highest in FCT, but remained low Oyo and Kogi states. The proportion of health facilities experiencing RDT or microscopy stock-outs declined in FCT (from 16.7% in 2021 to 8.3% in 2022), remained stable in Kogi (25.0% in both years), and increased in Oyo (from 16.7% during 2021 to 25.0% during 2022). SMC coverage (as the percentage of eligible children who received the first dose of SMC medicines) per cycle in 2022 was consistently high across states, ranging from 82.1–96.7%, with the proportion of children receiving SMC in all cycles being high in Kogi (78.4%) and Oyo (78.0%) but lower in FCT (56.5%). Insecticide-treated net (ITN) ownership and use improved across states, particularly in Oyo, where ownership increased from 53.7–72.8% and use rose from 31.2–59.2%. Other characteristics of participating health facilities are summarised in Tabe 1 below: Table 1 Characteristics of participating health facilities and states 2021–2022 Characteristic FCT Kogi Oyo 2021 2022 2021 2022 2021 2022 Catchment area population-<5yrs*** 1,134,828 1,245,430 1,039,491 1,071,148 307,747 318,390 Outpatient attendance-<5yrs** 381,083 403,482 39,576 58,735 369,883 376,037 Malaria testing rate (RDT)-<5yrs** 24.5 20.6 85.9 87.0 83.0 82.1 Malaria testing rate (microscopy)- <5yrs** 60.6 56.5 6.7 6.6 14.3 17.0 % of health facilities experiencing at least one RDT/microscopy stock-out 16.7 8.3 25.0 25.0 16.7 25.0 Completeness of monthly health facility records (% of monthly reports with zero counts for uncomplicated malaria cases among children aged 3–59 months 2021–2022) 16.0 5.6 45.1 26.4 4.9 17.4 SMC coverage per cycle (% eligible children receiving the first dose across cycles)* Not applicable 82.1–92.0 Not applicable 90.6–96.3 Not applicable 88.6–96.7 % of children who received SMC in all cycles in 2022* Not applicable 56.5 Not applicable 78.4 Not applicable 78.0 Net (ITN) ownership/coverage(%) * + 45.0 45.7 31.5 39.4 53.7 72.8 Net (ITN) use (%) * + 28.6 37.8 18.0 32.9 31.2 59.2 * + Sourced from MIS 2021 * Sourced from 2022 SMC EoR survey **S ourced from NHMIS DHIS2 Instance, downloaded 3rd February 2025 ***Sourced from NPC 2006 (projected at state growth rates) Incidence of uncomplicated malaria cases and all-cause fever episodes Across all reporting health facilities in the three states, the incidence of uncomplicated malaria cases was approximately 20 cases per 1000 children aged 3–59 months in 2021, and 9 cases per 1000 children in 2022. This represents a 55% crude reduction in incidence. The level of reduction in incidence varied across individual states, with the largest decline seen in Oyo state (62%) and the smallest reduction observed in the FCT (33%). Mean decline in Kogi state was estimated at 48% (Table 2 ). Figure 2 illustrates temporal trends in malaria incidence. Table 2 Incidence of confirmed malaria cases and all-cause fever per 1000 children aged 3–59 months (July – December 2021 vs July – December 2022) All states FCT Kogi Oyo Year Mean Std dev. Mean Std dev. Mean Std dev. Mean Std dev. Incidence of uncomplicated malaria cases 2021 20.16 70.31 13.28 37.09 5.50 8.72 41.72 113.05 2022 9.15 28.17 8.93 16.05 2.86 3.91 15.67 45.24 Incidence of all-cause fever 2021 38.36 103.05 53.86 115.57 10.67 17.72 50.54 131.59 2022 37.11 104.16 72.42 144.39 4.28 5.00 34.62 98.07 Estimates of mean incidence of all-cause fever episodes were 38 episodes per 1000 in 2021 and 37 episodes per 1000 children aged 3–59 months in 2022. In the FCT, there was a notably higher incidence for all-cause fever episodes in 2022 (72 episodes per 1000 children) compared with 2021 (54 episodes per 1000 children). State-level results for Kogi and Oyo show lower incidence of all-cause fever episodes in 2022 compared with 2021: 11 episodes per 1000 children aged 3–59 months in 2021 vs 4 episodes per 1000 children aged 3–59 months in 2022 in Kogi; and 51 episodes per 1000 children aged 3–59 months in 2021 vs 35 per 1000 children aged 3–59 months in Oyo (Table 2 ). The levels of reported severe malaria cases and malaria-associated deaths were too low to precisely measure their incidence over the study period. Estimates of impact of SMC on uncomplicated malaria cases and all-cause fever episodes Analyzing the data for all study states combined and adjusting for potential confounders (including RDT or microscopy testing rate, outpatient attendance rate, population growth, RDT stockouts, seasonality and rainfall, the incidence of confirmed malaria cases (per 1000 children) was 50% (95% CI: 39% − 60%) lower in 2022 compared with 2021 (adjusted IRR: 0.50; 95% CI: 0.40–0.61; p < 0.001) (Table 3 ) State-level estimates of impact varied widely. For the FCT, incidence was 41% (95% CI: 21% − 55%) lower in 2022 compared with 2021, adjusted IRR 0.59 (95% CI: 0.45–0.79, p < 0.001) and for Oyo state, the incidence was 63% (95% CI: 49% − 73%) lower in 2022 compared with 2021, adjusted IRR 0.37 (95% CI: 0.26–0.51, p < 0.001); lastly for Kogi state there was no evidence of impact observed from our analytic sample, adjusted IRR 1.19 (95% CI: 0.84–1.68, p = 0.340) (Table 3 ). Table 3 Estimates of SMC impact on confirmed malaria cases among children aged 3–59 months (July – December 2021 vs July - December 2022) Location Year Crude IRR (95% CI, p value) Adjusted IRR (95% CI, p value) All states 2021 Reference Reference 2022 0.47 (0.36–0.61, p < 0.001) 0.50 (0.40–0.61, p < 0.001) FCT 2021 Reference Reference 2022 0.79 (0.52–1.18, p = 0.247) 0.59 (0.45–0.79, p = 0.002) Kogi 2021 Reference Reference 2022 0. 46 (0.28–0.74, p = 0.001) 1.19 (0.84–1.68, p = 0.340) Oyo 2021 Reference Reference 2022 0.28 (0.17–0.45, p < 0.001) 0.37 (0.26–0.51, p < 0.001) FCT: Federal Capital Territory; IRR: incidence rate ratio; CI: confidence interval Incidence of all-cause fever per 1000 children aged 3–59 months was 29% (95% CI: 14% − 41%) lower in 2022 compared with 2021; adjusted IRR: 0.71 (95% CI: 0.59–0.86, p < 0.001). State-level estimates of impact on all-cause fever among children aged 3–59 months also varied widely. For Oyo, the incidence of all-cause fever was 29% (95% CI: 6% − 46%) lower in 2022 compared with 2021, adjusted IRR 0.71 (95% CI: 0.54–0.94, p = 0.016). There was however no evidence of impact on all-cause fever observed in Kogi state and the FCT (Table 4 ). The levels of reported severe malaria cases and malaria-associated deaths were too low to precisely measure their incidence and any impact that SMC might have had on them over the study period. Table 4 Estimates of SMC impact on all-cause fever among children aged 3–59 months (July – December 2021–2022) Location Year Crude IRR (95% CI, p value) Adjusted IRR (95% CI, p value) All states 2021 Reference Reference 2022 0.65 (0.51–0.83, p < 0.001) 0.71 (0.59–0.86, p < 0.001) FCT 2021 Reference Reference 2022 1.12 (0.77–1.61, p = 0.557) 1.01 (0.79–1.3, p = 0.924) Kogi 2021 Reference Reference 2022 0. 42 (0.28–0.64, p < 0.001) 0.92 (0.69–1.23, p = 0.582) Oyo 2021 Reference Reference 2022 0.56 (0.36–0.86, p = 0.009) 0.71 (0.54–0.94, p = 0.016) FCT: Federal Capital Territory; IRR: incidence rate ratio; CI: confidence interval Sensitivity and counterfactual analyses Results from sensitivity analyses, using zero-inflated negative binomial regression models to account for excess zeros ( Supplementary Table 1 ), were generally consistent with those of the primary analyses, indicating the robustness of main study findings. In the counterfactual age group, the incidence rate ratio (IRR) was 0.81 (95% CI: 0.66–1.00; p = 0.050), indicating a marginally significant 19% reduction in incidence in 2022 related to 2021 ( Supplementary Table 2 ). At the state level, however, the trends varied. There was no statistically significant difference in incidence between 2021 and 2022 in the FCT and Kogi state (p > 0.05). In contrast, a statistically significant reduction in incidence was observed in 2022 in Oyo state (IRR: 0.55; 95% CI: 0.40–0.77; p < 0.001), indicating a 45% decrease relative to 2021. DISCUSSION This study sought to evaluate the impact of SMC using SPAQ to SMC eligible children aged 3–59 months in three study locations in Nigeria. Using a plausibility evaluation design and comparing data from health facility records for the pre-SMC implementation period (2021) and the first year of SMC implementation (2022) across locations, the study makes an important and timely contribution to the existing evidence base on the impact SMC when delivered at scale under programmatic conditions. After accounting for measured potential confounders, the study found that mean incidence of uncomplicated clinical malaria cases confirmed via RDT, or microscopy was 50% (95% CI: 39–60%) lower in the first year of SMC delivery compared with the preceding year. While the observed magnitude of impact is lower than the level of effect typically found in randomised controlled trials and case-control studies, findings are consistent with those found in previous studies utilising routine data sources 11,22,25 . Differences in magnitude of impact between controlled research settings and real-world programmatic settings are likely due to a number of factors, including differences in implementation standards, contextual complexity, data quality and analytical methods. 26,27 Hence, these findings reflect the level of SMC effectiveness that is observable from routine health facility records - with notable data complexity and limitations in data quality and reporting – and is not necessarily a complete reflection of the level of SMC protection in those settings. Variations in the magnitude of impact across locations might have been due to factors such as differences in the quality of health facility records, epidemiological profiles, seasonality, coverage and quality of SMC programmatic delivery and broader contextual issues. In particular, the finding of no impact of SMC on incidence of confirmed malaria cases in Kogi state is likely due to the relatively lower completeness and overall quality of routinely reported case data, as Kogi had the lowest level of data completeness in terms of the percentage of monthly reports with zero counts for uncomplicated malaria cases among children aged 3–59 months in 2021 and 2022 as shown in Table 1 . This highlights the importance of efforts aimed at improving the quality of case management, parasitological testing surveillance and reporting both as part of the SMC programme and more broadly as part of health systems-strengthening measures. Such variations could also in part be attributable to differences in malaria diagnostic capacity and the level of effectiveness of the health reporting system in the different study locations 28 . It is possible that this could also reflect differences in the prevalence of resistance markers associated with SP within the population. However, while there are concerns that resistance to SP may reduce the protective effectiveness of SMC 29,30 , available evidence also suggests that high-grade resistance to SP remains relatively low in the Sahel region of West and Central Africa. 31 Future research may explore the influence of markers of SPAQ resistance on the level of SMC effectiveness and real-world impact in the region. The observable impact of SMC on the secondary outcomes of interest, including all-cause fever, were less clear. Incidence of all-cause fever per 1000 children aged 3–59 months was estimated at 29% (95% CI: 14% − 41%) lower in 2022 compared with 2021 in three locations combined, with no evidence of impact observed in Kogi state and the FCT. Since all fever episodes cannot be attributed to malaria, this trend is likely confounded by the imbalance in the occurrence of non-malarial fever episodes between the two years of interest. Paradoxically, for FCT there was an increase in all-cause fever episodes in 2022 compared to the previous year. While the exact reasons for these trends cannot be established through our study, we hypothesise that trends such as the increased fever episodes in the FCT might have been in part due to outbreaks of non-malarial febrile illnesses, such as diphtheria and cholera in some states in northern Nigeria, including the FCT in 2022. 32 The lack of available data to test for our secondary objectives, particularly severe malaria and mortality, highlights the need for improved routine data reporting. Only when routine data reporting is improved would the impact of SMC on these more concerning outcomes be measurable with high precision and accuracy. Feedback from data collectors engaged for the study’s fieldwork was that within secondary-level health facilities included in the study, there was very little documentation of severe malaria cases and malaria-attributable deaths recorded, with over 90% of monthly reporting having zero counts for both outcomes. In other words, data for severe malaria cases and malaria-associated deaths were mostly non-existent - much of the data for these specific secondary study outcomes had zero inputs in the sighted records of health facilities visited. Findings from the counterfactual analysis suggest a potential indirect or spillover chemoprevention effect of SMC, particularly in Oyo state. This is consistent with findings from a study conducted in the Gambia, which demonstrated that the risk of clinical malaria decreased by 20% in older children (who were ineligible for SMC) living in the same households and communities as eligible children who received SMC medicines. 33 This and previous evidence thus suggest that SMC provides indirect benefits to people who do not receive SMC by reducing malaria transmission in the wider community. As such, eligible populations such as older children residing in the same communities as those who received SMC may not be an ideal contemporaneous control group for counterfactual analysis of the impact of SMC in the real world. Further research is, however, needed to support the current evidence on the extent of the communal and indirect effect of SMC in untreated age groups. Overall, it is important to emphasise that these findings represent the observable magnitude of the impact of SMC on outcomes of interest, as derived from routine health facility data. While these data provide valuable insights into trends and changes in outcomes of interest, they are subject to inherent complexities and limitations related to data quality, completeness, and reporting accuracy in routine data sources. Consequently, the findings may not fully capture the comprehensive level of protection offered by SMC in the study settings. Therefore, while the results reflect important observable impacts, they should be interpreted with caution, recognising the complexities, potential for biases and limitations inherent in routine health facility data. Strengths, limitations and implications for future research To our knowledge, this is the first study to estimate SMC impact using routine HMIS data among children aged 3–59 months in Nigeria since the adoption and widespread implementation of SMC in the country following the WHO’s recommendation to deploy SMC for under-5 children. The plausibility evaluation approach and the incorporation of robust statistical methods enabled the estimation of SMC impact with reasonable levels of certainty and precision. A key strength of this study lies in its rigorous analytical approach, employing mixed-effects multilevel negative binomial regression models to appropriately account for the hierarchical structure of the data. By incorporating random intercepts at the LGA and state levels, the analysis captures unobserved heterogeneity, allowing for more accurate estimation of SMC impact across the different settings. The study’s adjustment for key time-varying confounders, including population growth, health-seeking behavior, malaria testing rates, and environmental factors such as rainfall and seasonality, enhances internal validity by reducing potential sources of bias 34 . The study also demonstrates methodological robustness through the use of sensitivity analyses with zero-inflated negative binomial models, addressing the common challenge of excess zeros in malaria surveillance data. Furthermore, incorporating data on incidence among SMC-ineligible children (aged 5–10 years) as a proxy counterfactual group helped to strengthen the study’s overall causal inferential utility. Like any study utilising routine data sources, our study has notable limitations. First, although we employed the plausible evaluation design plus appropriate data quality assurance and analytical methods which enabled us to estimate SMC impact with a high level of rigour, the study lacked a parallel control group. This was due to the unavailability of comparable control areas with similar epidemiological and SMC eligibility profiles to include as a contemporaneous counterfactual study arm, thereby limiting the extent to which the study was able to account for potential confounders in the estimation of impact. Second, while HMIS databases are a rich and readily available source of data for the evaluation of public health interventions, their use for assessing SMC impact raises several concerns, particularly regarding their data quality and reliance on passive surveillance. Concerns about data quality limitations in routine data sources have been acknowledged previously. The use of passive malaria surveillance systems presents an additional limitation in HMIS data, as only a fraction of infected individual cases seek treatment for malaria at public health facilities 35 . Besides, it is likely that not all those who sought treatment were primarily resident within the catchment areas of the health facility. Furthermore, of those who sought care with symptoms suggestive of malaria, not all were tested for parasitological confirmation. These factors may occur at different rates over time. Moreover, the exclusive sampling of public health facilities may have biased our data, especially if there are systematic differences in characteristics such as nutritional status, access to other malaria prevention interventions and socioeconomic status between children in households who primarily seek care in public health facilities relative to those in households who primarily seek care at private health facilities. That has implications on the external validity and generalisability of study findings. While we made efforts to adjust for population growth, adjustments were based on officially reported under-5 population estimates which may not be accurate. Our adjustments might not have completely accounted for other population dynamics such as migration. Neither did covariate adjustments explicitly account for broader contextual factors such as time-varying differences in healthcare access, health seeking behaviour and case management policy and practices and coverage of interventions (SMC and insecticide-treated bed nets), all of which could limit the validity of current estimates of SMC impact. These limitations nonetheless present opportunities for further HMIS data quality improvement and future research efforts to consider. As acknowledged earlier, efforts are needed to strengthen HMIS data capture and reporting tools, methods and processes. This will enable the availability of high-quality data to support future evaluations of SMC impact that utilise routine HMIS data as the primary or secondary data source. The inclusion of contemporaneous control groups as well as accounting for additional potential confounders, where feasible, are additional considerations in future SMC impact evaluations. CONCLUSION The study provides important and timely evidence on the impact of SMC when delivered at scale under routine programmatic conditions. It found significantly lower incidence of parasitologically-confirmed uncomplicated clinical malaria cases and modestly lower incidence of all-cause fever episodes following the introduction of SMC. It also enabled a better understanding of data quality gaps in routine data sources, while underscoring the need for efforts to strengthen HMIS data capture and reporting tools, methods and processes to improve data quality and support future evaluations of SMC impact in eligible settings. Declarations AUTHOR CONTRIBUTIONS E. Ikechukwu, E. Okereke, C. Nnaji and O. Oresanya conceptualised and designed the study. E. Ikechukwu, E. Okereke, O. Ogunmola, J. Chukwumerije, D. Emeto, E. Salifu and A. Balogun coordinated fieldwork and data collection. C. Nnaji and E. Ikechukwu conducted the statistical analysis. E. Okereke, E. Ikechukwu and C. Nnaji wrote the first draft of the manuscript. O. Ogunmola and J. Chukwumerije contributed to the refinement of the first draft of the manuscript. E. Ikechukwu, E. Okereke, O. Ogunmola, J. Chukwumerije, D. Emeto, E. Salifu, A. Balogun, C. Oguoma, E. Shekarau, N. Ogbulafor, E. Cassidy, C. Rassi, O. Oresanya and C. Nnaji reviewed and contributed to the subsequent versions of the manuscript. C. Nnaji provided overall methodological supervision. All authors approved the final version of the manuscript and contributed substantively to its intellectual content. AVAILABILITY OF DATA AND MATERIALS Processed data supporting the findings of this study are included in this published article and its supplementary information files. Original datasets analysed are available from the authors upon reasonable request. FUNDING This study was conducted as part of evaluation activities for Malaria Consortium’s SMC programme in Nigeria. The programme is supported using philanthropic funding received by Malaria Consortium, primarily as a result of being awarded Top Charity status by GiveWell. References Oladipo HJ, Tajudeen YA, Oladunjoye IO, Yusuff SI, Yusuf RO, Oluwaseyi EM, AbdulBasit MO, Adebisi YA, El-Sherbini MS. Increasing challenges of malaria control in sub-Saharan Africa: Priorities for public health research and policymakers. Ann Med Surg (Lond). 2022 Aug 18; 81:104366. WHO. World malaria report 2022. Geneva: World Health Organization; 2022. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2022. Accessed 31st May 2024. Baraka V, Nhama A, Aide P, Bassat Q, David A, Gesase S, Gwasupika J, Hachizovu S, Makenga G, Ntizimira CR, Obunge O, Tshefu KA, Cousin M, Otsyula N, Pathan R, Risterucci C, Su G, Manyando C. Prescription patterns and compliance with World Health Organization recommendations for the management of uncomplicated and severe malaria: A prospective, real-world study in sub-Saharan Africa. Malar J. 2023 Jul 25; 22(1): 215. WHO. World malaria report 2020. Geneva: World Health Organization; 2020. https://www.who.int/publications/i/item/9789240015791. Accessed 3rd June, 2024. Hessou-Djossou D, Djègbè I, Loko YLE, Boukari MKYG, Nonfodji OM, Tchigossou G, Djouaka R, Akogbeto M. Attitudes and prevention towards malaria in the context of COVID-19 pandemic in urban community in Benin, West Africa. Malar J. 2023 Aug 4; 22(1): 228. National Population Commission (NPC) [Nigeria], National Malaria Control Programme (NMCP) [Nigeria], and ICF International. 2012. Nigeria Malaria Indicator Survey 2010. Abuja, Nigeria: NPC, NMCP, and ICF International. FMOH. National Malaria Policy. Abuja: National Malaria Elimination Programme, Federal Ministry of Health; 2015. Ezennia IJ, Nduka SO, Ekwunife OI. Cost benefit analysis of malaria rapid diagnostic test: the perspective of Nigerian community pharmacists. Malar J. 2017 Jan 3;16(1):7. World Health Organization. WHO policy recommendation: Seasonal malaria chemoprevention (SMC) for Plasmodium falciparum malaria control in highly seasonal transmission areas of the Sahel sub-region in Africa. Geneva: WHO; 2012. WHO. Guidelines for Malaria. Geneva: World Health Organization; 2022. (WHO/UCN/GMP/2022.01 Rev. 2). License: CC BY-NC-SA 3.0 IGO ACCESS-SMC Partnership. Effectiveness of seasonal malaria chemoprevention at scale in west and central Africa: an observational study. Lancet. 2020 Dec 5;396(10265):1829-1840. doi: 10.1016/S0140-6736(20)32227-3 WHO. Seasonal Malaria Chemoprevention with Sulfadoxine-Pyrimethamine plus Amodiaquine in children, A field guide. Geneva, Switzerland; 2013. Cairns M, Roca-Feltrer A, Garske T, Wilson AL, Diallo D, Milligan PJ, Ghani AC, Greenwood BM. Estimating the potential public health impact of seasonal malaria chemoprevention in African children. Nat Commun. 2012 Jun 6; 3: 881. Cissé B, Ba EH, Sokhna C, NDiaye JL, Gomis JF, Dial Y, Pitt C, NDiaye M, Cairns M, Faye E, NDiaye M, Lo A, Tine R, Faye S, Faye B, Sy O, Konate L, Kouevijdin E, Flach C, Faye O, Trape JF, Sutherland C, Fall FB, Thior PM, Faye OK, Greenwood B, Gaye O, Milligan P. Effectiveness of Seasonal Malaria Chemoprevention in Children under Ten Years of Age in Senegal: A Stepped-Wedge Cluster-Randomized Trial. PLoS Med. 2016 Nov 22;13(11): e1002175. Thwing J, Williamson J, Cavros I, Gutman JR. Systematic Review and Meta-Analysis of Seasonal Malaria Chemoprevention. Am J Trop Med Hyg. 2023 Dec 11;110(1):20-31. doi: 10.4269/ajtmh.23-0481. PMID: 38081050; PMCID: PMC10793029. Cairns M, Ceesay SJ, Sagara I, Zongo I, Kessely H, Gamougam K, Diallo A, Ogboi JS, Moroso D, Van Hulle S, Eloike T, Snell P, Scott S, Merle C, Bojang K, Ouedraogo JB, Dicko A, Ndiaye JL, Milligan P. Effectiveness of seasonal malaria chemoprevention (SMC) treatments when SMC is implemented at scale: Case-control studies in 5 countries. PLoS Med. 2021 Sep 8;18(9): e1003727. Bakai TA, Thomas A, Iwaz J, Atcha-Oubou T, Tchadjobo T, Khanafer N, Rabilloud M, Voirin N. Effectiveness of seasonal malaria chemoprevention in three regions of Togo: a population-based longitudinal study from 2013 to 2020. Malar J. 2022 Dec 31;21(1):400. Adjei MR, Kubio C, Buamah M, Sarfo A, Suuri T, Ibrahim S, Sadiq A, Abubakari II, Baafi JV. Effectiveness of seasonal malaria chemoprevention in reducing under-five malaria morbidity and mortality in the Savannah Region, Ghana. Ghana Med J. 2022 Jun;56(2):64-70. Manga IA, Tairou F, Seck A, Kouevidjin E, Sylla K, Sow D, Gueye AB, Ba M, Ndiaye M, Tine RCK, Gaye O, Faye B, Ndiaye JLA. Effectiveness of seasonal malaria chemoprevention administered in a mass campaign in the Kedougou region of Senegal in 2016: a case-control study. Wellcome Open Res. 2023 Apr 12; 7: 216. Khan J, Suau Sans M, Okot F, Rom Ayuiel A, Magoola J, Rassi C, Huang S, Mubiru D, Bonnington C, Baker K, Ahmed J, Nnaji C, Richardson S. A quasi-experimental study to estimate effectiveness of seasonal malaria chemoprevention in Aweil South County in Northern Bahr El Ghazal, South Sudan. Malar J. 2024 Jan 24;23(1):33. Fottsoh Fokam A, Rouamba T, Samadoulougou S, Ye Y, Kirakoya-Samadoulougou F. A Bayesian spatio-temporal framework to assess the effect of seasonal malaria chemoprevention on children under 5 years in Cameroon from 2016 to 2021 using routine data. Malar J. 2023 Nov 11;22(1):347. Richardson S, Moukenet A, Diar MSI, de Cola MA, Rassi C, Counihan H, Roca-Feltrer A. Modeled Impact of Seasonal Malaria Chemoprevention on District-Level Suspected and Confirmed Malaria Cases in Chad Based on Routine Clinical Data (2013-2018). Am J Trop Med Hyg. 2021 Oct 18;105(6):1712-1721. doi: 10.4269/ajtmh.21-0314 Malaria Consortium. Coverage and quality of seasonal malaria chemoprevention supported by Malaria Consortium in 2023. Project Report; published 25th April, 2024. https://www.malariaconsortium.org/resources/publications/1774/coverage-and-quality-of-seasonal-malaria-chemoprevention-supported-by-malaria-consortium-in-2023. Accessed 3rd June 2024. StataCorp (2019) Stata Statistical Software: Release 16. StataCorp LLC, College Station, TX. Kirakoya-Samadoulougou F, De Brouwere V, Fokam AF, Ouédraogo M, Yé Y. Assessing the effect of seasonal malaria chemoprevention on malaria burden among children under 5 years in Burkina Faso. Malar J. 2022 May 6;21(1):143. doi: 10.1186/s12936-022-04172-z. Nordon C, Karcher H, Groenwold RH, Ankarfeldt MZ, Pichler F, Chevrou-Severac H, Rossignol M, Abbe A, Abenhaim L; GetReal consortium. The "Efficacy-Effectiveness Gap": Historical Background and Current Conceptualization. Value Health. 2016 Jan;19(1):75-81. doi: 10.1016/j.jval.2015.09.2938 Glasgow RE, Lichtenstein E, Marcus AC. Why don't we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am J Public Health. 2003 Aug;93(8):1261-7. doi: 10.2105/ajph.93.8.1261. Danwang C, Khalil É, Achu D, Ateba M, Abomabo M, Souopgui J, De Keukeleire M, Robert A. Fine scale analysis of malaria incidence in under-5: hierarchical Bayesian spatio-temporal modelling of routinely collected malaria data between 2012-2018 in Cameroon. Sci Rep. 2021 Jun 1;11(1):11408. Mahamar A, Sumner KM, Levitt B, Freedman B, Traore A, Barry A, et al. Effect of three years’ seasonal malaria chemoprevention on molecular markers of resistance of Plasmodium falciparum to sulfadoxine-pyrimethamine and amodiaquine in Ouelessebougou. Mali Malar J. 2022;21:39. Molina-de la Fuente, I., Sagrado Benito, M.J., Lasry, E. et al. Seasonal malaria chemoprevention in a context of high presumed sulfadoxine-pyrimethamine resistance: malaria morbidity and molecular drug resistance profiles in South Sudan. Malar J 22, 345 (2023). https://doi.org/10.1186/s12936-023-04740-x Baba E, Hamade P, Kivumbi H, Marasciulo M, Maxwell K, Moroso D, et al. Effectiveness of seasonal malaria chemoprevention at scale in west and central Africa: an observational study. Lancet. 2020;396:1829–40. WHO. Disease Outbreak News (Diphtheria – Nigeria). Available from Diphtheria-Nigeria (who.int). Accessed 28th May 2024. Soremekun, S., Conteh, B., Nyassi, A. et al. Household-level effects of seasonal malaria chemoprevention in the Gambia. Commun Med 4, 97 (2024). https://doi.org/10.1038/s43856-024-00503-0 Habicht JP, Victora CG, Vaughan JP. Evaluation designs for adequacy, plausibility and probability of public health programme performance and impact. Int J Epidemiol. 1999; 28(1):10–8. Thwing J, Camara A, Candrinho B, Zulliger R, Colborn J, Painter J, Plucinski MM. A Robust Estimator of Malaria Incidence from Routine Health Facility Data. Am J Trop Med Hyg. 2020 Apr;102(4):811-820. doi: 10.4269/ajtmh.19-0600. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Published Journal Publication published 11 Nov, 2025 Read the published version in Malaria Journal → Version 1 posted Editorial decision: Revision requested 13 Aug, 2025 Reviews received at journal 13 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviews received at journal 14 May, 2025 Reviewers agreed at journal 26 Apr, 2025 Reviewers invited by journal 21 Apr, 2025 Editor assigned by journal 10 Apr, 2025 Submission checks completed at journal 10 Apr, 2025 First submitted to journal 09 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6413508","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441144272,"identity":"2205eb2d-72dd-41fe-b586-cf8b39679d2d","order_by":0,"name":"Ebenezer C. 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Nnaji","email":"data:image/png;base64,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","orcid":"","institution":"Malaria Consortium","correspondingAuthor":true,"prefix":"","firstName":"Chukwudi","middleName":"A.","lastName":"Nnaji","suffix":""}],"badges":[],"createdAt":"2025-04-09 15:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6413508/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6413508/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12936-025-05604-2","type":"published","date":"2025-11-11T15:58:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80780819,"identity":"d37247cb-7f87-42e8-8a6c-3a65f0ba6907","added_by":"auto","created_at":"2025-04-17 04:32:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":448476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of Nigeria showing the study locations/areas\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6413508/v1/6a319c0fad7627feec0dbc63.jpeg"},{"id":80781394,"identity":"3a2c0965-f689-487d-8152-851e23c0dbe6","added_by":"auto","created_at":"2025-04-17 04:40:59","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":549218,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly confirmed malaria cases in children aged 3-59 months by year (with reference lines indicating the start of the evaluation period in each year)\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6413508/v1/40185b1aceb5814f4fa675f7.jpeg"},{"id":96105928,"identity":"3ed9793b-3360-41a7-bce4-13194f7991d5","added_by":"auto","created_at":"2025-11-17 16:12:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2140517,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6413508/v1/2488e9c8-1ab8-45f1-b29b-ba6858a6212d.pdf"},{"id":80780817,"identity":"d9821424-53a5-4387-b90e-e75891bd16a4","added_by":"auto","created_at":"2025-04-17 04:32:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15217,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6413508/v1/9243ad7527d6c27ad4b199f8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of seasonal malaria chemoprevention: a plausibility evaluation of routine data from health facilities in three implementing states in Nigeria","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMalaria continues to pose a significant public health concern in many sub-Saharan African countries.\u003csup\u003e1\u003c/sup\u003e Globally, there were an estimated 247\u0026nbsp;million malaria cases in 2021, with the African region accounting for an estimated 95% of all cases.\u003csup\u003e2,3\u003c/sup\u003e Between 2000 and 2019, malaria incidence declined in the region from 368 to 222 per 1000 population at risk. However, an increase to 232 per 1000 was reported 2020 due to the disruptions to health services during the COVID-19 pandemic.\u003csup\u003e4,5\u003c/sup\u003e In Nigeria, it is estimated that malaria is responsible for approximately 60% of outpatient visits and 30% of admissions.\u003csup\u003e6\u003c/sup\u003e The disease also contributes up to 11% of maternal mortality, 25% of infant mortality, and 30% of under-5 mortality.\u003csup\u003e7,8\u003c/sup\u003e The disease overburdens the already-stretched health system and exerts a severe social and economic burden on the country, contributing to substantial productivity losses and hindering economic growth.\u003csup\u003e7,8\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDefined as the intermittent administration of full treatment courses of an antimalarial medicine to children in areas of high seasonal malaria transmission, seasonal malaria chemoprevention (SMC) was first recommended by the WHO as a malaria chemoprevention strategy in eligible settings in 2012.\u003csup\u003e9,10,11\u003c/sup\u003e. In 2013, WHO released an implementation guide to help countries adopt and implement this new intervention.\u003csup\u003e12\u003c/sup\u003e Following pilot studies in seven local government areas (LGAs) in three Sahelian states, Nigeria adopted SMC as a chemoprevention strategy in 2014. By 2020, the intervention had been implemented across all nine states that were targeted for the initial scale-up phase. As part of the High Burden to High Impact approach for malaria control, a subnational stratification exercise enabled the identification of appropriate intervention-mixes for the different epidemiological settings in the country aimed at aiding prioritization for impact. This led to the expansion of SMC to additional states. As of 2024, SMC had been successfully implemented in all 20 eligible states and the Federal Capital Territory, targeting about 20\u0026nbsp;million children.\u003c/p\u003e \u003cp\u003eSMC involves the administration of sulfadoxine-pyrimethamine plus amodiaquine (SPAQ) to children aged 3\u0026ndash;59 months for four or five monthly cycles, given in 28-day intervals during the peak transmission season.\u003csup\u003e13\u003c/sup\u003e Evidence suggests that SMC using SPAQ monthly for up to at least 4 monthly cycles during the high malaria transmission season for children less than 5 years of age prevents approximately 73\u0026ndash;75% of uncomplicated malaria episodes\u003csup\u003e14,15,\u003c/sup\u003e and 62% of malaria parasitemia in children less than 5 years of age.\u003csup\u003e15\u003c/sup\u003e While previous evidence from both randomised and observational studies in different countries across sub-Saharan Africa has shown that SMC is highly effective\u003csup\u003e11,14,16,17,18,19,20,21\u003c/sup\u003e, much of the current evidence on SMC effectiveness is based on studies conducted several years ago. Considering the large-scale delivery of SMC over several years, there has been growing interest in assessing the impact of the intervention when delivered under programmatic conditions using routine health data. Generating up-to-date, real-world evidence on the impact of the programme when delivered at scale is essential for tracking progress, improving the programme\u0026rsquo;s performance and ensuring accountability to stakeholders and partners. However, obtaining reliable routine health management information system (HMIS) data to estimate the impact of the SMC programme can be challenging, as routine health data sources are known to have data quality challenges. Despite those limitations, existing HMIS data can offer a useful data source for evaluating and understanding the impact of SMC in real world settings.\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe goal of this study was thus to determine the contributions of SMC using SPAQ to the reduction in malaria disease burden and to provide information to guide decisions on the future of SMC and other malaria prevention and elimination interventions in Nigeria. The specific objectives of the study were to assess changes in the incidence of uncomplicated malaria and secondary epidemiological outcomes, including all-cause fever, severe malaria and malaria-associated deaths at the health facility level between the pre-SMC period and the start of SMC implementation in selected implementing states in Nigeria.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe study adopted a pre-post plausibility evaluation design involving routine HMIS data for the periods before and after the introduction of SMC in the study locations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy setting\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in three SMC implementing locations: Oyo, Kogi and the Federal Capital Territory (FCT) in Nigeria \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The FCT is located in the North Central geopolitical region in Nigeria and serve as the country's capital. It is situated within the savannah region with moderate climatic conditions. The peak of the rainy season in FCT typically lasts between July and October each year. Based on the duration of the high transmission season, it requires five monthly SMC cycles, typically starting in June. In partnership with the National Malaria Elimination Programme (NMEP), Malaria Consortium supported the implementation of SMC for the first time in the FCT in 2022. As of 2024, the target population of SMC eligible children in the FCT was about one million.\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKogi state is situated in the north-central Nigeria within the tropical Guinean forest\u0026ndash;savanna mosaic ecoregion. The peak of the rainy season in Kogi state lasts from April to October every year. Based on malaria transmission seasonality patterns, five monthly SMC cycles are required, typically starting in May or June. In partnership with the NMEP, Malaria Consortium supported the introduction of SMC in Kogi state in 2022. As of 2024, the target population of SMC eligible children in Kogi state was estimated at 1.2\u0026nbsp;million.\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOyo State is situated in the southwestern region of Nigeria and experiences an equatorial climate characterized by both dry and wet seasons, along with relatively high humidity. The dry season spans from November to March, while the wet season occurs from April to October. Due to seasonal patterns of malaria transmission, five monthly cycles of Seasonal Malaria Chemoprevention (SMC) are typically conducted, beginning in May or June. In 2022, Malaria Consortium, in partnership with the NMEP, supported the introduction of SMC in six out of the 33 LGAs in Oyo State. By 2024, the estimated target population of children eligible for SMC in these six LGAs was approximately 310,000.\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe study compared key malaria outcome measures between the pre-intervention (2021) year and the year when SMC implementation started (2022) in the three selected study locations. The pre-intervention year provides data on the outcome if the intervention had not been implemented, thus serving as the counterfactual. To measure the impact of SMC, key malaria indicators (incidence, severe malaria cases and malaria-related deaths) obtained from routine health facility data in 2021 were compared with data from 2022. In addition, data was collected for children aged 5\u0026ndash;10 years to check the trends in this older age group of children to act as a contemporaneous counterfactual group for the study. Thus, the study population consisted of children aged 3\u0026ndash;59 months and children aged 5\u0026ndash;10 years who attended the selected health facilities for any reason.\u003c/p\u003e\n\u003ch3\u003eSample size determination and sampling procedures\u003c/h3\u003e\n\u003cp\u003eThe number of health facilities selected was determined using a power calculation based on a mixed-effects negative binomial regression model. This approach accounts for the overdispersion often observed in count data and the clustering of observations within facilities over time. We assumed a baseline malaria incidence of 300 per 1,000 person months and aimed to detect a 50% reduction (to 150 per 1,000 person-months, corresponding to an incidence rate ratio of 0.5) with 80% power at a two-sided 5% significance level (α\u0026thinsp;=\u0026thinsp;0.05). Each health facility was expected to contribute 24 monthly observations (12 pre-intervention, 12 post-intervention). To account for within-facility correlation, we incorporated an intra-cluster correlation coefficient of 0.2, which yielded a design effect (DE) of 4.6 using the standard formula based on a cluster size of 24 (representing the number of monthly observations per facility). Further adjustments were made to accommodate instances of missing or incomplete monthly health facility data. Based on this, we estimated the required number of independent observations using standard power calculations for a mixed-effects negative binomial regression model and adjusted for clustering. The study sample, consisting of 36 health facilities across the three locations, with facilities each contributing 24 monthly observations over the two evaluation years, provides at least 80% power to detect a statistically significant reduction in malaria incidence based on the assumed baseline incidence and expected effect size.\u003c/p\u003e \u003cp\u003eA multi-stage sampling approach was followed, stratified by state and by LGA. First, three LGAs were randomly selected from each state, resulting in a total of nine LGAs across the three states. Based on pre-defined eligibility criteria, health facilities listed on the National Health Management Information System (NHMIS) District Health Information System (DHIS2) instance were identified and listed to create a sampling frame stratified by LGA. The selection criteria for health facilities to be included in the sampling frame for the SMC impact study included: health facilities listed on the NHMIS DHIS2 instance, facilities providing malaria testing services either by microscopy or rapid diagnostic tests (RDT), high volume health facilities with average monthly outpatient attendance of at least 120, availability of data reporting tools in the health facilities as well as health facilities with an average monthly reporting rate of at least 85% on the malaria indicators of interest in the years of interest (2021 and 2022). Health facilities in locations with security challenges were excluded. From the lists of potentially eligible health facilities, four facilities were randomly sampled per LGA, resulting in a total of 36 health facilities across the nine participating LGAs and three states. Where a secondary-level health facility exists and was not randomly picked, at least one is purposefully included in the sample in each selected LGA to provide data on outcomes that occur less at lower-level health facilities, such as data on severe malaria cases and malaria-related deaths.\u003c/p\u003e\n\u003ch3\u003eStudy outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the monthly count of malaria cases confirmed parasitologically using RDT or microscopy among children aged 3\u0026ndash;59 months, reported by each participating health facility. Secondary outcomes included monthly counts of all-cause fever episodes, severe malaria cases and malaria-associated deaths in age-eligible children.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection methods, procedures and sources\u003c/h2\u003e \u003cp\u003eData collection was done in December 2023 by trained research assistants at the respective health facilities. Data from January to December 2021 and 2022 were retrospectively extracted from the selected health facility registers (NHMIS tools) by direct observation and counting. The approach of directly extracting data from health facility registers enabled more granular age-disaggregation of data, as only data for children under 5 and persons above 5 years are available on the NHMIS DHIS2 instance in Nigeria. With the dichotomous age disaggregation in DHIS2 data, it is impossible to discount cases occurring in non-eligible children aged 0\u0026ndash;2 months in the estimation of impact using such data. The direct extraction of data from health facility records was also important to gather other necessary background information as additional data points required for analysis, including occurrences of RDT and microscopy stock-out. Monthly rainfall data were obtained for each LGA for both years of interest. A review of SMC programmatic records and End-of-Round (EoR) survey findings was conducted to obtain estimates of SMC coverage and other key indicators for the 2022 campaign in each LGA. Furthermore, desk reviews of the 2021 malaria indicator survey (MIS) and the 2006 national population census data conducted by the National Population Commission (NPC) were conducted to obtain other necessary background information such as coverage of other malaria control interventions like insecticide-treated bet nets in participating LGAs in 2021 and 2022. The review also facilitated an understanding of important contextual factors and potential confounders such as information on the occurrence of floods, droughts, insecurity or industrial strike actions by health workers which could influence the outcomes of interest.\u003c/p\u003e \u003cp\u003eThe data collection tool was adapted based on content- and data-focused piloting and field testing conducted in one health facility that was not selected for the study in the FCT. This enabled the team to flag any sensitive areas, determine survey duration and check for any aspect prone to missingness. The final version of the data collection tool was a structured questionnaire with logic rules, mandatory response and skip patterns for quality assurance. The questionnaire has sections on geographical characteristics, including catchment population; characteristics of health facility, including monthly outpatient attendance and occurrences of stock-out of RDT and microscopy supplies; and monthly fever cases, RDT and microscopy testing, RDT and microscopy-confirmed malaria cases, severe cases and mortality counts. It was preloaded on the SurveyCTO platform and used to extract data electronically from the records of participating health facilities. Android global positioning system (GPS) devices were used to collect GPS coordinates of all health facilities visited during fieldwork.\u003c/p\u003e \u003cp\u003eData were reviewed each day during data collection and spot-checked by state field supervisors before submission to a password-protected central database. All data sent to the central database were encrypted and daily quality assurance checks were carried out on them using Stata\u0026reg; version 16.\u003csup\u003e35\u003c/sup\u003e HMIS data submitted to the National DHIS2 for the period of the study were compared with data collected by the team of research assistants for completeness, consistency and accuracy as an additional data quality assurance measure. Where there were inconsistent reports, facility-based records were re-verified and assumed to be more accurate if inconsistency persisted upon re-verification. Data cleaning was done by verifying and querying all HMIS data uploaded on SurveyCTO for outliers and missing data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe unit of analysis was monthly observations of participating health facilities, with each facility-month treated as an observation within the analytical dataset. Descriptive and summary statistics were computed for each indicator, with categorical data summarised as frequencies and relative percentages, while count and continuous data were summarised as means and standard deviations. Mean counts and incidence of parasitologically-confirmed uncomplicated malaria cases and secondary outcomes (per 1000 children aged 3\u0026ndash;59 months) were computed for each period (2021 and 2022) for each state and all three states combined.\u003c/p\u003e \u003cp\u003eTo estimate the impact of SMC, we employed mixed-effects multilevel negative binomial regression models with random intercepts at the LGA and state levels. This analytical approach was chosen due to the hierarchical structure of the data, where observations were nested within health facilities, which were themselves nested within LGAs and states, reflecting the multilevel nature of healthcare delivery and intervention implementation. The decision to use negative binomial regression was based on the presence of overdispersion in the count data, where the variance exceeded the mean, making traditional Poisson regression unsuitable. Consequently, we adopted a three-level random-intercept model framework: health facilities constituted level 1, nested within LGAs (level 2), which were further nested within states (level 3). This hierarchical structure allowed us to account for unobserved heterogeneity at multiple levels, acknowledging that variations in malaria incidence could be influenced by contextual factors specific to each LGA or state.\u003c/p\u003e \u003cp\u003eModels were adjusted for a range of time-varying factors and potential confounders to mitigate bias. These included population growth, health-seeking behaviour (operationalised as monthly out-patient attendance per 1,000 children aged 3\u0026ndash;59 months), malaria testing rates, and environmental variables such as monthly rainfall and seasonality, captured by including the month of the year as a covariate. Such adjustments are critical in ecological studies to differentiate between changes attributable to the intervention and those due to external influences. For the impact evaluation, we focused on data from July to December for each of the study years\u0026mdash;specifically comparing July to December 2021 (pre-SMC period) with July to December 2022 (SMC implementation period). This comparison was necessitated by differences in the commencement of the first SMC cycle in 2022, which began in June for Oyo and Kogi states and July for the FCT. By harmonising the evaluation period to July to December 2022, we ensured consistency across the three states, thereby enhancing the comparability of findings.\u003c/p\u003e \u003cp\u003eImpact was quantified as incidence rate ratios (IRRs), accompanied by their 95% confidence intervals (95% CIs), providing a measure of relative change in malaria incidence due to the SMC intervention. Model fit and validity were assessed using the Akaike Information Criterion, which facilitated the comparison of competing models by balancing goodness-of-fit with model complexity. Given the frequency of zero values in reported monthly incidence data\u0026mdash;a common issue in malaria surveillance datasets\u0026mdash;we conducted sensitivity analyses using zero-inflated negative binomial regression models. This approach allowed us to account for excess zeros, potentially due to underreporting or true absence of cases, thus providing a more robust estimation of intervention impact. To further strengthen causal inference, a counterfactual analysis was conducted on incidence data of SMC-ineligible children (aged 5\u0026ndash;10 years) in the selected study locations. This age-group served as a natural control, to support the interpretation of findings as to whether observed changes in incidence may be attributable to the intervention rather than other contemporaneous factors. All statistical analyses were performed using Stata\u0026reg; version 16.\u003csup\u003e24\u003c/sup\u003e Statistical significance was set at p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eResearch ethical approval for the study was obtained from the National Health Research Ethics Committee (NHREC) (REF: NHREC/01/01/2007-05/06/2023). In addition, state-specific research ethical approval was obtained from each study state, except for FCT where the research ethical approval from NHREC sufficed. Institutional permissions, including from the state malaria elimination programmes were also secured from relevant authorities in each study state. In the interest of confidentiality and privacy, aggregate rather than individual patient level data were used for the study across all study locations.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of participating health facilities\u003c/h2\u003e\n \u003cp\u003eA total of 36 health facilities contributed data to this study, of which 12 were sampled in Kogi state, 12 in Oyo state, and 12 in the FCT. The majority (86.1%) of the facilities provided services at the primary level, while the rest (13.9%) were secondary-level facilities. Outpatient attendance among children under 5 was higher in 2022 compared to 2021 in all states, particularly in FCT. Malaria testing rates varied, with RDT usage being lowest in FCT and higher in Kogi and Oyo, while microscopy testing was highest in FCT, but remained low Oyo and Kogi states. The proportion of health facilities experiencing RDT or microscopy stock-outs declined in FCT (from 16.7% in 2021 to 8.3% in 2022), remained stable in Kogi (25.0% in both years), and increased in Oyo (from 16.7% during 2021 to 25.0% during 2022). SMC coverage (as the percentage of eligible children who received the first dose of SMC medicines) per cycle in 2022 was consistently high across states, ranging from 82.1\u0026ndash;96.7%, with the proportion of children receiving SMC in all cycles being high in Kogi (78.4%) and Oyo (78.0%) but lower in FCT (56.5%). Insecticide-treated net (ITN) ownership and use improved across states, particularly in Oyo, where ownership increased from 53.7\u0026ndash;72.8% and use rose from 31.2\u0026ndash;59.2%. Other characteristics of participating health facilities are summarised in \u003cstrong\u003eTabe 1\u003c/strong\u003e below:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of participating health facilities and states 2021\u0026ndash;2022\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFCT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eKogi\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOyo\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCatchment area population-\u0026lt;5yrs***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,134,828\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,245,430\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,039,491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,071,148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e307,747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e318,390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutpatient attendance-\u0026lt;5yrs**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381,083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e403,482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39,576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58,735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e369,883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e376,037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalaria testing rate (RDT)-\u0026lt;5yrs**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalaria testing rate (microscopy)- \u0026lt;5yrs**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% of health facilities experiencing at least one RDT/microscopy stock-out\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompleteness of monthly health facility records (% of monthly reports with zero counts for uncomplicated malaria cases among children aged 3\u0026ndash;59 months 2021\u0026ndash;2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSMC coverage per cycle (% eligible children receiving the first dose across cycles)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.1\u0026ndash;92.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.6\u0026ndash;96.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.6\u0026ndash;96.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% of children who received SMC in all cycles in 2022*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNet (ITN) ownership/coverage(%)\u003cem\u003e*\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNet (ITN) use (%)\u003cem\u003e*\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*\u003csup\u003e+\u003c/sup\u003e Sourced from MIS 2021\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003cp\u003e* Sourced from 2022 SMC EoR survey\u003c/p\u003e\n \u003cp\u003e**S\u003cem\u003eourced from NHMIS DHIS2 Instance, downloaded 3rd February 2025\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003cp\u003e***Sourced from NPC 2006 (projected at state growth rates)\u003c/p\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003eIncidence of uncomplicated malaria cases and all-cause fever episodes\u003c/h2\u003e\n \u003cp\u003eAcross all reporting health facilities in the three states, the incidence of uncomplicated malaria cases was approximately 20 cases per 1000 children aged 3\u0026ndash;59 months in 2021, and 9 cases per 1000 children in 2022. This represents a 55% crude reduction in incidence. The level of reduction in incidence varied across individual states, with the largest decline seen in Oyo state (62%) and the smallest reduction observed in the FCT (33%). Mean decline in Kogi state was estimated at 48% (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates temporal trends in malaria incidence.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncidence of confirmed malaria cases and all-cause fever per 1000 children aged 3\u0026ndash;59 months (July \u0026ndash; December 2021 vs July \u0026ndash; December 2022)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAll states\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFCT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eKogi\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOyo\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eIncidence of uncomplicated malaria cases\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncidence of all-cause fever\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eEstimates of mean incidence of all-cause fever episodes were 38 episodes per 1000 in 2021 and 37 episodes per 1000 children aged 3\u0026ndash;59 months in 2022. In the FCT, there was a notably higher incidence for all-cause fever episodes in 2022 (72 episodes per 1000 children) compared with 2021 (54 episodes per 1000 children). State-level results for Kogi and Oyo show lower incidence of all-cause fever episodes in 2022 compared with 2021: 11 episodes per 1000 children aged 3\u0026ndash;59 months in 2021 vs 4 episodes per 1000 children aged 3\u0026ndash;59 months in 2022 in Kogi; and 51 episodes per 1000 children aged 3\u0026ndash;59 months in 2021 vs 35 per 1000 children aged 3\u0026ndash;59 months in Oyo (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The levels of reported severe malaria cases and malaria-associated deaths were too low to precisely measure their incidence over the study period.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eEstimates of impact of SMC on uncomplicated malaria cases and all-cause fever episodes\u003c/h2\u003e\n \u003cp\u003eAnalyzing the data for all study states combined and adjusting for potential confounders (including RDT or microscopy testing rate, outpatient attendance rate, population growth, RDT stockouts, seasonality and rainfall, the incidence of confirmed malaria cases (per 1000 children) was 50% (95% CI: 39% \u0026minus;\u0026thinsp;60%) lower in 2022 compared with 2021 (adjusted IRR: 0.50; 95% CI: 0.40\u0026ndash;0.61; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eState-level estimates of impact varied widely. For the FCT, incidence was 41% (95% CI: 21% \u0026minus;\u0026thinsp;55%) lower in 2022 compared with 2021, adjusted IRR 0.59 (95% CI: 0.45\u0026ndash;0.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and for Oyo state, the incidence was 63% (95% CI: 49% \u0026minus;\u0026thinsp;73%) lower in 2022 compared with 2021, adjusted IRR 0.37 (95% CI: 0.26\u0026ndash;0.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); lastly for Kogi state there was no evidence of impact observed from our analytic sample, adjusted IRR 1.19 (95% CI: 0.84\u0026ndash;1.68, p\u0026thinsp;=\u0026thinsp;0.340) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimates of SMC impact on confirmed malaria cases among children aged 3\u0026ndash;59 months (July \u0026ndash; December 2021 vs July - December 2022)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude IRR (95% CI, p value)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted IRR (95% CI, p value)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAll states\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47 (0.36\u0026ndash;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.50 (0.40\u0026ndash;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.52\u0026ndash;1.18, p\u0026thinsp;=\u0026thinsp;0.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.59 (0.45\u0026ndash;0.79, p\u0026thinsp;=\u0026thinsp;0.002)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eKogi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 46 (0.28\u0026ndash;0.74, p\u0026thinsp;=\u0026thinsp;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.19 (0.84\u0026ndash;1.68, p\u0026thinsp;=\u0026thinsp;0.340)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOyo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28 (0.17\u0026ndash;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.37 (0.26\u0026ndash;0.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFCT: Federal Capital Territory; IRR: incidence rate ratio; CI: confidence interval\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003cp\u003eIncidence of all-cause fever per 1000 children aged 3\u0026ndash;59 months was 29% (95% CI: 14% \u0026minus;\u0026thinsp;41%) lower in 2022 compared with 2021; adjusted IRR: 0.71 (95% CI: 0.59\u0026ndash;0.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). State-level estimates of impact on all-cause fever among children aged 3\u0026ndash;59 months also varied widely. For Oyo, the incidence of all-cause fever was 29% (95% CI: 6% \u0026minus;\u0026thinsp;46%) lower in 2022 compared with 2021, adjusted IRR 0.71 (95% CI: 0.54\u0026ndash;0.94, p\u0026thinsp;=\u0026thinsp;0.016). There was however no evidence of impact on all-cause fever observed in Kogi state and the FCT (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The levels of reported severe malaria cases and malaria-associated deaths were too low to precisely measure their incidence and any impact that SMC might have had on them over the study period.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimates of SMC impact on all-cause fever among children aged 3\u0026ndash;59 months (July \u0026ndash; December 2021\u0026ndash;2022)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude IRR (95% CI, p value)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted IRR (95% CI, p value)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAll states\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65 (0.51\u0026ndash;0.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.71 (0.59\u0026ndash;0.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.77\u0026ndash;1.61, p\u0026thinsp;=\u0026thinsp;0.557)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.01 (0.79\u0026ndash;1.3, p\u0026thinsp;=\u0026thinsp;0.924)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eKogi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 42 (0.28\u0026ndash;0.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.92 (0.69\u0026ndash;1.23, p\u0026thinsp;=\u0026thinsp;0.582)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOyo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56 (0.36\u0026ndash;0.86, p\u0026thinsp;=\u0026thinsp;0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.71 (0.54\u0026ndash;0.94, p\u0026thinsp;=\u0026thinsp;0.016)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eFCT: Federal Capital Territory; IRR: incidence rate ratio; CI: confidence interval\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eSensitivity and counterfactual analyses\u003c/h2\u003e\n \u003cp\u003eResults from sensitivity analyses, using zero-inflated negative binomial regression models to account for excess zeros (\u003cstrong\u003eSupplementary Table\u0026nbsp;1\u003c/strong\u003e), were generally consistent with those of the primary analyses, indicating the robustness of main study findings. In the counterfactual age group, the incidence rate ratio (IRR) was 0.81 (95% CI: 0.66\u0026ndash;1.00; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050), indicating a marginally significant 19% reduction in incidence in 2022 related to 2021 (\u003cstrong\u003eSupplementary Table\u0026nbsp;2\u003c/strong\u003e). At the state level, however, the trends varied. There was no statistically significant difference in incidence between 2021 and 2022 in the FCT and Kogi state (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, a statistically significant reduction in incidence was observed in 2022 in Oyo state (IRR: 0.55; 95% CI: 0.40\u0026ndash;0.77; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a 45% decrease relative to 2021.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study sought to evaluate the impact of SMC using SPAQ to SMC eligible children aged 3\u0026ndash;59 months in three study locations in Nigeria. Using a plausibility evaluation design and comparing data from health facility records for the pre-SMC implementation period (2021) and the first year of SMC implementation (2022) across locations, the study makes an important and timely contribution to the existing evidence base on the impact SMC when delivered at scale under programmatic conditions.\u003c/p\u003e \u003cp\u003eAfter accounting for measured potential confounders, the study found that mean incidence of uncomplicated clinical malaria cases confirmed via RDT, or microscopy was 50% (95% CI: 39\u0026ndash;60%) lower in the first year of SMC delivery compared with the preceding year. While the observed magnitude of impact is lower than the level of effect typically found in randomised controlled trials and case-control studies, findings are consistent with those found in previous studies utilising routine data sources\u003csup\u003e11,22,25\u003c/sup\u003e. Differences in magnitude of impact between controlled research settings and real-world programmatic settings are likely due to a number of factors, including differences in implementation standards, contextual complexity, data quality and analytical methods.\u003csup\u003e26,27\u003c/sup\u003e Hence, these findings reflect the level of SMC effectiveness that is observable from routine health facility records - with notable data complexity and limitations in data quality and reporting \u0026ndash; and is not necessarily a complete reflection of the level of SMC protection in those settings.\u003c/p\u003e \u003cp\u003eVariations in the magnitude of impact across locations might have been due to factors such as differences in the quality of health facility records, epidemiological profiles, seasonality, coverage and quality of SMC programmatic delivery and broader contextual issues. In particular, the finding of no impact of SMC on incidence of confirmed malaria cases in Kogi state is likely due to the relatively lower completeness and overall quality of routinely reported case data, as Kogi had the lowest level of data completeness in terms of the percentage of monthly reports with zero counts for uncomplicated malaria cases among children aged 3\u0026ndash;59 months in 2021 and 2022 as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This highlights the importance of efforts aimed at improving the quality of case management, parasitological testing surveillance and reporting both as part of the SMC programme and more broadly as part of health systems-strengthening measures.\u003c/p\u003e \u003cp\u003eSuch variations could also in part be attributable to differences in malaria diagnostic capacity and the level of effectiveness of the health reporting system in the different study locations\u003csup\u003e28\u003c/sup\u003e. It is possible that this could also reflect differences in the prevalence of resistance markers associated with SP within the population. However, while there are concerns that resistance to SP may reduce the protective effectiveness of SMC\u003csup\u003e29,30\u003c/sup\u003e, available evidence also suggests that high-grade resistance to SP remains relatively low in the Sahel region of West and Central Africa.\u003csup\u003e31\u003c/sup\u003e Future research may explore the influence of markers of SPAQ resistance on the level of SMC effectiveness and real-world impact in the region.\u003c/p\u003e \u003cp\u003eThe observable impact of SMC on the secondary outcomes of interest, including all-cause fever, were less clear. Incidence of all-cause fever per 1000 children aged 3\u0026ndash;59 months was estimated at 29% (95% CI: 14% \u0026minus;\u0026thinsp;41%) lower in 2022 compared with 2021 in three locations combined, with no evidence of impact observed in Kogi state and the FCT. Since all fever episodes cannot be attributed to malaria, this trend is likely confounded by the imbalance in the occurrence of non-malarial fever episodes between the two years of interest. Paradoxically, for FCT there was \u003cem\u003ean increase\u003c/em\u003e in all-cause fever episodes in 2022 compared to the previous year. While the exact reasons for these trends cannot be established through our study, we hypothesise that trends such as the increased fever episodes in the FCT might have been in part due to outbreaks of non-malarial febrile illnesses, such as diphtheria and cholera in some states in northern Nigeria, including the FCT in 2022.\u003csup\u003e32\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe lack of available data to test for our secondary objectives, particularly severe malaria and mortality, highlights the need for improved routine data reporting. Only when routine data reporting is improved would the impact of SMC on these more concerning outcomes be measurable with high precision and accuracy. Feedback from data collectors engaged for the study\u0026rsquo;s fieldwork was that within secondary-level health facilities included in the study, there was very little documentation of severe malaria cases and malaria-attributable deaths recorded, with over 90% of monthly reporting having zero counts for both outcomes. In other words, data for severe malaria cases and malaria-associated deaths were mostly non-existent - much of the data for these specific secondary study outcomes had zero inputs in the sighted records of health facilities visited.\u003c/p\u003e \u003cp\u003eFindings from the counterfactual analysis suggest a potential indirect or spillover chemoprevention effect of SMC, particularly in Oyo state. This is consistent with findings from a study conducted in the Gambia, which demonstrated that the risk of clinical malaria decreased by 20% in older children (who were ineligible for SMC) living in the same households and communities as eligible children who received SMC medicines.\u003csup\u003e33\u003c/sup\u003e This and previous evidence thus suggest that SMC provides indirect benefits to people who do not receive SMC by reducing malaria transmission in the wider community. As such, eligible populations such as older children residing in the same communities as those who received SMC may not be an ideal contemporaneous control group for counterfactual analysis of the impact of SMC in the real world. Further research is, however, needed to support the current evidence on the extent of the communal and indirect effect of SMC in untreated age groups.\u003c/p\u003e \u003cp\u003eOverall, it is important to emphasise that these findings represent the observable magnitude of the impact of SMC on outcomes of interest, as derived from routine health facility data. While these data provide valuable insights into trends and changes in outcomes of interest, they are subject to inherent complexities and limitations related to data quality, completeness, and reporting accuracy in routine data sources. Consequently, the findings may not fully capture the comprehensive level of protection offered by SMC in the study settings. Therefore, while the results reflect important observable impacts, they should be interpreted with caution, recognising the complexities, potential for biases and limitations inherent in routine health facility data.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStrengths, limitations and implications for future research\u003c/h2\u003e \u003cp\u003eTo our knowledge, this is the first study to estimate SMC impact using routine HMIS data among children aged 3\u0026ndash;59 months in Nigeria since the adoption and widespread implementation of SMC in the country following the WHO\u0026rsquo;s recommendation to deploy SMC for under-5 children. The plausibility evaluation approach and the incorporation of robust statistical methods enabled the estimation of SMC impact with reasonable levels of certainty and precision. A key strength of this study lies in its rigorous analytical approach, employing mixed-effects multilevel negative binomial regression models to appropriately account for the hierarchical structure of the data. By incorporating random intercepts at the LGA and state levels, the analysis captures unobserved heterogeneity, allowing for more accurate estimation of SMC impact across the different settings. The study\u0026rsquo;s adjustment for key time-varying confounders, including population growth, health-seeking behavior, malaria testing rates, and environmental factors such as rainfall and seasonality, enhances internal validity by reducing potential sources of bias\u003csup\u003e34\u003c/sup\u003e. The study also demonstrates methodological robustness through the use of sensitivity analyses with zero-inflated negative binomial models, addressing the common challenge of excess zeros in malaria surveillance data. Furthermore, incorporating data on incidence among SMC-ineligible children (aged 5\u0026ndash;10 years) as a proxy counterfactual group helped to strengthen the study\u0026rsquo;s overall causal inferential utility.\u003c/p\u003e \u003cp\u003eLike any study utilising routine data sources, our study has notable limitations. First, although we employed the plausible evaluation design plus appropriate data quality assurance and analytical methods which enabled us to estimate SMC impact with a high level of rigour, the study lacked a parallel control group. This was due to the unavailability of comparable control areas with similar epidemiological and SMC eligibility profiles to include as a contemporaneous counterfactual study arm, thereby limiting the extent to which the study was able to account for potential confounders in the estimation of impact. Second, while HMIS databases are a rich and readily available source of data for the evaluation of public health interventions, their use for assessing SMC impact raises several concerns, particularly regarding their data quality and reliance on passive surveillance. Concerns about data quality limitations in routine data sources have been acknowledged previously. The use of passive malaria surveillance systems presents an additional limitation in HMIS data, as only a fraction of infected individual cases seek treatment for malaria at public health facilities\u003csup\u003e35\u003c/sup\u003e. Besides, it is likely that not all those who sought treatment were primarily resident within the catchment areas of the health facility. Furthermore, of those who sought care with symptoms suggestive of malaria, not all were tested for parasitological confirmation. These factors may occur at different rates over time. Moreover, the exclusive sampling of public health facilities may have biased our data, especially if there are systematic differences in characteristics such as nutritional status, access to other malaria prevention interventions and socioeconomic status between children in households who primarily seek care in public health facilities relative to those in households who primarily seek care at private health facilities. That has implications on the external validity and generalisability of study findings. While we made efforts to adjust for population growth, adjustments were based on officially reported under-5 population estimates which may not be accurate. Our adjustments might not have completely accounted for other population dynamics such as migration. Neither did covariate adjustments explicitly account for broader contextual factors such as time-varying differences in healthcare access, health seeking behaviour and case management policy and practices and coverage of interventions (SMC and insecticide-treated bed nets), all of which could limit the validity of current estimates of SMC impact.\u003c/p\u003e \u003cp\u003eThese limitations nonetheless present opportunities for further HMIS data quality improvement and future research efforts to consider. As acknowledged earlier, efforts are needed to strengthen HMIS data capture and reporting tools, methods and processes. This will enable the availability of high-quality data to support future evaluations of SMC impact that utilise routine HMIS data as the primary or secondary data source. The inclusion of contemporaneous control groups as well as accounting for additional potential confounders, where feasible, are additional considerations in future SMC impact evaluations.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe study provides important and timely evidence on the impact of SMC when delivered at scale under routine programmatic conditions. It found significantly lower incidence of parasitologically-confirmed uncomplicated clinical malaria cases and modestly lower incidence of all-cause fever episodes following the introduction of SMC. It also enabled a better understanding of data quality gaps in routine data sources, while underscoring the need for efforts to strengthen HMIS data capture and reporting tools, methods and processes to improve data quality and support future evaluations of SMC impact in eligible settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE. Ikechukwu, E. Okereke, C. Nnaji and O. Oresanya conceptualised and designed the study. E. Ikechukwu, E. Okereke, O. Ogunmola, J. Chukwumerije, D. Emeto, E. Salifu and A. Balogun coordinated fieldwork and data collection. C. Nnaji and E. Ikechukwu conducted the statistical analysis. E. Okereke, E. Ikechukwu and C. Nnaji wrote the first draft of the manuscript. O. Ogunmola and J. Chukwumerije contributed to the refinement of the first draft of the manuscript. E. Ikechukwu, E. Okereke, O. Ogunmola, J. Chukwumerije, D. Emeto, E. Salifu, A. Balogun, C. Oguoma, E. Shekarau, N. Ogbulafor, E. Cassidy, C. Rassi, O. Oresanya and C. Nnaji reviewed and contributed to the subsequent versions of the manuscript. C. Nnaji provided overall methodological supervision. All authors approved the final version of the manuscript and contributed substantively to its intellectual content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA AND MATERIALS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProcessed data supporting the findings of this study are included in this published article and its supplementary information files. \u0026nbsp;Original datasets analysed are available from the authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as part of evaluation activities for Malaria Consortium’s SMC programme in Nigeria. The programme is supported using philanthropic funding received by Malaria Consortium, primarily as a result of being awarded Top Charity status by GiveWell.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOladipo HJ, Tajudeen YA, Oladunjoye IO, Yusuff SI, Yusuf RO, Oluwaseyi EM, AbdulBasit MO, Adebisi YA, El-Sherbini MS. Increasing challenges of malaria control in sub-Saharan Africa: Priorities for public health research and policymakers. Ann Med Surg (Lond). 2022 Aug 18; 81:104366.\u003c/li\u003e\n \u003cli\u003eWHO. World malaria report 2022. Geneva: World Health Organization; 2022. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2022. Accessed 31st May 2024.\u003c/li\u003e\n \u003cli\u003eBaraka V, Nhama A, Aide P, Bassat Q, David A, Gesase S, Gwasupika J, Hachizovu S, Makenga G, Ntizimira CR, Obunge O, Tshefu KA, Cousin M, Otsyula N, Pathan R, Risterucci C, Su G, Manyando C. 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PLoS Med. 2016 Nov 22;13(11): e1002175.\u003c/li\u003e\n \u003cli\u003eThwing J, Williamson J, Cavros I, Gutman JR. Systematic Review and Meta-Analysis of Seasonal Malaria Chemoprevention. Am J Trop Med Hyg. 2023 Dec 11;110(1):20-31. doi: 10.4269/ajtmh.23-0481. PMID: 38081050; PMCID: PMC10793029.\u003c/li\u003e\n \u003cli\u003eCairns M, Ceesay SJ, Sagara I, Zongo I, Kessely H, Gamougam K, Diallo A, Ogboi JS, Moroso D, Van Hulle S, Eloike T, Snell P, Scott S, Merle C, Bojang K, Ouedraogo JB, Dicko A, Ndiaye JL, Milligan P. Effectiveness of seasonal malaria chemoprevention (SMC) treatments when SMC is implemented at scale: Case-control studies in 5 countries. PLoS Med. 2021 Sep 8;18(9): e1003727.\u003c/li\u003e\n \u003cli\u003eBakai TA, Thomas A, Iwaz J, Atcha-Oubou T, Tchadjobo T, Khanafer N, Rabilloud M, Voirin N. Effectiveness of seasonal malaria chemoprevention in three regions of Togo: a population-based longitudinal study from 2013 to 2020. Malar J. 2022 Dec 31;21(1):400.\u003c/li\u003e\n \u003cli\u003eAdjei MR, Kubio C, Buamah M, Sarfo A, Suuri T, Ibrahim S, Sadiq A, Abubakari II, Baafi JV. Effectiveness of seasonal malaria chemoprevention in reducing under-five malaria morbidity and mortality in the Savannah Region, Ghana. Ghana Med J. 2022 Jun;56(2):64-70.\u003c/li\u003e\n \u003cli\u003eManga IA, Tairou F, Seck A, Kouevidjin E, Sylla K, Sow D, Gueye AB, Ba M, Ndiaye M, Tine RCK, Gaye O, Faye B, Ndiaye JLA. Effectiveness of seasonal malaria chemoprevention administered in a mass campaign in the Kedougou region of Senegal in 2016: a case-control study. Wellcome Open Res. 2023 Apr 12; 7: 216.\u003c/li\u003e\n \u003cli\u003eKhan J, Suau Sans M, Okot F, Rom Ayuiel A, Magoola J, Rassi C, Huang S, Mubiru D, Bonnington C, Baker K, Ahmed J, Nnaji C, Richardson S. A quasi-experimental study to estimate effectiveness of seasonal malaria chemoprevention in Aweil South County in Northern Bahr El Ghazal, South Sudan. Malar J. 2024 Jan 24;23(1):33.\u003c/li\u003e\n \u003cli\u003eFottsoh Fokam A, Rouamba T, Samadoulougou S, Ye Y, Kirakoya-Samadoulougou F. A Bayesian spatio-temporal framework to assess the effect of seasonal malaria chemoprevention on children under 5 years in Cameroon from 2016 to 2021 using routine data. Malar J. 2023 Nov 11;22(1):347.\u003c/li\u003e\n \u003cli\u003eRichardson S, Moukenet A, Diar MSI, de Cola MA, Rassi C, Counihan H, Roca-Feltrer A. Modeled Impact of Seasonal Malaria Chemoprevention on District-Level Suspected and Confirmed Malaria Cases in Chad Based on Routine Clinical Data (2013-2018). Am J Trop Med Hyg. 2021 Oct 18;105(6):1712-1721. doi: 10.4269/ajtmh.21-0314\u003c/li\u003e\n \u003cli\u003eMalaria Consortium. Coverage and quality of seasonal malaria chemoprevention supported by Malaria Consortium in 2023. Project Report; published 25th April, 2024. https://www.malariaconsortium.org/resources/publications/1774/coverage-and-quality-of-seasonal-malaria-chemoprevention-supported-by-malaria-consortium-in-2023. Accessed 3rd June 2024.\u003c/li\u003e\n \u003cli\u003eStataCorp (2019) Stata Statistical Software: Release 16. StataCorp LLC, College Station, TX.\u003c/li\u003e\n \u003cli\u003eKirakoya-Samadoulougou F, De Brouwere V, Fokam AF, Ou\u0026eacute;draogo M, Y\u0026eacute; Y. Assessing the effect of seasonal malaria chemoprevention on malaria burden among children under 5 years in Burkina Faso. Malar J. 2022 May 6;21(1):143. doi: 10.1186/s12936-022-04172-z.\u003c/li\u003e\n \u003cli\u003eNordon C, Karcher H, Groenwold RH, Ankarfeldt MZ, Pichler F, Chevrou-Severac H, Rossignol M, Abbe A, Abenhaim L; GetReal consortium. The \u0026quot;Efficacy-Effectiveness Gap\u0026quot;: Historical Background and Current Conceptualization. Value Health. 2016 Jan;19(1):75-81. doi: 10.1016/j.jval.2015.09.2938\u003c/li\u003e\n \u003cli\u003eGlasgow RE, Lichtenstein E, Marcus AC. Why don\u0026apos;t we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am J Public Health. 2003 Aug;93(8):1261-7. doi: 10.2105/ajph.93.8.1261.\u003c/li\u003e\n \u003cli\u003eDanwang C, Khalil \u0026Eacute;, Achu D, Ateba M, Abomabo M, Souopgui J, De Keukeleire M, Robert A. Fine scale analysis of malaria incidence in under-5: hierarchical Bayesian spatio-temporal modelling of routinely collected malaria data between 2012-2018 in Cameroon. Sci Rep. 2021 Jun 1;11(1):11408.\u003c/li\u003e\n \u003cli\u003eMahamar A, Sumner KM, Levitt B, Freedman B, Traore A, Barry A, et al. Effect of three years\u0026rsquo; seasonal malaria chemoprevention on molecular markers of resistance of Plasmodium falciparum to sulfadoxine-pyrimethamine and amodiaquine in Ouelessebougou. Mali Malar J. 2022;21:39.\u003c/li\u003e\n \u003cli\u003eMolina-de la Fuente, I., Sagrado Benito, M.J., Lasry, E. et al. Seasonal malaria chemoprevention in a context of high presumed sulfadoxine-pyrimethamine resistance: malaria morbidity and molecular drug resistance profiles in South Sudan. Malar J 22, 345 (2023). https://doi.org/10.1186/s12936-023-04740-x\u003c/li\u003e\n \u003cli\u003eBaba E, Hamade P, Kivumbi H, Marasciulo M, Maxwell K, Moroso D, et al. Effectiveness of seasonal malaria chemoprevention at scale in west and central Africa: an observational study. Lancet. 2020;396:1829\u0026ndash;40.\u003c/li\u003e\n \u003cli\u003eWHO. Disease Outbreak News (Diphtheria \u0026ndash; Nigeria). Available from Diphtheria-Nigeria (who.int). Accessed 28th May 2024.\u003c/li\u003e\n \u003cli\u003eSoremekun, S., Conteh, B., Nyassi, A. et al. Household-level effects of seasonal malaria chemoprevention in the Gambia. Commun Med 4, 97 (2024). https://doi.org/10.1038/s43856-024-00503-0\u003c/li\u003e\n \u003cli\u003eHabicht JP, Victora CG, Vaughan JP. Evaluation designs for adequacy, plausibility and probability of public health programme performance and impact. Int J Epidemiol. 1999; 28(1):10\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eThwing J, Camara A, Candrinho B, Zulliger R, Colborn J, Painter J, Plucinski MM. A Robust Estimator of Malaria Incidence from Routine Health Facility Data. Am J Trop Med Hyg. 2020 Apr;102(4):811-820. doi: 10.4269/ajtmh.19-0600.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6413508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6413508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSeasonal malaria chemoprevention (SMC) has been recommended by the World Health Organization since 2012 for children aged 3\u0026ndash;59 months in areas where malaria transmission is highly seasonal. By 2024, SMC had been successfully implemented in all 21 eligible states in Nigeria. Given this widespread implementation, there has been increasing interest in understanding the impact of the intervention under programmatic conditions. This study assessed changes in malaria incidence and related epidemiological outcomes among the target population of children in three SMC implementing states in Nigeria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA pre-post study plausibility evaluation design was used for this study. Data from routine health management information systems were extracted from selected health facilities to compare the incidence of parasitologically-confirmed uncomplicated malaria cases and secondary outcomes among children aged 3\u0026ndash;59 months within the catchment populations of those health facilities. Mixed-effects, multilevel, negative binomial regression models were employed to estimate the impact of SMC on outcomes of interest between the pre-SMC period (2021) and SMC period (2022).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eData were collected in 36 health facilities: 12 each in Kogi state, Oyo state, and the Federal Capital Territory. The mean incidence of uncomplicated malaria was 20 cases per 1000 children aged 3\u0026ndash;59 months in 2021, and 9 cases per 1000 children in 2022. After accounting for potential confounders, malaria incidence was 50% (95% confidence interval [CI]: 39\u0026ndash;60) lower in the SMC period compared with the pre-SMC period (adjusted incidence rate ratio (IRR): 0.50, 95% CI: 0.40\u0026ndash;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with notable variations in the level of reduction across the three study locations. Incidence of all-cause fever per 1000 children was 29% (95% CI: 14\u0026ndash;41) lower in 2022 compared with 2021 (adjusted IRR: 0.71, 95% CI: 0.59\u0026ndash;0.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Observed levels of severe malaria and attributable deaths were too low to measure the impact of SMC on those outcomes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study found significantly lower levels of incidence of uncomplicated malaria following the introduction of SMC. It thus provides evidence on the potential impact of the intervention in real-world settings while underscoring the need for further improvement to and utilisation of routine data to monitor impact in eligible settings.\u003c/p\u003e","manuscriptTitle":"Impact of seasonal malaria chemoprevention: a plausibility evaluation of routine data from health facilities in three implementing states in Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 04:32:54","doi":"10.21203/rs.3.rs-6413508/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-13T18:23:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-13T18:18:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190877056139610872540787196029226604037","date":"2025-08-13T17:55:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-14T15:37:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85827014290159139498810570982937271216","date":"2025-04-27T00:32:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-21T23:54:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-10T11:29:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-10T11:26:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2025-04-09T15:47:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b8948b5-a08f-4b32-955f-8aa8e911efd8","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:10:29+00:00","versionOfRecord":{"articleIdentity":"rs-6413508","link":"https://doi.org/10.1186/s12936-025-05604-2","journal":{"identity":"malaria-journal","isVorOnly":false,"title":"Malaria Journal"},"publishedOn":"2025-11-11 15:58:25","publishedOnDateReadable":"November 11th, 2025"},"versionCreatedAt":"2025-04-17 04:32:54","video":"","vorDoi":"10.1186/s12936-025-05604-2","vorDoiUrl":"https://doi.org/10.1186/s12936-025-05604-2","workflowStages":[]},"version":"v1","identity":"rs-6413508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6413508","identity":"rs-6413508","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}