Impact of Indoor Residual Spraying and Insecticide-Treated Nets on Malaria Burden in Uganda: A Quasi-Experimental Study in 8 Districts in West Nile and Acholi Regions

Impact of Indoor Residual Spraying and Insecticide-Treated Nets on Malaria Burden in Uganda: A Quasi-Experimental Study in 8 Districts in West Nile and Acholi Regions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Indoor Residual Spraying and Insecticide-Treated Nets on Malaria Burden in Uganda: A Quasi-Experimental Study in 8 Districts in West Nile and Acholi Regions Jane Frances Namuganga, Daniel P. McDermott, Adrienne Epstein, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7518583/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: In Uganda, where malaria transmission is high, insecticide treated nets (ITNs) have been distributed nationwide every three years since 2013. In West Nile, northern Uganda, indoor residual spraying (IRS) was first implemented with clothianidin-deltamethrin (Fludora Fusion®) in 2022, followed by pirimiphos-methyl (Actellic 300CS®) in 2023. We utilized a quasi-experimental study to assess the impact of IRS+ITNs on malaria incidence in West Nile. Methods: Data were collected from three malaria reference centres (MRCs) in West Nile (IRS+ITNs, intervention) and five MRCs in neighbouring Acholi (ITNs only, control) over 4 years: (1) Baseline (December 2018-November 2020), prior to IRS; (2) IRS-1 (December 2022-December 2023) following IRS with clothianidin-deltamethrin; (3) IRS-2 (January 2024-December 2024) following IRS with pirimiphos-methyl. The primary outcome was monthly malaria incidence (number of laboratory-confirmed malaria cases from each MRC target area per 1000 person-years). Data were analysed using negative binomial regression models with a difference-in-difference approach, comparing pre-post trends in malaria incidence between intervention and control groups. Adjusted models accounted for seasonality and care-seeking behaviours. Results: During IRS-1, mean observed malaria incidence fell from baseline in both arms (intervention: 720.9 to 547.9; and control: 523.4 to 455.2 per 1000 person-years). We detected a 14% difference in predicted mean malaria incidence between intervention and control during IRS-1 relative to baseline, but this was not significant (adjusted IRR = 0.86, 95% CI 0.70–1.06, p=0.17). During IRS-2, incidence in the intervention arm declined by 79.3% compared to baseline (720.9 to 149.1), while in the control arm, incidence fell by 24.3% (523.4 to 396.2). We detected a 70% reduction in predicted mean malaria incidence in the intervention arm compared to control relative to baseline (aIRR 0.30, 95% CI 0.24 – 0.38, p<0.01). During IRS-2, there was strong evidence of an immediate and sustained reduction in incidence in the intervention arm over one year. Conclusion: In West Nile, the reduction in malaria incidence after clothianidin-based IRS (plus ITNs) was modest and non-significant. Subsequent IRS with pirimiphos-methyl (plus ITNs) substantially reduced malaria incidence. These results highlight the importance of selecting context-specific insecticides for vector control programs and the potential synergistic effect of dual interventions in areas of high pyrethroid resistance. Malaria IRS clothianidin-deltamethrin pirimiphos-methyl insecticide-treated nets impact of vector control interventions ITNs Figures Figure 1 Figure 2 Figure 3 Introduction Despite concerted efforts to eliminate malaria, the global burden remains high, with an increasing trend in incidence observed in some endemic countries since 2015 [ 1 ]. Sub-Saharan Africa remains disproportionately affected relative to the rest of the world [ 1 ]. Insecticide treated nets (ITNs) and indoor residual spraying of insecticide (IRS) are the principal vector control tools targeting anopheline mosquitoes that feed and rest indoors [ 2 ]. However, their effectiveness is determined by the susceptibility of primary vectors to insecticides, population-level intervention uptake, and intervention coverage [ 3 ]. Unfortunately, insecticide resistance remains a major threat to malaria control efforts [ 1 ], affecting four of the five major insecticide classes (pyrethroids, carbamates, organophosphates and neonicotinoids) [ 1 , 4 , 5 ]. The World Health Organization (WHO) recommends rotation of IRS insecticides at pre-set intervals, and diversification from pyrethroid insecticides to mitigate the development and spread of resistance [ 2 , 3 ]. Where pyrethroid resistance is high, replacing standard (pyrethroid-only) nets with new generation ITNs is encouraged, including pyrethroid plus either piperonyl butoxide (PBO), chlorfenapyr, or pyriproxyfen [ 2 ]. Co-deployment of IRS and ITNs is not recommended by WHO [ 2 ], but widespread pyrethroid resistance [ 5 ] has prompted deployment of these interventions together to reduce malaria transmission in high burden areas [ 2 , 6 ]. In Uganda, the Ministry of Health (MOH) has adopted rotation of IRS insecticides every three years, and the co-deployment strategy in areas hardest hit by malaria. Although globally IRS coverage is low [ 1 ], many sub-Saharan African countries have benefited from IRS campaigns over the past two decades [ 7 – 15 ]. In 2018, clothianidin-based products, Sumishield® (clothianidin-only) [ 16 ], and Fludora Fusion® (clothianidin-deltamethrin mixture) [ 17 ] were adopted and deployed for IRS in several countries, replacing pirimiphos-methyl [ 8 , 10 , 11 , 13 , 14 ]. Replacement often coincided with country-specific predefined timelines for IRS insecticide rotation and aligned with WHO recommendations for insecticide resistance mitigation [ 2 ]. Previously, these insecticides demonstrated prolonged residual efficacy (up to 48 weeks) [ 18 – 20 ], and high vector species susceptibility [ 18 , 19 , 21 ]. Nevertheless, their population-level impact has differed across countries [ 10 – 12 , 14 , 15 , 22 ] underscoring the need for continued assessment of effectiveness in real-world settings. In 2022, clothianidin-deltamethrin-based IRS was deployed in West Nile Uganda, an area with intense malaria transmission [ 23 ]. IRS was repeated in 2023 with pirimiphos-methyl. This rather premature switch in insecticides was prompted by a dramatic malaria resurgence in Eastern Uganda [ 12 , 22 ]. Additionally, a combination of standard and new-generation ITNs were distributed in 2020/2021 and 2023/2024 during Uganda’s campaigns to distribute long-lasting insecticidal nets (LLINs), presenting an opportunity to evaluate IRS plus ITNs in a high transmission setting. To evaluate the effect of co-deployment of these interventions, we utilised a quasi-experimental, difference-in-differences study of health facility-based surveillance data to compare malaria indicators in West Nile to those in neighbouring Acholi region that received ITNs but not IRS. Methods Study sites This study was embedded within the Program for Resistance, Immunology, Surveillance and Modelling of Malaria [PRISM] project, which conducts enhanced surveillance at selected public health facilities in Uganda. West Nile and Acholi are high transmission regions in northern Uganda [ 23 – 26 ], an area bordering South Sudan, Democratic Republic of Congo (DRC) and Kenya. The regions are separated by the Albert Nile, with West Nile to the west and Acholi to the east. The terrain in both regions is predominantly flat, with tropical climatic conditions comprised of one distinct wet and dry season. The burden of malaria in northern Uganda is high [ 23 , 24 ]. In the 2019 Malaria Indicator Survey (MIS), parasite prevalence by microscopy in children under five years was 22% and 12% in West Nile and Acholi respectively [ 23 ]. Anopheles gambiae ss and Anopheles funestus are the primary malaria vectors in Uganda, with An. gambiae ss being the predominant species [ 25 , 27 ]. However, IRS implementation prompted a shift in vector species, with An. arabiensis , an outdoor-biting mosquito, becoming more prevalent, particularly in districts with sustained IRS coverage [ 32 ]. Where ITNs are the sole vector control measure, An. funestus and An. gambiae ss are dominant [ 26 – 28 ]. IRS deployment in northern Uganda The Acholi region received IRS from 2009 to 2014 [ 8 ]. Deployment of IRS was then halted, with the exception of a single round of IRS with pirimiphos-methyl which was delivered in 2017 in response to a resurgence of malaria [ 8 ]. In West Nile, IRS was first introduced in December 2022 with clothianidin-deltamethrin. IRS coverage, defined as the proportion of houses found that were sprayed, averaged 92% [ 26 ] and 88% [ 29 ] in 2022 and 2023 respectively. Mass distribution of ITNs ITNs are Uganda’s primary vector control intervention, distributed through government-sponsored mass distribution campaigns. Between 2013 and 2024, four mass campaigns were conducted country wide. Standard nets were distributed in the first round (2013/2014). Thereafter, a combination of new generation ITNs and standard nets were delivered in selected areas during subsequent rounds[ 30 , 31 ]. In 2019, ITN ownership was 92% and 83%, and net use was 76% and 62% in West Nile and Acholi respectively [ 23 ]. In 2019/2020 mass campaign, selected sub-counties in West Nile and Acholi (including the study sites) received either pyrethroid-PBO or pyrethroid-pyriproxyfen ITNs while the remaining areas received standard, pyrethroid-only nets [ 31 ]. All 8 study sites were in sub-counties that received pyrethroid-PBO nets in the 2020/21 campaign and either pyrethroid-PBO or pyrethroid-chlorfenapyr nets in the 2023/2024 campaign. Health facility-based surveillance We leveraged an existing malaria sentinel site surveillance network previously managed by the Uganda Malaria Surveillance Project (UMSP) at Malaria Reference Centres (MRCs) and described elsewhere [ 32 ]. Surveillance activities are ongoing at 38 high-volume level III/IV public health facilities across Uganda that provide free general medical services to surrounding populations. At each MRC, individual-level outpatient data collected in paper-based health management information system (HMIS) forms are transcribed into an electronic database monthly. A detailed description of data collection, management, analysis and dissemination processes at these sites has been published previously [ 33 ]. Selection of target areas for incidence measurements A target area was defined as the village where the MRC is located and adjacent villages that lack another health facility, are within the same sub-county, have a similar malaria incidence, and together have an estimated population of at least 1,500 persons. These were 1–7 villages around each MRC from where at least 90% of patients that sought care from each site resided and were identified using at least 6 months’ data from each MRC (collected between November 2018 and November 2019). Enumeration surveys were conducted to estimate the population of each target area. These estimates serve as the denominator for incidence measures for each target area, considering a fixed population growth rate of 0.29% per month. This approach is described in detail elsewhere [ 31 , 33 , 34 ]. Site selection This study included eight MRCs (Fig. 1 ), three in West Nile (intervention) and five in Acholi region (control). All MRC sites within West Nile were included as intervention sites. Control sites were selected from the adjacent Acholi region (with similar malaria endemicity, climate and geographical conditions) using the following criteria: 1) within Acholi region; 2) two years of individual-level data available from December 2020 to December 2022); 3) received pyrethroid-PBO ITNs (PermaNet® 3·0) in 2020/202, and either pyrethroid-PBO or pyrethroid-chlorfenapyr (PermaNet® Dual) nets in 2023. Defining IRS periods (independent variables) The study was divided into three follow-up periods: (1) baseline, defined as the 24-month period before IRS was implemented in West Nile (December 2020 to November 2022); (2) IRS-1, the 13-month period following deployment of clothianidin-deltamethrin (Fludora Fusion®) in West Nile (December 2022 to December 2023); and (3) IRS-2, the 12-month period following IRS with pirimiphos-methyl (Actellic® 300CS) in West Nile (January 2024 to December 2024). For our analysis, the independent variable was the IRS period, specified as a categorical variable representing the first (IRS-1) and second (IRS-2) periods. Outcome measures The dependent variable for the primary analyses was site-specific monthly estimates of malaria incidence, defined as the number of laboratory-confirmed malaria cases from the target area of each MRC per 1000-person years of observation. We corrected for variation in monthly testing rates to account for missed testing during commodity stock-outs by multiplying the number of patients with suspected malaria but not tested by the test positivity rate (defined as the proportion of patients with laboratory-confirmed malaria of all patients tested for malaria). Results remained consistent with and without this correction. The result was then added to the number of confirmed malaria cases in that month. The study arm was specified as a categorical variable denoting the intervention and control arms. Statistical analysis To evaluate the impact of IRS in the West Nile region, we employed a difference-in-differences (DID) approach, which compared pre-post trends in malaria incidence after the implementation of IRS in intervention groups to average pre-post change in the control group [ 35 ]. The outcome model for our DID was as follows: $$\:{Y}_{it}\:=\:{\beta\:}_{0\:}+\:{\beta\:}_{1\:}Trea{t}_{i}\:+\:{\beta\:}_{2\:}Pos{t}_{it}\:+\:{\beta\:}_{3}Trea{t}_{i}\cdot\:Pos{t}_{it}\:+\:{\beta\:}_{4}{X}_{it\:}+{\epsilon\:}_{it\:}$$ Where \(\:{Y}_{it}\:\) is the outcome (malaria incidence) per MRC i in month t , \(\:Trea{t}_{i}\) is a binary variable representing whether an site is in the treatment or control arm, \(\:Pos{t}_{it}\:\) is a categorical variable representing the treatment period (pre-IRS, IRS-1, and IRS-2), and \(\:{X}_{it\:}\) represents a vector of site- and time-varying covariates. \(\:{\beta\:}_{3}\) is the key DID parameter in this model which represents the treatment effects of IRS-1 and IRS-2 on malaria incidence. DID analyses were implemented using generalised linear mixed models using the negative binomial distribution and random intercepts for site. The outcome for this model was the MRC-level monthly count of malaria cases in the target area, with an offset for the target area population. Model coefficients were exponentiated into incidence rate ratios (IRR) representing the treatment effect for each IRS in period relative to the baseline period. We adjusted for seasonality, rainfall, and incidence of care-seeking for non-malarial illnesses. Seasonality was specified as a factor variable denoting the rainy (March to May and September to November every year) and dry (December to February and June to August every year) seasons. Rainfall data were retrieved from the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) database [ 36 ]. These were extracted as monthly site-specific estimates in millimetres for each study site. Incidence of care-seeking for non-malarial illnesses was defined as the monthly number of patients who sought care at each health facility, were not suspected of having malaria, and resided in the target area of each MRC per 1000 person-years of observation. Adjusted models included a categorical variable for seasonality, a continuous variable for monthly site-level rainfall estimates (in millimetres), and incidence of care-seeking for non-malarial illnesses (to account for potential changes in care-seeking over time). To produce valid estimates, DID relies on the assumption of parallel trends, which states that the average trend of the treated and control groups would have been parallel in the absence of the intervention [ 35 ]. While this assumption is formally untestable, parallel trends were assessed in the pre-IRS period to ensure the pre-intervention trends met these criteria. This was done by restricting the model to the pre-IRS period and including an interaction term between treatment arm (binary) and a linear time trend in months, with the same covariates as the primary DID analysis and checking if the slopes were significantly different. We used Stata version 18.5 (StataCorp LLC, College Station, TX, USA) statistical software to clean data and generate aggregate monthly estimates for key variables including malaria incidence estimates. Further cleaning, analysis, and model fitting to generate tables, and graphs was performed in R (version 4.4.0) using the glmmTMB, and lme4 packages. Results Descriptive characteristics Overall, 118,916 outpatient visits were recorded for residents from the 8 MRC target areas (Table 1 ), including 61,828 visits during the 24-month baseline period, 30,229 during the 13-month period following deployment of clothianidin-deltamethrin (IRS-1), and 26,869 visits during the 12-month period following deployment of pirimiphos-methyl (IRS-2). The median age of patients was 15 years (IQR 4.5–25.2), and two-thirds were female (77,547 [65.2%]). In total, 88,999 (74.8%) patients were suspected to have malaria, of whom 88,033 (98.9%) were tested using either a malaria rapid diagnostic test (RDT) or microscopy. Confirmatory testing for malaria was predominantly done with RDTs (77,787 [88%]) (Supplemental Fig. 1). Baseline period During the baseline period, malaria was suspected in 21,632 (83.1%) of 26,033 intervention outpatient visits vs 25,951 (72.5%) of 35,795 control visits. Of patients tested for malaria, 12,865 of 21,012 in the intervention arm were confirmed positive (TPR 61.2%) versus 15,261 of 25,620 in the control arm (TPR 59.6%, Table 1 ). Observed mean malaria incidence in the intervention arm was 721 malaria cases per 1000 person-years (SD 307) and 523 malaria cases per 1000 person-years (SD 261) in the control arm (Table 2 ). Monthly observed mean incidence was consistently higher in the intervention arm than in the control throughout the baseline period. Parallel trend analysis detected no difference in the slope of malaria incidence trends between arms at baseline (aIRR = 0.99. 95% CI 0.98–1.01, p = 0.486). Table 1 Target area-specific statistics stratified by MRC, study arm and study period Study period Region Study arm MRC (District) Outpatient visits Median age (IQR) Female sex n (%) Suspected malaria cases n (% outpatient visits) Suspected cases tested n (%) Confirmatory testing by RDT n (%) Confirmed malaria cases n (%[TPR]) Mean malaria incidence per 1000 person-years of observation Baseline (24-month period pre-IRS with Fludora Fusion) West Nile Intervention Ayipe HCIII (Koboko) 9316 12 (2.0–22.0) 5814 (62.4) 8423 (90.4) 7848 (93.2) 7429 (94.7) 5320 (67.8) 873 Metu HCIII (Moyo) 8458 16 (6.0–26.0) 5299 (62.7) 6217 (73.5) 6182 (99.4) 5527 (89.4) 3425 (55.4) 733 Opia HCIII (Arua) 8259 13 (4.5–21.5) 5293 (64.1) 6992 (84.7) 6982 (99.9) 6914 (99) 4120 (59) 557 Intervention arm estimates 26033 14 (4.5–23.5) 16406 (63) 21632 (83.1) 21012 (97.1) 19870 (94.6) 12865 (61.2) 721 Acholi Control Atiak HCIV (Amuru) 5990 13 (2.5–23.5) 3935 (65.7) 4262 (71.2) 4262 (100) 3702 (86.9) 2862 (67.2) 385 Awach HCIV (Gulu) 9340 15 (3.5–26.4) 6495 (69.5) 6349 (68) 6020 (94.8) 5242 (87.1) 3722 (61.8) 733 Namokora HCIV (Kitgum) 5192 15 (2.5–27.5) 3306 (63.7) 3594 (69.2) 3594 (100) 2723 (75.8) 2094 (58.3) 644 Padibe HCIV (Lamwo) 8444 17 (6.5–27.5) 5301 (62.8) 6089 (72.1) 6087 (100) 5967 (98) 3622 (59.5) 474 Patongo HCIII (Agago) 6829 16 (5.5–26.5) 4559 (66.8) 5657 (82.8) 5657 (100) 5289 (93.5) 2961 (52.3) 381 Control arm estimates 35795 15 (3.5–26.5) 23596 (65.9) 25951 (72.5) 25620 (98.7) 22923 (89.5) 15261 (59.6) 523 IRS-1 (13-month period post 1st IRS round with Fludora Fusion) West Nile Intervention Ayipe HCIII (Koboko) 3828 12 (3.0–21.0) 2590 (67.7) 3663 (95.7) 3663 (100) 3489 (95.2) 2228 (60.8) 602 Metu HCIII (Moyo) 4825 14 (5.0–23.0) 3079 (63.8) 3763 (78) 3763 (100) 3043 (80.9) 1906 (50.7) 712 Opia HCIII (Arua) 3806 14 (5.0–23.0) 2413 (63.4) 3130 (82.2) 3128 (99.9) 2559 (81.8) 1399 (44.7) 331 Intervention arm estimates 12459 13 (4.5–21.5) 8082 (64.9) 10556 (84.7) 10554 (100) 9091 (86.1) 5533 (52.4) 548 Acholi Control Atiak HCIV (Amuru) 2901 15 (2.5–27.5) 1896 (65.4) 1831 (63.1) 1829 (99.9) 1299 (71) 1239 (67.7) 289 Awach HCIV (Gulu) 4598 15 (3.5–26.5) 3056 (66.5) 3213 (69.9) 3204 (99.7) 3027 (94.5) 1965 (61.3) 635 Namokora HCIV (Kitgum) 2767 16 (3.5–28.5) 1777 (64.2) 1962 (70.9) 1960 (99.9) 1724 (88) 1126 (57.4) 605 Padibe HCIV (Lamwo) 3676 17 (6.5–27.5) 2351 (64) 2546 (69.3) 2546 (100) 2135 (83.9) 1524 (59.9) 349 Patongo HCIII (Agago) 3828 17 (6.5–27.5) 2514 (65.7) 3211 (83.9) 3211 (100) 2652 (82.6) 1757 (54.7) 397 Control arm estimates 17770 16 (4.5–27.5) 11594 (65.2) 12763 (71.8) 12750 (99.9) 10837 (85) 7611 (59.7) 455 IRS-2 (12-month period post 2nd IRS with Actellic West Nile Intervention Ayipe HCIII (Koboko) 3274 13 (2.5–23.5) 2216 (67.7) 3091 (94.4) 3091 (100) 2977 (96.3) 694 (22.5) 197 Metu HCIII (Moyo) 3057 17 (5.5–28.5) 1903 (62.3) 1998 (65.4) 1998 (100) 1615 (80.8) 428 (21.4) 168 Opia HCIII (Arua) 2797 16 (4.5–27.5) 1806 (64.6) 1889 (67.5) 1889 (100) 1781 (94.3) 335 (17.7) 83 Intervention arm estimates 9128 15 (4.0–26.0) 5925 (64.9) 6978 (76.4) 6978 (100) 6373 (91.3) 1457 (20.9) 149 Acholi Control Atiak HCIV (Amuru) 2849 17 (4.0–20.0) 1941 (68.1) 1418 (49.8) 1418 (100) 994 (70.1) 794 (56) 194 Awach HCIV (Gulu) 5013 17 (4.6–29.5) 3477 (69.4) 2833 (56.5) 2833 (100) 1983 (70) 1460 (51.5) 494 Namokora HCIV (Kitgum) 3200 14.5 (2.5–26.5) 2056 (64.3) 2276 (71.1) 2276 (100) 1847 (81.2) 1210 (53.2) 682 Padibe HCIV (Lamwo) 4221 19 (8.5–29.5) 2841 (67.3) 2713 (64.3) 2713 (100) 2474 (91.2) 1494 (55.1) 359 Patongo HCIII (Agago) 2448 16 (5.5–26.5) 1629 (66.5) 1879 (76.8) 1879 (100) 1395 (74.2) 1063 (56.6) 251 Control arm estimates 17731 17 (5.5–28.5) 11944 (67.4) 11119 (62.7) 11119 (100) 8693 (78.2) 6021 (54.2) 396 Total 118916 15 (4.5–25.2) 77547 (65.2) 88999 (74.8) 88033 (98.9) 77787 (88.4) 48748 (55.4) 497 ⴕIRS-1 denotes the first IRS period 13 months post-IRS with Fludora Fusion *IRS-2 denotes the second IRS period 12 months post-IRS with Actellic Impact of IRS with clothianidin-deltamethrin (IRS-1) During the 13-month period following IRS with clothianidin-deltamethrin (December 2022 to December 2023), malaria was suspected in 10,556 (84.7%) of 12,459 intervention outpatient visits versus 12,763 (71.8%) of 17,770 control visits (Table 1 ; Supplemental Fig. 1). Malaria was diagnosed in 5,533 of intervention patients (TPR 52.4%) and 7,611 of control patients (TPR 59.7%). A 56.4% reduction in confirmed malaria case-counts was observed in the intervention arm relative to baseline, corresponding to 7,332 confirmed malaria cases averted. Similarly, confirmed cases in the control arm reduced by 50.1% relative to baseline, corresponding to 7,650 malaria cases averted. In the intervention arm, TPR declined by 9% from baseline during IRS-1, while TPR in the control arm remained stable (Table 1 ; Supplemental Fig. 2). An overall decline in mean TPR was observed in both arms relative to baseline (Supplemental Fig. 2B). In the intervention arm, an immediate decline in TPR was observed within the first three months of IRS-1 (December 2022 to February 2023). Thereafter, TPR increased and remained high throughout the remaining 10 months (March 2023 to December 2023). A similar reduction in TPR trends was observed in the control arm, mirroring trends in the intervention arm throughout this period (Supplemental Fig. 2B). Mean observed incidence in the intervention arm declined by 24% from 720.9 (SD 306.7) per 1000 person-years at baseline to 547.9 (SD 281.1) during IRS-1. Concurrently, incidence in the control arm declined by 13% from 523.4 (SD 261.1) per 1000 person-years at baseline to 455.2 (SD 263.2) during this period. Using unadjusted and adjusted models (Table 2 ), we detected a 14% difference in predicted mean malaria incidence between the intervention and control arms relative to baseline, but there was no evidence that predicted incidence differed between arms during IRS-1 (unadjusted IRR = 0.86, 95% CI 0.69–1.06, p = 0.16; aIRR = 0.86, 95% CI 0.70–1.06, p = 0.17). Monthly adjusted IRR estimates showed no difference in mean incidence between study arms following IRS with clothianidin-deltamethrin (Table 3 , Fig. 4) except in November 2023 (12th month post-IRS) when mean incidence was 46% lower in the intervention arm versus control (aIRR = 0.54, 95% CI 0.26–0.8, p = 0.04). Trends in mean malaria incidence during IRS-1 were like those observed for TPR, initially declining over 5 months from December 2023 to April 2023, then increasing and remaining high throughout the next 7 months (May 2023 to December 2023, Fig. 2 and Supplemental Fig. 2A). Incidence was consistently higher in the intervention arm than the control throughout this period (Fig. 2 ). Impact of IRS with pirimiphos-methyl (IRS-2) During the 12-month period following IRS with pirimiphos-methyl (January 2024 to December 2024), malaria was suspected in 6,978 (76.4%) of 9,128 intervention outpatient visits, and 11,119 (62.7%) of 17,731 control visits (Table 1 , Supplemental Fig. 1). Malaria was confirmed in 1,457 intervention patients (TPR 20.9%) versus 6,021 control patients (TPR 54.2%). An 88.7% reduction in confirmed malaria case- Table 2 Difference-in-difference coefficients and malaria incidence estimates between study arms in the pre- and post-IRS periods Time-period Study arm Malaria incidence per 1000 person-years of observation Observed (SD) Unadjusted estimates Adjusted estimates ⱡ Predicted, 95% CI DID IRR (95% CI) P value Predicted DID IRR (95% CI) P value Baseline* Control 523.4 (261.1) 529 (462.6–605.2) 529.4 (455.1–616.1) Intervention 720.9 (306.7) 731.9 (634.9–843.8) 739.6 (634.0–862.9) IRS-1 ⴕ (13 months post IRS with Fludora Fusion) Control 455.2 (263.2) 455.8 (391.3–530.9) Reference Reference 447.4 (379.3–527.9) Reference Reference Intervention 547.9 (281.1) 540.3 (456.1–640.1) 0.86 (0.69–1.06) 0.16 534.7 (448.0–638.1) 0.86 (0.70–1.06) 0.17 IRS-2 ŧ (12 months post IRS with Actellic) Control 396.2 (237.5) 380.6 (325.9–444.6) Reference Reference 381.6 (321.5–453.2) Reference Reference Intervention 149.1 (100.6) 145.3 (121.5–173.9) 0.28 (0.22–0.35) < 0.001 149.2 (123.1–180.9) 0.30 (0.24–0.38) < 0.001 *Baseline period: December 2020 – November 2022, 24 months ⴕ IRS-1: December 2022 – December 2023, 13 months ŧ IRS-2: January 2024 – December 2024, 12 months ⱡ Adjusted for site-specific monthly rainfall, seasonality and incidence of care-seeking for non-malarial illnesses counts were observed in the intervention arm relative to baseline, corresponding to 11,408 confirmed malaria cases averted (Table 1 ). Confirmed malaria cases in the control arm declined by 60.5%, corresponding to 9,240 cases averted in this period relative to baseline (Table 1 ). Mean TPR declined by 40.3% in the intervention arm but only reduced by 5.4% in the control arm (Table 1 ). An immediate and substantial drop in mean TPR was observed in the intervention arm within the first 3 months of IRS-2 (January 2024 to March 2024), which was sustained over the remaining 9 months (April 2024 to December 2024) with minor seasonal peaks. In the control arm, a modest decline in TPR occurred in the first 4 months (January 2024 to April 2024). Thereafter, TPR increased and remained high for the remainder of IRS-2 (May 2024 to December 2024) (Supplemental Fig. 2A). In the intervention arm, observed mean incidence declined by 79.3% during IRS-2 from 720.9 cases per 1000 person-years (SD 306.7) at baseline to 149.1 (SD 100.6) during IRS-2. In the control arm, observed mean incidence declined by 24.3% from 523.4 (SD 261.1) cases per 1000 person-years at baseline to 396.2 (SD 237.5) during this period (Table 1 & Table 2 ). In the unadjusted model (Table 2 ), we detected a 72% difference in predicted mean malaria incidence between the intervention and control arms during IRS-2 relative to baseline (unadjusted IRR = 0.28, 95% CI 0.22–0.45, p < 0.001). Model adjustment estimated a 70% reduction in predicted mean malaria incidence in the intervention arm compared to control relative to baseline (aIRR 0.30, 95% CI 0.24–0.38, p < 0.01). Further stratification by month revealed a 49% reduction in incidence in the intervention arm within the first month (January 2024) following IRS with pirimiphos-methyl (adjusted IRR = 0.51, 95% CI 0.30–0.86, p = 0.01) (Table 3 ; Fig. 3 ). The Table 3 Monthly adjusted difference-in-difference coefficients and predicted mean incidence by study arm during IRS-1 and IRS-2. Intervention period Month and year Predicted mean malaria incidence per 1000 person-years of observation (95% CI) aIRR (95% CI) P value Intervention arm Control arm IRS − 1 (13-month period following IRS with Fludora Fusion) Jan-23 508.3 (423.7–609.7) 437 (371–514.9) 0.83 (0.49–1.4 0.49 Feb-23 535.1 (445.5–642.7) 434.5 (369–511.6) 0.73 (0.43–1.24) 0.25 Mar-23 502.2 (422.6–597) 411.2 (349.8–483.4) 1.21 (0.71–2.06) 0.48 Apr-23 485.3 (407.9–577.3) 392.6 (330–467.2) 1.52 (0.89–2.60) 0.13 May-23 478.2 (402.1–568.7) 379.3 (319.8–450) 0.89 (0.53–1.51) 0.67 Jun-23 641.3 (534.8–769) 505.3 (421–606.7) 0.88 (0.53–1.48) 0.63 Jul-23 575.8 (485.7–682.7) 508.2 (434.8–594) 0.83 (0.50–1.40) 0.49 Aug-23 593.1 (499.8–703.8) 509.5 (435.7–595.8) 0.88 (0.52–1.47) 0.62 Sept-23 481.7 (404.8–573.2) 419.6 (358.9–490.6) 0.71 (0.42–1.19) 0.2 Oct-23 570.2 (472.5–688.1) 492.7 (412–589.6) 1.08 (0.64–1.81) 0.78 Nov-23 527.5 (442.6–628.7) 424.3 (360.8–498.9) 0.57 (0.34–0.96) 0.04*** Dec-23 511.9 (427.7–612.7) 460.5 (391.7–541.4) 0.9 (0.53–1.50) 0.68 IRS − 2 (12-month period following IRS with Actellic) Jan-24 137 (113.5–165.4) 368.7 (311.4–436.7) 0.51 (0.30–0.86) 0.01*** Feb-24 142.5 (118.6–171.3) 378.2 (321.6–444.8) 0.44 (0.26–0.76) < 0.01*** Mar-24 125.1 (103.7–150.9) 314.6 (266.3–371.8) 0.54 (0.31–0.92) 0.02*** Apr-24 123.1 (102.2–148.1) 346.2 (294.6–406.8) 0.46 (0.26–0.80) < 0.01*** May-24 134 (111.8–160.7) 353.7 (299.6–417.6) 0.26 (0.15–0.44) < 0.01*** Jun-24 168.2 (138.8–203.7) 420.1 (357.5–493.9) 0.21 (0.12–0.37) < 0.01*** Jul-24 181.4 (148.7–221.4) 485.3 (400.2–588.7) 0.18 (0.10–0.31) < 0.01*** Aug-24 186.8 (150.8–231.5) 439.9 (368.6–524.9) 0.21 (0.12–0.36) < 0.01*** Sept-24 155 (126.8–189.4) 351.2 (299.6–411.7) 0.19 (0.11–0.34) < 0.01*** Oct-24 152.4 (125.4–185.2) 396.4 (329.9–476.9) 0.22 (0.13–0.38) < 0.01*** Nov-24 138.5 (115.2–166.5) 365.1 (303.4–440) 0.19 (0.11–0.34) < 0.01*** Dec-24 146.2 (121.3–176.2) 359.8 (305–424.5) 0.21 (0.12–0.36) < 0.01*** reduction in incidence in the intervention arm was sustained until the twelfth month, with a 79% difference detected in December 2024 (12-month aIRR = 0.21, 95% CI 0.12–0.36, p < 0.01) (Table 3 ; Fig. 3 ). No seasonal peaks in malaria incidence were observed in the intervention arm during IRS-2 (Supplemental Fig. 2B; Fig. 2 ). Incidence trends for the control arm during this period were similar to TPR trends, with an immediate decline that lasted 3 months (January 2024 to March 2024). Thereafter, incidence in the control arm peaked and remained high until December 2024 (Supplemental Fig. 2B; Fig. 2 ). Discussion Assessing the impact of population-level vector control interventions is critical for tracking progress in malaria control and guiding future deployment strategies. In this study, enhanced health facility-based surveillance data were analysed to estimate the combined impact of ITNs and IRS with two insecticides, clothianidin-deltamethrin and pirimiphos-methyl, on malaria incidence in West Nile, Uganda, as compared to ITNs without IRS in Acholi region. We found no significant difference in predicted incidence in the 13 months following initial IRS with clothianidin-deltamethrin compared to control. In contrast, an immediate, substantial and sustained reduction in malaria incidence was observed when IRS with pirimiphos-methyl was deployed in the subsequent year. These findings suggest that IRS with pirimiphos-methyl (plus ITNs) was markedly more effective than IRS with clothianidin-deltamethrin in northern Uganda. Clothianidin-based IRS for malaria control has generated mixed results across sub-Saharan Africa. Entomological studies assessing insecticide efficacy with WHO tube tests and bottle bioassays, and CDC bottle bioassays [ 3 ], have generally yielded positive results for clothianidin [ 18 , 19 , 21 , 37 – 40 ]. However, findings from these assessments may over-estimate actual field effectiveness; further validation with results from epidemiological studies is needed [ 10 , 11 , 15 ]. Our findings are consistent with other studies conducted in eastern Uganda, where a switch from pirimiphos-methyl to clothianidin-based IRS was associated with an 8-fold increase in malaria incidence and a 4-fold increase in parasite prevalence [ 12 , 22 ]. Similarly, a study in Zambia [ 15 ], using routine surveillance data, found that transitioning from pirimiphos-methyl to clothianidin-deltamethrin did not reduce parasite prevalence. In Cote d’Ivoire [ 11 ], an analysis of routine surveillance data was undertaken to determine incidence changes in IRS vs non-IRS districts following the introduction of clothianidin-based IRS. This study found that the month-to-month incidence trend did not change in either area after the initial round, monthly malaria cases increased at a rate 2.5x higher than in non-IRS areas, and following the second IRS round a significantly higher increase in cases was observed in IRS areas vs control [ 11 ]. In Burkina Faso, another epidemiological study utilising routine surveillance data found that the impact of clothianidin-based IRS on malaria case rates varied by region [ 10 ]. Where reductions in malaria case rates were observed, they were only modest, diminished with each round, and hardly differed from those observed in non-IRS areas [ 10 ]. Taken together, these studies suggest that clothianidin was ineffective. In contrast, an evaluation of IRS in Madagascar [ 14 ], including IRS with clothianidin-based insecticides, yielded positive results. Here, IRS was associated with a 30% reduction in incidence, with incremental reductions in subsequent years. However, this study did not disaggregate IRS impact by insecticide type, rendering it difficult to attribute these positive findings solely to clothianidin-based IRS. The lack of effectiveness of clothianidin-deltamethrin in West Nile could be attributed to multiple factors including insecticide resistance, shifts in vector species composition, housing quality, and the timing and quality of spraying. High-level pyrethroid resistance [ 41 – 44 ] and high clothianidin tolerance [ 44 ] both exist in Uganda. Predominant malaria vectors in Uganda are resistant to deltamethrin [ 42 , 45 , 46 ], rendering it generally ineffective. In 2016–2017, signs of clothianidin resistance in wild mosquito species were detected in Uganda [ 18 ], prior to the WHO prequalification of clothianidin-based insecticides for IRS. Other studies have since confirmed that while clothianidin generally remains effective in most settings, susceptibility patterns vary among wild mosquito populations [ 26 , 48 – 50 ]. More recently, waning mortality rates for An. funestus were observed following IRS with clothianidin-based insecticides in eastern Uganda [ 46 , 47 ]. Clothianidin resistance could have resulted from widespread use of neonicotinoid-containing pesticides in agriculture in Uganda [ 48 ]. High concentrations of neonicotinoids have been detected in waterbodies [ 49 ], groundwater in non-IRS areas [ 50 ], on fresh fruits and vegetables [ 51 ], and in honeybee products [ 52 ] in Uganda. Neonicotinoid insecticides are hydro-soluble, persisting in surface water, including in potential breeding sites for mosquito larvae, and exceedingly high concentrations beyond regulatory thresholds have previously been detected in African settings [ 49 ]. However, attempts to confirm this hypothesis in West Africa have yielded mixed results. Neonicotinoid use in agriculture was found to reduce wild mosquito vector susceptibility to clothianidin in Cameroon [ 53 , 54 ], but not in Benin [ 40 ], and while wild vectors collected from agricultural settings in Cameroon demonstrated lower mortality on exposure to clothianidin [ 54 ], susceptibility remained high for wild An. gambiae ss from 18 agricultural settings in Benin [ 40 ]. These mixed results underscore the need to guide deployment of clothianidin-based IRS on resistance patterns of local vectors [ 2 ]. Shifts in predominant malaria vector species, documented elsewhere in Uganda, may also have reduced the effect of clothianidin-deltamethrin [ 22 ]. Historically, An. gambiae s.s. was the principal malaria vector in Uganda, with smaller populations of An. funestus and An. arabiensis [ 55 ]. With deployment of vector control interventions, An. gambiae s.s. and An. funestus populations reduced in regions receiving IRS and ITNs, while An. arabiensis and An. funestus populations markedly, although relatively increased in districts receiving ITNs only (not IRS) [ 9 , 26 , 28 , 46 , 55 ]. This pattern was also observed in Eastern Uganda following several years of sustained IRS with carbamates, organophosphates, and neonicotinoids [ 55 ], where both An. gambiae ss and An. funestus populations reduced and were initially replaced by An. arabiensis . More recently, however, An. funestus populations have been restored [ 22 , 46 , 47 , 55 ]. Although An. arabiensis remained highly susceptible to clothianidin, An. funestus has exhibited high tolerance [ 22 ]. A similar shift occurred in West Nile between 2021 and 2023 when the proportion of field-collected An. funestus increased markedly from 0–7% before IRS with Fludora® Fusion, to 69–80% after, while those of An. gambiae ss reduced from 93–100%, to 18–31% [ 26 ]. The evidence suggests that An. funestus was likely the dominant vector in West Nile in 2022 and 2023, which may not have been susceptible to the clothianidin-based insecticides. Entomological surveillance conducted in 2023 in West Nile revealed that field-collected An. funestus species from this region had 50% higher sporozoite rates, and 30x higher indoor human biting rates than An. gambiae , demonstrating higher propensity to transmit malaria [ 22 , 26 ]. Coupled with the higher tolerance of An. funestus to clothianidin the lack of impact following IRS with clothianidin-deltamethrin in West Nile is not surprising. Housing quality in West Nile may also have reduced the effectiveness of clothianidin-deltamethrin. Traditionally constructed housing, characterised by mud walls, thatch roof and open eaves is highly prevalent in northern Uganda [ 34 ]. Clothianidin is known to exhibit lower mortality and residual efficacy when applied to mud walls as compared to cement [ 18 , 20 , 21 ], presumably due to the high porosity of mud [ 56 ]. Incorporating cement substrate within mud bricks has been shown to markedly improve clothianidin’s residual efficacy close to levels observed on cement [ 38 ]. In eastern Uganda, concentrations of clothianidin on sprayed walls were below the target dose, while excess concentrations of pirimiphos-methyl were detected [ 47 ]. These differences could be attributed to inadequate deployment, especially where differences in spray operations exist, but the same teams delivered IRS in these areas, making variability in implementation less likely. Timing of IRS deployment, at the start of the long dry season, is counter to WHO’s recommendation and may also have reduced the impact of clothianidin-deltamethrin as concentrations of the insecticides may have dwindled long before the start of the peak transmission season. We found that pirimiphos-methyl performed exceptionally well in West Nile, substantially reducing malaria incidence when deployed one year after the single round of clothianidin-deltamethrin. Historically, pirimiphos-methyl has been highly effective in Uganda, with the 3-year period of sustained IRS providing the greatest malaria control [ 8 , 12 , 22 , 57 ]. Other African countries have documented remarkable results following IRS with pirimiphos-methyl. In Benin, one round of IRS with pirimiphos-methyl was associated with 7.5x lower biting rates, 75% reduction in vector density, and 34% reduction in blood feeding rates compared to control, which was consistent through subsequent IRS rounds [ 37 , 58 ]. Similar results were observed in Kenya when entomological monitoring conducted before and after IRS with pirimiphos-methyl found an 88% and 93% reduction in field-collected An. funestus vector species by light traps and pyrethrum spray catches (PSC) respectively, and a 69% reduction in field-collected An. arabiensis [ 7 ]. In this same study, sporozoite infections and human biting rates were reduced to nearly zero. Conversely, pirimiphos-methyl conferred only modest reductions in incidence (9–25%) in Zambia [ 13 ], and malaria case-rates increased when a round of pirimophos-methyl was implemented following three rounds of clothianidin-based IRS in Burkina Faso [ 10 ]. Why was pirimiphos-methyl more effective for IRS in West Nile? Relative to clothianidin-deltamethrin, it is faster-acting [ 18 , 19 , 37 , 59 ], exhibiting an immediate and much stronger lethal effect (nearly 2.5x higher) within 24 hours [ 59 ]. In contrast, clothianidin-deltamethrin requires at least five days to attain similar results [ 19 , 56 , 59 , 60 ]. Moreover, pirimiphos-methyl’s residual efficacy extends at least 10 months [ 61 ], with some variation by location and type of wall substrate [ 62 – 64 ], providing protection against malaria throughout the transmission season and allowing for annual deployment. Importantly, pirimiphos-methyl has remained highly efficacious against all mosquito vector species in Uganda, including An. funestus [ 46 , 65 ]. Wild An. gambiae vector species in Burkina Faso, Togo, and Benin also remain highly susceptible to pirimiphos-methyl despite high resistance to DDT, pyrethroids and bendiocarb [ 18 , 21 , 37 , 61 , 66 ]. Levels and prevalence of organophosphate resistance remain either low or largely undetermined globally [ 5 ], but resistance is emerging within sub-Saharan Africa [ 67 , 68 ]. This may explain why in Burkina Faso, a single IRS round with pirimiphos-methyl following multiple rounds with clothianidin-based IRS was associated with an increase in malaria case-rates [ 10 ]. Also, like neonicotinoids, organophosphates are also widely used in agriculture [ 51 , 52 ] and continued exposure to sub-lethal doses will potentially increase selection pressure for resistant malaria vectors. Continued investment in entomological surveillance monitoring and related insecticide resistance research is critical. Our study had several strengths. We applied a quasi-experimental study design to assess IRS impact in West Nile using enhanced health facility-based surveillance data, providing a cost-effective means for population-level impact evaluation. Including data from control health facilities located in neighbouring Acholi region with similar climatic and geographic conditions allowed us to disaggregate incidence changes unrelated to IRS from those associated with IRS. Additionally, we utilised individual-level data from our MRC sentinel site surveillance system where monthly quality assurance checks ensured high quality data. However, our study also had some limitations. First, our study arms were unbalanced because only a limited number of sentinel sites existed within IRS districts in West Nile. However, we believe that ensuring high quality data from our MRC sites outweighed any potential benefit of expanding to sites outside of our MRC surveillance system. Second, it was impossible to dissociate the impact of IRS from the effect of ITNs. To counter this, intervention and control sites were matched based on ITN type and timing of distribution campaigns. Conclusion In West Nile, we found that clothianidin-based IRS (plus ITNs) did not reduce malaria incidence, while a subsequent round of IRS with pirimiphos-methyl (plus ITNs), one year after clothianidin-deltamethrin, substantially reduced malaria incidence as compared to ITNs alone in the neighbouring Acholi region. Whilst periodic IRS insecticide rotation is a pragmatic approach to resistance management, it is only useful if the replacement insecticide is equally effective. Our findings underscore the need to assess wild vector susceptibility to insecticides prior to implementing large-scale vector control measures in endemic settings. Evaluating insecticides in real-world settings remains a priority, rather than relying solely on modelling or experimental hut trials. Organophosphates may be preferred over clothianidin-based insecticides for population-level IRS coverage in similar settings. Continued entomological and epidemiological monitoring of IRS impact and for signals of resistance to both organophosphates and neonicotinoids is warranted. Malaria vector control programs should be guided by context-specific data on insecticide effectiveness. The synergistic effect of dual interventions should be considered in areas of high pyrethroid resistance. Abbreviations aIRR: adjusted incidence rate ratio; CDC: Centre for Disease Control; DDT: Dichloro-diphenyl-trichloroethane; DID: difference-in-difference.EES: Enhanced Entomological Surveillance; HMIS: Health Management Information System; IDRC: Infectious Diseases Research Collaboration; IRR: incidence rate ratio; IRS: indoor residual spraying of insecticide; ITNs: Insecticide-treated nets; LLINs: long-lasting insecticidal nets; MRC: malaria reference centre; PBO: piperonyl butoxide; PRISM: Program for Resistance, Immunology, Surveillance and Modelling of Malaria; SD: standard deviation; TPR: test positivity rate; WHO: World Health Organisation. Declarations Ethical approval and consent to participate Ethical approval to conduct this study was sought from Makerere University College of Health Sciences School of Public Health Research and Ethics Committee (SPHREC) in Uganda, approval number SPH-2025-551, and the University of California, San Francisco Ethics Committee. Waiver of informed consent was granted for this study by SPHREC because the study involved extraction of secondary data from patient records without using any personal identifiers. Availability of data and materials The datasets supporting the conclusions of this article is(are) available in the [repository name] repository, [unique persistent identifier and hyperlink to dataset(s) in http:// format]." Competing interests All authors declare no conflict of interest. Funding Funding for all research-related activities was provided by the National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Health (NIH), USA and the Commonwealth Scholarship Commission (CSC) in the UK, under the Commonwealth Scholarship Commission. More information can be found at https://cscuk.fcdo.gov.uk. Author’s contributions JFN: conceptualization, methodology, formal analysis, investigation, data curation, writing – original draft, writing – review and editing, visualisation, and project administration; DPM: methodology, formal analysis, data curation, writing – review and editing, and visualisation; AE: methodology, formal analysis, writing – review and editing, data curation, and visualisation; JIN: writing – review and editing, securing funding, project administration; SG: writing – review and editing, project administration; JO: project administration; IN: investigation, data curation; CMS: writing – review and editing, project administration; MRK: writing – review and editing, project administration, securing funding and supervision; MJD: conceptualisation, methodology, writing – review and editing, securing funding, and supervision; GD: conceptualisation, methodology, investigation, data curation, writing – review and editing, supervision, and securing funding; and SGS: conceptualisation, methodology, investigation, writing – review and editing, supervision, securing funding, and project administration. 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Syme T, N'Dombidjé B, Odjo A, Gbegbo M, Todjinou D, Ngufor C: Laboratory evaluation of the contact irritancy of a clothianidin solo formulation vs. clothianidin-deltamethrin mixture formulations for indoor residual spraying against pyrethroid-resistant Anopheles gambiae sensu lato . Parasit Vectors 2024, 17 (1):183. Hougbe SZ, Sovi A, Koumodji K, Ahouandjinou MJ, Affolabi ZK, Towakinou L, Chitou S, Agbo-Ola A, Tokponnon F, Zoungbédji DM et al : Susceptibility of Anopheles gambiae s.l. to the neonicotinoid insecticide clothianidin in eighteen sites located along the south-north transect of Benin . Trop Med Health 2025, 53 (1):21. Morgan JC, Irving H, Okedi LM, Steven A, Wondji CS: Pyrethroid resistance in an Anopheles funestus population from Uganda . PLoS One 2010, 5 (7):e11872. Mawejje HD, Weetman D, Epstein A, Lynd A, Opigo J, Maiteki-Sebuguzi C, Lines J, Kamya MR, Rosenthal PJ, Donnelly MJ et al : Characterizing pyrethroid resistance and mechanisms in Anopheles gambiae (s.s.) and Anopheles arabiensis from 11 districts in Uganda . Curr Res Parasitol Vector Borne Dis 2023, 3 :100106. Mulamba C, Riveron JM, Ibrahim SS, Irving H, Barnes KG, Mukwaya LG, Birungi J, Wondji CS: Widespread pyrethroid and DDT resistance in the major malaria vector Anopheles funestus in East Africa is driven by metabolic resistance mechanisms . PLoS One 2014, 9 (10):e110058. Okia M, Hoel DF, Kirunda J, Rwakimari JB, Mpeka B, Ambayo D, Price A, Oguttu DW, Okui AP, Govere J: Insecticide resistance status of the malaria mosquitoes: Anopheles gambiae and Anopheles funestus in eastern and northern Uganda . Malar J 2018, 17 (1):157. Tchouakui M, Mugenzi LMJ, B DM, Khaukha JNT, Tchapga W, Tchoupo M, Wondji MJ, Wondji CS: Pyrethroid Resistance Aggravation in Ugandan Malaria Vectors Is Reducing Bednet Efficacy . Pathogens 2021, 10 (4). Oruni A, Tchouakui M, Tagne CSD, Hearn J, Kayondo J, Wondji CS: Temporal evolution of insecticide resistance and bionomics in Anopheles funestus, a key malaria vector in Uganda . Sci Rep 2024, 14 (1):32027. Oruni A, Arinaitwe E, Adiga J, Otto G, Kyagamba P, Okoth J, Ayo D, Asiimwe JR, Zedi M, Rek J et al : Significant variations in tolerance to clothianidin and pirimiphos-methyl in Anopheles gambiae and Anopheles funestus populations during a dramatic malaria resurgence despite sustained indoor residual spraying in Uganda . bioRxiv 2025. Orikpete OF, Kikanme KN, Falade TDO, Dennis NM, Ejike Ewim DR, Fadare OO: Neonicotinoid pesticides in African agriculture: What do we know and what should be the focus for future research? Chemosphere 2025, 372 :144057. Stehle S, Ovcharova V, Wolfram J, Bub S, Herrmann LZ, Petschick LL, Schulz R: Neonicotinoid insecticides in global agricultural surface waters - Exposure, risks and regulatory challenges . Sci Total Environ 2023, 867 :161383. Oltramare C, Weiss FT, Staudacher P, Kibirango O, Atuhaire A, Stamm C: Pesticides monitoring in surface water of a subsistence agricultural catchment in Uganda using passive samplers . Environ Sci Pollut Res Int 2023, 30 (4):10312-10328. Ssemugabo C, Bradman A, Ssempebwa JC, Sillé F, Guwatudde D: Pesticide Residues in Fresh Fruit and Vegetables from Farm to Fork in the Kampala Metropolitan Area, Uganda . Environ Health Insights 2022, 16 :11786302221111866. Amulen DR, Spanoghe P, Houbraken M, Tamale A, de Graaf DC, Cross P, Smagghe G: Environmental contaminants of honeybee products in Uganda detected using LC-MS/MS and GC-ECD . PLoS One 2017, 12 (6):e0178546. Ashu FA, Fouet C, Ambadiang MM, Penlap-Beng V, Kamdem C: Adult mosquitoes of the sibling species Anopheles gambiae and Anopheles coluzzii exhibit contrasting patterns of susceptibility to four neonicotinoid insecticides along an urban-to-rural gradient in Yaoundé, Cameroon . Malar J 2024, 23 (1):65. Fouet C, Ashu FA, Ambadiang MM, Tchapga W, Wondji CS, Kamdem C: Clothianidin-resistant Anopheles gambiae adult mosquitoes from Yaoundé, Cameroon, display reduced susceptibility to SumiShield® 50WG, a neonicotinoid formulation for indoor residual spraying . BMC Infect Dis 2024, 24 (1):133. Mawejje HD, Kilama M, Kigozi SP, Musiime AK, Kamya M, Lines J, Lindsay SW, Smith D, Dorsey G, Donnelly MJ et al : Impact of seasonality and malaria control interventions on Anopheles density and species composition from three areas of Uganda with differing malaria endemicity . Malar J 2021, 20 (1):138. Pambit Zong CM, Coleman S, Mohammed AR, Owusu-Asenso CM, Akuamoah-Boateng Y, Sraku IK, Attah SK, Cui L, Afrane YA: Baseline susceptibility of Anopheles gambiae to clothianidin in northern Ghana . Malar J 2024, 23 (1):12. Tugume A, Muneza F, Oporia F, Kiconco A, Kihembo C, Kisakye AN, Nsubuga P, Deogratias S, Yeka A: Effects and factors associated with indoor residual spraying with Actellic 300 CS on malaria morbidity in Lira District, Northern Uganda . Malar J 2019, 18 (1):44. Salako AS, Dagnon F, Sovi A, Padonou GG, Aïkpon R, Ahogni I, Syme T, Govoétchan R, Sagbohan H, Sominahouin AA et al : Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin . Parasit Vectors 2019, 12 (1):612. Chabi J, Seyoum A, Edi CVA, Kouassi BL, Yihdego Y, Oxborough R, Gbalegba CGN, Johns B, Desale S, Irish SR et al : Efficacy of partial spraying of SumiShield, Fludora Fusion and Actellic against wild populations of Anopheles gambiae s.l. in experimental huts in Tiassalé, Côte d'Ivoire . Sci Rep 2023, 13 (1):11364. Tchicaya ES, Nsanzabana C, Smith TA, Donzé J, de Hipsl ML, Tano Y, Müller P, Briët OJ, Utzinger J, Koudou BG: Micro-encapsulated pirimiphos-methyl shows high insecticidal efficacy and long residual activity against pyrethroid-resistant malaria vectors in central Côte d'Ivoire . Malar J 2014, 13 :332. Soma DD, Zogo B, Hien DFS, Hien AS, Kaboré DA, Kientega M, Ouédraogo AG, Pennetier C, Koffi AA, Moiroux N et al : Insecticide resistance status of malaria vectors Anopheles gambiae (s.l.) of southwest Burkina Faso and residual efficacy of indoor residual spraying with microencapsulated pirimiphos-methyl insecticide . Parasit Vectors 2021, 14 (1):58. Mugenyi L, Nankabirwa JI, Arinaitwe E, Rek J, Hens N, Kamya M, Dorsey G: Estimating the optimal interval between rounds of indoor residual spraying of insecticide using malaria incidence data from cohort studies . PLoS One 2020, 15 (10):e0241033. Mashauri FM, Manjurano A, Kinung'hi S, Martine J, Lyimo E, Kishamawe C, Ndege C, Ramsan MM, Chan A, Mwalimu CD et al : Indoor residual spraying with micro-encapsulated pirimiphos-methyl (Actellic® 300CS) against malaria vectors in the Lake Victoria basin, Tanzania . PLoS One 2017, 12 (5):e0176982. Dugassa S, Mekonnen S, Muthee PW, Peter R, Zinyengere D, Feyasa MB, Sievert K: Evaluation of the Residual Efficacy of Actellic300 CS in Simple Huts in Central Ethiopia . J Med Entomol 2021, 58 (6):2308-2313. Project TPV: Uganda Annual Entomological Surveillance Report, January 1 - December 31, 2021 . In . Rockville, MD: Abt Associates; 2022: 49. Medjigbodo AA, Djogbenou LS, Koumba AA, Djossou L, Badolo A, Adoha CJ, Ketoh GK, Mavoungou JF: Phenotypic Insecticide Resistance in Anopheles gambiae (Diptera: Culicidae): Specific Characterization of Underlying Resistance Mechanisms Still Matters . J Med Entomol 2021, 58 (2):730-738. Nagi SC, Lucas ER, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, Djogbénou LS, Medjigbodo AA, Edi CV, Ketoh GK et al : Parallel Evolution in Mosquito Vectors—A Duplicated Esterase Locus is Associated With Resistance to Pirimiphos-methyl in Anopheles gambiae . Molecular Biology and Evolution 2024, 41 (7). Lucas ER, Nagi SC, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, Djogbénou LS, Medjigbodo AA, Edi CV, Kétoh GK et al : Genome-wide association studies reveal novel loci associated with pyrethroid and organophosphate resistance in Anopheles gambiae and Anopheles coluzzii . Nature Communications 2023, 14 (1):4946. Additional Declarations No competing interests reported. Supplementary Files SupplementalFigureslegends.docx SUPPLE1.jpe SupplementalFigure2.TrendsinmeanTPRandmeanincidencebystudyarm..jpeg SUPPLE2.jpe Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Nov, 2025 Reviews received at journal 28 Oct, 2025 Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers invited by journal 13 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 02 Sep, 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. 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Nankabirwa","email":"","orcid":"","institution":"Infectious Diseases Research Collaboration","correspondingAuthor":false,"prefix":"","firstName":"Joaniter","middleName":"I.","lastName":"Nankabirwa","suffix":""},{"id":511833678,"identity":"910fe60f-b5b9-4fd9-877a-9a006b157320","order_by":4,"name":"Samuel Gonahasa","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Gonahasa","suffix":""},{"id":511833679,"identity":"f99c8ca4-bae4-4925-af0a-fa5eb738f89d","order_by":5,"name":"Jimmy Opigo","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Jimmy","middleName":"","lastName":"Opigo","suffix":""},{"id":511833680,"identity":"8cd884dc-654f-4992-8391-6df5705280e3","order_by":6,"name":"Isaiah Nabende","email":"","orcid":"","institution":"Infectious Diseases Research Collaboration","correspondingAuthor":false,"prefix":"","firstName":"Isaiah","middleName":"","lastName":"Nabende","suffix":""},{"id":511833681,"identity":"843f6344-14d2-4d39-8422-f282a338b11d","order_by":7,"name":"Catherine Maiteki-Sebuguzi","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Maiteki-Sebuguzi","suffix":""},{"id":511833682,"identity":"ef53d5ea-1120-4fb1-8e65-8a69ee9d0ce7","order_by":8,"name":"Moses R. Kamya","email":"","orcid":"","institution":"Infectious Diseases Research Collaboration","correspondingAuthor":false,"prefix":"","firstName":"Moses","middleName":"R.","lastName":"Kamya","suffix":""},{"id":511833683,"identity":"46e6bc75-4ef9-402c-868b-98a178dfb9ed","order_by":9,"name":"Martin J. Donnelly","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"J.","lastName":"Donnelly","suffix":""},{"id":511833684,"identity":"8d9d0e55-8e5a-4fa4-9180-dd419059d16e","order_by":10,"name":"Grant Dorsey","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Grant","middleName":"","lastName":"Dorsey","suffix":""},{"id":511833685,"identity":"c7fd29af-dd8e-4cbd-ad4e-72d0aeaa98d8","order_by":11,"name":"Sarah G. Staedke","email":"","orcid":"","institution":"Liverpool School of Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"G.","lastName":"Staedke","suffix":""}],"badges":[],"createdAt":"2025-09-02 14:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7518583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7518583/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91199385,"identity":"46db958f-9e98-4a37-9479-750ad7e3cae8","added_by":"auto","created_at":"2025-09-12 15:19:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":538380,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of study sites.\u003c/p\u003e","description":"","filename":"Figure1.Geographicallocationofstudysites.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7518583/v1/d98e4f1e62a42d4aeaa0d997.jpeg"},{"id":91199386,"identity":"44d6b8af-e188-4c51-8849-718d03a27a1b","added_by":"auto","created_at":"2025-09-12 15:19:27","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49904,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in predicted mean malaria incidence stratified by study arm.\u003c/p\u003e","description":"","filename":"Figure2.Trendsinpredictedmeanmalariaincidencestratifiedbystudyarm.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7518583/v1/d32f507a05bc91dee8379391.jpg"},{"id":91200874,"identity":"9129e5c0-e78b-4750-8458-a5e8c9a150bf","added_by":"auto","created_at":"2025-09-12 15:35:27","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144917,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly adjusted difference-in-difference IRR estimates for the interaction between the 1st and 2nd IRS periods and study arm.\u003c/p\u003e","description":"","filename":"FIGURE2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7518583/v1/3aaa760e2ea5f2e2128f7cf7.jpg"},{"id":91332556,"identity":"77ad2705-acaf-4e6f-a23c-05223080cf57","added_by":"auto","created_at":"2025-09-15 11:12:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6192943,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7518583/v1/6bec8095-5cd4-4ea2-ba28-6fb15ca5e114.pdf"},{"id":91200560,"identity":"5432cbd2-ad66-4f10-be63-ff45d9898695","added_by":"auto","created_at":"2025-09-12 15:27:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13879,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigureslegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-7518583/v1/3fec09c87f515df10e16f833.docx"},{"id":91199391,"identity":"5a83673d-759d-4bfe-923a-5b165cd5df90","added_by":"auto","created_at":"2025-09-12 15:19:27","extension":"jpe","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":456395,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SUPPLE1.jpe","url":"https://assets-eu.researchsquare.com/files/rs-7518583/v1/3222abfc939d7b8fad4d1111.jpe"},{"id":91201987,"identity":"63c16f83-55dc-4ea3-9441-ffa8b61f51cc","added_by":"auto","created_at":"2025-09-12 15:51:27","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":587137,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementalFigure2.TrendsinmeanTPRandmeanincidencebystudyarm..jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7518583/v1/bc8eae13d8ed6b27eada4035.jpeg"},{"id":91199393,"identity":"ad6eedb6-ff79-444a-9a1c-5545a8c4cf4b","added_by":"auto","created_at":"2025-09-12 15:19:27","extension":"jpe","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":723869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SUPPLE2.jpe","url":"https://assets-eu.researchsquare.com/files/rs-7518583/v1/62c15ddf036f02ceff9c18e9.jpe"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Indoor Residual Spraying and Insecticide-Treated Nets on Malaria Burden in Uganda: A Quasi-Experimental Study in 8 Districts in West Nile and Acholi Regions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite concerted efforts to eliminate malaria, the global burden remains high, with an increasing trend in incidence observed in some endemic countries since 2015 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Sub-Saharan Africa remains disproportionately affected relative to the rest of the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Insecticide treated nets (ITNs) and indoor residual spraying of insecticide (IRS) are the principal vector control tools targeting anopheline mosquitoes that feed and rest indoors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, their effectiveness is determined by the susceptibility of primary vectors to insecticides, population-level intervention uptake, and intervention coverage [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Unfortunately, insecticide resistance remains a major threat to malaria control efforts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], affecting four of the five major insecticide classes (pyrethroids, carbamates, organophosphates and neonicotinoids) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe World Health Organization (WHO) recommends rotation of IRS insecticides at pre-set intervals, and diversification from pyrethroid insecticides to mitigate the development and spread of resistance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Where pyrethroid resistance is high, replacing standard (pyrethroid-only) nets with new generation ITNs is encouraged, including pyrethroid plus either piperonyl butoxide (PBO), chlorfenapyr, or pyriproxyfen [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Co-deployment of IRS and ITNs is not recommended by WHO [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], but widespread pyrethroid resistance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] has prompted deployment of these interventions together to reduce malaria transmission in high burden areas [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In Uganda, the Ministry of Health (MOH) has adopted rotation of IRS insecticides every three years, and the co-deployment strategy in areas hardest hit by malaria.\u003c/p\u003e\u003cp\u003eAlthough globally IRS coverage is low [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], many sub-Saharan African countries have benefited from IRS campaigns over the past two decades [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12 CR13 CR14\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In 2018, clothianidin-based products, Sumishield\u0026reg; (clothianidin-only) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and Fludora Fusion\u0026reg; (clothianidin-deltamethrin mixture) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] were adopted and deployed for IRS in several countries, replacing pirimiphos-methyl [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Replacement often coincided with country-specific predefined timelines for IRS insecticide rotation and aligned with WHO recommendations for insecticide resistance mitigation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Previously, these insecticides demonstrated prolonged residual efficacy (up to 48 weeks) [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and high vector species susceptibility [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Nevertheless, their population-level impact has differed across countries [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] underscoring the need for continued assessment of effectiveness in real-world settings.\u003c/p\u003e\u003cp\u003eIn 2022, clothianidin-deltamethrin-based IRS was deployed in West Nile Uganda, an area with intense malaria transmission [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. IRS was repeated in 2023 with pirimiphos-methyl. This rather premature switch in insecticides was prompted by a dramatic malaria resurgence in Eastern Uganda [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, a combination of standard and new-generation ITNs were distributed in 2020/2021 and 2023/2024 during Uganda\u0026rsquo;s campaigns to distribute long-lasting insecticidal nets (LLINs), presenting an opportunity to evaluate IRS plus ITNs in a high transmission setting. To evaluate the effect of co-deployment of these interventions, we utilised a quasi-experimental, difference-in-differences study of health facility-based surveillance data to compare malaria indicators in West Nile to those in neighbouring Acholi region that received ITNs but not IRS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy sites\u003c/h2\u003e\u003cp\u003eThis study was embedded within the Program for Resistance, Immunology, Surveillance and Modelling of Malaria [PRISM] project, which conducts enhanced surveillance at selected public health facilities in Uganda. West Nile and Acholi are high transmission regions in northern Uganda [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], an area bordering South Sudan, Democratic Republic of Congo (DRC) and Kenya. The regions are separated by the Albert Nile, with West Nile to the west and Acholi to the east. The terrain in both regions is predominantly flat, with tropical climatic conditions comprised of one distinct wet and dry season. The burden of malaria in northern Uganda is high [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In the 2019 Malaria Indicator Survey (MIS), parasite prevalence by microscopy in children under five years was 22% and 12% in West Nile and Acholi respectively [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. \u003cem\u003eAnopheles gambiae ss\u003c/em\u003e and \u003cem\u003eAnopheles funestus\u003c/em\u003e are the primary malaria vectors in Uganda, with \u003cem\u003eAn. gambiae ss\u003c/em\u003e being the predominant species [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, IRS implementation prompted a shift in vector species, with \u003cem\u003eAn. arabiensis\u003c/em\u003e, an outdoor-biting mosquito, becoming more prevalent, particularly in districts with sustained IRS coverage [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Where ITNs are the sole vector control measure, \u003cem\u003eAn. funestus\u003c/em\u003e and \u003cem\u003eAn. gambiae ss\u003c/em\u003e are dominant [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIRS deployment in northern Uganda\u003c/h3\u003e\n\u003cp\u003eThe Acholi region received IRS from 2009 to 2014 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Deployment of IRS was then halted, with the exception of a single round of IRS with pirimiphos-methyl which was delivered in 2017 in response to a resurgence of malaria [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In West Nile, IRS was first introduced in December 2022 with clothianidin-deltamethrin. IRS coverage, defined as the proportion of houses found that were sprayed, averaged 92% [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and 88% [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] in 2022 and 2023 respectively.\u003c/p\u003e\n\u003ch3\u003eMass distribution of ITNs\u003c/h3\u003e\n\u003cp\u003eITNs are Uganda\u0026rsquo;s primary vector control intervention, distributed through government-sponsored mass distribution campaigns. Between 2013 and 2024, four mass campaigns were conducted country wide. Standard nets were distributed in the first round (2013/2014). Thereafter, a combination of new generation ITNs and standard nets were delivered in selected areas during subsequent rounds[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In 2019, ITN ownership was 92% and 83%, and net use was 76% and 62% in West Nile and Acholi respectively [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In 2019/2020 mass campaign, selected sub-counties in West Nile and Acholi (including the study sites) received either pyrethroid-PBO or pyrethroid-pyriproxyfen ITNs while the remaining areas received standard, pyrethroid-only nets [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. All 8 study sites were in sub-counties that received pyrethroid-PBO nets in the 2020/21 campaign and either pyrethroid-PBO or pyrethroid-chlorfenapyr nets in the 2023/2024 campaign.\u003c/p\u003e\n\u003ch3\u003eHealth facility-based surveillance\u003c/h3\u003e\n\u003cp\u003eWe leveraged an existing malaria sentinel site surveillance network previously managed by the Uganda Malaria Surveillance Project (UMSP) at Malaria Reference Centres (MRCs) and described elsewhere [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Surveillance activities are ongoing at 38 high-volume level III/IV public health facilities across Uganda that provide free general medical services to surrounding populations. At each MRC, individual-level outpatient data collected in paper-based health management information system (HMIS) forms are transcribed into an electronic database monthly. A detailed description of data collection, management, analysis and dissemination processes at these sites has been published previously [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSelection of target areas for incidence measurements\u003c/h3\u003e\n\u003cp\u003eA target area was defined as the village where the MRC is located and adjacent villages that lack another health facility, are within the same sub-county, have a similar malaria incidence, and together have an estimated population of at least 1,500 persons. These were 1\u0026ndash;7 villages around each MRC from where at least 90% of patients that sought care from each site resided and were identified using at least 6 months\u0026rsquo; data from each MRC (collected between November 2018 and November 2019). Enumeration surveys were conducted to estimate the population of each target area. These estimates serve as the denominator for incidence measures for each target area, considering a fixed population growth rate of 0.29% per month. This approach is described in detail elsewhere [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSite selection\u003c/h2\u003e\u003cp\u003eThis study included eight MRCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), three in West Nile (intervention) and five in Acholi region (control). All MRC sites within West Nile were included as intervention sites. Control sites were selected from the adjacent Acholi region (with similar malaria endemicity, climate and geographical conditions) using the following criteria: 1) within Acholi region; 2) two years of individual-level data available from December 2020 to December 2022); 3) received pyrethroid-PBO ITNs (PermaNet\u0026reg; 3\u0026middot;0) in 2020/202, and either pyrethroid-PBO or pyrethroid-chlorfenapyr (PermaNet\u0026reg; Dual) nets in 2023.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefining IRS periods (independent variables)\u003c/h3\u003e\n\u003cp\u003eThe study was divided into three follow-up periods: (1) baseline, defined as the 24-month period before IRS was implemented in West Nile (December 2020 to November 2022); (2) IRS-1, the 13-month period following deployment of clothianidin-deltamethrin (Fludora Fusion\u0026reg;) in West Nile (December 2022 to December 2023); and (3) IRS-2, the 12-month period following IRS with pirimiphos-methyl (Actellic\u0026reg; 300CS) in West Nile (January 2024 to December 2024). For our analysis, the independent variable was the IRS period, specified as a categorical variable representing the first (IRS-1) and second (IRS-2) periods.\u003c/p\u003e\n\u003ch3\u003eOutcome measures\u003c/h3\u003e\n\u003cp\u003eThe dependent variable for the primary analyses was site-specific monthly estimates of malaria incidence, defined as the number of laboratory-confirmed malaria cases from the target area of each MRC per 1000-person years of observation. We corrected for variation in monthly testing rates to account for missed testing during commodity stock-outs by multiplying the number of patients with suspected malaria but not tested by the test positivity rate (defined as the proportion of patients with laboratory-confirmed malaria of all patients tested for malaria). Results remained consistent with and without this correction. The result was then added to the number of confirmed malaria cases in that month. The study arm was specified as a categorical variable denoting the intervention and control arms.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eTo evaluate the impact of IRS in the West Nile region, we employed a difference-in-differences (DID) approach, which compared pre-post trends in malaria incidence after the implementation of IRS in intervention groups to average pre-post change in the control group [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The outcome model for our DID was as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{it}\\:=\\:{\\beta\\:}_{0\\:}+\\:{\\beta\\:}_{1\\:}Trea{t}_{i}\\:+\\:{\\beta\\:}_{2\\:}Pos{t}_{it}\\:+\\:{\\beta\\:}_{3}Trea{t}_{i}\\cdot\\:Pos{t}_{it}\\:+\\:{\\beta\\:}_{4}{X}_{it\\:}+{\\epsilon\\:}_{it\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{it}\\:\\)\u003c/span\u003e\u003c/span\u003eis the outcome (malaria incidence) per MRC \u003cem\u003ei\u003c/em\u003e in month \u003cem\u003et\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Trea{t}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a binary variable representing whether an site is in the treatment or control arm, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Pos{t}_{it}\\:\\)\u003c/span\u003e\u003c/span\u003eis a categorical variable representing the treatment period (pre-IRS, IRS-1, and IRS-2), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it\\:}\\)\u003c/span\u003e\u003c/span\u003erepresents a vector of site- and time-varying covariates. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{3}\\)\u003c/span\u003e\u003c/span\u003e is the key DID parameter in this model which represents the treatment effects of IRS-1 and IRS-2 on malaria incidence.\u003c/p\u003e\u003cp\u003eDID analyses were implemented using generalised linear mixed models using the negative binomial distribution and random intercepts for site. The outcome for this model was the MRC-level monthly count of malaria cases in the target area, with an offset for the target area population. Model coefficients were exponentiated into incidence rate ratios (IRR) representing the treatment effect for each IRS in period relative to the baseline period. We adjusted for seasonality, rainfall, and incidence of care-seeking for non-malarial illnesses. Seasonality was specified as a factor variable denoting the rainy (March to May and September to November every year) and dry (December to February and June to August every year) seasons. Rainfall data were retrieved from the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) database [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These were extracted as monthly site-specific estimates in millimetres for each study site. Incidence of care-seeking for non-malarial illnesses was defined as the monthly number of patients who sought care at each health facility, were not suspected of having malaria, and resided in the target area of each MRC per 1000 person-years of observation. Adjusted models included a categorical variable for seasonality, a continuous variable for monthly site-level rainfall estimates (in millimetres), and incidence of care-seeking for non-malarial illnesses (to account for potential changes in care-seeking over time).\u003c/p\u003e\u003cp\u003eTo produce valid estimates, DID relies on the assumption of parallel trends, which states that the average trend of the treated and control groups would have been parallel in the absence of the intervention [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. While this assumption is formally untestable, parallel trends were assessed in the pre-IRS period to ensure the pre-intervention trends met these criteria. This was done by restricting the model to the pre-IRS period and including an interaction term between treatment arm (binary) and a linear time trend in months, with the same covariates as the primary DID analysis and checking if the slopes were significantly different.\u003c/p\u003e\u003cp\u003eWe used Stata version 18.5 (StataCorp LLC, College Station, TX, USA) statistical software to clean data and generate aggregate monthly estimates for key variables including malaria incidence estimates. Further cleaning, analysis, and model fitting to generate tables, and graphs was performed in R (version 4.4.0) using the glmmTMB, and lme4 packages.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive characteristics\u003c/h2\u003e\u003cp\u003eOverall, 118,916 outpatient visits were recorded for residents from the 8 MRC target areas (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), including 61,828 visits during the 24-month baseline period, 30,229 during the 13-month period following deployment of clothianidin-deltamethrin (IRS-1), and 26,869 visits during the 12-month period following deployment of pirimiphos-methyl (IRS-2). The median age of patients was 15 years (IQR 4.5\u0026ndash;25.2), and two-thirds were female (77,547 [65.2%]). In total, 88,999 (74.8%) patients were suspected to have malaria, of whom 88,033 (98.9%) were tested using either a malaria rapid diagnostic test (RDT) or microscopy. Confirmatory testing for malaria was predominantly done with RDTs (77,787 [88%]) (Supplemental Fig.\u0026nbsp;1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eBaseline period\u003c/h2\u003e\u003cp\u003eDuring the baseline period, malaria was suspected in 21,632 (83.1%) of 26,033 intervention outpatient visits vs 25,951 (72.5%) of 35,795 control visits. Of patients tested for malaria, 12,865 of 21,012 in the intervention arm were confirmed positive (TPR 61.2%) versus 15,261 of 25,620 in the control arm (TPR 59.6%, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Observed mean malaria incidence in the intervention arm was 721 malaria cases per 1000 person-years (SD 307) and 523 malaria cases per 1000 person-years (SD 261) in the control arm (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Monthly observed mean incidence was consistently higher in the intervention arm than in the control throughout the baseline period. Parallel trend analysis detected no difference in the slope of malaria incidence trends between arms at baseline (aIRR\u0026thinsp;=\u0026thinsp;0.99. 95% CI 0.98\u0026ndash;1.01, p\u0026thinsp;=\u0026thinsp;0.486).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTarget area-specific statistics stratified by MRC, study arm and study period\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudy period\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStudy arm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMRC (District)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOutpatient visits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian age (IQR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFemale sex n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSuspected malaria cases n (% outpatient visits)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSuspected cases tested n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eConfirmatory testing by RDT n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eConfirmed malaria cases n (%[TPR])\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eMean malaria incidence per 1000 person-years of observation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eBaseline (24-month period pre-IRS with Fludora Fusion)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eWest Nile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAyipe HCIII (Koboko)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (2.0\u0026ndash;22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5814 (62.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8423 (90.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7848 (93.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7429 (94.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5320 (67.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e873\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMetu HCIII (Moyo)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16 (6.0\u0026ndash;26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5299 (62.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6217 (73.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6182 (99.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5527 (89.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3425 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e733\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOpia HCIII (Arua)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 (4.5\u0026ndash;21.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5293 (64.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6992 (84.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6982 (99.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6914 (99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4120 (59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eIntervention arm estimates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e26033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e14 (4.5\u0026ndash;23.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e16406 (63)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e21632 (83.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e21012 (97.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e19870 (94.6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e12865 (61.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e721\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eAcholi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAtiak HCIV (Amuru)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 (2.5\u0026ndash;23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3935 (65.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4262 (71.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4262 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3702 (86.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2862 (67.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAwach HCIV (Gulu)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (3.5\u0026ndash;26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6495 (69.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6349 (68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6020 (94.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5242 (87.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3722 (61.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e733\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNamokora HCIV (Kitgum)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (2.5\u0026ndash;27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3306 (63.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3594 (69.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3594 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2723 (75.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2094 (58.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePadibe HCIV (Lamwo)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (6.5\u0026ndash;27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5301 (62.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6089 (72.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6087 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5967 (98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3622 (59.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePatongo HCIII (Agago)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16 (5.5\u0026ndash;26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4559 (66.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5657 (82.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5657 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5289 (93.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2961 (52.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e381\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eControl arm estimates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e35795\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e15 (3.5\u0026ndash;26.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e23596 (65.9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e25951 (72.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e25620 (98.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e22923 (89.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e15261 (59.6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e523\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eIRS-1 (13-month period post 1st IRS round with Fludora Fusion)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eWest Nile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAyipe HCIII (Koboko)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (3.0\u0026ndash;21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2590 (67.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3663 (95.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3663 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3489 (95.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2228 (60.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e602\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMetu HCIII (Moyo)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14 (5.0\u0026ndash;23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3079 (63.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3763 (78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3763 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3043 (80.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1906 (50.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e712\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOpia HCIII (Arua)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14 (5.0\u0026ndash;23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2413 (63.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3130 (82.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3128 (99.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2559 (81.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1399 (44.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eIntervention arm estimates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e12459\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e13 (4.5\u0026ndash;21.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e8082 (64.9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e10556 (84.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e10554 (100)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e9091 (86.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e5533 (52.4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e548\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eAcholi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAtiak HCIV (Amuru)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (2.5\u0026ndash;27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1896 (65.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1831 (63.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1829 (99.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1299 (71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1239 (67.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAwach HCIV (Gulu)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (3.5\u0026ndash;26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3056 (66.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3213 (69.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3204 (99.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3027 (94.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1965 (61.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e635\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNamokora HCIV (Kitgum)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16 (3.5\u0026ndash;28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1777 (64.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1962 (70.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1960 (99.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1724 (88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1126 (57.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePadibe HCIV (Lamwo)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (6.5\u0026ndash;27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2351 (64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2546 (69.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2546 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2135 (83.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1524 (59.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePatongo HCIII (Agago)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (6.5\u0026ndash;27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2514 (65.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3211 (83.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3211 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2652 (82.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1757 (54.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eControl arm estimates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e17770\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e16 (4.5\u0026ndash;27.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e11594 (65.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e12763 (71.8)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e12750 (99.9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e10837 (85)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e7611 (59.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e455\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e\u003cp\u003eIRS-2 (12-month period post 2nd IRS with Actellic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eWest Nile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAyipe HCIII (Koboko)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 (2.5\u0026ndash;23.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2216 (67.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3091 (94.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3091 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2977 (96.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e694 (22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMetu HCIII (Moyo)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (5.5\u0026ndash;28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1903 (62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1998 (65.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1998 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1615 (80.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e428 (21.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOpia HCIII (Arua)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16 (4.5\u0026ndash;27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1806 (64.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1889 (67.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1889 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1781 (94.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e335 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eIntervention\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003earm estimates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e9128\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e15 (4.0\u0026ndash;26.0)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e5925 (64.9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e6978 (76.4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e6978 (100)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e6373 (91.3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e1457 (20.9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e149\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eAcholi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAtiak HCIV (Amuru)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (4.0\u0026ndash;20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1941 (68.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1418 (49.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1418 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e994 (70.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e794 (56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAwach HCIV (Gulu)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17 (4.6\u0026ndash;29.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3477 (69.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2833 (56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2833 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1983 (70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1460 (51.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNamokora HCIV (Kitgum)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.5 (2.5\u0026ndash;26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2056 (64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2276 (71.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2276 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1847 (81.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1210 (53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePadibe HCIV (Lamwo)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (8.5\u0026ndash;29.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2841 (67.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2713 (64.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2713 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2474 (91.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1494 (55.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e359\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePatongo HCIII (Agago)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16 (5.5\u0026ndash;26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1629 (66.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1879 (76.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1879 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1395 (74.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1063 (56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e251\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eControl arm estimates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e17731\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e17 (5.5\u0026ndash;28.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e11944 (67.4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e11119 (62.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e11119 (100)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e8693 (78.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e6021 (54.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e396\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e118916\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e15 (4.5\u0026ndash;25.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e77547 (65.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e88999 (74.8)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e88033 (98.9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e77787 (88.4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e48748 (55.4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e497\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eⴕIRS-1 denotes the first IRS period 13 months post-IRS with Fludora Fusion\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e*IRS-2 denotes the second IRS period 12 months post-IRS with Actellic\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section4\"\u003e\u003ch2\u003eImpact of IRS with clothianidin-deltamethrin (IRS-1)\u003c/h2\u003e\u003cp\u003eDuring the 13-month period following IRS with clothianidin-deltamethrin (December 2022 to December 2023), malaria was suspected in 10,556 (84.7%) of 12,459 intervention outpatient visits versus 12,763 (71.8%) of 17,770 control visits (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplemental Fig.\u0026nbsp;1). Malaria was diagnosed in 5,533 of intervention patients (TPR 52.4%) and 7,611 of control patients (TPR 59.7%). A 56.4% reduction in confirmed malaria case-counts was observed in the intervention arm relative to baseline, corresponding to 7,332 confirmed malaria cases averted. Similarly, confirmed cases in the control arm reduced by 50.1% relative to baseline, corresponding to 7,650 malaria cases averted.\u003c/p\u003e\u003cp\u003eIn the intervention arm, TPR declined by 9% from baseline during IRS-1, while TPR in the control arm remained stable (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplemental Fig.\u0026nbsp;2). An overall decline in mean TPR was observed in both arms relative to baseline (Supplemental Fig.\u0026nbsp;2B). In the intervention arm, an immediate decline in TPR was observed within the first three months of IRS-1 (December 2022 to February 2023). Thereafter, TPR increased and remained high throughout the remaining 10 months (March 2023 to December 2023). A similar reduction in TPR trends was observed in the control arm, mirroring trends in the intervention arm throughout this period (Supplemental Fig.\u0026nbsp;2B).\u003c/p\u003e\u003cp\u003eMean observed incidence in the intervention arm declined by 24% from 720.9 (SD 306.7) per 1000 person-years at baseline to 547.9 (SD 281.1) during IRS-1. Concurrently, incidence in the control arm declined by 13% from 523.4 (SD 261.1) per 1000 person-years at baseline to 455.2 (SD 263.2) during this period. Using unadjusted and adjusted models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), we detected a 14% difference in predicted mean malaria incidence between the intervention and control arms relative to baseline, but there was no evidence that predicted incidence differed between arms during IRS-1 (unadjusted IRR\u0026thinsp;=\u0026thinsp;0.86, 95% CI 0.69\u0026ndash;1.06, p\u0026thinsp;=\u0026thinsp;0.16; aIRR\u0026thinsp;=\u0026thinsp;0.86, 95% CI 0.70\u0026ndash;1.06, p\u0026thinsp;=\u0026thinsp;0.17). Monthly adjusted IRR estimates showed no difference in mean incidence between study arms following IRS with clothianidin-deltamethrin (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;4) except in November 2023 (12th month post-IRS) when mean incidence was 46% lower in the intervention arm versus control (aIRR\u0026thinsp;=\u0026thinsp;0.54, 95% CI 0.26\u0026ndash;0.8, p\u0026thinsp;=\u0026thinsp;0.04). Trends in mean malaria incidence during IRS-1 were like those observed for TPR, initially declining over 5 months from December 2023 to April 2023, then increasing and remaining high throughout the next 7 months (May 2023 to December 2023, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplemental Fig.\u0026nbsp;2A). Incidence was consistently higher in the intervention arm than the control throughout this period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eImpact of IRS with pirimiphos-methyl (IRS-2)\u003c/h2\u003e\u003cp\u003eDuring the 12-month period following IRS with pirimiphos-methyl (January 2024 to December 2024), malaria was suspected in 6,978 (76.4%) of 9,128 intervention outpatient visits, and 11,119 (62.7%) of 17,731 control visits (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplemental Fig.\u0026nbsp;1). Malaria was confirmed in 1,457 intervention patients (TPR 20.9%) versus 6,021 control patients (TPR 54.2%). An 88.7% reduction in confirmed malaria case-\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDifference-in-difference coefficients and malaria incidence estimates between study arms in the pre- and post-IRS periods\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTime-period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eStudy arm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e\u003cp\u003eMalaria incidence per 1000 person-years of observation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eObserved (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003eUnadjusted estimates\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003eAdjusted estimates\u003c/b\u003e\u003csup\u003e\u003cb\u003eⱡ\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ePredicted, 95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eDID IRR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ePredicted\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eDID IRR (95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBaseline*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eControl\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e523.4 (261.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e529 (462.6\u0026ndash;605.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e529.4 (455.1\u0026ndash;616.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eIntervention\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e720.9 (306.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e731.9 (634.9\u0026ndash;843.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e739.6 (634.0\u0026ndash;862.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIRS-1\u003csup\u003eⴕ\u003c/sup\u003e (13 months post IRS with Fludora Fusion)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eControl\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e455.2 (263.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e455.8 (391.3\u0026ndash;530.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e447.4 (379.3\u0026ndash;527.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eIntervention\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e547.9 (281.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e540.3 (456.1\u0026ndash;640.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.86 (0.69\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e534.7 (448.0\u0026ndash;638.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.86 (0.70\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIRS-2\u003csup\u003e\u003cem\u003eŧ\u003c/em\u003e\u003c/sup\u003e (12 months post IRS with Actellic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eControl\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e396.2 (237.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e380.6 (325.9\u0026ndash;444.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e381.6 (321.5\u0026ndash;453.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eIntervention\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e149.1 (100.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e145.3 (121.5\u0026ndash;173.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.28 (0.22\u0026ndash;0.35)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e149.2 (123.1\u0026ndash;180.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.30 (0.24\u0026ndash;0.38)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e*Baseline period: December 2020 \u0026ndash; November 2022, 24 months\u003c/h2\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003eⴕ\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eIRS-1: December 2022 \u0026ndash; December 2023, 13 months\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cem\u003eŧ\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eIRS-2: January 2024 \u0026ndash; December 2024, 12 months\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003eⱡ\u003c/b\u003e\u003c/sup\u003e\u003cem\u003eAdjusted for site-specific monthly rainfall, seasonality and incidence of care-seeking for non-malarial illnesses\u003c/em\u003e\u003c/p\u003e\u003cp\u003ecounts were observed in the intervention arm relative to baseline, corresponding to 11,408 confirmed malaria cases averted (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Confirmed malaria cases in the control arm declined by 60.5%, corresponding to 9,240 cases averted in this period relative to baseline (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Mean TPR declined by 40.3% in the intervention arm but only reduced by 5.4% in the control arm (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). An immediate and substantial drop in mean TPR was observed in the intervention arm within the first 3 months of IRS-2 (January 2024 to March 2024), which was sustained over the remaining 9 months (April 2024 to December 2024) with minor seasonal peaks. In the control arm, a modest decline in TPR occurred in the first 4 months (January 2024 to April 2024). Thereafter, TPR increased and remained high for the remainder of IRS-2 (May 2024 to December 2024) (Supplemental Fig.\u0026nbsp;2A).\u003c/p\u003e\u003cp\u003eIn the intervention arm, observed mean incidence declined by 79.3% during IRS-2 from 720.9 cases per 1000 person-years (SD 306.7) at baseline to 149.1 (SD 100.6) during IRS-2. In the control arm, observed mean incidence declined by 24.3% from 523.4 (SD 261.1) cases per 1000 person-years at baseline to 396.2 (SD 237.5) during this period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the unadjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), we detected a 72% difference in predicted mean malaria incidence between the intervention and control arms during IRS-2 relative to baseline (unadjusted IRR\u0026thinsp;=\u0026thinsp;0.28, 95% CI 0.22\u0026ndash;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Model adjustment estimated a 70% reduction in predicted mean malaria incidence in the intervention arm compared to control relative to baseline (aIRR 0.30, 95% CI 0.24\u0026ndash;0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Further stratification by month revealed a 49% reduction in incidence in the intervention arm within the first month (January 2024) following IRS with pirimiphos-methyl (adjusted IRR\u0026thinsp;=\u0026thinsp;0.51, 95% CI 0.30\u0026ndash;0.86, p\u0026thinsp;=\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMonthly adjusted difference-in-difference coefficients and predicted mean incidence by study arm during IRS-1 and IRS-2.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIntervention period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMonth and year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ePredicted mean malaria incidence per 1000 person-years of observation (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eaIRR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntervention arm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl arm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eIRS \u0026minus;\u0026thinsp;1 (13-month period following IRS with Fludora Fusion)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJan-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e508.3 (423.7\u0026ndash;609.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e437 (371\u0026ndash;514.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83 (0.49\u0026ndash;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeb-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e535.1 (445.5\u0026ndash;642.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e434.5 (369\u0026ndash;511.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.73 (0.43\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMar-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e502.2 (422.6\u0026ndash;597)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e411.2 (349.8\u0026ndash;483.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.21 (0.71\u0026ndash;2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApr-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e485.3 (407.9\u0026ndash;577.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e392.6 (330\u0026ndash;467.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.52 (0.89\u0026ndash;2.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMay-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e478.2 (402.1\u0026ndash;568.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e379.3 (319.8\u0026ndash;450)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89 (0.53\u0026ndash;1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJun-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e641.3 (534.8\u0026ndash;769)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e505.3 (421\u0026ndash;606.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88 (0.53\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJul-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e575.8 (485.7\u0026ndash;682.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e508.2 (434.8\u0026ndash;594)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83 (0.50\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAug-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e593.1 (499.8\u0026ndash;703.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e509.5 (435.7\u0026ndash;595.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.88 (0.52\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSept-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e481.7 (404.8\u0026ndash;573.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e419.6 (358.9\u0026ndash;490.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71 (0.42\u0026ndash;1.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOct-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e570.2 (472.5\u0026ndash;688.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e492.7 (412\u0026ndash;589.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08 (0.64\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNov-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e527.5 (442.6\u0026ndash;628.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e424.3 (360.8\u0026ndash;498.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57 (0.34\u0026ndash;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDec-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e511.9 (427.7\u0026ndash;612.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e460.5 (391.7\u0026ndash;541.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9 (0.53\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eIRS \u0026minus;\u0026thinsp;2 (12-month period following IRS with Actellic)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJan-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137 (113.5\u0026ndash;165.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e368.7 (311.4\u0026ndash;436.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.51 (0.30\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeb-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142.5 (118.6\u0026ndash;171.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e378.2 (321.6\u0026ndash;444.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.44 (0.26\u0026ndash;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMar-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125.1 (103.7\u0026ndash;150.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e314.6 (266.3\u0026ndash;371.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54 (0.31\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApr-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123.1 (102.2\u0026ndash;148.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e346.2 (294.6\u0026ndash;406.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46 (0.26\u0026ndash;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMay-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134 (111.8\u0026ndash;160.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e353.7 (299.6\u0026ndash;417.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26 (0.15\u0026ndash;0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJun-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168.2 (138.8\u0026ndash;203.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e420.1 (357.5\u0026ndash;493.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21 (0.12\u0026ndash;0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJul-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181.4 (148.7\u0026ndash;221.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e485.3 (400.2\u0026ndash;588.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18 (0.10\u0026ndash;0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAug-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e186.8 (150.8\u0026ndash;231.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e439.9 (368.6\u0026ndash;524.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21 (0.12\u0026ndash;0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSept-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155 (126.8\u0026ndash;189.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e351.2 (299.6\u0026ndash;411.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19 (0.11\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOct-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152.4 (125.4\u0026ndash;185.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e396.4 (329.9\u0026ndash;476.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.22 (0.13\u0026ndash;0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNov-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138.5 (115.2\u0026ndash;166.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e365.1 (303.4\u0026ndash;440)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19 (0.11\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDec-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146.2 (121.3\u0026ndash;176.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e359.8 (305\u0026ndash;424.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21 (0.12\u0026ndash;0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ereduction in incidence in the intervention arm was sustained until the twelfth month, with a 79% difference detected in December 2024 (12-month aIRR\u0026thinsp;=\u0026thinsp;0.21, 95% CI 0.12\u0026ndash;0.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). No seasonal peaks in malaria incidence were observed in the intervention arm during IRS-2 (Supplemental Fig.\u0026nbsp;2B; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Incidence trends for the control arm during this period were similar to TPR trends, with an immediate decline that lasted 3 months (January 2024 to March 2024). Thereafter, incidence in the control arm peaked and remained high until December 2024 (Supplemental Fig.\u0026nbsp;2B; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAssessing the impact of population-level vector control interventions is critical for tracking progress in malaria control and guiding future deployment strategies. In this study, enhanced health facility-based surveillance data were analysed to estimate the combined impact of ITNs and IRS with two insecticides, clothianidin-deltamethrin and pirimiphos-methyl, on malaria incidence in West Nile, Uganda, as compared to ITNs without IRS in Acholi region. We found no significant difference in predicted incidence in the 13 months following initial IRS with clothianidin-deltamethrin compared to control. In contrast, an immediate, substantial and sustained reduction in malaria incidence was observed when IRS with pirimiphos-methyl was deployed in the subsequent year. These findings suggest that IRS with pirimiphos-methyl (plus ITNs) was markedly more effective than IRS with clothianidin-deltamethrin in northern Uganda.\u003c/p\u003e\u003cp\u003eClothianidin-based IRS for malaria control has generated mixed results across sub-Saharan Africa. Entomological studies assessing insecticide efficacy with WHO tube tests and bottle bioassays, and CDC bottle bioassays [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], have generally yielded positive results for clothianidin [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, findings from these assessments may over-estimate actual field effectiveness; further validation with results from epidemiological studies is needed [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our findings are consistent with other studies conducted in eastern Uganda, where a switch from pirimiphos-methyl to clothianidin-based IRS was associated with an 8-fold increase in malaria incidence and a 4-fold increase in parasite prevalence [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, a study in Zambia [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], using routine surveillance data, found that transitioning from pirimiphos-methyl to clothianidin-deltamethrin did not reduce parasite prevalence. In Cote d\u0026rsquo;Ivoire [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], an analysis of routine surveillance data was undertaken to determine incidence changes in IRS vs non-IRS districts following the introduction of clothianidin-based IRS. This study found that the month-to-month incidence trend did not change in either area after the initial round, monthly malaria cases increased at a rate 2.5x higher than in non-IRS areas, and following the second IRS round a significantly higher increase in cases was observed in IRS areas vs control [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In Burkina Faso, another epidemiological study utilising routine surveillance data found that the impact of clothianidin-based IRS on malaria case rates varied by region [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Where reductions in malaria case rates were observed, they were only modest, diminished with each round, and hardly differed from those observed in non-IRS areas [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Taken together, these studies suggest that clothianidin was ineffective. In contrast, an evaluation of IRS in Madagascar [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], including IRS with clothianidin-based insecticides, yielded positive results. Here, IRS was associated with a 30% reduction in incidence, with incremental reductions in subsequent years. However, this study did not disaggregate IRS impact by insecticide type, rendering it difficult to attribute these positive findings solely to clothianidin-based IRS.\u003c/p\u003e\u003cp\u003eThe lack of effectiveness of clothianidin-deltamethrin in West Nile could be attributed to multiple factors including insecticide resistance, shifts in vector species composition, housing quality, and the timing and quality of spraying. High-level pyrethroid resistance [\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and high clothianidin tolerance [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] both exist in Uganda. Predominant malaria vectors in Uganda are resistant to deltamethrin [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], rendering it generally ineffective. In 2016\u0026ndash;2017, signs of clothianidin resistance in wild mosquito species were detected in Uganda [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], prior to the WHO prequalification of clothianidin-based insecticides for IRS. Other studies have since confirmed that while clothianidin generally remains effective in most settings, susceptibility patterns vary among wild mosquito populations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. More recently, waning mortality rates for \u003cem\u003eAn. funestus\u003c/em\u003e were observed following IRS with clothianidin-based insecticides in eastern Uganda [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Clothianidin resistance could have resulted from widespread use of neonicotinoid-containing pesticides in agriculture in Uganda [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. High concentrations of neonicotinoids have been detected in waterbodies [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], groundwater in non-IRS areas [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], on fresh fruits and vegetables [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and in honeybee products [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] in Uganda. Neonicotinoid insecticides are hydro-soluble, persisting in surface water, including in potential breeding sites for mosquito larvae, and exceedingly high concentrations beyond regulatory thresholds have previously been detected in African settings [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, attempts to confirm this hypothesis in West Africa have yielded mixed results. Neonicotinoid use in agriculture was found to reduce wild mosquito vector susceptibility to clothianidin in Cameroon [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], but not in Benin [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and while wild vectors collected from agricultural settings in Cameroon demonstrated lower mortality on exposure to clothianidin [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], susceptibility remained high for wild \u003cem\u003eAn. gambiae ss\u003c/em\u003e from 18 agricultural settings in Benin [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These mixed results underscore the need to guide deployment of clothianidin-based IRS on resistance patterns of local vectors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eShifts in predominant malaria vector species, documented elsewhere in Uganda, may also have reduced the effect of clothianidin-deltamethrin [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Historically, \u003cem\u003eAn. gambiae s.s.\u003c/em\u003e was the principal malaria vector in Uganda, with smaller populations of \u003cem\u003eAn. funestus and An. arabiensis\u003c/em\u003e [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. With deployment of vector control interventions, \u003cem\u003eAn. gambiae s.s. and An. funestus\u003c/em\u003e populations reduced in regions receiving IRS and ITNs, while \u003cem\u003eAn. arabiensis\u003c/em\u003e and \u003cem\u003eAn. funestus\u003c/em\u003e populations markedly, although relatively increased in districts receiving ITNs only (not IRS) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This pattern was also observed in Eastern Uganda following several years of sustained IRS with carbamates, organophosphates, and neonicotinoids [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], where both \u003cem\u003eAn. gambiae ss and An. funestus\u003c/em\u003e populations reduced and were initially replaced by \u003cem\u003eAn. arabiensis\u003c/em\u003e. More recently, however, \u003cem\u003eAn. funestus\u003c/em\u003e populations have been restored [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Although \u003cem\u003eAn. arabiensis\u003c/em\u003e remained highly susceptible to clothianidin, \u003cem\u003eAn. funestus\u003c/em\u003e has exhibited high tolerance [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A similar shift occurred in West Nile between 2021 and 2023 when the proportion of field-collected \u003cem\u003eAn. funestus\u003c/em\u003e increased markedly from 0\u0026ndash;7% before IRS with Fludora\u0026reg; Fusion, to 69\u0026ndash;80% after, while those of \u003cem\u003eAn. gambiae ss\u003c/em\u003e reduced from 93\u0026ndash;100%, to 18\u0026ndash;31% [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The evidence suggests that \u003cem\u003eAn. funestus\u003c/em\u003e was likely the dominant vector in West Nile in 2022 and 2023, which may not have been susceptible to the clothianidin-based insecticides. Entomological surveillance conducted in 2023 in West Nile revealed that field-collected \u003cem\u003eAn. funestus\u003c/em\u003e species from this region had 50% higher sporozoite rates, and 30x higher indoor human biting rates than \u003cem\u003eAn. gambiae\u003c/em\u003e, demonstrating higher propensity to transmit malaria [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Coupled with the higher tolerance of \u003cem\u003eAn. funestus\u003c/em\u003e to clothianidin the lack of impact following IRS with clothianidin-deltamethrin in West Nile is not surprising.\u003c/p\u003e\u003cp\u003eHousing quality in West Nile may also have reduced the effectiveness of clothianidin-deltamethrin. Traditionally constructed housing, characterised by mud walls, thatch roof and open eaves is highly prevalent in northern Uganda [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Clothianidin is known to exhibit lower mortality and residual efficacy when applied to mud walls as compared to cement [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], presumably due to the high porosity of mud [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Incorporating cement substrate within mud bricks has been shown to markedly improve clothianidin\u0026rsquo;s residual efficacy close to levels observed on cement [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In eastern Uganda, concentrations of clothianidin on sprayed walls were below the target dose, while excess concentrations of pirimiphos-methyl were detected [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These differences could be attributed to inadequate deployment, especially where differences in spray operations exist, but the same teams delivered IRS in these areas, making variability in implementation less likely. Timing of IRS deployment, at the start of the long dry season, is counter to WHO\u0026rsquo;s recommendation and may also have reduced the impact of clothianidin-deltamethrin as concentrations of the insecticides may have dwindled long before the start of the peak transmission season.\u003c/p\u003e\u003cp\u003eWe found that pirimiphos-methyl performed exceptionally well in West Nile, substantially reducing malaria incidence when deployed one year after the single round of clothianidin-deltamethrin. Historically, pirimiphos-methyl has been highly effective in Uganda, with the 3-year period of sustained IRS providing the greatest malaria control [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Other African countries have documented remarkable results following IRS with pirimiphos-methyl. In Benin, one round of IRS with pirimiphos-methyl was associated with 7.5x lower biting rates, 75% reduction in vector density, and 34% reduction in blood feeding rates compared to control, which was consistent through subsequent IRS rounds [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Similar results were observed in Kenya when entomological monitoring conducted before and after IRS with pirimiphos-methyl found an 88% and 93% reduction in field-collected \u003cem\u003eAn. funestus\u003c/em\u003e vector species by light traps and pyrethrum spray catches (PSC) respectively, and a 69% reduction in field-collected \u003cem\u003eAn. arabiensis\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this same study, sporozoite infections and human biting rates were reduced to nearly zero. Conversely, pirimiphos-methyl conferred only modest reductions in incidence (9\u0026ndash;25%) in Zambia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and malaria case-rates increased when a round of pirimophos-methyl was implemented following three rounds of clothianidin-based IRS in Burkina Faso [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhy was pirimiphos-methyl more effective for IRS in West Nile? Relative to clothianidin-deltamethrin, it is faster-acting [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], exhibiting an immediate and much stronger lethal effect (nearly 2.5x higher) within 24 hours [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In contrast, clothianidin-deltamethrin requires at least five days to attain similar results [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Moreover, pirimiphos-methyl\u0026rsquo;s residual efficacy extends at least 10 months [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], with some variation by location and type of wall substrate [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], providing protection against malaria throughout the transmission season and allowing for annual deployment. Importantly, pirimiphos-methyl has remained highly efficacious against all mosquito vector species in Uganda, including \u003cem\u003eAn. funestus\u003c/em\u003e [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Wild \u003cem\u003eAn. gambiae\u003c/em\u003e vector species in Burkina Faso, Togo, and Benin also remain highly susceptible to pirimiphos-methyl despite high resistance to DDT, pyrethroids and bendiocarb [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Levels and prevalence of organophosphate resistance remain either low or largely undetermined globally [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], but resistance is emerging within sub-Saharan Africa [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. This may explain why in Burkina Faso, a single IRS round with pirimiphos-methyl following multiple rounds with clothianidin-based IRS was associated with an increase in malaria case-rates [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Also, like neonicotinoids, organophosphates are also widely used in agriculture [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and continued exposure to sub-lethal doses will potentially increase selection pressure for resistant malaria vectors. Continued investment in entomological surveillance monitoring and related insecticide resistance research is critical.\u003c/p\u003e\u003cp\u003eOur study had several strengths. We applied a quasi-experimental study design to assess IRS impact in West Nile using enhanced health facility-based surveillance data, providing a cost-effective means for population-level impact evaluation. Including data from control health facilities located in neighbouring Acholi region with similar climatic and geographic conditions allowed us to disaggregate incidence changes unrelated to IRS from those associated with IRS. Additionally, we utilised individual-level data from our MRC sentinel site surveillance system where monthly quality assurance checks ensured high quality data. However, our study also had some limitations. First, our study arms were unbalanced because only a limited number of sentinel sites existed within IRS districts in West Nile. However, we believe that ensuring high quality data from our MRC sites outweighed any potential benefit of expanding to sites outside of our MRC surveillance system. Second, it was impossible to dissociate the impact of IRS from the effect of ITNs. To counter this, intervention and control sites were matched based on ITN type and timing of distribution campaigns.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn West Nile, we found that clothianidin-based IRS (plus ITNs) did not reduce malaria incidence, while a subsequent round of IRS with pirimiphos-methyl (plus ITNs), one year after clothianidin-deltamethrin, substantially reduced malaria incidence as compared to ITNs alone in the neighbouring Acholi region. Whilst periodic IRS insecticide rotation is a pragmatic approach to resistance management, it is only useful if the replacement insecticide is equally effective. Our findings underscore the need to assess wild vector susceptibility to insecticides prior to implementing large-scale vector control measures in endemic settings. Evaluating insecticides in real-world settings remains a priority, rather than relying solely on modelling or experimental hut trials. Organophosphates may be preferred over clothianidin-based insecticides for population-level IRS coverage in similar settings. Continued entomological and epidemiological monitoring of IRS impact and for signals of resistance to both organophosphates and neonicotinoids is warranted. Malaria vector control programs should be guided by context-specific data on insecticide effectiveness. The synergistic effect of dual interventions should be considered in areas of high pyrethroid resistance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eaIRR: adjusted incidence rate ratio; CDC: Centre for Disease Control; DDT: Dichloro-diphenyl-trichloroethane; DID: difference-in-difference.EES: Enhanced Entomological Surveillance; HMIS: Health Management Information System; IDRC: Infectious Diseases Research Collaboration; IRR: incidence rate ratio; IRS: indoor residual spraying of insecticide; ITNs: Insecticide-treated nets; LLINs: long-lasting insecticidal nets; MRC: malaria reference centre; PBO: piperonyl butoxide; PRISM: Program for Resistance, Immunology, Surveillance and Modelling of Malaria; SD: standard deviation; TPR: test positivity rate; WHO: World Health Organisation.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthical approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval to conduct this study was sought from Makerere University College of Health Sciences School of Public Health Research and Ethics Committee (SPHREC) in Uganda, approval number SPH-2025-551, and the University of California, San Francisco Ethics Committee. Waiver of informed consent was granted for this study by SPHREC because the study involved extraction of secondary data from patient records without using any personal identifiers.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article is(are) available in the [repository name] repository, [unique persistent identifier and hyperlink to dataset(s) in http:// format].\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFunding for all research-related activities was provided by the National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Health (NIH), USA and the Commonwealth Scholarship Commission (CSC) in the UK, under the Commonwealth Scholarship Commission. More information can be found at https://cscuk.fcdo.gov.uk. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor\u0026rsquo;s contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJFN: conceptualization, methodology, formal analysis, investigation, data curation, writing \u0026ndash; original draft, writing \u0026ndash; review and editing, visualisation, and project administration; DPM: methodology, formal analysis, data curation, writing \u0026ndash; review and editing, and visualisation; AE: methodology, formal analysis, writing \u0026ndash; review and editing, data curation, and visualisation; JIN: writing \u0026ndash; review and editing, securing funding, project administration; SG: writing \u0026ndash; review and editing, project administration; JO: project administration; IN: investigation, data curation; CMS: writing \u0026ndash; review and editing, project administration; MRK: writing \u0026ndash; review and editing, project administration, securing funding and supervision; MJD: conceptualisation, methodology, writing \u0026ndash; review and editing, securing funding, and supervision; GD: conceptualisation, methodology, investigation, data curation, writing \u0026ndash; review and editing, supervision, and securing funding; and SGS: conceptualisation, methodology, investigation, writing \u0026ndash; review and editing, supervision, securing funding, and project administration.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe author thanks members of the UMSP core surveillance team at IDRC, health care workers at public health facilities with ongoing MRC surveillance activities, district local government officials at respective districts in Uganda and government officials at Ministry of Health Uganda National Malaria Elimination Division.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization: \u003cstrong\u003eWorld malaria report 2023\u003c/strong\u003e: World Health Organization; 2023.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization: \u003cstrong\u003eWHO guidelines for malaria, 16 October 2023.\u003c/strong\u003e Geneva; 2023.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization: \u003cstrong\u003eManual for monitoring insecticide resistance in mosquito vectors and selecting appropriate interventions\u003c/strong\u003e: World Health Organization; 2022.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization: \u003cstrong\u003eGlobal report on insecticide resistance in malaria vectors: 2010\u0026ndash;2016\u003c/strong\u003e. 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In\u003cem\u003e.\u003c/em\u003e Rockville, MD: Abt Associates; 2022: 49.\u003c/li\u003e\n\u003cli\u003eMedjigbodo AA, Djogbenou LS, Koumba AA, Djossou L, Badolo A, Adoha CJ, Ketoh GK, Mavoungou JF: \u003cstrong\u003ePhenotypic Insecticide Resistance in Anopheles gambiae (Diptera: Culicidae): Specific Characterization of Underlying Resistance Mechanisms Still Matters\u003c/strong\u003e. \u003cem\u003eJ Med Entomol \u003c/em\u003e2021, \u003cstrong\u003e58\u003c/strong\u003e(2):730-738.\u003c/li\u003e\n\u003cli\u003eNagi SC, Lucas ER, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, Djogb\u0026eacute;nou LS, Medjigbodo AA, Edi CV, Ketoh GK\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eParallel Evolution in Mosquito Vectors\u0026mdash;A Duplicated Esterase Locus is Associated With Resistance to Pirimiphos-methyl in Anopheles gambiae\u003c/strong\u003e. \u003cem\u003eMolecular Biology and Evolution \u003c/em\u003e2024, \u003cstrong\u003e41\u003c/strong\u003e(7).\u003c/li\u003e\n\u003cli\u003eLucas ER, Nagi SC, Egyir-Yawson A, Essandoh J, Dadzie S, Chabi J, Djogb\u0026eacute;nou LS, Medjigbodo AA, Edi CV, K\u0026eacute;toh GK\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGenome-wide association studies reveal novel loci associated with pyrethroid and organophosphate resistance in Anopheles gambiae and Anopheles coluzzii\u003c/strong\u003e. \u003cem\u003eNature Communications \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e(1):4946.\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":"bmc-global-and-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)","snPcode":"44263","submissionUrl":"https://submission.springernature.com/new-submission/44263/3","title":"BMC Global and Public Health","twitterHandle":"@BMC_GPH","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria, IRS, clothianidin-deltamethrin, pirimiphos-methyl, insecticide-treated nets, impact of vector control interventions, ITNs","lastPublishedDoi":"10.21203/rs.3.rs-7518583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7518583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e In Uganda, where malaria transmission is high, insecticide treated nets (ITNs) have been distributed nationwide every three years since 2013. In West Nile, northern Uganda, indoor residual spraying (IRS) was first implemented with clothianidin-deltamethrin (Fludora Fusion®) in 2022, followed by pirimiphos-methyl (Actellic 300CS®) in 2023. We utilized a quasi-experimental study to assess the impact of IRS+ITNs on malaria incidence in West Nile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data were collected from three malaria reference centres (MRCs) in West Nile (IRS+ITNs, intervention) and five MRCs in neighbouring Acholi (ITNs only, control) over 4 years: (1) Baseline (December 2018-November 2020), prior to IRS; (2) IRS-1 (December 2022-December 2023) following IRS with clothianidin-deltamethrin; (3) IRS-2 (January 2024-December 2024) following IRS with pirimiphos-methyl. The primary outcome was monthly malaria incidence (number of laboratory-confirmed malaria cases from each MRC target area per 1000 person-years). Data were analysed using negative binomial regression models with a difference-in-difference approach, comparing pre-post trends in malaria incidence between intervention and control groups. Adjusted models accounted for seasonality and care-seeking behaviours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e During IRS-1, mean observed malaria incidence fell from baseline in both arms (intervention: 720.9 to 547.9; and control: 523.4 to 455.2 per 1000 person-years). We detected a 14% difference in predicted mean malaria incidence between intervention and control during IRS-1 relative to baseline, but this was not significant (adjusted IRR = 0.86, 95% CI 0.70–1.06, p=0.17). During IRS-2, incidence in the intervention arm declined by 79.3% compared to baseline (720.9 to 149.1), while in the control arm, incidence fell by 24.3% (523.4 to 396.2). We detected a 70% reduction in predicted mean malaria incidence in the intervention arm compared to control relative to baseline (aIRR 0.30, 95% CI 0.24 – 0.38, p\u0026lt;0.01). During IRS-2, there was strong evidence of an immediate and sustained reduction in incidence in the intervention arm over one year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn West Nile, the reduction in malaria incidence after clothianidin-based IRS (plus ITNs) was modest and non-significant. Subsequent IRS with pirimiphos-methyl (plus ITNs) substantially reduced malaria incidence. These results highlight the importance of selecting context-specific insecticides for vector control programs and the potential synergistic effect of dual interventions in areas of high pyrethroid resistance.\u003c/p\u003e","manuscriptTitle":"Impact of Indoor Residual Spraying and Insecticide-Treated Nets on Malaria Burden in Uganda: A Quasi-Experimental Study in 8 Districts in West Nile and Acholi Regions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 15:19:22","doi":"10.21203/rs.3.rs-7518583/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-18T05:15:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T16:17:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T03:24:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335802315525035321399696317783202834951","date":"2025-09-30T13:18:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26088816867022614260569101518854842038","date":"2025-09-29T08:25:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77777202831836245708374687020691170818","date":"2025-09-22T02:32:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-13T18:39:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T06:20:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T06:04:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Global and Public Health","date":"2025-09-02T13:57:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-global-and-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)","snPcode":"44263","submissionUrl":"https://submission.springernature.com/new-submission/44263/3","title":"BMC Global and Public Health","twitterHandle":"@BMC_GPH","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad682db6-c82b-4866-951e-39ec281ff699","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T11:55:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 15:19:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7518583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7518583","identity":"rs-7518583","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}