The accuracy of recording malaria rapid diagnostic test (RDT) results in public health facilities in Benin; results from the MaCRA project

The accuracy of recording malaria rapid diagnostic test (RDT) results in public health facilities in Benin; results from the MaCRA project | 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 The accuracy of recording malaria rapid diagnostic test (RDT) results in public health facilities in Benin; results from the MaCRA project Idelphonse Ahogni, Hospice Avanon, Corneille Hueha, Augustin Kpemasse, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7002558/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2026 Read the published version in Malaria Journal → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Accurate interpretation and recording of malaria rapid diagnostic tests (RDTs) are critical for case management and surveillance in malaria-endemic settings. In Benin, where over 90% of malaria diagnoses rely on RDTs, concerns remain about the accuracy of the reporting and recording of RDT results. This study assessed the fidelity of RDT recording by healthcare workers (HCWs) in public health facilities and explored associated factors. Methods: A six-month mixed-methods, prospective observational study was conducted in 16 public health facilities across two departments in Benin. For each RDT performed, an image was captured using a digital RDT reader (HealthPulse, Audere, Seattle, WA USA) and independently interpreted by an external trained panel. HCW-recorded results were compared to panel interpretations. A knowledge, attitudes, practices, and beliefs (KAPB) survey and structured observations of RDT performance were conducted, alongside in-depth interviews with selected HCWs. Results: Of 35,706 RDTs assessed, overall agreement between HCW and reference panel interpretations was 94.3% (Cohen’s kappa = 0.88). Results misrecorded as positive (5.0%) were more frequent than results misrecorded as negative (0.7%). Agreement varied by patient age, HCW experience, and facility characteristics. Accuracy was highest with children under 5 years (96.7%) and lowest with patients over 15 years (91.6%). HCWs with ≥ 10 years of experience, and access to electricity and internet performed better. From 226 HCWs surveyed, 89.4% believed a patient with malaria could have a negative RDT, though only 19.5% supported treating such cases with antimalarials. While most HCWs were proficient in performing RDTs, only 40.5% waited the recommended time before reading results, and glove use was low (15.6%) highlighting safety gaps. RDT use was primarily motivated by adherence to guidelines (60.2%), rather than patient or supervisor expectations. Qualitative interviews highlighted contextual challenges including workload, lighting conditions in health facilities, and resource constraints. Conclusion: HCWs in Benin showed high accuracy in interpreting and reporting malaria RDT results, likely supported by recent nationwide RDT cassette validations. Performance was strongest among those with more experience, training, and adequate infrastructure. However, negative results misrecorded as positive, especially in adult patients, remains a concern. Targeted training and supportive supervision may help strengthen confidence in negative results and improve overall diagnostic accuracy. malaria rapid diagnostic test RDT healthcare workers diagnostic accuracy Benin surveillance case management KAPB survey Figures Figure 1 Figure 2 Figure 3 BACKGROUND Malaria remains a leading cause of mortality among children under five and a major contributor to morbidity in adults in Benin. In 2022 alone, an estimated 5 million malaria cases and over 11,000 related deaths were reported [ 1 ]. To address this burden, Benin’s national malaria policy promotes early diagnosis and prompt treatment at all levels of the healthcare system in line with World Health Organization (WHO) guidance [ 2 ]. This includes the use of rapid diagnostic tests (RDTs) or microscopy for confirming all suspected cases of malaria prior to treatment. Patients who test positive are expected to receive a full course of WHO-recommended antimalarial treatment. Conversely, those who test negative should not be prescribed antimalarials but should undergo thorough clinical evaluation to identify alternative causes of fever. Malaria diagnosis in Benin relies heavily on RDTs, which account for over 90% of all confirmed malaria cases [ 3 , 4 ]. RDTs are simple, easy-to-use immunochromatographic devices that detect parasite-specific antigens or enzymes in the blood, targeting either the Plasmodium genus or specific species [ 5 ]. They are accessible across all levels of the health system in Benin and are especially critical in peripheral health facilities where access to microscopy may be limited. Since their introduction in 2008, RDTs have been provided free of charge within the public health sector, contributing significantly to improved diagnostic coverage and case management across the country [ 4 ]. Patients presenting at public health facilities with a body temperature of 37.5°C or higher, or with a reported history of fever within the previous 48 hours, are routinely tested for malaria using RDTs. Following administration of the RDT, patient information (including clinical presentation, diagnostic test results, and treatment provided) is recorded in the health facility registers and later entered into the national Health Management Information System (HMIS). Given the reliance on RDTs for diagnosis, effective malaria surveillance in Benin therefore hinges on the proper management of RDT results. Although RDTs generally demonstrate high diagnostic performance, the quality of malaria surveillance data in Benin remains susceptible to challenges associated with their routine implementation [ 6 ]. Key issues include healthcare workers’ (HCWs) ability to correctly administer and interpret test results, adherence to national treatment guidelines based on RDT outcomes, and the accurate recording of diagnostic and treatment information in patient registers and national HMIS reporting systems. These data form the backbone of the HMIS informing public health decision-making, guiding malaria control strategies, and supporting the efficient allocation of resources. Ensuring accurate recording of RDT results is essential for tracking malaria trends and supporting timely, evidence-based policy responses at both national and sub-national levels. However, surveys conducted by the National Malaria Control Programme (NMCP) of Benin in selected public health facilities in 2019 revealed significant issues with HCWs’ adherence to and recording of negative malaria RDT results [ 7 ]. In many instances, HCWs recorded patients with negative test outcomes as malaria-positive and treated them with artemisinin-based combination therapies (ACTs), opting to rely on presumptive diagnosis rather than test outcomes. In response, the Ministry of Health in 2023 launched a decentralized programme to validate malaria data on a monthly basis [ 8 ]. This initiative involves cross-checking data using primary sources from health facilities, including patient registers and used RDT cassettes, before the information is entered into the HMIS. These issues are not unique to Benin and have been documented in several countries across sub-Saharan Africa [ 6 , 9 – 11 ]. They compromise the accuracy of malaria surveillance data and can lead to inappropriate clinical management, including the unnecessary use of antimalarial drugs in patients who do not have malaria. To better understand and address these challenges, we conducted a mixed-methods, prospective observational study in public health facilities in Benin as part of the broader multi-country Malaria RDT Capture and Reporting Assessment (MaCRA project) [ 12 ]. The study aimed to evaluate the accuracy of RDT result reporting by comparing outcomes recorded in health facility registers with those independently verified by an external panel of trained reviewers. Over a six-month period, RDT results captured in routine facility registers were compared to results interpreted by the external panel reviewing images of the RDTs, which had been taken using a smartphone application (HealthPulse, Audere, Seattle, WA USA). The study also sought to identify key factors contributing to reporting discrepancies within the Beninese context. Here, we present a descriptive analysis of the results to highlight key trends, patterns, and variations observed in HCWs’ accuracy of recording malaria RDT results across health facility, patient and HCW characteristics in Benin. METHODS Study sites in Benin The study was conducted between June and December 2023 in four Health Zones across two Departments in Benin; one located in the north, where malaria transmission is seasonal, and one in the south, where transmission is perennial. In collaboration with national stakeholders and the National Malaria Control Programme (NMCP), the Health Zones of Bembèrèkè-Sinendé and Nikki-Kalalé-Pèrèrè in the Borgou Department were selected to represent the northern site. For the southern site, the Health Zones of Bohicon-Za-Kpota-Zogbodomey and Djidja-Abomey-Agbangnizoun in the Zou Department were chosen (Fig. 1 ). Selection of the study sites was based on several criteria; the absence of major ongoing malaria control interventions (such as seasonal malaria chemoprevention [SMC] and perennial malaria chemoprevention [PMC]) that could confound study outcomes; the presence of a sufficient number of public health facilities; patient volume; a consistently high RDT test positivity rate (TPR) over recent years; availability of routine malaria data for at least nine months per year over the past three years; and logistical and financial feasibility, including site accessibility. The selected Health Zones exhibited persistently high RDT TPRs with minimal variation between 2019 and 2021, further supporting their suitability for inclusion in the study. A total of 32 public health facilities (16 in the northern site and 16 in the southern site) were selected (Fig. 1 ). Eligible health facilities were those with at least two to three years of malaria reporting to the national HMIS, with data available for at least nine out of twelve months per year and a minimum of 50 RDTs per month. Facilities within each administrative district were stratified into four categories based on median values of patient volume (high/low) and average TPRs (high/low). From each of the four strata in each district, one health facility was randomly selected to participate in the study, resulting in 16 study facilities per site. Additionally, one control facility was selected from each stratum in each district; these control sites did not participate in study activities and were used to assess the potential impact of the study on TPR trends in a separate interrupted time series analysis. To ensure uninterrupted diagnostic and treatment services, all study and control facilities were supplied with adequate stocks of RDTs and antimalarial medicines throughout the study period. Overview of study design A summary of the study methodology, which is described in detail elsewhere [ 12 ], is provided below: Health facility survey At the beginning of the study, each health facility was surveyed through interviews and direct observation to assess operational capacity. The survey documented geolocation, staff numbers (especially those performing RDTs), availability of registers, guidelines, equipment, diagnostics, antimalarial medicines, and any recent stockouts. Infrastructure such as electricity and internet connectivity was also reviewed. Knowledge, attitudes, perceptions and behavior (KAPB) and RDT proficiency At baseline, a survey was administered to all HCWs in the evaluation health facilities who were currently involved (or likely to be involved) in malaria rapid diagnostic testing, interpretation of results, treatment decisions, or documentation of RDT outcomes. Informed consent was obtained from all participants. The survey gathered information on HCWs’ training and professional experience, knowledge of malaria transmission and case management, attitudes toward RDTs, perceptions of RDT accuracy, prescribing behaviors, and perceived norms related to malaria diagnosis, treatment, and surveillance practices. HCWs were observed while performing malaria RDTs and assessed using a standardized checklist. The evaluation focused on key areas including procedural steps, accuracy in test administration and interpretation, and adherence to biosafety protocols. Healthcare worker interpretations of RDT results Over the six-month study period, the accuracy of HCWs’ recording of malaria RDTs was evaluated by comparing results recorded in facility registers with those assessed by an independent panel of trained reviewers. The panel reviewed images of completed RDTs captured using the HealthPulse smartphone digital RDT reader application, developed by Audere (Seattle, WA USA). Data collectors used project-issued smartphones to capture RDT images, including the HCW’s unique ID, their recorded interpretation, and basic patient details such as age and sex. To accurately link images with corresponding register entries, barcodes were affixed to both the RDT cassette and the matching register line. The external panel was blinded to all patient and facility information. They classified each RDT image as positive or negative based solely on the presence or absence of control and test lines, following the manufacturer’s guidelines. Patient care was managed entirely by health facility staff based on their own RDT interpretations, in line with national case management protocols. Data collectors had no interaction with patients and did not influence clinical decision-making. In-depth interviews At the end of the study, in-depth interviews were conducted with a random selection of 16 HCWs demonstrating both high (8) and low (8) agreement with the reference panel. These aimed to explore key drivers and root causes of discrepancies in RDT performance, documentation, and adherence to diagnostic results. HCWs were asked open-ended questions exploring their experiences with malaria RDTs, including their understanding of procedures, challenges in interpreting results, and responses to cases where clinical symptoms contradict RDT outcomes. They also discussed how they manage inconclusive results, stockouts of RDTs and supplies, and high patient volumes. Finally, HCWs were asked about the influence of the monthly malaria RDT validation exercise in Benin on their diagnostic and reporting practices. Sample size The primary objective of the study was the degree of agreement between the RDT results reported in the health facility register by HCWs and the panel RDT results measured using Cohen’s kappa which corrects for chance agreement. The sample size was thus based on the precision for estimating Cohen’s kappa for an individual HCW [ 13 ]. A range of kappa scores between 0.7–0.9 was assumed and the number of RDTs needed for different levels of precision calculated. The probability of a positive rating was estimated by the TPR. Assuming TPRs between 30–70%, the maximum sample size needed to calculate 95% CI with a width of no more than 0.2 was 236 RDTs. It was estimated that individual HCWs would likely interpret between 40 and 80 RDTs each month, for a possible range of 320–480 observations per HCW over the course of the evaluation, which would be sufficient for a precise measurement of Cohen’s kappa at both the HCW and health facility level. The number of study facilities included was fixed at 16 due to budget limitations. Data management and analysis All questionnaires were digitized and deployed on smartphones using the Open Data Kit (ODK) platform. Electronic data were securely stored in password-protected database systems, with access limited to designated project staff. Data were exported from KoboToolbox and the HealthPulse application into centralized databases. Standardized scripts were developed in R (R Foundation for Statistical Computing, Vienna, Austria) to generate cleaned and analysable datasets. Statistical analyses were performed using R. The accuracy of HCWs’ interpretation of malaria RDTs was assessed by measuring agreement between the RDT results they recorded in health facility registers and those determined by the external panel. Agreement was quantified using Cohen’s kappa statistic, which accounts for agreement occurring by chance. Discrepancies, where HCWs recorded a result different from that reported by the panel, were classified as results that were either misrecorded as positive or negative (Table 1 ). Table 1 Illustration of cross-tabulation of HCW and external panel RDT results HCW RDT result Panel RDT result Positive Negative Invalid Positive A (Misrecorded as positive) B (Misrecorded as positive) E Negative (Misrecorded as negative) C D (Misrecorded as negative) F The agreement between HCW and panel RDT results was calculated by health facility and a random effects meta-analysis used to summarize results according to various factors at the health facility and patient level. A bespoke R function was developed to summarize Cohen’s kappa scores by calculating a great mean and weighting by the inverse of the standard error (SE) for each health facility [ 16 ]. Ethical Considerations The study received ethical approval from the Comité National d’Éthique pour la Recherche en Santé (CNERS) under the Ministry of Health in Benin (Approval No. CNERS019/2023), as well as from the Western Institutional Review Board (WCG IRB). Written informed consent was obtained from HWCs participating in the study. Patient consent was not required, as RDT images were anonymized and data extracted from health facility registers constituted secondary, non-identifiable information. RESULTS Study profile During the six-month study period, a total of 36,413 RDT images and data for 36,438 patients who underwent malaria RDT testing were captured using the HealthPulse app. Among these, 36,407 matching records were successfully identified (Fig. 2 ). There were 687 records excluded due to various issues: 17 did not meet image quality standards, 283 had indeterminate results as interpreted by the external panel, 386 were missing from the health facility register, and 1 had an invalid HCW identification code, resulting in 35,720 observations for analysis. Of the 408 HCWs present across the study facilities, 354 (86.8%) were eligible to participate in the KAPB survey. The remaining 54 HCWs were not involved in RDT administration. Among the eligible HCWs, 226 (63.8%) completed the KAPB survey, and 205 (57.9%) were observed performing an RDT using the standardized checklist. A total of 128 (36.2%) HCWs did not participate, primarily due to absence during the study period. By the end of the study, complete RDT observation data were available for 35,720 RDTs performed and interpreted by 182 (51.4%) HCWs. Characteristics of study health facilities Among the 16 health facilities included in the study, malaria prevalence data derived from malariaAtlas, (an R package developed by the Malaria Atlas Project to facilitate accessing their data[ 14 ]) ranged from 30–39% in 15 facilities, while one facility had a lower prevalence of 20–29%. Four facilities were equipped with laboratories; however, all 16 facilities recorded malaria RDT results exclusively in outpatient and antenatal clinic registers. Job aids for performing RDTs were available in 11 facilities, and malaria case management guidelines were present in 14. Most facilities (11 out of 16) had more than five HCWs who routinely performed malaria RDTs. There were no recent shortages of first- or second-line antimalarial medications in any of the facilities, although minor stockouts of antibiotics were observed. Only one facility reported a temporary shortage of RDTs at baseline. Throughout the study period, a total of only seven days of RDT stockouts were recorded across all sites. With regard to medical equipment, all facilities had thermometers and weighing scales, but only three had timers specifically for RDTs. Microscopes for malaria diagnosis were available exclusively in the four facilities with laboratory capacity. As for infrastructure, 13 facilities had internet access, 8 had piped water, and 2 lacked an electricity supply. Characteristics of healthcare workers The median number of HCWs who participated in the KAPB survey per health facility was 11.5 (IQR: 7.75–17.5). Among the 226 participating HCWs, 71.2% were women and approximately 70% were under the age of 40 (Table 2 ). Fewer than 8% were over 50 years old. The largest occupational group was medical auxiliary staff (42.9%), followed by nurses (19.9%) and students/interns/volunteers (18.1%). Only 8 medical doctors (3.5%) and 2 community health workers (0.9%) were included. Educational attainment was generally low: 64.2% had completed only primary education or less, while just 14.2% had reached university level. Nearly 40% had more than 10 years of experience, whereas 17.3% had one year or less. All HCWs who participated in the survey had experience performing RDTs and were also involved in related tasks. More than 80% were responsible for determining diagnoses and recording results in patient cards and registers. Regarding training in RDT procedures, only half of the HCWs reported having received offsite training, and just 18.6% of these had received such training within the past year. Similarly, 45% reported receiving onsite training, with 38% indicating that this occurred in the past year. A greater proportion (68%) stated that they had been observed by a supervisor while performing an RDT at some point, although only 35.8% reported that such supervision had taken place in the past year. Table 2 Characteristics of healthcare workers Characteristic Value Frequency Percentage Sex Female 161 71.2 Male 65 28.8 Age < 30 years 89 39.4 30–39 years 69 30.5 40–49 years 50 22.1 50–59 years 17 7.5 60 + years 1 0.4 Occupational category Community health worker 2 0.9 Medical auxiliary staff 97 42.9 Medical doctor 8 3.5 Midwife 31 13.7 Non-medical staff 2 0.9 Nurse 45 19.9 Student, intern or volunteer 41 18.1 Highest qualification achieved Primary school or below 145 64.2 Secondary school 49 21.7 University 32 14.2 Years of experience 0–1 years 39 17.3 2–4 years 57 25.2 5–9 years 42 18.6 10 + years 88 38.9 Hours worked in a week >=50 hours 181 80.1 0–19 hours 4 1.8 20–39 hours 5 2.2 40–49 hours 36 15.9 Tasks performed related to RDTs Performs RDTs 226 100.0 Performs microscopy 23 10.2 Determines diagnosis/treatment 204 90.3 Dispenses medicine 100 44.2 Writes results in patient cards 192 85.0 Writes information in registers 195 86.3 Completes monthly record forms 88 38.9 Frequency of performing RDTs Very often (every day) 195 86.3 Once in a while to often 22 9.7 Never 9 4.0 Frequency of recording RDT results Very often (every day) 180 79.6 Once in a while to often 16 7.1 Never 30 13.3 Knowledge, attitudes, perceptions and beliefs of healthcare workers The vast majority of HCWs (93.4%) believed that RDTs provide certainty in diagnosing malaria, and 63.3% agreed or strongly agreed that RDTs can accurately diagnose the disease (Table 3 ). Similarly, 64.2% considered RDTs the best available method for malaria diagnosis, while 14.2% disagreed with this view. Confidence in performing RDTs was high; 89.8% agreed or strongly agreed that they could perform the test correctly, and 91.6% found RDTs easy to use. Most HCWs (85.8%) felt they had enough time to conduct the test, and 82.3% indicated they had sufficient time to wait for the results. In terms of resources, 84.5% reported having sufficient RDT supplies, and 65.9% stated that reference materials for consultation were available. However, nearly one in five HCWs (19.5%) disagreed with the availability of such reference materials, suggesting room for improvement in supporting documentation and guidance. A large proportion of HCWs (89.4%) believed that a patient with a negative RDT could still have malaria (Table 3 ). However, only 19.5% believed that patients should still be treated with an antimalarial in such cases, while 79.2% disagreed with treating RDT-negative patients. Motivations for RDT use varied; 60.2% reported using RDTs because guidelines require it, while fewer HCWs cited external expectations as the main driver; only 25.2% agreed they used RDTs because supervisors expected it, and 23.5% said they used them because patients expected it. A majority (65–68.6%) disagreed that supervisor or patient expectations influenced their use of RDTs. Regarding the availability of alternative diagnostics and treatments, 47.3% agreed that other diagnostic tests for febrile illnesses were available at their facilities, while 41.6% disagreed. Most HCWs (73.9%) reported that medicines other than antimalarials were available for treating febrile illnesses. Table 3 Knowledge, attitudes and perceptions of healthcare workers Characteristic Response Frequency Percentage RDT provides certainty of malaria Yes 211 93.4 No 15 6.6 RDTs can accurately diagnose malaria Agree or strongly agree 143 63.3 Neutral 47 20.8 Disagree or strongly disagree 36 15.9 RDTs believed to be the best way to diagnose malaria Agree or strongly agree 145 64.2 Neutral 49 21.7 Disagree or strongly disagree 32 14.2 Able to perform malaria RDT correctly Agree or strongly agree 203 89.8 Neutral 20 8.8 Disagree or strongly disagree 3 1.3 RDTs considered easy to perform Agree or strongly agree 207 91.6 Neutral 18 8 Disagree or strongly disagree 1 0.4 Have sufficient time to perform RDTs Agree or strongly agree 194 85.8 Neutral 23 10.2 Disagree or strongly disagree 9 4 Have sufficient time to wait for RDT result Agree or strongly agree 186 82.3 Neutral 28 12.4 Disagree or strongly disagree 12 5.3 Sufficient supplies for RDT Agree or strongly agree 191 84.5 Neutral 27 11.9 Disagree or strongly disagree 8 3.5 Reference material for consultation available Agree or strongly agree 149 65.9 Neutral 33 14.6 Disagree or strongly disagree 44 19.5 Is it possible for a patient to have a negative RDT test when they actually have a malaria infection? Yes 202 89.4 No 19 8.4 I don’t know 5 2.2 Do you think you should treat a patient with an antimalarial even if their RDT returns a negative result? Yes 44 19.5 No 179 79.2 I don’t know 3 1.3 Are you worried that a test is incorrect if the patient is febrile with a negative RDT? Agree or strongly agree 70 31 Neutral 47 20.8 Disagree or strongly disagree 109 48.2 I use RDTs because the patients expect it Agree or strongly agree 53 23.5 Neutral 18 8 Disagree or strongly disagree 155 68.6 I use RDTs because supervisors expect it Agree or strongly agree 57 25.2 Neutral 22 9.7 Disagree or strongly disagree 147 65 I use RDTs because guidelines require it Agree or strongly agree 136 60.2 Neutral 29 12.8 Disagree or strongly disagree 61 27 Other diagnostic tests for febrile illnesses available Agree or strongly agree 107 47.3 Neutral 25 11.1 Disagree or strongly disagree 94 41.6 Medicines besides antimalarials available to treat febrile illness Agree or strongly agree 167 73.9 Neutral 28 12.4 Disagree or strongly disagree 31 13.7 Healthcare workers’ proficiency in performing RDTs Overall, HCWs demonstrated high proficiency in several key procedural, safety, and accuracy-related steps during the observed administration of RDTs (Table 4 ). All HCWs (100%) successfully opened the test package and collected an adequate amount of blood. Nearly all (99.5%) correctly pricked the finger with a sterile lancet and dispensed the blood in the appropriate well. A similar proportion (99.0%) properly cleaned the finger with alcohol and allowed it to dry beforehand. For accuracy, 97.6% of HCWs correctly interpreted the test results, and 95.6% accurately identified the control line. Most (90.2%) dispensed the correct amount of buffer, but only 40.5% waited the appropriate amount of time before reading the result, highlighting a key area for improvement. Checking the expiry date of the RDT, another critical accuracy step, was performed by just 40.5% of HCWs. In terms of procedural adherence, 93.7% wrote the patient identifier on the test, 73.7% disposed of waste appropriately, and 73.2% assembled all necessary materials before beginning. Only 61.0% avoided excessive squeezing of the finger, and just over half (52.2%) selected the correct finger for blood collection. Less than half (43.9%) explained the procedure to the patient. Safety practices showed mixed results. While nearly all HCWs (99.5%) pricked the finger with a sterile lancet and 75.6% discarded it in a sharps bin, only 15.6% wore gloves. Similarly, 74.1% correctly discarded the pipette in a sharps container. These findings suggest strong overall performance in blood collection and test execution but point to weaknesses in timing, safety practices (especially glove use), and communication with patients that should be addressed through targeted training and supervision. Table 4 Proficiency of HCWs in performing an observed RDT Proficiency of HCWs in performing an observed RDT Frequency Percentage Assembles all materials (procedural) 150 73.2 Checks expiry date (accuracy) 83 40.5 Wears gloves (safety) 32 15.6 Opens the package and removes contents (procedural) 205 100.0 Writes the patients identifier on the RDT (procedural) 192 93.7 Explains the procedure to the patient (procedural) 90 43.9 Selects the correct finger for blood collection (procedural) 107 52.2 Cleans the finger with alcohol and allows to dry (safety) 203 99.0 Pricks finger firmly with sterile lancet (safety) 204 99.5 Discards lancet in sharps bin (safety) 155 75.6 Does not squeeze finger excessively (procedural) 125 61.0 Collects and adequate amount of blood (accuracy) 205 100.0 Dispenses blood in the appropriate well (accuracy) 204 99.5 Discards the pipette in the sharps bin (safety) 152 74.1 Dispenses the correct amount of buffer (accuracy) 185 90.2 Disposes of waste in appropriate container (procedural) 151 73.7 Waits for the appropriate time after adding buffer to read the result (accuracy) 83 40.5 Interprets the test correctly (accuracy) 200 97.6 Identifies the control line correctly (accuracy) 196 95.6 Characteristics of RDTs observed in the study Overall, the study team successfully captured 82% of all RDTs reported by the participating facilities. Of the 35,720 RDTs captured during the study period, 54.5% were recorded as positive and 45.5% as negative in the health facility registers (Table 5 ). The largest number of RDTs was performed in July (22.4%), aligning with the peak malaria transmission season. RDT testing remained relatively consistent from August through November, with each month accounting for approximately 15–19% of the total. Bioline-pf was the predominant RDT brand used, representing 99.4% of all tests. Very small proportions of First-response (0.01%) and Parahit (0.6%) RDTs were also used. Most patients tested were female (56.9%), and 40.3% were over the age of 15. Children under 5 years accounted for 33.9% of those tested, while 25.9% were aged 5–14 years. Malaria was diagnosed in 62.9% of patients tested. Over half (54.1%) of patients received ACTs, and 39.2% were prescribed antibiotics. Table 5 Characteristics of RDTs observed in the study Characteristic Value Frequency Percentage RDT result recorded in the health facility register Negative 16,240 45.5 Positive 19,480 54.5 Month RDT was performed June* 3,108 8.7 July 8,002 22.4 August 5,578 15.6 September 5,653 15.8 October 6,865 19.2 November 6,514 18.2 RDT Brand Bioline-pf 35,504 99.4 First-response 3 0.01 Parahit 213 0.6 Patient sex Female 20,216 56.9 Male 15,297 43.1 Patient age 15 years 14,379 40.3 Patient diagnosis Malaria 22,486 63.0 Antimalarial medicine prescribed ACT 19,341 54.1 Antibiotic medicine prescribed Antibiotics 13,995 39.2 * Data from June represented only the latter half of the month (8.7%) due to the study start. Agreement between results interpreted and recorded by healthcare workers and results interpreted by an external panel Overall trends in agreement: Overall, there was a high level of agreement (94.3%) between RDT interpretations recorded by HCWs and those of the external reference panel. Only 0.7% of positive results were misrecorded as negative, while 5.0% of negative results were misrecorded as positive (Table 6 ). Results misrecorded as positive were more common than those misrecorded as negative, a pattern that was consistent across different health facility and patient characteristics. The overall Cohen’s Kappa score was 0.88, indicating strong agreement between HCW and reference interpretations (Fig. 3 ). At the facility level, 13 out of 16 health facilities achieved a kappa score above 0.8, reflecting strong agreement, while three facilities had lower scores (< 0.8). In the Borgou Department, 6 of the 8 facilities achieved near-perfect agreement, as did 7 of the 8 facilities in the Zou Department. Table 6 Overall agreement of HCWs RDT results with external panel result (n = 35,20) Health Care Workers’ result External Panel Result (Reference) Positive Negative Invalid Positive Positive agreement 17,675 (49.5%) Misrecorded as positive 1797 (5.0%) Misrecorded as positive 8 (0.0%) Negative Misrecorded as negative 238 (0.7%) Negative agreement 15,996 (44.8%) Misrecorded as negative 6 (0.0%) Agreement by health facility characteristics: Although overall agreement was high, the disaggregated analysis based on health facility characteristics revealed important variation by region, health zone, operational stratum, staffing, and infrastructure (Table 7 ). Regionally, HCWs in Zou achieved higher agreement (95.5%, Kappa: 0.90) than those in Borgou (93.7%, Kappa: 0.86). RDT results misrecorded as positives were lower in Zou (3.6%) than Borgou (5.8%), though Zou had slightly more results misrecorded as negatives (0.9% vs. 0.5%). Across health zones, Bohicon-Za-Kpota-Zogbodomey recorded the highest agreement (96.4%), while Nikki-Kalalé-Pèrèrè had the lowest (92.6%) and the highest rate of results miscrecorded as positive (6.7%). Djidja-Abomey-Agbangnizoun recorded the highest rate of results misrecorded as negative (1.4%). Performance also varied across operational strata. Facilities with high volume but low TPRs showed the best results (96.8% agreement, Kappa: 0.93), while high volume/high positivity facilities had lower agreement (91.1%) and the highest rate of results misrecorded as positive (8.2%), suggesting that workload and disease burden can affect accuracy. By malaria prevalence, the single health facility with 20–29% prevalence showed the highest agreement (97.2%) and the lowest rates of misrecorded positives (2.4%) and misrecorded negatives (0.4%) while facilities with 30–39% prevalence had slightly lower agreement (93.9%) and higher misrecorded positives (5.4%). Staffing levels also influenced performance. Facilities with one or two HCWs had the highest agreement (96.7%) and the lowest rate of results misrecorded as positive (2.7%). Facilities with three to four HCWs showed reduced agreement (92.6%) and higher misrecorded positives (6.7%), while those with larger teams had moderate agreement (94.6%). Infrastructure access had a clear impact. Facilities with internet access had higher agreement (95.0%) and fewer misrecorded positives (4.3%) than those without (91.3%, 8.1%). Facilities with grid electricity also performed better (95.2% agreement, 4.0% misrecorded positives) compared to those relying on solar (92.3%, 7.3%) or no electricity (92.6%, 7.1%). These findings show that, while HCW performance in interpreting RDTs was generally high, diagnostic errors were more frequent in high-volume, high-burden, and infrastructure-limited settings. Table 7 Agreement of HCWs and external panel RDT results by health facility characteristics Characteristic Characteristic N Agreement (%) Kappa Score Misrecorded as Positive (n, %) Misrecorded as Negative (n, %) Overall Overall 35720 94.3 0.88 1805 (5.1%) 244 (0.7%) Region Borgou 23898 93.7 0.86 1383 (5.8%) 134 (0.6%) Zou 11822 95.5 0.90 422 (3.6%) 110 (0.9%) Health Zone Bembèrèkè-Sinendé 16477 94.1 0.88 883 (5.4%) 88 (0.5%) Bohicon-Za-Kpota-Zogbodomey 7564 96.4 0.90 223 (2.9%) 50 (0.7%) Djidja-Abomey-Agbangnizoun 4258 93.9 0.88 199 (4.7%) 60 (1.4%) Nikki-Kalalé-Pèrèrè 7421 92.6 0.84 500 (6.7%) 46 (0.6%) Stratum High volume/High positivity 10906 91.1 0.83 894 (8.2%) 76 (0.7%) High volume/Low positivity 9812 96.8 0.93 236 (2.4%) 86 (0.9%) Low volume/High positivity 7179 94.9 0.86 311 (4.3%) 53 (0.7%) Low volume/Low positivity 7823 95.0 0.90 364 (4.7%) 29 (0.4%) Parasite prevalence 20–29% 4407 97.2 0.91 104 (2.4%) 19 (0.4%) 30–39% 31313 93.8 0.87 1701 (5.4%) 225 (0.7%) Number of staff performing RDTS 1–2 staff 817 96.7 0.93 22 (2.7%) 5 (0.6%) 3–4 staff 5400 92.5 0.84 351 (6.7%) 43 (0.8%) 5 + staff 29503 94.5 0.89 1422 (4.8%) 196 (0.7%) Internet access Internet: No 6662 91.3 0.80 545 (8.2%) 37 (0.6%) Internet: Yes 29058 95.0 0.90 1260 (4.3%) 207 (0.7%) Main electricity source Power: Grid 24030 95.2 0.85 958 (4.0%) 194 (0.8%) Power: None 3027 92.6 0.84 215 (7.1%) 9 (0.3%) Power: Solar 8663 92.2 0.86 632 (7.3%) 41 (0.5%) Agreement by patient and HCW characteristics: Agreement between HCW-interpreted malaria RDT results and those of the reference panel, also varied across patient and HCW characteristics, occupational categories, and training levels (Table 8 ). Among patient characteristics, agreement was similar between females (94.1%) and males (94.6%), with slightly fewer misrecorded positives among males. RDT interpretation accuracy varied by patient age, with the highest agreement in children under five (96.7%) and the lowest in adults aged 15 and above (91.6%).. Among HCWs, females demonstrated slightly better performance than males (95.0% vs. 93.5% agreement), with fewer results misrecorded as positive. HCW performance also improved with age. Those aged 50–59 achieved the highest agreement (97.6%) and lowest misrecorded positives (1.5%). HCWs aged 30–39 had the lowest agreement (94.0%) and more misrecorded positives (5.5%). Among occupational groups, nurses and doctors showed high agreement (95.4% and 94.5%), while midwives and student/intern/volunteers performed less well, with student/interns having the highest misrecorded positive (9.6%) and misrecorded negative (1.7%) rates. Educational level made a small difference. HCWs with university education had slightly better performance than those with secondary school education. Agreement also increased with experience. HCWs with 10 or more years of experience had the highest agreement (96.1%) and lowest misrecorded positive rate (3.2%). Those with less than 5 years of experience showed more diagnostic errors. Workload influenced accuracy; HCWs working over 50 hours per week had slightly lower agreement (94%) and more misrecorded positives (5.4%) compared to those with lighter workloads. Training was beneficial; those who received onsite RDT training showed better agreement (94.7%) and fewer misrecorded positives (4.7%). However, recent onsite training (2022–2023) had almost no impact suggesting the importance of ongoing supervision and support. Table 8 Agreement of HCW and external panel RDT results by patient and HCWs’ characteristics Characteristic Variable N Positives Negatives Agreement (%) Misrecorded Pos N (%) Misrecorded Neg N (%) Patient Sex female 20216 9507 10703 19019 (94.1%) 1057 (5.2%) 140 (0.7%) male 15297 8299 6990 14457 (94.6%) 737 (4.8%) 103 (0.7%) Patient Age < 5 12099 6419 5673 11691 (96.6%) 343 (2.8%) 65 (0.5%) 5–14 9241 6815 2424 8814 (95.4%) 393 (4.3%) 34 (0.4%) 15+ 14379 4678 9696 13165 (91.6%) 1069 (7.4%) 145 (1.0%) HCW Sex Female 19654 9298 10352 18660 (94.9%) 854 (4.3%) 140 (0.7%) male 16066 8615 7441 15011 (93.4%) 951 (5.9%) 104 (0.6%) HCW Age < 30 12953 6482 11885 (91.8) 973 (7.5%) 95 (0.7%) 30–39 9647 5258 4385 9062 (93.9%) 532 (5.5%) 53 (0.5%) 40–49 9880 4574 5302 9563 (96.8%) 253 (2.6%) 64 (0.6%) 50–59 3240 1599 1641 3161 (97.6%) 47 (1.5%) 32 (1%) Occupational category Community health worker 31 16 15 29 (93.5%) 2 (6.5%) 0 (0.0%) Medical auxiliary staff 15843 7850 7987 14973 (94.5%) 787 (5%) 83 (0.5%) Medical doctor 1738 1025 712 1641 (94.4%) 87 (5%) 10 (0.6%) Midwife 2062 823 1237 1890 (91.7%) 156 (7.6%) 16 (0.8%) Nurse 13627 7052 6571 12993 (95.3%) 540 (4%) 94 (0.7%) Student/intern/volunteer 2419 1147 1271 2145 (88.7%) 233 (9.6%) 41 (1.7%) HCW qualification Primary school or below 24995 12294 12691 23590 (94.4%) 1227 (4.9%) 178 (0.7%) Secondary school 7282 3718 3562 6829 (93.8%) 402 (5.5%) 51 (0.7%) University 3443 1901 1540 3252 (94.5%) 176 (5.1%) 15 (0.4%) Years of experience 0–1 years 2218 1100 1117 2011 (90.7%) 189 (8.5%) 18 (0.8%) 2–4 years 11930 6098 5825 11053 (92.6%) 799 (6.7%) 78 (0.7%) 5–9 years 6238 3266 2972 5872 (94.1%) 329 (5.3%) 37 (0.6%) 10 + years 15334 7449 7879 14735 (96.1%) 488 (3.2%) 111 (0.7%) Average hours per week 0–19 hours 1057 592 465 1006 (95.2%) 48 (4.5%) 3 (0.3%) 20–39 hours 452 233 219 429 (94.9%) 19 (4.2%) 4 (0.9%) 40–49 hours 5325 2906 2414 5092 (95.6%) 183 (3.4%) 50 (0.9%) > 50 hours 28886 14182 14695 27144 (94.0%) 1555 (5.4%) 187 (0.6%) Received onsite training in RDTs No 15726 7952 7766 14751 (93.8%) 858 (5.5%) 117 (0.7%) Yes 19994 9961 10027 18920 (94.6%) 947 (4.7%) 127 (0.6%) Received onsite training in RDTs within past year No 20523 10294 10221 19377 (94.4%) 996 (4.9%) 150 (0.7%) Yes, 2022 or 2023 15197 7619 7572 14294 (94.1%) 809 (5.3%) 94 (0.6%) Agreement by HCWs’ perceptions and beliefs: Agreement between HCW recorded RDT results and external panel readings varied according to HCWs’ knowledge, beliefs, and perceptions related to malaria diagnosis and RDT use (Table 9 ). HCWs who demonstrated correct knowledge such as knowing that the control line is essential for a valid test and that RDTs must be repeated if the control line is absent, had higher agreement rates (95.8% and 94.5%, respectively) and lower misrecorded positive results compared to those lacking this knowledge. Beliefs also influenced performance. HCWs who believed it is acceptable to give antimalarials despite a negative RDT had substantially lower agreement (90.3%) and higher rates of misrecorded positives (8.6%) and negatives (1.2%) than those who did not hold this belief. Higher agreement was observed among HCWs who reported using RDTs because supervisors expect it or clinical guidelines require it (94.9% and 94.8%, respectively), compared to those who disagreed with these statements (93.9% and 93.3%, respectively). Agreement was also higher among HCWs with access to alternative treatments for febrile illnesses (96.3%) and those with stronger RDT performance scores, which increased agreement from 93.3% among the lowest-scoring group to 95.7% among those scoring 16–19. These findings suggest that HCW knowledge and diagnostic attitudes, including trust in RDT validity, supervision and adherence to protocols, are important drivers of accurate test interpretation. Table 9 Agreement of HCWs and external panel RDT results by patient and HCWs’ perceptions and beliefs Characteristic Variable N Positives Negatives Agreement (%) Misrecorded Pos N (%) Misrecorded Neg N (%) Knows that the control line is required for an RDT test to be valid no 25726 12907 12819 24107 (93.7%) 1438 (5.6) 181 (0.7%) Yes 9984 5006 4974 9564 (95.8%) 362 (3.6%) 58 (0.6%) Knows an RDT test must be repeated if no control line is present no 5669 2865 2804 5268 (92.9%) 361 (6.4%) 40 (0.7%) yes 30048 15048 14989 28403 (94.5%) 1443 (4.8%) 202 (0.7%) Believes a patient with malaria can have a negative RDT no 2794 1489 1305 2610 (93.4%) 165 (5.9%) 19 (0.7%) yes 32249 16063 16173 30425 (94.3%) 1601 (5%) 223 (0.7%) Could provide antimalarial to a patient even if the RDT is negative no 30032 15176 14856 28549 (95.1%) 1308 (4.4%) 175 (0.6%) yes 5567 2697 2866 5025 (90.3%) 477 (8.6%) 65 (1.2%) Would be worried that a test is incorrect if patient is febrile but the RDT is negative Disagree or strongly disagree 18574 9624 8950 17303 (93.2%) 1150 (6.2%) 121 (0.7%) Agree or strongly agree 5579 2646 2931 5215 (93.5%) 327 (5.9%) 37 (0.7%) Uses RDTs because the patients expect it Disagree or strongly disagree 25175 12903 12272 23759 (94.4%) 1241 (4.9%) 175 (0.7%) Agree or strongly agree 8588 4148 4437 8091 (94.2%) 447 (5.2%) 50 (0.6%) Uses RDTs because the supervisors expect it Disagree or strongly disagree 23814 12155 11659 22351 (93.9%) 1285 (5.4%) 178 (0.7%) Agree or strongly agree 8929 4260 4665 8468 (94.9%) 413 (4.6%) 48 (0.5%) Uses RDTs because the guidelines require it Disagree or strongly disagree 4932 2446 2486 4601 (93.3%) 267 (5.4%) 64 (1.3%) Agree or strongly agree 25997 13077 12911 24533 (94.8%) 1311 (5%) 153 (0.6%) Other diagnostic tests for febrile illnesses are available Disagree or strongly disagree 14627 7301 7326 13735 (93.9%) 780 (5.3%) 112 (0.8%) Agree or strongly agree 16865 8570 8285 15909 (94.4%) 842 (5%) 114 (0.7%) Has access to medicines besides antimalarials available to treat febrile illness Disagree or strongly disagree 4441 1943 2498 4275 (96.3%) 133 (3%) 33 (0.7%) Agree or strongly agree 25996 13254 12730 24415 (93.9%) 1418 (5.5%) 163 (0.6%) RDT performance score 0–12 4438 1835 2601 4140 (93.3%) 253 (5.7%) 45 (1%) 13–15 18895 10029 8858 17674 (93.5%) 1096 (5.8%) 117 (0.6%) 16–19 12366 6035 6327 11839 (95.7%) 447 (3.6%) 76 (0.6%) Findings from health care worker in-depth interviews Characteristics of HCWs participating in in-depth interviews A total of 16 health care workers (HCWs) participated in the in-depth interviews, evenly split between high and low RDT agreement groups. Most were nursing assistants (11), had less than 15 years of experience (11), and were primarily involved in support roles. Fewer were directly engaged in malaria diagnosis (7) or treatment (6), with clinical responsibilities slightly more common among high agreement HCWs. Nearly all had received training in malaria services (14) and RDT use (13).. Use and Interpretation of RDTs Most HCWs demonstrated a clear understanding of the value of malaria RDTs and could accurately describe the procedures involved. While many found RDT interpretation straightforward, some reported challenges—particularly in reading faint results or distinguishing positive from negative outcomes. Both high and low agreement HCWs cited poor lighting, especially at night, as a barrier to accurate interpretation. Concerns were expressed when RDT results were negative despite clinical symptoms suggestive of malaria. In such cases, HCWs often repeated the test or referred the patient for microscopy, acknowledging that treatment without confirmation went against national guidelines. Some low agreement HCWs reported instances where microscopy confirmed malaria despite an initial negative RDT, leading to doubts about RDT reliability, particularly in cases of low parasitaemia. “Sometimes we struggle to read the result after 15 minutes... We return later and see it’s positive.” — High agreement nursing assistant, Tchikandou “At night, we struggle to read the test correctly as the lights are too dim..….” — High agreement nursing assistant, Tchikandou “The RDT is effective, but sometimes it gives a negative result, and the thick blood smear is positive.” — High agreement nursing assistant, Ouassaho Stockouts of RDTs and Supplies Although RDT and ACT stockouts were uncommon in the study sites, glove shortages were frequently reported. In the event of RDT stockouts, HCWs contacted nearby facilities to borrow supplies or resorted to microscopy or presumptive treatment. Responses to glove shortages varied; some HCWs asked patients to purchase gloves, while others proceeded without them, taking precautions to avoid direct contact with blood. This behavior did not differ significantly between HCW groups. “When we run out of RDTs, we borrow or treat symptomatically while awaiting confirmation...” — High agreement nursing assistant, Ouassaho “Sometimes patients are asked to buy gloves. If not, we manage without touching the blood…” — Low agreement nursing assistant, Bohicon Patient Volume and Workflow High patient loads, especially during the rainy season, were cited as a challenge. Nonetheless, most HCWs said they continued testing all suspected cases and prioritized urgent patients. No major differences in RDT interpretation were observed between high and low agreement HCWs during busy periods. “We strive to perform all RDTs, regardless of patient numbers...” — Low agreement nursing assistant, Bembèrèkè “Severe cases are managed first, which can delay RDTs.” — High agreement nursing assistant, Ouassaho Impact of Monthly Validation Exercises Most HCWs were aware of the monthly RDT validation exercises and participated in sorting used RDTs for review. The initiative was seen as supportive rather than burdensome. While high agreement HCWs reported that the exercise did not change their practices, lower agreement HCWs described it as helpful in aligning their work with national guidelines; suggesting it may be an important tool for quality improvement and data reliability. “It motivates us to work more thoroughly.” — Low agreement nursing assistant, Bohicon “The validation hasn’t changed how I record results—I’ve always followed the guidelines.” — High agreement nursing assistant, Agbokpa DISCUSSION This study provides one of the most comprehensive assessments to date of the accuracy of recording malaria RDT results in public health facilities in Benin. Across over 35,000 RDTs evaluated, there was a high level of agreement (94.3%) between interpretations recorded by HCWs and an external reference panel, with a Cohen’s Kappa score of 0.88 indicating strong reliability. These results highlight the overall accuracy of HCWs' interpretation and recording of RDT results under routine programmatic conditions and offer reassurance about the reliability of malaria data within the national HMIS. This should however be interpreted in the context of the ongoing nationwide decentralized monthly validation of malaria RDT results, introduced six months before the study, which likely contributed to improved HCW diagnostic practices by increasing scrutiny and accountability. The monthly validation involves a physical review and counting of positive and negative RDT cassettes recorded in health facility registers before the data are integrated into the HMIS [ 8 , 15 ]. An interrupted time series analysis to investigate the impact of this strategy on malaria surveillance outcomes is underway. Despite high overall agreement, we observed important variation in RDT interpretation accuracy. RDT results misrecorded as positives were significantly more common (5.0%) than those misrecorded as negative (0.7%), indicating a tendency among HCWs to deliberately override negative test results and treat presumptively. This finding is consistent with responses from the KAPB survey, where 89% of HCWs believed that a patient infected with malaria could still test negative on an RDT. This perception, commonly reported in other settings such as Kenya, Uganda, and Nigeria [ 10 , 16 – 19 ], may erode HCWs’ confidence in negative results, leading to unnecessary antimalarial prescriptions and potential overuse of ACTs. HCWs’ non-adherence to negative RDT results has been extensively studied and is attributed to their awareness of key limitations of RDTs such as the risk of missing low-density infections and failure to detect non-falciparum species as well as the lack of alternative diagnostic tools or treatments, which may prompt precautionary antimalarial prescriptions for RDT-negative patients [ 10 , 20 , 21 ]. Training and supervision programmes designed to reinforce trust in negative results and build confidence in following test-based treatment protocols may be particularly impactful. Interestingly, contrary to expectations that children under five, who are most vulnerable to malaria, might be more likely to be over-diagnosed, we found the highest accuracy and lowest error rates in this age group. Patients over 15 years of age had the lowest agreement and the highest rate of results misrecorded as positive (7.4%). This contradicts trends seen in other studies where caution may lead to overdiagnosis in young children [ 22 ]. One possible explanation is that adults are more likely to pressure HCWs into prescribing antimalarials despite negative results; a hypothesis also supported by qualitative interviews and findings from other contexts [ 9 , 10 , 23 ]. Variation in RDT recording accuracy was also observed across facility and HCW characteristics. Facilities with internet access and grid electricity had better performance than those lacking infrastructure, highlighting the role of basic facility readiness. High-volume, high-TPR settings were associated with lower agreement and higher error rates, suggesting that both workload and caseload intensity can compromise diagnostic accuracy. Among HCWs, performance improved with age and years of experience, with older HCWs and those with more than 10 years of experience achieving the highest agreement. Student interns and volunteers showed the poorest performance, reinforcing the importance of training and supervision for newer or less experienced cadres. The impact of HCW beliefs and motivations was also evident. HCWs who admitted they might treat despite a negative RDT result demonstrated the highest rate of results misrecorded as positive, suggesting that personal beliefs can undermine test-based treatment. Conversely, those who cited national guidelines or supervisory expectations as the reason for using RDTs tended to perform better. This suggests that accountability mechanisms and performance expectations may support improved practice. While the study provides valuable insights, it was limited to peripheral public health facilities. These were selected because they serve the largest share of rural populations, where malaria burden is typically highest, and contribute the majority of routine HMIS data. They also benefit from stronger stock control and supervision systems compared to private providers. However, private health facilities and pharmacies also perform malaria diagnosis and contribute to national data. Given their more limited support from the Ministry of Health, diagnostic practices in private settings may be more variable. Future studies are recommended to assess RDT interpretation and reporting in private sector contexts to inform a broader national quality improvement strategy. Conclusion This study found high accuracy in the interpretation and recording of malaria rapid diagnostic test (RDT) results by HCWs in public health facilities in Benin. Performance was strongest among HCWs with more experience, recent training, and access to supportive infrastructure. Nonetheless, efforts are needed to address persistent misrecorded positives, particularly among adult patients, and to reinforce HCWs’ confidence in reporting negative RDTs. Abbreviations ACT(s) Artemisinin-based Combination Therapy AI Artificial Intelligence CI Confidence Interval CNERS Comité National d’Éthique pour la Recherche en Santé HCW Health Care Worker HCW(s) Health Care Worker(s) / Healthcare Worker(s) HMIS Health Management Information System KAPB Knowledge, Attitude, Perception, and Behavior / Knowledge, Attitudes, Perceptions, and Behavior / Knowledge, Attitudes, Practices, and Beliefs MaCRA Malaria RDT Capturing and Reporting Assessment NMCP National Malaria Control Programme (Benin) ODK Open Data Kit OPD Outpatient Department Pe Probability of expected (chance) agreement PMC Perennial Malaria Chemoprevention Po Probability of observed agreement RDT Rapid Diagnostic Test SMC Seasonal Malaria Chemoprevention SSA Sub-Saharan Africa TPR True Positive Rate TPR(s) Test Positivity Rate(s) WHO World Health Organization WCG IRB Western Institutional Review Board WMR World Malaria Report κ (kappa) Cohen’s kappa coefficient Declarations ACKNOWLEDGEMENTS The authors gratefully acknowledge the many individuals and institutions who contributed to the successful implementation of the MaCRA study in Benin. We extend our sincere thanks to Imelda Glele (Centre de Recherche Entomologique de Cotonou, CREC) and Saadjo Sow (PMIInsights/PATH) for their exceptional administrative and logistical support. We are also thankful to Megan Littrell, Kim Vu, and Taj Munson for their leadership and coordination of the PMI Insights project. Sasha Frade and Sam Smedinghoff (Audere, Johannesburg, South Africa) played a key role in developing and refining the HealthPulse application and in designing the data dashboards used during the study. We further acknowledge the contributions of Audere’s AI data creation and labeling teams at the Centre for HIV-AIDS Prevention Studies (South Africa) and Indivillage (India). We are especially grateful to the PMI/USAID Benin team, notably Virgile Gnanguenon and Raoul Olokoi, for their insights and ongoing support. Finally, we thank the Directors of Health for the Departments of Borgou and Zou, the Medical Coordinators of the participating Health Zones, the heads of the health facilities, and all the healthcare workers whose dedication and participation made this study possible. FUNDING This study was co-funded by PMI Insights and the Bill & Melinda Gates Foundation. PMI Insights was the global operational research and program evaluation project of the U.S. President’s Malaria Initiative (PMI). Funding for this study is made possible by the generous support of the American people through the United States Agency for International Development (USAID). The contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Contributions KL, MH, and KG conceptualised and designed the evaluation, with contributions from CN, IA, and AK. CN led the study implementation and supervised data collection with support from IA, HA, MA and CA. SC led development of the HealthPulse application used for capturing RDT images. Data analysis was performed by JH, EH, NG, and KL, with input from CN and IA. CA, AK, and JA contributed to site selection and facilitated coordination with the Ministry of Health and study health facilities. CN and IA drafted the manuscript. All authors reviewed the manuscript and approved the final version for submission. Corresponding author Correspondence to [email protected] Ethical approval and consent to participate The study received ethical approval from the Comité National d’Éthique pour la Recherche en Santé (CNERS) under the Ministry of Health in Benin (Approval No. CNERS019/2023), as well as from the Western Institutional Review Board (WCG IRB). Written informed consent was obtained from HWCs participating in the study. Patient consent was not required, as RDT images were anonymized and data extracted from health facility registers constituted secondary, non-identifiable information. Permission to conduct the study in the selected health facilities was obtained from the Ministry of Health. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study can be provided by the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. References WHO: World Malaria report. World Health Organisation, Geneva 2023. WHO: Guidelines for malaria vector control. Geneva, Switzerland: World Health Organization 2025. Zinsou C, Cherifath AB: The malaria testing and treatment landscape in Benin. Malar J 2017, 16: 174. PMI: President’s malaria initiative Benin malaria operational plan. 2024. PMI website. https://www.pmi.gov/where-we-work/benin . . Accessed 4 January 2025 . Aidoo M, Incardona S: Ten Years of Universal Testing: How the Rapid Diagnostic Test Became a Game Changer for Malaria Case Management and Improved Disease Reporting. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7002558","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482372192,"identity":"dea53d4e-4e56-40cd-84c0-e0bf48648dae","order_by":0,"name":"Idelphonse Ahogni","email":"","orcid":"","institution":"Centre de Recherche Entomologique de Cotonou, CREC","correspondingAuthor":false,"prefix":"","firstName":"Idelphonse","middleName":"","lastName":"Ahogni","suffix":""},{"id":482372193,"identity":"5e1480a5-7ec2-4a1c-8215-fc5721e7f932","order_by":1,"name":"Hospice Avanon","email":"","orcid":"","institution":"Centre de Recherche Entomologique de Cotonou, CREC","correspondingAuthor":false,"prefix":"","firstName":"Hospice","middleName":"","lastName":"Avanon","suffix":""},{"id":482372194,"identity":"81b2d6fa-15b0-4ecb-8c69-149b35a96c3b","order_by":2,"name":"Corneille Hueha","email":"","orcid":"","institution":"Centre de Recherche Entomologique de Cotonou, CREC","correspondingAuthor":false,"prefix":"","firstName":"Corneille","middleName":"","lastName":"Hueha","suffix":""},{"id":482372195,"identity":"c5203732-5972-4c4c-9c79-4866db7b76d9","order_by":3,"name":"Augustin Kpemasse","email":"","orcid":"","institution":"Programme National de Lutte contre le Paludisme (PNLP)","correspondingAuthor":false,"prefix":"","firstName":"Augustin","middleName":"","lastName":"Kpemasse","suffix":""},{"id":482372196,"identity":"2b976cfa-b8cc-44a7-b119-56e916f44e7d","order_by":4,"name":"Julien Aissan","email":"","orcid":"","institution":"Programme National de Lutte contre le Paludisme (PNLP)","correspondingAuthor":false,"prefix":"","firstName":"Julien","middleName":"","lastName":"Aissan","suffix":""},{"id":482372197,"identity":"1605c14a-7ed1-48ee-8814-549f9f7ce22b","order_by":5,"name":"Cyriaque Affoukou","email":"","orcid":"","institution":"Programme National de Lutte contre le Paludisme (PNLP)","correspondingAuthor":false,"prefix":"","firstName":"Cyriaque","middleName":"","lastName":"Affoukou","suffix":""},{"id":482372198,"identity":"a02fbe14-24f1-4276-b0c2-739d7593d8dc","order_by":6,"name":"Manfred Accrombessi","email":"","orcid":"","institution":"Centre de Recherche Entomologique de Cotonou, CREC","correspondingAuthor":false,"prefix":"","firstName":"Manfred","middleName":"","lastName":"Accrombessi","suffix":""},{"id":482372201,"identity":"888331f6-b3a8-4bb9-9398-b6acfeb67f65","order_by":7,"name":"John J. Aponte","email":"","orcid":"","institution":"PMI Insights Project/PATH","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"J.","lastName":"Aponte","suffix":""},{"id":482372203,"identity":"95043837-b1ea-4dc7-a192-fe7753e4a7d0","order_by":8,"name":"Emily Hilton","email":"","orcid":"","institution":"PMI Insights Project/PATH","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"Hilton","suffix":""},{"id":482372204,"identity":"b4b722fd-993c-494d-bf4f-f4fa92451030","order_by":9,"name":"Shawna Cooper","email":"","orcid":"","institution":"Audere","correspondingAuthor":false,"prefix":"","firstName":"Shawna","middleName":"","lastName":"Cooper","suffix":""},{"id":482372208,"identity":"812574bd-a34d-45d9-85df-b07b84c8b66d","order_by":10,"name":"Kevin Griffith","email":"","orcid":"","institution":"United States President’s Malaria Initiative","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Griffith","suffix":""},{"id":482372209,"identity":"7affccf8-9d2d-4c21-84b7-14763ba28afe","order_by":11,"name":"Michael Humes","email":"","orcid":"","institution":"United States President’s Malaria Initiative","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Humes","suffix":""},{"id":482372210,"identity":"6abbfa6c-a3a0-41e8-a682-444c0adb3dbd","order_by":12,"name":"Kim A. Lindblade","email":"","orcid":"","institution":"PMI Insights Project/PATH","correspondingAuthor":false,"prefix":"","firstName":"Kim","middleName":"A.","lastName":"Lindblade","suffix":""},{"id":482372211,"identity":"92327e83-486b-4559-a509-a02ffe7e1ba5","order_by":13,"name":"Corine Ngufor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYJCCAxCK8QEDQ4UFgwFMWIKwFmag4jMSDAZsRGhhgGthbCNCC39778PDBRUMefIRyYyPK+dJ2JvLdycw/KhhSJzZgF2LxJnjBodnnGEoNryRzGx4dptE4s423g2MPccYEmfjsMVAIo3hMG8bQ+LGGfnHJBu3SSQYHOPdwMDbwJA4D6+WfyAtyWySjXMk7EFaGP8S1AJUMF8CpKVBgnEDUAszSASXwyTOHGM4zHNMInEDz2NmwwYQ41juhsMyxySMcXmfv72N+TNPjU3i/PZkxocNNTb2BofPbnz4psZGdsYBHNZALWMwQFZwgKiIlMfhjFEwCkbBKBgFDAB3cFdDGKjDYgAAAABJRU5ErkJggg==","orcid":"","institution":"Centre de Recherche Entomologique de Cotonou, CREC","correspondingAuthor":true,"prefix":"","firstName":"Corine","middleName":"","lastName":"Ngufor","suffix":""}],"badges":[],"createdAt":"2025-06-29 12:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7002558/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7002558/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12936-026-05871-7","type":"published","date":"2026-03-28T16:10:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86656949,"identity":"b1744689-9bd5-46e7-8dfe-e4757b963dd8","added_by":"auto","created_at":"2025-07-14 10:21:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":240682,"visible":true,"origin":"","legend":"\u003cp\u003eMap of study sites and health facilities involved in study\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7002558/v1/9a4d907b9d14a02528d876cb.png"},{"id":86655467,"identity":"bddb844a-c146-46e0-bb4f-529f9d958189","added_by":"auto","created_at":"2025-07-14 10:13:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61857,"visible":true,"origin":"","legend":"\u003cp\u003eStudy profile\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7002558/v1/714c91b2fc2234474e66d603.png"},{"id":86655465,"identity":"6ab69a58-da62-4a5c-abf2-9ba3bddd633c","added_by":"auto","created_at":"2025-07-14 10:13:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51150,"visible":true,"origin":"","legend":"\u003cp\u003eA forest plot of Cohen's kappa estimates by health facility and the overall estimate from the random-effects (RE) model\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7002558/v1/72fe993ae0959447db290b3b.png"},{"id":105755971,"identity":"5ef3c08e-69c7-4b1c-ac1a-9bb3562175fc","added_by":"auto","created_at":"2026-03-30 16:33:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3677002,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7002558/v1/4e35e1de-e999-48b8-83ca-211d0b68bdbe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The accuracy of recording malaria rapid diagnostic test (RDT) results in public health facilities in Benin; results from the MaCRA project","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eMalaria remains a leading cause of mortality among children under five and a major contributor to morbidity in adults in Benin. In 2022 alone, an estimated 5\u0026nbsp;million malaria cases and over 11,000 related deaths were reported [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. To address this burden, Benin\u0026rsquo;s national malaria policy promotes early diagnosis and prompt treatment at all levels of the healthcare system in line with World Health Organization (WHO) guidance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This includes the use of rapid diagnostic tests (RDTs) or microscopy for confirming all suspected cases of malaria prior to treatment. Patients who test positive are expected to receive a full course of WHO-recommended antimalarial treatment. Conversely, those who test negative should not be prescribed antimalarials but should undergo thorough clinical evaluation to identify alternative causes of fever.\u003c/p\u003e\u003cp\u003eMalaria diagnosis in Benin relies heavily on RDTs, which account for over 90% of all confirmed malaria cases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. RDTs are simple, easy-to-use immunochromatographic devices that detect parasite-specific antigens or enzymes in the blood, targeting either the \u003cem\u003ePlasmodium\u003c/em\u003e genus or specific species [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. They are accessible across all levels of the health system in Benin and are especially critical in peripheral health facilities where access to microscopy may be limited. Since their introduction in 2008, RDTs have been provided free of charge within the public health sector, contributing significantly to improved diagnostic coverage and case management across the country [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Patients presenting at public health facilities with a body temperature of 37.5\u0026deg;C or higher, or with a reported history of fever within the previous 48 hours, are routinely tested for malaria using RDTs. Following administration of the RDT, patient information (including clinical presentation, diagnostic test results, and treatment provided) is recorded in the health facility registers and later entered into the national Health Management Information System (HMIS).\u003c/p\u003e\u003cp\u003eGiven the reliance on RDTs for diagnosis, effective malaria surveillance in Benin therefore hinges on the proper management of RDT results. Although RDTs generally demonstrate high diagnostic performance, the quality of malaria surveillance data in Benin remains susceptible to challenges associated with their routine implementation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Key issues include healthcare workers\u0026rsquo; (HCWs) ability to correctly administer and interpret test results, adherence to national treatment guidelines based on RDT outcomes, and the accurate recording of diagnostic and treatment information in patient registers and national HMIS reporting systems. These data form the backbone of the HMIS informing public health decision-making, guiding malaria control strategies, and supporting the efficient allocation of resources.\u003c/p\u003e\u003cp\u003eEnsuring accurate recording of RDT results is essential for tracking malaria trends and supporting timely, evidence-based policy responses at both national and sub-national levels. However, surveys conducted by the National Malaria Control Programme (NMCP) of Benin in selected public health facilities in 2019 revealed significant issues with HCWs\u0026rsquo; adherence to and recording of negative malaria RDT results [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In many instances, HCWs recorded patients with negative test outcomes as malaria-positive and treated them with artemisinin-based combination therapies (ACTs), opting to rely on presumptive diagnosis rather than test outcomes. In response, the Ministry of Health in 2023 launched a decentralized programme to validate malaria data on a monthly basis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This initiative involves cross-checking data using primary sources from health facilities, including patient registers and used RDT cassettes, before the information is entered into the HMIS. These issues are not unique to Benin and have been documented in several countries across sub-Saharan Africa [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. They compromise the accuracy of malaria surveillance data and can lead to inappropriate clinical management, including the unnecessary use of antimalarial drugs in patients who do not have malaria.\u003c/p\u003e\u003cp\u003eTo better understand and address these challenges, we conducted a mixed-methods, prospective observational study in public health facilities in Benin as part of the broader multi-country Malaria RDT Capture and Reporting Assessment (MaCRA project) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The study aimed to evaluate the accuracy of RDT result reporting by comparing outcomes recorded in health facility registers with those independently verified by an external panel of trained reviewers. Over a six-month period, RDT results captured in routine facility registers were compared to results interpreted by the external panel reviewing images of the RDTs, which had been taken using a smartphone application (HealthPulse, Audere, Seattle, WA USA). The study also sought to identify key factors contributing to reporting discrepancies within the Beninese context. Here, we present a descriptive analysis of the results to highlight key trends, patterns, and variations observed in HCWs\u0026rsquo; accuracy of recording malaria RDT results across health facility, patient and HCW characteristics in Benin.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy sites in Benin\u003c/h2\u003e\u003cp\u003eThe study was conducted between June and December 2023 in four Health Zones across two Departments in Benin; one located in the north, where malaria transmission is seasonal, and one in the south, where transmission is perennial. In collaboration with national stakeholders and the National Malaria Control Programme (NMCP), the Health Zones of Bemb\u0026egrave;r\u0026egrave;k\u0026egrave;-Sinend\u0026eacute; and Nikki-Kalal\u0026eacute;-P\u0026egrave;r\u0026egrave;r\u0026egrave; in the Borgou Department were selected to represent the northern site. For the southern site, the Health Zones of Bohicon-Za-Kpota-Zogbodomey and Djidja-Abomey-Agbangnizoun in the Zou Department were chosen (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSelection of the study sites was based on several criteria; the absence of major ongoing malaria control interventions (such as seasonal malaria chemoprevention [SMC] and perennial malaria chemoprevention [PMC]) that could confound study outcomes; the presence of a sufficient number of public health facilities; patient volume; a consistently high RDT test positivity rate (TPR) over recent years; availability of routine malaria data for at least nine months per year over the past three years; and logistical and financial feasibility, including site accessibility. The selected Health Zones exhibited persistently high RDT TPRs with minimal variation between 2019 and 2021, further supporting their suitability for inclusion in the study.\u003c/p\u003e\u003cp\u003eA total of 32 public health facilities (16 in the northern site and 16 in the southern site) were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Eligible health facilities were those with at least two to three years of malaria reporting to the national HMIS, with data available for at least nine out of twelve months per year and a minimum of 50 RDTs per month. Facilities within each administrative district were stratified into four categories based on median values of patient volume (high/low) and average TPRs (high/low). From each of the four strata in each district, one health facility was randomly selected to participate in the study, resulting in 16 study facilities per site. Additionally, one control facility was selected from each stratum in each district; these control sites did not participate in study activities and were used to assess the potential impact of the study on TPR trends in a separate interrupted time series analysis. To ensure uninterrupted diagnostic and treatment services, all study and control facilities were supplied with adequate stocks of RDTs and antimalarial medicines throughout the study period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOverview of study design\u003c/h3\u003e\n\u003cp\u003eA summary of the study methodology, which is described in detail elsewhere [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], is provided below:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHealth facility survey\u003c/strong\u003e\u003cp\u003eAt the beginning of the study, each health facility was surveyed through interviews and direct observation to assess operational capacity. The survey documented geolocation, staff numbers (especially those performing RDTs), availability of registers, guidelines, equipment, diagnostics, antimalarial medicines, and any recent stockouts. Infrastructure such as electricity and internet connectivity was also reviewed.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eKnowledge, attitudes, perceptions and behavior (KAPB) and RDT proficiency\u003c/strong\u003e\u003cp\u003eAt baseline, a survey was administered to all HCWs in the evaluation health facilities who were currently involved (or likely to be involved) in malaria rapid diagnostic testing, interpretation of results, treatment decisions, or documentation of RDT outcomes. Informed consent was obtained from all participants. The survey gathered information on HCWs\u0026rsquo; training and professional experience, knowledge of malaria transmission and case management, attitudes toward RDTs, perceptions of RDT accuracy, prescribing behaviors, and perceived norms related to malaria diagnosis, treatment, and surveillance practices. HCWs were observed while performing malaria RDTs and assessed using a standardized checklist. The evaluation focused on key areas including procedural steps, accuracy in test administration and interpretation, and adherence to biosafety protocols.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHealthcare worker interpretations of RDT results\u003c/strong\u003e\u003cp\u003eOver the six-month study period, the accuracy of HCWs\u0026rsquo; recording of malaria RDTs was evaluated by comparing results recorded in facility registers with those assessed by an independent panel of trained reviewers. The panel reviewed images of completed RDTs captured using the HealthPulse smartphone digital RDT reader application, developed by Audere (Seattle, WA USA). Data collectors used project-issued smartphones to capture RDT images, including the HCW\u0026rsquo;s unique ID, their recorded interpretation, and basic patient details such as age and sex. To accurately link images with corresponding register entries, barcodes were affixed to both the RDT cassette and the matching register line. The external panel was blinded to all patient and facility information. They classified each RDT image as positive or negative based solely on the presence or absence of control and test lines, following the manufacturer\u0026rsquo;s guidelines. Patient care was managed entirely by health facility staff based on their own RDT interpretations, in line with national case management protocols. Data collectors had no interaction with patients and did not influence clinical decision-making.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eIn-depth interviews\u003c/strong\u003e\u003cp\u003eAt the end of the study, in-depth interviews were conducted with a random selection of 16 HCWs demonstrating both high (8) and low (8) agreement with the reference panel. These aimed to explore key drivers and root causes of discrepancies in RDT performance, documentation, and adherence to diagnostic results. HCWs were asked open-ended questions exploring their experiences with malaria RDTs, including their understanding of procedures, challenges in interpreting results, and responses to cases where clinical symptoms contradict RDT outcomes. They also discussed how they manage inconclusive results, stockouts of RDTs and supplies, and high patient volumes. Finally, HCWs were asked about the influence of the monthly malaria RDT validation exercise in Benin on their diagnostic and reporting practices.\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe primary objective of the study was the degree of agreement between the RDT results reported in the health facility register by HCWs and the panel RDT results measured using Cohen\u0026rsquo;s kappa which corrects for chance agreement. The sample size was thus based on the precision for estimating Cohen\u0026rsquo;s kappa for an individual HCW [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A range of kappa scores between 0.7\u0026ndash;0.9 was assumed and the number of RDTs needed for different levels of precision calculated. The probability of a positive rating was estimated by the TPR. Assuming TPRs between 30\u0026ndash;70%, the maximum sample size needed to calculate 95% CI with a width of no more than 0.2 was 236 RDTs. It was estimated that individual HCWs would likely interpret between 40 and 80 RDTs each month, for a possible range of 320\u0026ndash;480 observations per HCW over the course of the evaluation, which would be sufficient for a precise measurement of Cohen\u0026rsquo;s kappa at both the HCW and health facility level. The number of study facilities included was fixed at 16 due to budget limitations.\u003c/p\u003e\n\u003ch3\u003eData management and analysis\u003c/h3\u003e\n\u003cp\u003eAll questionnaires were digitized and deployed on smartphones using the Open Data Kit (ODK) platform. Electronic data were securely stored in password-protected database systems, with access limited to designated project staff. Data were exported from KoboToolbox and the HealthPulse application into centralized databases. Standardized scripts were developed in R (R Foundation for Statistical Computing, Vienna, Austria) to generate cleaned and analysable datasets. Statistical analyses were performed using R. The accuracy of HCWs\u0026rsquo; interpretation of malaria RDTs was assessed by measuring agreement between the RDT results they recorded in health facility registers and those determined by the external panel. Agreement was quantified using Cohen\u0026rsquo;s kappa statistic, which accounts for agreement occurring by chance. Discrepancies, where HCWs recorded a result different from that reported by the panel, were classified as results that were either misrecorded as positive or negative (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eIllustration of cross-tabulation of HCW and external panel RDT results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHCW RDT result\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePanel RDT result\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInvalid\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Misrecorded as positive) B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Misrecorded as positive) E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Misrecorded as negative) C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(Misrecorded as negative) F\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 The agreement between HCW and panel RDT results was calculated by health facility and a random effects meta-analysis used to summarize results according to various factors at the health facility and patient level. A bespoke R function was developed to summarize Cohen\u0026rsquo;s kappa scores by calculating a great mean and weighting by the inverse of the standard error (SE) for each health facility [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The study received ethical approval from the Comit\u0026eacute; National d\u0026rsquo;\u0026Eacute;thique pour la Recherche en Sant\u0026eacute; (CNERS) under the Ministry of Health in Benin (Approval No. CNERS019/2023), as well as from the Western Institutional Review Board (WCG IRB). Written informed consent was obtained from HWCs participating in the study. Patient consent was not required, as RDT images were anonymized and data extracted from health facility registers constituted secondary, non-identifiable information.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStudy profile\u003c/h2\u003e\u003cp\u003eDuring the six-month study period, a total of 36,413 RDT images and data for 36,438 patients who underwent malaria RDT testing were captured using the HealthPulse app. Among these, 36,407 matching records were successfully identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There were 687 records excluded due to various issues: 17 did not meet image quality standards, 283 had indeterminate results as interpreted by the external panel, 386 were missing from the health facility register, and 1 had an invalid HCW identification code, resulting in 35,720 observations for analysis.\u003c/p\u003e\u003cp\u003eOf the 408 HCWs present across the study facilities, 354 (86.8%) were eligible to participate in the KAPB survey. The remaining 54 HCWs were not involved in RDT administration. Among the eligible HCWs, 226 (63.8%) completed the KAPB survey, and 205 (57.9%) were observed performing an RDT using the standardized checklist. A total of 128 (36.2%) HCWs did not participate, primarily due to absence during the study period. By the end of the study, complete RDT observation data were available for 35,720 RDTs performed and interpreted by 182 (51.4%) HCWs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCharacteristics of study health facilities\u003c/h3\u003e\n\u003cp\u003eAmong the 16 health facilities included in the study, malaria prevalence data derived from malariaAtlas, (an R package developed by the Malaria Atlas Project to facilitate accessing their data[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]) ranged from 30\u0026ndash;39% in 15 facilities, while one facility had a lower prevalence of 20\u0026ndash;29%. Four facilities were equipped with laboratories; however, all 16 facilities recorded malaria RDT results exclusively in outpatient and antenatal clinic registers. Job aids for performing RDTs were available in 11 facilities, and malaria case management guidelines were present in 14. Most facilities (11 out of 16) had more than five HCWs who routinely performed malaria RDTs. There were no recent shortages of first- or second-line antimalarial medications in any of the facilities, although minor stockouts of antibiotics were observed. Only one facility reported a temporary shortage of RDTs at baseline. Throughout the study period, a total of only seven days of RDT stockouts were recorded across all sites.\u003c/p\u003e\u003cp\u003eWith regard to medical equipment, all facilities had thermometers and weighing scales, but only three had timers specifically for RDTs. Microscopes for malaria diagnosis were available exclusively in the four facilities with laboratory capacity. As for infrastructure, 13 facilities had internet access, 8 had piped water, and 2 lacked an electricity supply.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of healthcare workers\u003c/h2\u003e\u003cp\u003eThe median number of HCWs who participated in the KAPB survey per health facility was 11.5 (IQR: 7.75\u0026ndash;17.5). Among the 226 participating HCWs, 71.2% were women and approximately 70% were under the age of 40 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Fewer than 8% were over 50 years old. The largest occupational group was medical auxiliary staff (42.9%), followed by nurses (19.9%) and students/interns/volunteers (18.1%). Only 8 medical doctors (3.5%) and 2 community health workers (0.9%) were included. Educational attainment was generally low: 64.2% had completed only primary education or less, while just 14.2% had reached university level. Nearly 40% had more than 10 years of experience, whereas 17.3% had one year or less. All HCWs who participated in the survey had experience performing RDTs and were also involved in related tasks. More than 80% were responsible for determining diagnoses and recording results in patient cards and registers.\u003c/p\u003e\u003cp\u003eRegarding training in RDT procedures, only half of the HCWs reported having received offsite training, and just 18.6% of these had received such training within the past year. Similarly, 45% reported receiving onsite training, with 38% indicating that this occurred in the past year. A greater proportion (68%) stated that they had been observed by a supervisor while performing an RDT at some point, although only 35.8% reported that such supervision had taken place in the past year.\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\u003eCharacteristics of healthcare workers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u0026ndash;59 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eOccupational category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommunity health worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical auxiliary staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical doctor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMidwife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-medical staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudent, intern or volunteer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eHighest qualification achieved\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eYears of experience\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;1 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026ndash;4 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026ndash;9 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eHours worked in a week\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;=50 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;19 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;39 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;49 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eTasks performed related to RDTs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePerforms RDTs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePerforms microscopy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDetermines diagnosis/treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDispenses medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWrites results in patient cards\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWrites information in registers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCompletes monthly record forms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eFrequency of performing RDTs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery often (every day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOnce in a while to often\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eFrequency of recording RDT results\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery often (every day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOnce in a while to often\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.3\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=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eKnowledge, attitudes, perceptions and beliefs of healthcare workers\u003c/h2\u003e\u003cp\u003eThe vast majority of HCWs (93.4%) believed that RDTs provide certainty in diagnosing malaria, and 63.3% agreed or strongly agreed that RDTs can accurately diagnose the disease (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Similarly, 64.2% considered RDTs the best available method for malaria diagnosis, while 14.2% disagreed with this view. Confidence in performing RDTs was high; 89.8% agreed or strongly agreed that they could perform the test correctly, and 91.6% found RDTs easy to use. Most HCWs (85.8%) felt they had enough time to conduct the test, and 82.3% indicated they had sufficient time to wait for the results. In terms of resources, 84.5% reported having sufficient RDT supplies, and 65.9% stated that reference materials for consultation were available. However, nearly one in five HCWs (19.5%) disagreed with the availability of such reference materials, suggesting room for improvement in supporting documentation and guidance.\u003c/p\u003e\u003cp\u003eA large proportion of HCWs (89.4%) believed that a patient with a negative RDT could still have malaria (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, only 19.5% believed that patients should still be treated with an antimalarial in such cases, while 79.2% disagreed with treating RDT-negative patients. Motivations for RDT use varied; 60.2% reported using RDTs because guidelines require it, while fewer HCWs cited external expectations as the main driver; only 25.2% agreed they used RDTs because supervisors expected it, and 23.5% said they used them because patients expected it. A majority (65\u0026ndash;68.6%) disagreed that supervisor or patient expectations influenced their use of RDTs. Regarding the availability of alternative diagnostics and treatments, 47.3% agreed that other diagnostic tests for febrile illnesses were available at their facilities, while 41.6% disagreed. Most HCWs (73.9%) reported that medicines other than antimalarials were available for treating febrile illnesses.\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\u003eKnowledge, attitudes and perceptions of healthcare workers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRDT provides certainty of malaria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRDTs can accurately diagnose malaria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRDTs believed to be the best way to diagnose malaria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAble to perform malaria RDT correctly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRDTs considered easy to perform\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHave sufficient time to perform RDTs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHave sufficient time to wait for RDT result\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSufficient supplies for RDT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eReference material for consultation available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIs it possible for a patient to have a negative RDT test when they actually have a malaria infection?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDo you think you should treat a patient with an antimalarial even if their RDT returns a negative result?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI don\u0026rsquo;t know\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAre you worried that a test is incorrect if the patient is febrile with a negative RDT?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eI use RDTs because the patients expect it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eI use RDTs because supervisors expect it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eI use RDTs because guidelines require it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eOther diagnostic tests for febrile illnesses available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMedicines besides antimalarials available to treat febrile illness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.7\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=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eHealthcare workers\u0026rsquo; proficiency in performing RDTs\u003c/h2\u003e\u003cp\u003eOverall, HCWs demonstrated high proficiency in several key procedural, safety, and accuracy-related steps during the observed administration of RDTs (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All HCWs (100%) successfully opened the test package and collected an adequate amount of blood. Nearly all (99.5%) correctly pricked the finger with a sterile lancet and dispensed the blood in the appropriate well. A similar proportion (99.0%) properly cleaned the finger with alcohol and allowed it to dry beforehand.\u003c/p\u003e\u003cp\u003eFor accuracy, 97.6% of HCWs correctly interpreted the test results, and 95.6% accurately identified the control line. Most (90.2%) dispensed the correct amount of buffer, but only 40.5% waited the appropriate amount of time before reading the result, highlighting a key area for improvement. Checking the expiry date of the RDT, another critical accuracy step, was performed by just 40.5% of HCWs. In terms of procedural adherence, 93.7% wrote the patient identifier on the test, 73.7% disposed of waste appropriately, and 73.2% assembled all necessary materials before beginning. Only 61.0% avoided excessive squeezing of the finger, and just over half (52.2%) selected the correct finger for blood collection. Less than half (43.9%) explained the procedure to the patient. Safety practices showed mixed results. While nearly all HCWs (99.5%) pricked the finger with a sterile lancet and 75.6% discarded it in a sharps bin, only 15.6% wore gloves. Similarly, 74.1% correctly discarded the pipette in a sharps container.\u003c/p\u003e\u003cp\u003eThese findings suggest strong overall performance in blood collection and test execution but point to weaknesses in timing, safety practices (especially glove use), and communication with patients that should be addressed through targeted training and supervision.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eProficiency of HCWs in performing an observed RDT\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProficiency of HCWs in performing an observed RDT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssembles all materials (procedural)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChecks expiry date (accuracy)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWears gloves (safety)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpens the package and removes contents (procedural)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWrites the patients identifier on the RDT (procedural)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplains the procedure to the patient (procedural)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelects the correct finger for blood collection (procedural)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCleans the finger with alcohol and allows to dry (safety)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePricks finger firmly with sterile lancet (safety)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscards lancet in sharps bin (safety)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDoes not squeeze finger excessively (procedural)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollects and adequate amount of blood (accuracy)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDispenses blood in the appropriate well (accuracy)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscards the pipette in the sharps bin (safety)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDispenses the correct amount of buffer (accuracy)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisposes of waste in appropriate container (procedural)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaits for the appropriate time after adding buffer to read the result (accuracy)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInterprets the test correctly (accuracy)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIdentifies the control line correctly (accuracy)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.6\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=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of RDTs observed in the study\u003c/h2\u003e\u003cp\u003eOverall, the study team successfully captured 82% of all RDTs reported by the participating facilities. Of the 35,720 RDTs captured during the study period, 54.5% were recorded as positive and 45.5% as negative in the health facility registers (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The largest number of RDTs was performed in July (22.4%), aligning with the peak malaria transmission season. RDT testing remained relatively consistent from August through November, with each month accounting for approximately 15\u0026ndash;19% of the total. Bioline-pf was the predominant RDT brand used, representing 99.4% of all tests. Very small proportions of First-response (0.01%) and Parahit (0.6%) RDTs were also used. Most patients tested were female (56.9%), and 40.3% were over the age of 15. Children under 5 years accounted for 33.9% of those tested, while 25.9% were aged 5\u0026ndash;14 years. Malaria was diagnosed in 62.9% of patients tested. Over half (54.1%) of patients received ACTs, and 39.2% were prescribed antibiotics.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of RDTs observed in the study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eRDT result recorded in the health facility register\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16,240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19,480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonth RDT was performed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJune*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJuly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAugust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeptember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOctober\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNovember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6,514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRDT Brand\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBioline-pf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35,504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst-response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParahit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatient sex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20,216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15,297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatient age\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12,099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5_14 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9,241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;15 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14,379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatient diagnosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMalaria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22,486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAntimalarial medicine prescribed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eACT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19,341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAntibiotic medicine prescribed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAntibiotics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13,995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.2\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\u003cem\u003e* Data from June represented only the latter half of the month (8.7%) due to the study start.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAgreement between results interpreted and recorded by healthcare workers and results interpreted by an external panel\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eOverall trends in agreement:\u003c/h2\u003e\u003cp\u003eOverall, there was a high level of agreement (94.3%) between RDT interpretations recorded by HCWs and those of the external reference panel. Only 0.7% of positive results were misrecorded as negative, while 5.0% of negative results were misrecorded as positive (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Results misrecorded as positive were more common than those misrecorded as negative, a pattern that was consistent across different health facility and patient characteristics. The overall Cohen\u0026rsquo;s Kappa score was 0.88, indicating strong agreement between HCW and reference interpretations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At the facility level, 13 out of 16 health facilities achieved a kappa score above 0.8, reflecting strong agreement, while three facilities had lower scores (\u0026lt;\u0026thinsp;0.8). In the Borgou Department, 6 of the 8 facilities achieved near-perfect agreement, as did 7 of the 8 facilities in the Zou Department.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverall agreement of HCWs RDT results with external panel result (n\u0026thinsp;=\u0026thinsp;35,20)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHealth Care Workers\u0026rsquo; result\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eExternal Panel Result (Reference)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInvalid\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive agreement\u003c/p\u003e\u003cp\u003e17,675 (49.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMisrecorded as positive\u003c/p\u003e\u003cp\u003e1797 (5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMisrecorded as positive\u003c/p\u003e\u003cp\u003e8 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMisrecorded as negative\u003c/p\u003e\u003cp\u003e238 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNegative agreement\u003c/p\u003e\u003cp\u003e15,996 (44.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMisrecorded as negative\u003c/p\u003e\u003cp\u003e6 (0.0%)\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\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eAgreement by health facility characteristics:\u003c/h2\u003e\u003cp\u003eAlthough overall agreement was high, the disaggregated analysis based on health facility characteristics revealed important variation by region, health zone, operational stratum, staffing, and infrastructure (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegionally, HCWs in Zou achieved higher agreement (95.5%, Kappa: 0.90) than those in Borgou (93.7%, Kappa: 0.86). RDT results misrecorded as positives were lower in Zou (3.6%) than Borgou (5.8%), though Zou had slightly more results misrecorded as negatives (0.9% vs. 0.5%). Across health zones, Bohicon-Za-Kpota-Zogbodomey recorded the highest agreement (96.4%), while Nikki-Kalal\u0026eacute;-P\u0026egrave;r\u0026egrave;r\u0026egrave; had the lowest (92.6%) and the highest rate of results miscrecorded as positive (6.7%). Djidja-Abomey-Agbangnizoun recorded the highest rate of results misrecorded as negative (1.4%).\u003c/p\u003e\u003cp\u003ePerformance also varied across operational strata. Facilities with high volume but low TPRs showed the best results (96.8% agreement, Kappa: 0.93), while high volume/high positivity facilities had lower agreement (91.1%) and the highest rate of results misrecorded as positive (8.2%), suggesting that workload and disease burden can affect accuracy. By malaria prevalence, the single health facility with 20\u0026ndash;29% prevalence showed the highest agreement (97.2%) and the lowest rates of misrecorded positives (2.4%) and misrecorded negatives (0.4%) while facilities with 30\u0026ndash;39% prevalence had slightly lower agreement (93.9%) and higher misrecorded positives (5.4%).\u003c/p\u003e\u003cp\u003eStaffing levels also influenced performance. Facilities with one or two HCWs had the highest agreement (96.7%) and the lowest rate of results misrecorded as positive (2.7%). Facilities with three to four HCWs showed reduced agreement (92.6%) and higher misrecorded positives (6.7%), while those with larger teams had moderate agreement (94.6%). Infrastructure access had a clear impact. Facilities with internet access had higher agreement (95.0%) and fewer misrecorded positives (4.3%) than those without (91.3%, 8.1%). Facilities with grid electricity also performed better (95.2% agreement, 4.0% misrecorded positives) compared to those relying on solar (92.3%, 7.3%) or no electricity (92.6%, 7.1%).\u003c/p\u003e\u003cp\u003eThese findings show that, while HCW performance in interpreting RDTs was generally high, diagnostic errors were more frequent in high-volume, high-burden, and infrastructure-limited settings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAgreement of HCWs and external panel RDT results by health facility characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgreement (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKappa Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMisrecorded as Positive\u003c/p\u003e\u003cp\u003e(n, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMisrecorded as Negative\u003c/p\u003e\u003cp\u003e(n, %)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1805 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e244 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRegion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBorgou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1383 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e134 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZou\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e422 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e110 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHealth Zone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBemb\u0026egrave;r\u0026egrave;k\u0026egrave;-Sinend\u0026eacute;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e883 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e88 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBohicon-Za-Kpota-Zogbodomey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e223 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e50 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDjidja-Abomey-Agbangnizoun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e199 (4.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e60 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNikki-Kalal\u0026eacute;-P\u0026egrave;r\u0026egrave;r\u0026egrave;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e500 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e46 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eStratum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh volume/High positivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e894 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e76 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh volume/Low positivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e236 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e86 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow volume/High positivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e311 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e53 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow volume/Low positivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e364 (4.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e29 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParasite prevalence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;29%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e104 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u0026ndash;39%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1701 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e225 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eNumber of staff performing RDTS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;2 staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22 (2.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;4 staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e351 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e43 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026thinsp;+\u0026thinsp;staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1422 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e196 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eInternet access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet: No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e545 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e37 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternet: Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1260 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e207 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMain electricity source\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePower: Grid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e958 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e194 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePower: None\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e215 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePower: Solar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e632 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41 (0.5%)\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=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eAgreement by patient and HCW characteristics:\u003c/h2\u003e\u003cp\u003eAgreement between HCW-interpreted malaria RDT results and those of the reference panel, also varied across patient and HCW characteristics, occupational categories, and training levels (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Among patient characteristics, agreement was similar between females (94.1%) and males (94.6%), with slightly fewer misrecorded positives among males. RDT interpretation accuracy varied by patient age, with the highest agreement in children under five (96.7%) and the lowest in adults aged 15 and above (91.6%)..\u003c/p\u003e\u003cp\u003eAmong HCWs, females demonstrated slightly better performance than males (95.0% vs. 93.5% agreement), with fewer results misrecorded as positive. HCW performance also improved with age. Those aged 50\u0026ndash;59 achieved the highest agreement (97.6%) and lowest misrecorded positives (1.5%). HCWs aged 30\u0026ndash;39 had the lowest agreement (94.0%) and more misrecorded positives (5.5%). Among occupational groups, nurses and doctors showed high agreement (95.4% and 94.5%), while midwives and student/intern/volunteers performed less well, with student/interns having the highest misrecorded positive (9.6%) and misrecorded negative (1.7%) rates. Educational level made a small difference. HCWs with university education had slightly better performance than those with secondary school education. Agreement also increased with experience. HCWs with 10 or more years of experience had the highest agreement (96.1%) and lowest misrecorded positive rate (3.2%). Those with less than 5 years of experience showed more diagnostic errors.\u003c/p\u003e\u003cp\u003eWorkload influenced accuracy; HCWs working over 50 hours per week had slightly lower agreement (94%) and more misrecorded positives (5.4%) compared to those with lighter workloads. Training was beneficial; those who received onsite RDT training showed better agreement (94.7%) and fewer misrecorded positives (4.7%). However, recent onsite training (2022\u0026ndash;2023) had almost no impact suggesting the importance of ongoing supervision and support.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAgreement of HCW and external panel RDT results by patient and HCWs\u0026rsquo; characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositives\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNegatives\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAgreement (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMisrecorded Pos\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMisrecorded Neg\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePatient Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19019 (94.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1057 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e140 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14457 (94.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e737 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e103 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePatient Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11691 (96.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e343 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e65 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026ndash;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8814 (95.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e393 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13165 (91.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1069 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e145 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHCW Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18660 (94.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e854 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e140 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8615\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15011 (93.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e951 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e104 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eHCW Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11885 (91.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e973 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9062 (93.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e532 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e53 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9563 (96.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e253 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e64 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3161 (97.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32 (1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eOccupational category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCommunity health worker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29 (93.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical auxiliary staff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14973 (94.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e787 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical doctor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1641 (94.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e87 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMidwife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1890 (91.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e156 (7.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNurse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12993 (95.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e540 (4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudent/intern/volunteer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2145 (88.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e233 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e41 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHCW qualification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23590 (94.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1227 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e178 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6829 (93.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e402 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e51 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUniversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3252 (94.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e176 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eYears of experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;1 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2011 (90.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e189 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026ndash;4 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11053 (92.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e799 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e78 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026ndash;9 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5872 (94.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e329 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14735 (96.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e488 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e111 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eAverage hours per week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;19 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1006 (95.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e48 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;39 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e429 (94.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19 (4.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;49 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5092 (95.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e183 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e50 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;50 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27144 (94.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1555 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e187 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eReceived onsite training in RDTs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14751 (93.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e858 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e117 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18920 (94.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e947 (4.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e127 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eReceived onsite training in RDTs within past year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19377 (94.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e996 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e150 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes, 2022 or 2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14294 (94.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e809 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94 (0.6%)\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=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eAgreement by HCWs\u0026rsquo; perceptions and beliefs:\u003c/h2\u003e\u003cp\u003eAgreement between HCW recorded RDT results and external panel readings varied according to HCWs\u0026rsquo; knowledge, beliefs, and perceptions related to malaria diagnosis and RDT use (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). HCWs who demonstrated correct knowledge such as knowing that the control line is essential for a valid test and that RDTs must be repeated if the control line is absent, had higher agreement rates (95.8% and 94.5%, respectively) and lower misrecorded positive results compared to those lacking this knowledge.\u003c/p\u003e\u003cp\u003eBeliefs also influenced performance. HCWs who believed it is acceptable to give antimalarials despite a negative RDT had substantially lower agreement (90.3%) and higher rates of misrecorded positives (8.6%) and negatives (1.2%) than those who did not hold this belief. Higher agreement was observed among HCWs who reported using RDTs because supervisors expect it or clinical guidelines require it (94.9% and 94.8%, respectively), compared to those who disagreed with these statements (93.9% and 93.3%, respectively). Agreement was also higher among HCWs with access to alternative treatments for febrile illnesses (96.3%) and those with stronger RDT performance scores, which increased agreement from 93.3% among the lowest-scoring group to 95.7% among those scoring 16\u0026ndash;19.\u003c/p\u003e\u003cp\u003eThese findings suggest that HCW knowledge and diagnostic attitudes, including trust in RDT validity, supervision and adherence to protocols, are important drivers of accurate test interpretation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAgreement of HCWs and external panel RDT results by patient and HCWs\u0026rsquo; perceptions and beliefs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePositives\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNegatives\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAgreement (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMisrecorded Pos\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMisrecorded Neg N (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eKnows that the control line is required for an RDT test to be valid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24107 (93.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1438 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e181 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9564 (95.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e362 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e58 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eKnows an RDT test must be repeated if no control line is present\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5268 (92.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e361 (6.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e40 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28403 (94.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1443 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e202 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBelieves a patient with malaria can have a negative RDT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2610 (93.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e165 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30425 (94.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1601 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e223 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCould provide antimalarial to a patient even if the RDT is negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eno\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28549 (95.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1308 (4.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e175 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5025 (90.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e477 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e65 (1.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWould be worried that a test is incorrect if patient is febrile but the RDT is negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17303 (93.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1150 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e121 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5215 (93.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e327 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUses RDTs because the patients expect it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23759 (94.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1241 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e175 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4437\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8091 (94.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e447 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e50 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUses RDTs because the supervisors expect it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22351 (93.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1285 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e178 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8468 (94.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e413 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUses RDTs because the guidelines require it\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4601 (93.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e267 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e64 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24533 (94.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1311 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e153 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOther diagnostic tests for febrile illnesses are available\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13735 (93.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e780 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e112 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15909 (94.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e842 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e114 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHas access to medicines besides antimalarials available to treat febrile illness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDisagree or strongly disagree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4275 (96.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e133 (3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree or strongly agree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24415 (93.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1418 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e163 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRDT performance score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4140 (93.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e253 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e45 (1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13\u0026ndash;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17674 (93.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1096 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e117 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11839 (95.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e447 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e76 (0.6%)\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\u003eFindings from health care worker in-depth interviews\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eCharacteristics of HCWs participating in in-depth interviews\u003c/strong\u003e\u003cp\u003eA total of 16 health care workers (HCWs) participated in the in-depth interviews, evenly split between high and low RDT agreement groups. Most were nursing assistants (11), had less than 15 years of experience (11), and were primarily involved in support roles. Fewer were directly engaged in malaria diagnosis (7) or treatment (6), with clinical responsibilities slightly more common among high agreement HCWs. Nearly all had received training in malaria services (14) and RDT use (13)..\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eUse and Interpretation of RDTs\u003c/strong\u003e\u003cp\u003eMost HCWs demonstrated a clear understanding of the value of malaria RDTs and could accurately describe the procedures involved. While many found RDT interpretation straightforward, some reported challenges\u0026mdash;particularly in reading faint results or distinguishing positive from negative outcomes. Both high and low agreement HCWs cited poor lighting, especially at night, as a barrier to accurate interpretation. Concerns were expressed when RDT results were negative despite clinical symptoms suggestive of malaria. In such cases, HCWs often repeated the test or referred the patient for microscopy, acknowledging that treatment without confirmation went against national guidelines. Some low agreement HCWs reported instances where microscopy confirmed malaria despite an initial negative RDT, leading to doubts about RDT reliability, particularly in cases of low parasitaemia.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u0026ldquo;Sometimes we struggle to read the result after 15 minutes... We return later and see it\u0026rsquo;s positive.\u0026rdquo; \u0026mdash; \u003cem\u003eHigh agreement nursing assistant, Tchikandou\u003c/em\u003e\u0026ldquo;At night, we struggle to read the test correctly as the lights are too dim..\u0026hellip;.\u0026rdquo; \u0026mdash; \u003cem\u003eHigh agreement nursing assistant, Tchikandou\u003c/em\u003e\u0026ldquo;The RDT is effective, but sometimes it gives a negative result, and the thick blood smear is positive.\u0026rdquo; \u0026mdash; \u003cem\u003eHigh agreement nursing assistant, Ouassaho\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStockouts of RDTs and Supplies\u003c/strong\u003e\u003cp\u003eAlthough RDT and ACT stockouts were uncommon in the study sites, glove shortages were frequently reported. In the event of RDT stockouts, HCWs contacted nearby facilities to borrow supplies or resorted to microscopy or presumptive treatment. Responses to glove shortages varied; some HCWs asked patients to purchase gloves, while others proceeded without them, taking precautions to avoid direct contact with blood. This behavior did not differ significantly between HCW groups.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u0026ldquo;When we run out of RDTs, we borrow or treat symptomatically while awaiting confirmation...\u0026rdquo; \u0026mdash; \u003cem\u003eHigh agreement nursing assistant, Ouassaho\u003c/em\u003e\u0026ldquo;Sometimes patients are asked to buy gloves. If not, we manage without touching the blood\u0026hellip;\u0026rdquo; \u0026mdash; \u003cem\u003eLow agreement nursing assistant, Bohicon\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePatient Volume and Workflow\u003c/strong\u003e\u003cp\u003eHigh patient loads, especially during the rainy season, were cited as a challenge. Nonetheless, most HCWs said they continued testing all suspected cases and prioritized urgent patients. No major differences in RDT interpretation were observed between high and low agreement HCWs during busy periods.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u0026ldquo;We strive to perform all RDTs, regardless of patient numbers...\u0026rdquo; \u0026mdash; \u003cem\u003eLow agreement nursing assistant, Bemb\u0026egrave;r\u0026egrave;k\u0026egrave;\u003c/em\u003e\u0026ldquo;Severe cases are managed first, which can delay RDTs.\u0026rdquo; \u0026mdash; \u003cem\u003eHigh agreement nursing assistant, Ouassaho\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eImpact of Monthly Validation Exercises\u003c/strong\u003e\u003cp\u003eMost HCWs were aware of the monthly RDT validation exercises and participated in sorting used RDTs for review. The initiative was seen as supportive rather than burdensome. While high agreement HCWs reported that the exercise did not change their practices, lower agreement HCWs described it as helpful in aligning their work with national guidelines; suggesting it may be an important tool for quality improvement and data reliability.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u0026ldquo;It motivates us to work more thoroughly.\u0026rdquo; \u0026mdash; \u003cem\u003eLow agreement nursing assistant, Bohicon\u003c/em\u003e\u0026ldquo;The validation hasn\u0026rsquo;t changed how I record results\u0026mdash;I\u0026rsquo;ve always followed the guidelines.\u0026rdquo; \u0026mdash; \u003cem\u003eHigh agreement nursing assistant, Agbokpa\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study provides one of the most comprehensive assessments to date of the accuracy of recording malaria RDT results in public health facilities in Benin. Across over 35,000 RDTs evaluated, there was a high level of agreement (94.3%) between interpretations recorded by HCWs and an external reference panel, with a Cohen\u0026rsquo;s Kappa score of 0.88 indicating strong reliability. These results highlight the overall accuracy of HCWs' interpretation and recording of RDT results under routine programmatic conditions and offer reassurance about the reliability of malaria data within the national HMIS. This should however be interpreted in the context of the ongoing nationwide decentralized monthly validation of malaria RDT results, introduced six months before the study, which likely contributed to improved HCW diagnostic practices by increasing scrutiny and accountability. The monthly validation involves a physical review and counting of positive and negative RDT cassettes recorded in health facility registers before the data are integrated into the HMIS [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. An interrupted time series analysis to investigate the impact of this strategy on malaria surveillance outcomes is underway.\u003c/p\u003e\u003cp\u003eDespite high overall agreement, we observed important variation in RDT interpretation accuracy. RDT results misrecorded as positives were significantly more common (5.0%) than those misrecorded as negative (0.7%), indicating a tendency among HCWs to deliberately override negative test results and treat presumptively. This finding is consistent with responses from the KAPB survey, where 89% of HCWs believed that a patient infected with malaria could still test negative on an RDT. This perception, commonly reported in other settings such as Kenya, Uganda, and Nigeria [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], may erode HCWs\u0026rsquo; confidence in negative results, leading to unnecessary antimalarial prescriptions and potential overuse of ACTs. HCWs\u0026rsquo; non-adherence to negative RDT results has been extensively studied and is attributed to their awareness of key limitations of RDTs such as the risk of missing low-density infections and failure to detect non-falciparum species as well as the lack of alternative diagnostic tools or treatments, which may prompt precautionary antimalarial prescriptions for RDT-negative patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Training and supervision programmes designed to reinforce trust in negative results and build confidence in following test-based treatment protocols may be particularly impactful.\u003c/p\u003e\u003cp\u003eInterestingly, contrary to expectations that children under five, who are most vulnerable to malaria, might be more likely to be over-diagnosed, we found the highest accuracy and lowest error rates in this age group. Patients over 15 years of age had the lowest agreement and the highest rate of results misrecorded as positive (7.4%). This contradicts trends seen in other studies where caution may lead to overdiagnosis in young children [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. One possible explanation is that adults are more likely to pressure HCWs into prescribing antimalarials despite negative results; a hypothesis also supported by qualitative interviews and findings from other contexts [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Variation in RDT recording accuracy was also observed across facility and HCW characteristics. Facilities with internet access and grid electricity had better performance than those lacking infrastructure, highlighting the role of basic facility readiness. High-volume, high-TPR settings were associated with lower agreement and higher error rates, suggesting that both workload and caseload intensity can compromise diagnostic accuracy. Among HCWs, performance improved with age and years of experience, with older HCWs and those with more than 10 years of experience achieving the highest agreement. Student interns and volunteers showed the poorest performance, reinforcing the importance of training and supervision for newer or less experienced cadres. The impact of HCW beliefs and motivations was also evident. HCWs who admitted they might treat despite a negative RDT result demonstrated the highest rate of results misrecorded as positive, suggesting that personal beliefs can undermine test-based treatment. Conversely, those who cited national guidelines or supervisory expectations as the reason for using RDTs tended to perform better. This suggests that accountability mechanisms and performance expectations may support improved practice.\u003c/p\u003e\u003cp\u003eWhile the study provides valuable insights, it was limited to peripheral public health facilities. These were selected because they serve the largest share of rural populations, where malaria burden is typically highest, and contribute the majority of routine HMIS data. They also benefit from stronger stock control and supervision systems compared to private providers. However, private health facilities and pharmacies also perform malaria diagnosis and contribute to national data. Given their more limited support from the Ministry of Health, diagnostic practices in private settings may be more variable. Future studies are recommended to assess RDT interpretation and reporting in private sector contexts to inform a broader national quality improvement strategy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found high accuracy in the interpretation and recording of malaria rapid diagnostic test (RDT) results by HCWs in public health facilities in Benin. Performance was strongest among HCWs with more experience, recent training, and access to supportive infrastructure. Nonetheless, efforts are needed to address persistent misrecorded positives, particularly among adult patients, and to reinforce HCWs\u0026rsquo; confidence in reporting negative RDTs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eACT(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eArtemisinin-based Combination Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eArtificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eCNERS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eComit\u0026eacute; National d\u0026rsquo;\u0026Eacute;thique pour la Recherche en Sant\u0026eacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eHCW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eHealth Care Worker\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eHCW(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eHealth Care Worker(s) / Healthcare Worker(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eHMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eHealth Management Information System\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eKAPB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eKnowledge, Attitude, Perception, and Behavior / Knowledge, Attitudes, Perceptions, and Behavior / Knowledge, Attitudes, Practices, and Beliefs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eMaCRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eMalaria RDT Capturing and Reporting Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eNMCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eNational Malaria Control Programme (Benin)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eODK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eOpen Data Kit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eOutpatient Department\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eProbability of expected (chance) agreement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003ePerennial Malaria Chemoprevention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eProbability of observed agreement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eRapid Diagnostic Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eSeasonal Malaria Chemoprevention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eTPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eTrue Positive Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eTPR(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eTest Positivity Rate(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eWCG IRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eWestern Institutional Review Board\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eWMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eWorld Malaria Report\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026kappa; (kappa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 552px;\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s kappa coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors gratefully acknowledge the many individuals and institutions who contributed to the successful implementation of the MaCRA study in Benin. We extend our sincere thanks to Imelda Glele (Centre de Recherche Entomologique de Cotonou, CREC) and Saadjo Sow (PMIInsights/PATH) for their exceptional administrative and logistical support. We are also thankful to Megan Littrell, Kim Vu, and Taj Munson for their leadership and coordination of the PMI Insights project. Sasha Frade and Sam Smedinghoff (Audere, Johannesburg, South Africa) played a key role in developing and refining the HealthPulse application and in designing the data dashboards used during the study. We further acknowledge the contributions of Audere\u0026rsquo;s AI data creation and labeling teams at the Centre for HIV-AIDS Prevention Studies (South Africa) and Indivillage (India). We are especially grateful to the PMI/USAID Benin team, notably Virgile Gnanguenon and Raoul Olokoi, for their insights and ongoing support. Finally, we thank the Directors of Health for the Departments of Borgou and Zou, the Medical Coordinators of the participating Health Zones, the heads of the health facilities, and all the healthcare workers whose dedication and participation made this study possible.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was co-funded by PMI Insights and the Bill \u0026amp; Melinda Gates Foundation. PMI Insights was the global operational research and program evaluation project of the U.S. President\u0026rsquo;s Malaria Initiative (PMI). Funding for this study is made possible by the generous support of the American people through the United States Agency for International Development (USAID). The contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eKL, MH, and KG conceptualised and designed the evaluation, with contributions from CN, IA, and AK. CN led the study implementation and supervised data collection with support from IA, HA, MA and CA. SC led development of the HealthPulse application used for capturing RDT images. Data analysis was performed by JH, EH, NG, and KL, with input from CN and IA. CA, AK, and JA contributed to site selection and facilitated coordination with the Ministry of Health and study health facilities. CN and IA drafted the manuscript. All authors reviewed the manuscript and approved the final version for submission.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval from the Comit\u0026eacute; National d\u0026rsquo;\u0026Eacute;thique pour la Recherche en Sant\u0026eacute; (CNERS) under the Ministry of Health in Benin (Approval No. CNERS019/2023), as well as from the Western Institutional Review Board (WCG IRB). Written informed consent was obtained from HWCs participating in the study. Patient consent was not required, as RDT images were anonymized and data extracted from health facility registers constituted secondary, non-identifiable information. Permission to conduct the study in the selected health facilities was obtained from the Ministry of Health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study can be provided by the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO: \u003cstrong\u003eWorld Malaria report.\u003c/strong\u003e \u003cem\u003eWorld Health Organisation, Geneva \u003c/em\u003e2023.\u003c/li\u003e\n\u003cli\u003eWHO: \u003cstrong\u003eGuidelines for malaria vector control.\u003c/strong\u003e \u003cem\u003eGeneva, Switzerland: World Health Organization \u003c/em\u003e2025.\u003c/li\u003e\n\u003cli\u003eZinsou C, Cherifath AB: \u003cstrong\u003eThe malaria testing and treatment landscape in Benin.\u003c/strong\u003e \u003cem\u003eMalar J \u003c/em\u003e2017, \u003cstrong\u003e16:\u003c/strong\u003e174.\u003c/li\u003e\n\u003cli\u003ePMI: \u003cstrong\u003ePresident\u0026rsquo;s malaria initiative Benin malaria operational plan. 2024. 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In Benin, where over 90% of malaria diagnoses rely on RDTs, concerns remain about the accuracy of the reporting and recording of RDT results. This study assessed the fidelity of RDT recording by healthcare workers (HCWs) in public health facilities and explored associated factors.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eA six-month mixed-methods, prospective observational study was conducted in 16 public health facilities across two departments in Benin. For each RDT performed, an image was captured using a digital RDT reader (HealthPulse, Audere, Seattle, WA USA) and independently interpreted by an external trained panel. HCW-recorded results were compared to panel interpretations. A knowledge, attitudes, practices, and beliefs (KAPB) survey and structured observations of RDT performance were conducted, alongside in-depth interviews with selected HCWs.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eOf 35,706 RDTs assessed, overall agreement between HCW and reference panel interpretations was 94.3% (Cohen\u0026rsquo;s kappa\u0026thinsp;=\u0026thinsp;0.88). Results misrecorded as positive (5.0%) were more frequent than results misrecorded as negative (0.7%). Agreement varied by patient age, HCW experience, and facility characteristics. Accuracy was highest with children under 5 years (96.7%) and lowest with patients over 15 years (91.6%). HCWs with \u0026ge;\u0026thinsp;10 years of experience, and access to electricity and internet performed better. From 226 HCWs surveyed, 89.4% believed a patient with malaria could have a negative RDT, though only 19.5% supported treating such cases with antimalarials. While most HCWs were proficient in performing RDTs, only 40.5% waited the recommended time before reading results, and glove use was low (15.6%) highlighting safety gaps. RDT use was primarily motivated by adherence to guidelines (60.2%), rather than patient or supervisor expectations. Qualitative interviews highlighted contextual challenges including workload, lighting conditions in health facilities, and resource constraints.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eHCWs in Benin showed high accuracy in interpreting and reporting malaria RDT results, likely supported by recent nationwide RDT cassette validations. Performance was strongest among those with more experience, training, and adequate infrastructure. However, negative results misrecorded as positive, especially in adult patients, remains a concern. Targeted training and supportive supervision may help strengthen confidence in negative results and improve overall diagnostic accuracy.\u003c/p\u003e","manuscriptTitle":"The accuracy of recording malaria rapid diagnostic test (RDT) results in public health facilities in Benin; results from the MaCRA project","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:13:42","doi":"10.21203/rs.3.rs-7002558/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-14T05:22:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T11:00:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T13:17:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40460771640464160850152502978351930731","date":"2026-01-30T08:01:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11278764335559346680209988784093664116","date":"2026-01-30T05:44:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T09:30:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134870898576982932422832789964133527912","date":"2026-01-06T06:23:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-08T12:16:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-30T11:18:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-30T11:18:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2025-06-29T12:01:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a2d6eda-d622-48b3-8b3e-a69ec4116274","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:29:54+00:00","versionOfRecord":{"articleIdentity":"rs-7002558","link":"https://doi.org/10.1186/s12936-026-05871-7","journal":{"identity":"malaria-journal","isVorOnly":false,"title":"Malaria Journal"},"publishedOn":"2026-03-28 16:10:52","publishedOnDateReadable":"March 28th, 2026"},"versionCreatedAt":"2025-07-14 10:13:42","video":"","vorDoi":"10.1186/s12936-026-05871-7","vorDoiUrl":"https://doi.org/10.1186/s12936-026-05871-7","workflowStages":[]},"version":"v1","identity":"rs-7002558","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7002558","identity":"rs-7002558","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}