Accuracy of reporting of malaria rapid diagnostic test results in Uganda

Accuracy of reporting of malaria rapid diagnostic test results in Uganda | 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 Accuracy of reporting of malaria rapid diagnostic test results in Uganda Nelson Ssewante, Jane Frances Namuganga, Anne Katahoire, Jenipher Musoke, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6603474/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Nov, 2025 Read the published version in Malaria Journal → Version 1 posted 9 You are reading this latest preprint version Abstract Background: Malaria rapid diagnostic tests (RDTs) have been critical in promoting the rational use of antimalarials and strengthening malaria surveillance. However, the accuracy of routinely reported RDT results in Uganda remains unclear. The study’s objective was to measure the level of agreement between healthcare workers (HCWs) and an external panel’s RDT results among lower-level public health facilities in Busoga and Lango regions, Uganda. Methods: A prospective study was conducted in 16 public health facilities in four purposively selected districts in Uganda. At each study site, images of all RDTs were taken as soon as the HCW had finished interpreting the test results and uploaded into HealthPulse (Audere, Seattle, WA USA), a digital RDT reader. Corresponding patient data was captured from the outpatient department (OPD) register, including demographics, RDT test results and prescribed treatment. RDT images were sent to a trained, external panel for interpretation. Cohen’s kappa statistic (κ) was used to determine agreement. The associations between characteristics of health facilities, HCWs and RDTs and the level of agreement were analyzed using meta-analytical approaches. Results: From June to November 2023, 40,049 RDT images were captured, of which 37,020 (92.4%) were included in the analysis. Overall, the test positivity rate based on OPD records was 61.8%. The overall agreement was strong (κ 0.82, 95% confidence interval [CI] 0.79, 0.84). Where disagreement occurred, HCWs misrecorded more RDT results as positive (7.1%) than negative (1.8%). Agreement was higher in Busoga (κ 0.86, 95% CI 0.83, 0.88) compared to Lango (κ 0.78, 95% CI 0.75, 0.81). Lower agreement levels were also associated with older patients, RDTs with faint lines and RDTs with two test lines. Conclusion: The study found a strong level of agreement between HCWs' RDT results and an external panel. However, significant proportions of results were misrecorded as positive or negative, particularly in the Lango region. Targeted interventions, such as RDT validation exercises and tailored refresher training, are recommended to enhance RDT reporting accuracy in Uganda. malaria diagnosis testing accuracy agreement Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Over the past two decades, Uganda has made remarkable progress in malaria control, achieving a 47% decline in malaria incidence and an 81% reduction in mortality between 2000 and 2023 ( 1 ). One of the major contributors to this success has been improvement in malaria case detection and management, largely driven by the promotion and widespread use of malaria rapid diagnostic tests (RDTs) ( 2 ). Despite this progress, Uganda remains a significant contributor to the global malaria burden, with recent World Health Organization (WHO) estimates ranking the country third in malaria cases and tenth in malaria-related deaths in 2023 ( 1 ). RDTs are immunochromatographic test strips that detect parasite antigens or enzymes in blood that are either genus- or species-specific ( 3 ). Since 2011, Uganda adopted the WHO policy of mandatory parasitological confirmation of malaria with either microscopy or RDTs before the prescription of antimalarials ( 4 ). At the inception of the policy, microscopy was to remain the “gold standard” for malaria diagnosis at health centre (HC) III (i.e. health units with laboratory services) and higher-level facilities, while RDTs were limited for use at HC IIs and other facilities whenever microscopy was not possible ( 5 ). However, microscopy is labor-intensive, and its accuracy is dependent on the expertise of the laboratorian, the equipment, and the quality of staining reagents ( 6 ). Given the high patient load amidst staffing shortages, easier-to-use RDTs have become the preferred test type across all facility types, especially at lower-level health facilities (HCs II and III). Increased use of RDTs has boosted testing rates, resulting in a significant decline in the presumptive treatment of malaria among febrile patients ( 7 ). The scale-up of RDTs has also improved the accuracy of reported malaria surveillance data, thereby enabling Uganda to better monitor malaria trends and the effectiveness of interventions ( 8 ). For malaria RDTs to serve the purpose for which they are intended, test results must be accurate, healthcare workers (HCWs) must prescribe antimalarials based on the test results, and results must be accurately recorded in health facility registers. Studies investigating compliance of HCWs with malaria RDT results have reported varying levels of adherence ( 9 – 13 ). A meta-analysis of 14 studies reported an overall compliance with RDT results of 83%, with factors such as patient expectations, HCW cadre, work experience, and perceived test accuracy influencing adherence ( 14 ). In Uganda, previous studies have documented high proficiency in administering and interpreting malaria RDTs, including among trained community health workers (CHWs) ( 15 , 16 ). However, none of these studies have assessed the accuracy of malaria RDT data recorded in public health facilities. This study aimed to assess the accuracy of RDT results recorded by HCWs by measuring the level of agreement between RDT results recorded in health facility registers and those of a trained external panel at lower-level public health facilities in Uganda’s Busoga and Lango regions. Methods Study design This was a prospective study conducted as part of a larger multi-country study in sub-Saharan Africa, including Benin, Côte d’Ivoire and Nigeria. More detailed methods are available in the study overview paper (Lindblade et al.). In brief, between June and November 2023, we captured high-quality images of malaria RDTs performed at health facilities among patients with suspected malaria cases using an AI-powered digital RDT reader smartphone application (HealthPulse, Audere, Seattle, WA USA). These images were sent to an external panel for interpretation. RDT results recorded by HCWs were compared to the interpretation by the external panel to determine the level of agreement. Study sites The study was conducted in 16 public health facilities (HC II and III) located in two high-burden regions: Busoga and Lango in Eastern and Northern Uganda, respectively. HC II facilities are defined as those at parish level that provide basic outpatient services and treat common illnesses such as uncomplicated malaria, while HC III facilities offer diagnostic, outpatient, and maternity services at sub-county level. In each region, two districts were purposively selected based on two criteria: 1) the presence of HC II or III public facilities, and 2) the absence of ongoing external interventions supporting malaria testing capacity at the facilities. All lower-level public health facilities in the selected districts were considered eligible if they met the following criteria: 1) three years (2020 to 2022) of complete outpatient malaria data (defined as at least 9 of 12 months each year) reported to the District Health Information System 2 (DHIS-2), which serves as Uganda’s health management information system, and 2) a minimum of 50 RDTs performed monthly from 2020 to 2022. Using medians, eligible facilities within each district were grouped into four strata based on patient volume and test positivity rate (TPR). One facility from each stratum was randomly selected, resulting in four study facilities per district (Figure 1). Data collection procedures Trained research assistants (RAs) collected RDT images and data using the HealthPulse application on weekdays. At the start of the study, a facility survey was conducted to assess infrastructure and resource capacity for malaria case management. HCWs were surveyed to collect their demographic information and training and experience using RDTs. RAs observed HCWs as they performed an RDT, using a 19-point standardized checklist to assess proficiency (Supplemental file 1). RAs photographed RDTs using the HealthPulse app as soon as practically feasible after the HCW completed interpreting the results, without interfering with or obstructing patient care. The app had an image quality assurance component that immediately flagged images that did not meet quality standards and prompted users to retake the photo when needed. RDT images were matched using barcode labels to corresponding patient data in the outpatient department (OPD) registers, which were captured by RAs using the HealthPulse application. Images were sent to an external panel of trained reviewers who determined whether control and test lines were present in an RDT image. Panelists flagged anomalies in the RDTs and the presence of faint lines. The overview paper [citation] provides further information on the quality control process for image results. Data analysis Characteristics of health facilities, HCWs and patients for whom RDTs were performed were presented as proportions disaggregated by region. The latitude and longitude of health facilities were used to calculate Plasmodium falciparum parasite prevalence for children 2 – 10 years (PfPR 2-10 ) within 5 km using the malariaAtlas package in R (R Foundation for Statistical Computing, Vienna, Austria) (17). HCWs were divided into terciles based on their RDT proficiency scores. Results interpreted by the external panel as positive but recorded as negative by the HCW were termed ‘misrecorded as negative,’ and those interpreted as negative by the external panel and positive by the HCW were termed ‘misrecorded as positive.’ The positive predictive value (true positives / [true positives + misrecorded as positive]) and the negative predictive value (true negatives / [true negatives + misrecorded as negative]) were calculated to measure the likelihood that a positive or negative test recorded in a facility register was a true positive or negative, respectively. Cohen’s kappa was used to measure the level of agreement between malaria RDT results recorded by HCWs in health facility registers and those interpreted by the external panel. To generate an overall estimate of agreement across study facilities, we applied a random-effects meta-analytic approach (16). Specifically, we computed a weighted mean kappa, with each facility-level kappa estimate weighted by the inverse of its variance to account for variation in measurement precision across facilities. In addition to calculating the pooled kappa, we explored potential sources of variability in agreement by including key characteristics of health facilities, HCWs, RDTs and patients on whom RDTs were performed as moderators in the model. All statistical analysis was conducted using R version 4.3.3 (18). Results Characteristics of study health facilities Of the 16 study health facilities, 10 (62.5%) were HCs II while the remaining six were HCs III (Table 1). The malaria prevalence among children 2 to 10 years old in the 5 km radius around the health facility was >30% in 5 (31.3%) health facilities. Most facilities (10, 62.0%) had at least five staff who performed RDTs. Seven facilities (43.8%) had a laboratory technician present. Lango had fewer HCs II (50.0%) compared to Busoga (75.0%). Additionally, more facilities in Lango had a parasite prevalence >30% (8, 50%) compared to those in Busoga (2, 12.5%). Most RDTs (17,112, 46.2%) were recorded at health facilities in Dokolo district over the study period (Table 1). Health facilities from the high RDT volume stratum performed more RDTs (20,414, 55.1%) than those from the low volume stratum (16,606, 44.9%). Similar numbers of RDTs were performed in facilities with a laboratory technician present (18,441, 49.8%) as without (18,579, 50.2%). Characteristics of HCWs Among 217 health facility staff, 171 (78.8%) reported involvement in the administration, recording, and reporting of malaria RDTs and 169 (98.8%) were interviewed. Over the six months of the study implementation, 93 (55.0%) HCWs contributed at least one RDT recording to the analytical dataset. Of these, 46 (49.5%) were female, and a plurality (36, 38.7%) were aged 30-39 years (Table 2). Nurses constituted the largest cadre of HCWs (49, 52.7%), followed by nonmedical or volunteer staff (12, 12.9%) and medical auxiliary staff (10, 10.8%). Most HCWs (76, 81.7%) had a university degree, while more than half (48, 51.6%) had at least 10 years of experience in their professional roles. Most HCWs reported frequently performing RDTs (60, 64.5%). Compared to Busoga, health facilities in Lango had a higher proportion of male HCWs (63.6% vs. 38.8%) and a greater proportion aged 50–59 years (15.9% vs. 4.1%) (Table 2). In contrast, fewer HCWs in Lango had 10 or more years of experience (45.5% vs. 57.1%). The proportion of HCWs who reported performing RDTs very often was similar between the two regions. However, a larger proportion of HCWs in Lango had RDT proficiency scores in the upper tercile (46.2% vs. 22.9%). The distribution of RDTs recorded by HCWs was broadly proportional to the frequency of their demographic and professional characteristics. However, several notable deviations were observed. HCWs under 30 accounted for 15.9% of RDTs recorded, despite representing 23.7% of the HCWs, whereas those aged 50–59 years recorded 16.5% of RDTs but comprised only 9.7% of HCWs. Nurses and clinical officers recorded a higher-than-expected proportion of RDTs relative to their representation, while other cadres contributed less than expected. HCWs who reported performing RDTs very often made up 64.5% of the total but were responsible for 85.7% of RDTs recorded. Interestingly, HCWs in the middle tercile of RDT proficiency conducted 43.6% of RDTs, compared to 21.1% among those in the highest proficiency tercile. Characteristics of RDTs observed in this study During the study period, a total of 45,838 RDT results was recorded in OPD registers. Of these, 40,049 (87.4%) had images captured in the HealthPulse application. Most missing RDT images were performed on weekends or evenings when RAs were not present. After excluding images without corresponding patient data, 37,020 (92.4%) RDT results were included in the final analysis (Figure 2). The overall TPR among RDTs recorded in the health facility registers was 61.8% (Table 3). The most common RDT product was Bioline Malaria Pf (Abbott, IL, USA), which accounted for 19,148 (51.7%) cases, while STANDARD Q Malaria Pf (SD Biosensor, Gyeonggi-do, Republic of Korea) and Bioline Malaria Pf/Pan each comprised more than 10% of RDTs performed. A total of 4980 (13.5%) RDTs were tagged as having a faint line. Most RDTs were performed on female patients (24,804, 67.0%) and those aged 15 years or more (15,804, 42.7%). There were more patients recorded in OPD registers with a diagnosis of malaria (24,220, 65.4%), of whom 22,834 (61.7%) received antimalarial prescriptions, than there the number of positive RDTs recorded (22,887, 61.8%). Close to a third of RDT results (11,159, 30.1%) were recorded in the OPD register by the same individual who performed the RDT. RDTs from health facilities in Busoga had a lower TPR than Lango (54.5% vs 63.3%) (Table 3). STANDARD Q Malaria Pf was the main RDT product used in Busoga, while Bioline Malaria Pf was the main product in Lango. The age distribution of patients differed by region, with a lower proportion of patients in Lango aged 0 – 4 years (17.9%) compared with Busoga (30.7%). RDTs in Busoga were more likely to be performed and recorded by the same person (45.0%) compared to Lango (21.0%). Agreement between healthcare workers’ and external panel’s RDT results Out of the 37,020 RDTs reviewed, 33,743 (91.1%) of the HCW results were in agreement with the external panel results, including 20,254 (54.7%) true positives and 13,489 (36.4%) true negatives. Among the disagreements, 2618 (7.1%) were misrecorded as positive and 659 (1.8%) were misrecorded as negative. The positive predictive value was 88.6% and the negative predictive value was 95.3%. While the TPR declined over the study period in line with seasonal patterns (19), the proportion misrecorded as positive or negative did not vary substantially by study week (Figure 3). Cohen's kappa (κ) for the overall level of agreement was 0.82 (95% CI, 0.79, 0.84). There was variability in the estimate of κ by health facility, with individual facility-level varying from a low of 0.73 (95% CI 0.69, 0.77) to a high of 0.92 (95% CI 0.90, 0.94) (Figure 4). Generally, health facilities in Busoga (facilities 1-8) had higher agreement levels compared to those in Lango (facilities 9-16). The level of agreement in Busoga was higher (κ 0.86, 95% CI 0.83, 0.88) compared to Lango (κ 0.78, 95% CI 0.75, 0.81). There were a few factors other than region found to be associated with the level of agreement. The two districts of Busoga (Buyende and Bugweri) had higher measures of κ than the two districts of Lango (Otuke and Dokolo) (Figure 5). There was no association between either the baseline strata for TPR or RDT volume and the level of agreement. There was a decreasing level of agreement with increasing parasite prevalence, but confidence intervals overlapped. Neither the number of staff who performed RDTs nor the presence of a laboratory technician within the facility had any association with the level of agreement. Among HCW characteristics, no factor was found to moderate the level of agreement. Several RDT characteristics, however, were associated with κ, including RDT product and the presence of faint lines. The level of agreement was lower for Bioline Malaria Pf (HRP2/pLDH) (κ 0.73, 95% CI 0.56, 0.89), Bioline Malaria Pf/Pan (κ 0.78, 95% CI 0.73, 0.82) and First Response Malaria (pLDH/HRP2) (κ 0.78, 95% CI 0.72, 0.84), all of which have two test lines, compared to other, single test line RDT products. Faint lines were associated with significantly lower levels of agreement (κ 0.16, 95% CI 0.04, 0.28). Discussion In this study, we evaluated the accuracy of RDT reporting in Ugandan health facilities by comparing RDT results recorded in facility registers with results interpreted by an external panel reviewing images of the RDTs. Overall, there was a strong level of agreement between HCW’s records and the panel’s interpretations, indicating that RDT results reported from health facilities are generally reliable. However, 7.1% of results were misrecorded as positive, resulting in an 88.6% probability that a positive result in the register was a true positive. These findings suggest that while RDT reporting is largely accurate, positive results may be over-reported, potentially obscuring declines in TPR over time and masking the impact of malaria control efforts. Notable regional discrepancies were observed, with lower agreement levels in the Lango region compared to Busoga. This regional discrepancy may be attributed to several factors. First, compared to Busoga, health facilities in Lango reported a higher proportion of older patients, who were associated with lower agreement levels. This aligns with previous studies indicating that patient expectations may influence HCWs’ adherence to RDT results ( 9 , 20 , 21 ). Older patients, including colleagues and community leaders, may exert pressure on HCWs to prescribe antimalarials regardless of test outcomes. In such cases, HCWs might alter recorded RDT results in OPD registers to justify prescriptions, thereby compromising the accuracy of recorded malaria RDT data. Second, most health facilities in Lango experienced a higher patient volume but had relatively fewer HCWs responsible for administering and recording RDT results. A low HCW-to-patient ratio can contribute to reduced accuracy in reporting, as increased workloads and burnout are known to elevate the risk of medical errors ( 22 ). Therefore, efforts to improve data quality and malaria surveillance should prioritize increasing the number of trained HCWs in lower-level health facilities, particularly in high-burden districts to enhance service delivery. Third, the study also found that RDTs with two test lines were more frequently reported in Lango than in Busoga, and they were associated with lower agreement levels. In Uganda, single test line RDTs are the most commonly used, making them more familiar to HCWs, especially in rural districts. However, in 2020, molecular surveillance studies in Uganda provided evidence of HRP2/3 deletions in certain regions, which prompted a policy decision to provide RDTs that included pLDH in the affected areas ( 23 , 24 ). Newly introduced two test-line RDTs, such as Bioline Malaria Pf/Pan and Bioline Malaria Pf (HRP2/pLDH), require additional training to ensure accurate interpretation. Since the introduction of these tests was not universal across study facilities, it is likely that HCWs did not receive adequate training, leading to misinterpretations. This finding highlights the importance of ensuring that HCWs receive proper training and updated reference materials for newly introduced RDTs, particularly those with different characteristics. To improve the accuracy of malaria RDT reporting, continuous medical education and refresher training should be prioritized, particularly in lower-level facilities. Strengthening HCW capacity and standardizing RDT use will enhance data quality, ensuring reliable malaria surveillance and case management across Uganda. Results misrecorded as positive were more common than results misrecorded as negative, suggesting a possible systematic bias or set of factors influencing the direction of recording errors When a HCW is in doubt of how to interpret an RDT result or lacks the expertise to confidently explain the cause of fever in the presence of a negative test, they would rather err in the direction of diagnosing malaria to minimize the risk of missing a potentially life-threatening malaria diagnosis. This partly explains the higher prevalence of non-compliance to negative than positive RDT results that have been observed in multiple studies in the region ( 21 , 25 , 26 ). Moreover, skilled and experienced HCWs such as nurses and those with access to reference materials were less likely to misrecord RDT results. Though uncommon, results misrecorded as negative could be partially explained by transcription errors introduced in the health facility register during the recording of test results. Moreover, in this study, about 70% of RDT results were recorded in the health facility register by a staff member different from the one who performed the test. This has serious implications for the quality of OPD register data, regardless of whether it was intentional or not. Innovative solutions such as validation of malaria RDT results could be considered, especially in the Lango region, to improve data quality. The other highly plausible explanation for these results could be premature interpretation of RDTs potentially attributed to the overwhelmingly large patient numbers. This was especially common in the Lango region which contributed the majority of RDTs observed in this study. To cope with overwhelming patient numbers, we witnessed HCWs processing RDTs in batches. While batched RDTs were clearly labelled with patient identification information to ensure patients were assigned correct results, this practice could have led to some RDTs being interpreted prematurely, before the recommended manufacturer test time, affecting the accuracy of the final result. Due to the unavoidable time lag between HCW’s interpretation and image capture in such scenarios, some tests initially interpreted as negative could havefinished processing, indicating a positive at the time of image capture, resulting in discrepant interpretations by the external panel. The appearance of the test line on an RDT is a time-dependent function. Therefore, premature interpretation of an RDT as negative due to non-appearance of the test line could easily turn out to be a result misrecorded as negative ( 27 ). Incorrect interpretation of RDTs and subsequent misdiagnosis of malaria may lead to inappropriate patient treatment. Results misinterpreted as negative lead to under-diagnosis and consequently under-treatment of malaria, with the danger of allowing the progression of illness to severe malaria, a life-threatening condition ( 16 , 28 , 29 ). Patients incorrectly diagnosed with malaria lead to the wastage of antimalarials and may contribute to mismanagement of nonmalarial causes of fever, potentially contributing to poor outcomes ( 30 , 31 ). Furthermore, incorrect records may result in under- or overestimation of malaria cases, affecting resource allocations and policy decisions in affected regions. This highlights the importance of implementing standardized training programs and regular skills assessments to address these gaps and enhance the reliability of RDT results ( 32 , 33 ). Strengths and limitations This study benefited from a large sample size with over 37,000 individual RDT observations. Unlike previous evaluations using digital RDT readers that have compared TPRs between health facility registers and a subset of patients whose results were read by an RDT reader, we conducted comparisons at the individual RDT cassette level. This approach reduced potential bias associated with sampling. Nonetheless, the study had several limitations. First, RDT test results recorded in laboratory registers were not captured, limiting our ability to assess whether these entries were more accurate than those in the facility register. While including these data could have enhanced understanding of within-facility consistency, it is unlikely to have affected the overall conclusions. Second, the external panel interpreted RDT results based on images captured by a smartphone camera. This interpretation could have been affected by the quality of the images submitted limiting visibility of particular details such as faint lines. However, we believe that the study’s robust quality control procedures for image interpretation minimized the likelihood of such errors. Finally, although some evidence suggests that misrecording of RDT results, particularly negative results, may be linked to over-treatment and could be intentional, we cannot determine whether these misrecordings were due to deliberate actions, unintentional errors or misinterpretation of the results. Conclusion The level of agreement between RDT results recorded by HCWs and those interpreted by an external panel was strong, suggesting that RDT results reported from health facilities are generally accurate. However, important proportions of RDTs were misrecorded as positive or negative, particularly in the Lango region where factors such as high patient volume, limited HCW staffing, and the use of newer two-line RDTs were more common. To reduce workload and minimize reporting errors, increasing the number of trained HCWs, especially in high-burden districts, should be prioritized. In addition, measures are needed to limit the number of RDT products in circulation, while the government should ensure that HCWs receive standardized training and reference materials. Finally, innovative strategies, such as validation mechanisms for RDT results should be explored to enhance data quality, particularly in settings with high patient volumes. Abbreviations CI Confidence interval HC Health centre HCW Healthcare worker MOH Ministry of Health OPD Outpatient department Pf Plasmodium falciparum PR Prevalence RDT Rapid diagnostic test TPR Test positivity rate WHO World Health Organization Declarations ACKNOWLEDGEMENTS We gratefully acknowledge the many healthcare workers and Ministry of Health officials who gave generously of their time to participate in this evaluation. We appreciate the technical support received from staff at the CHDC, including the director, Herbert Muyinda, for his supervisory support. Paul Opira and Martin Omello assisted with data management, Faith Bagatya led technical coordination of ethics and regulatory approvals, Donald Ngarombo managed logistics, and Margaret Nakuya supported financial accountability. The team received support from Timothy Ogwang and Paul Oketch who were the surveillance coordinators in Lango and Busoga regions, respectively. They provided direct supervision to the research assistants, including Jolly Job Odongo, Nelson Opira, Rebecca Adongo, Denis Opira, Zam Fadir, Kevina Nakamya, John Mary Kibirige, Maria Nabuule, Grace Uwimana, Maureen Biira, Joseph Kyabaggu, Julius Nkuma, Catherine, Taima Hamimu and Fred Birungi. We received excellent research support from Saadjo Sow, Annie Arnzen and Maia Cullen (PMI Insights). Megan Littrell, Kim Vu and Taj Munson provided overall direction and administrative support to the PMI Insights project. Aysu Uygur (Bill & Melinda Gates Foundation) is greatly appreciated for her contributions during the design of the evaluation. We thank the Uganda PMI staff for their support: Edgar Agaba, Grace Appiah, Patrick Condo. Emily Hilton and Natalie Galles (PATH) supported data analysis. Sasha Frade, Sam Smedinghoff, and the Audere development team (USA and South Africa), supported customization of the HealthPulse application and designed dashboards used during the study. We appreciate the engagement of Julia Mwesigwa (PATH) in the initial protocol harmonization meeting. FUNDING This evaluation was co-funded by PMI Insights and the Bill & Melinda Gates Foundation (Investment # INV-043942). PMI Insights was the global operational research and program evaluation project of the U.S. President’s Malaria Initiative (PMI). Funding for this evaluation was made possible by the generous support of the American people through the United States Agency for International Development (USAID) (Cooperative agreement # 7200AA20CA00031). The contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. AUTHOR INFORMATION Authors and Affiliations Child Health and Development Centre, Makerere University, Kampala, Uganda Nelson Ssewante Jane Frances Namuganga Anne Katahoire Jenipher Musoke Noel Mutesi Arthur Mpimbaza U.S. President’s Malaria Initiative, United States Agency for International Development, Washington, DC USA Michael Humes Kevin Griffith Radina Soebiyanto Audere, Seattle, WA USA Shawna Cooper PMI Insights Project/PATH, Geneva, Switzerland John J. Aponte Kim A. Lindblade National Malaria Control Division, Ministry of Health, Kampala, Uganda Bosco Agaba Jimmy Opigo Contributions KAL, MH and KG conceived and designed the evaluation. NS, JFN, AK, JM, NM, AM, and BA oversaw data collection activities. SC oversaw the development of the HealthPulse application used in the study. NS and KAL drafted the manuscript. JJA, RS and KAL analyzed the data. NS, JFN, AK, JM, NM, MH, KG, JJA, RS, SC, BA, JO, KAL and AM critically reviewed the manuscript. All authors read and approved the final manuscript. Corresponding author Correspondence to Nelson Ssewante Upper Mulago Hospital Complex, P.O. Box 6717, Kampala, Uganda Child Health and Development Centre, Makerere University, Kampala, Uganda [email protected] Ethical approval and consent to participate Ethical approval was obtained from the Vector Control Division Research and Ethics Committee (Ref # VCDREC172) and the Uganda National Council of Science and Technology (Ref # HS2747ES) in Uganda, and the WGC IRB in the USA (Ref # 20231373). All participants provided written, informed consent to participate. 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Assessing village health workers’ ability to perform and interpret rapid diagnostic tests for malaria 4 years after initial training: a cross-sectional study. Am J Trop Med Hyg. 2020;104(1):294. Boyce MR, O’Meara WP. Use of malaria RDTs in various health contexts across sub-Saharan Africa: a systematic review. BMC Public Health. 2017 May 18;17(1):470. Pfeffer DA, Lucas TCD, May D, Harris J, Rozier J, Twohig KA, et al. malariaAtlas: an R interface to global malariometric data hosted by the Malaria Atlas Project. Malar J. 2018 Oct 5;17(1):352. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2024. Available from: https://www.R-project.org/ Kigozi SP, Kigozi RN, Sebuguzi CM, Cano J, Rutazaana D, Opigo J, et al. Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019. BMC Public Health. 2020;20:1–14. Rakotonandrasana DH, Tsukahara T, Yamamoto-Mitani N. Antimalarial drug prescribing by healthcare workers when malaria testing is negative: a qualitative study in Madagascar. Trop Med Health. 2018;46:1–9. Altaras R, Nuwa A, Agaba B, Streat E, Tibenderana JK, Strachan CE. Why do health workers give anti-malarials to patients with negative rapid test results? A qualitative study at rural health facilities in western Uganda. Malar J. 2016 Jan 11;15(1):23. Naseralallah L, Stewart D, Azfar Ali R, Paudyal V. An umbrella review of systematic reviews on contributory factors to medication errors in health-care settings. Expert Opin Drug Saf. 2022;21(11):1379–99. Bosco AB, Anderson K, Gresty K, Prosser C, Smith D, Nankabirwa JI, et al. Molecular surveillance reveals the presence of pfhrp2 and pfhrp3 gene deletions in Plasmodium falciparum parasite populations in Uganda, 2017–2019. Malar J. 2020;19:1–14. Ministry of Health. The Uganda malaria reduction and elimination strategic plan 2021–2025. 2020th ed. Kampala, Uganda: Ministry of Health; Bonful HA, Awua AK, Adjuik M, Tsekpetse D, Adanu RMK, Nortey PA, et al. Extent of inappropriate prescription of artemisinin and anti-malarial injections to febrile outpatients, a cross-sectional analytic survey in the Greater Accra region, Ghana. Malar J. 2019;18:1–11. Lal S, Ndyomugenyi R, Paintain L, Alexander ND, Hansen KS, Magnussen P, et al. Community health workers adherence to referral guidelines: evidence from studies introducing RDTs in two malaria transmission settings in Uganda. Malar J. 2016;15:1–13. Watson OJ, Sumner KM, Janko M, Goel V, Winskill P, Slater HC, et al. False-negative malaria rapid diagnostic test results and their impact on community-based malaria surveys in sub-Saharan Africa. BMJ Glob Health. 2019 Jul 29;4(4):e001582. Kozycki CT, Umulisa N, Rulisa S, Mwikarago EI, Musabyimana JP, Habimana JP, et al. False-negative malaria rapid diagnostic tests in Rwanda: impact of Plasmodium falciparum isolates lacking hrp2 and declining malaria transmission. Malar J. 2017;16:1–11. Kolekang AS, Afrane Y, Apanga S, Zurovac D, Kwarteng A, Afari-Asiedu S, et al. Challenges with adherence to the ‘test, treat, and track’malaria case management guideline among prescribers in Ghana. Malar J. 2022;21(1):332. Shelus V, Mumbere N, Masereka A, Masika B, Kiitha J, Nyangoma G, et al. “Testing for malaria does not cure any pain” A qualitative study exploring low use of malaria rapid diagnostic tests at drug shops in rural Uganda. PLOS Glob Public Health. 2022 Dec 13;2(12):e0001235. Parsel SM, Gustafson SA, Friedlander E, Shnyra AA, Adegbulu AJ, Liu Y, et al. Malaria over-diagnosis in Cameroon: diagnostic accuracy of Fluorescence and Staining Technologies (FAST) Malaria Stain and LED microscopy versus Giemsa and bright field microscopy validated by polymerase chain reaction. Infect Dis Poverty. 2017;6(02):20–8. Eliades MJ, Wun J, Burnett SM, Alombah F, Amoo-Sakyi F, Chirambo P, et al. Effect of Supportive Supervision on Performance of Malaria Rapid Diagnostic Tests in Sub-Saharan Africa. Am J Trop Med Hyg. 2019 Apr;100(4):876–81. Martiáñez-Vendrell X, Skjefte M, Sikka R, Gupta H. Factors Affecting the Performance of HRP2-Based Malaria Rapid Diagnostic Tests. Trop Med Infect Dis. 2022 Sep 25;7(10):265. Tables Table 1. Characteristics of study health facilities, Uganda 2023 Characteristic Overall N=16 n (%) Region No. RDTs recorded N=37,020 n (%) Busoga N=8 n (%) Lango N=8 n (%) District Otuke 4 (25.0) 0 (0) 4 (50.0) 5808 (15.7) Dokolo 4 (25.0) 0 (0) 4 (50.0) 17,112 (46.2) Buyende 4 (25.0) 4 (50.0) 0 (0) 5808 (15.7) Bugweri 4 (25.0) 4 (50.0) 0 (0) 8292 (22.4) Health centre level Level II 10 (62.5) 6 (75.0) 4 (50.0) 20,116 (54.3) Level III 6 (37.5) 2 (25.0) 4 (50.0) 16,904 (45.7) Test positivity rate stratum High 8 (50.0) 4 (50.0) 4 (50.0) 18,513 (50.0) Low 8 (50.0) 4 (50.0) 4 (50.0) 18,507 (50.0) RDT volume stratum High 8 (50.0) 4 (50.0) 4 (50.0) 20,414 (55.1) Low 8 (50.0) 4 (50.0) 4 (50.0) 16,606 (44.9) Parasite prevalence (PfPR 2-10 ) 0 – 9% 1 (6.2) 1 (12.5) 0 (0) 2704 (7.3) 10 – 19% 1 (6.2) 1 (12.5) 0 (0) 1855 (5.0) 20 – 29% 9 (56.2) 5 (62.5) 4 (50.0) 14,248 (38.5) 30 – 39% 5 (31.2) 1 (12.5) 4 (50.0) 18,213 (49.2) Number of HCWs who perform RDTs 1 – 2 2 (12.5) 1 (12.5) 1 (12.5) 6833 (18.5) 3 – 4 4 (25.0) 3 (37.5) 1 (12.5) 7273 (19.6) 5 or more 10 (62.5) 4 (50.0) 6 (75.0) 22,914 (61.9) Laboratory technician present Yes 7 (43.8) 3 (37.5) 4 (50.0) 18,441 (49.8) No 9 (56.2) 5 (62.5) 4 (50.0) 18,579 (50.2) HCWs: healthcare workers; No.: Number; PfPR 2-10 : P. falciparum prevalence among children 2-10 years old; RDTs: rapid diagnostic tests Table 2. Characteristics of healthcare workers who recorded rapid diagnostic test (RDT) results during the MaCRA study and number of RDTs recorded, Uganda 2023 Characteristic Overall N=93 n (%) Region No. RDTs recorded N=37,020 n (%) Busoga N=49 n (%) Lango N=44 n (%) Sex Female 46 (49.5) 30 (61.2) 16 (36.4) 16,745 (45.2) Male 47 (50.5) 19 (38.8) 28 (63.6) 20,275 (54.8) Age (years) < 30 22 (23.7) 13 (26.5) 9 (20.5) 5899 (15.9) 30 – 39 36 (38.7) 18 (36.7) 18 (40.9) 16,019 (43.3) 40 – 49 26 (28.0) 16 (32.7) 10 (22.7) 8993 (24.3) 50 – 59 9 (9.7) 2 (4.1) 7 (15.9) 6109 (16.5) Occupational cadre Nurse 49 (52.7) 27 (55.1) 22 (50.0) 24,598 (66.4) Clinical officer 9 (9.7) 4 (8.2) 5 (11.4) 8179 (22.1) Nonmedical or volunteer staff 12 (12.9) 5 (10.2) 7 (15.9) 2527 (6.8) Community health worker 8 (8.6) 4 (8.2) 4 (9.1) 97 (0.3) Lab technician 5 (5.4) 2 (4.1) 3 (6.8) 19 (0.1) Medical auxiliary staff 10 (10.8) 7 (14.3) 3 (6.8) 1600 (4.3) Highest educational qualification Primary or below 3 (3.2) 3 (6.1) 0 (0) 89 (0.2) Secondary 14 (15.1) 7 (14.3) 7 (15.9) 2270 (6.1) University 76 (81.7) 39 (79.6) 37 (84.1) 34,661 (93.6) Years of experience 0 – 1 8 (8.6) 5 (10.2) 3 (6.8) 2754 (7.4) 2 – 4 12 (12.9) 8 (16.3) 4 (9.1) 2843 (7.7) 5 – 9 25 (26.9) 8 (16.3) 17 (38.6) 13,439 (36.3) 10 or more 48 (51.6) 28 (57.1) 20 (45.5) 17,984 (48.6) Frequency of performing RDTs Very often (every day) 60 (64.5) 31 (63.3) 29 (65.9) 31,744 (85.7) Once in a while to often 27 (29.0) 16 (32.7) 11 (25.0) 5201 (14.0) Never 6 (6.5) 2 (4.1) 4 (9.1) 75 (0.2) RDT proficiency score tercile Low 29 (33.3) 22 (45.8) 7 (17.9) 10,543 (35.3) Middle 29 (33.3) 15 (31.2) 14 (35.9) 13,021 (43.6) High 29 (33.3) 11 (22.9) 18 (46.2) 6313 (21.1) RDTs: rapid diagnostic tests. Table 3. Characteristics of rapid diagnostic tests observed in the MaCRA study by region, Uganda 2023 Characteristic Overall N=37,020 n (%) Region Busoga N=14,100 n (%) Lango N=22,920 n (%) RDT result recorded in OPD register Positive 22,872 (61.8) 7686 (54.5) 15,186 (66.3) Negative 14,148 (38.2) 6414 (45.5) 7734 (33.7) RDT result interpreted by external panel Positive 20,913 (56.5) 7124 (50.5) 13,789 (60.2) Negative 16,107 (43.5) 6976 (49.5) 9131 (39.8) RDT brand Bioline Malaria Pf 19,148 (51.7) 3882 (27.5) 15,266 (66.6) STANDARD Q Malaria Pf 7289 (19.7) 7036 (49.9) 253 (1.1) Bioline Malaria Pf/Pan 4294 (11.6) 1 (0) 4293 (18.7) First Response Malaria Ag (pLDH/HRP2) 3128 (8.4) 1903 (13.5) 1225 (5.3) First Response Malaria Pf 2275 (6.1) 520 (3.7) 1755 (7.7) ParaHIT Malaria Pf 741 (2.0) 741 (5.3) 0 (0) Bioline Malaria Pf (HRP2/pLDH) 128 (0.3) 0 128 (0.6) Other 17 (0) 17 (0) 0 (0) Faint line Yes 4980 (13.5) 1693 (12.0) 3287 (14.3) No 32,040 (86.5) 12,407 (88.0) 19,633 (85.7) Patient sex Female 24,804 (67.0) 9044 (64.2) 15,760 (68.8) Male 12,194 (33.0) 5047 (35.8) 7147 (31.2) Patient age (years) 0 – 4 8442 (22.8) 4333 (30.7) 4109 (17.9) 5 – 14 12,772 (34.5) 4267 (30.3) 8505 (37.1) 15 and more 15,804 (42.7) 5500 (39.0) 10,304 (45.0) Patient diagnosed with malaria Yes 24,220 (65.4) 7691 (54.5) 16,529 (72.1) No 12,800 (34.6) 6409 (45.5) 6391 (27.9) Patient prescribed antimalarial Yes 22,834 (61.7) 7676 (54.4) 15,158 (66.1) No 14,186 (38.3) 6424 (45.6) 7762 (33.9) RDT performed and recorded by the same person Yes 11,159 (30.1) 6338 (45.0) 4821 (21.0) No 25,861 (69.9) 7762 (55.0) 18099 (79.0) HRP2: histidine-rich protein 2; pLDH: parasite lactate dehydrogenase; RDT: rapid diagnostic test Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.docx Cite Share Download PDF Status: Published Journal Publication published 10 Nov, 2025 Read the published version in Malaria Journal → Version 1 posted Editorial decision: Revision requested 02 Jun, 2025 Reviews received at journal 28 May, 2025 Reviews received at journal 18 May, 2025 Reviewers agreed at journal 18 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers invited by journal 14 May, 2025 Editor assigned by journal 08 May, 2025 Submission checks completed at journal 08 May, 2025 First submitted to journal 06 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Lindblade","email":"","orcid":"","institution":"PMI Insights Project, PATH","correspondingAuthor":false,"prefix":"","firstName":"Kim","middleName":"A.","lastName":"Lindblade","suffix":""},{"id":458210217,"identity":"ddc4b6e0-b0e6-4c0f-ad82-ec2c68a3b41c","order_by":13,"name":"Arthur Mpimbaza","email":"","orcid":"","institution":"Child Health and Development Centre, College of Health Sciences, Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Arthur","middleName":"","lastName":"Mpimbaza","suffix":""}],"badges":[],"createdAt":"2025-05-06 13:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6603474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6603474/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12936-025-05637-7","type":"published","date":"2025-11-10T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83125852,"identity":"e6ba004a-63b8-4253-bd94-b3a4b2b4b7e8","added_by":"auto","created_at":"2025-05-20 09:40:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":377417,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Uganda showing the study area\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6603474/v1/c7072a05264f25d76c324b86.jpeg"},{"id":83124467,"identity":"b578c617-e706-4440-bece-fce4c66b6bba","added_by":"auto","created_at":"2025-05-20 09:32:21","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":320065,"visible":true,"origin":"","legend":"\u003cp\u003eStudy profile for rapid diagnostic tests observed in the study\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6603474/v1/93e0575053fd890c5e3896eb.jpeg"},{"id":83126652,"identity":"819009ff-649a-42e4-8828-1046cf174e00","added_by":"auto","created_at":"2025-05-20 09:48:19","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":293618,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of malaria rapid diagnostic test results based on external panel interpretation by study week, Uganda, N=37,020\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6603474/v1/acd3a67e438e308f24f347c0.jpeg"},{"id":83125847,"identity":"2088f821-c490-4935-8209-9ae0b99c7513","added_by":"auto","created_at":"2025-05-20 09:40:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51685,"visible":true,"origin":"","legend":"\u003cp\u003eA forest plot of Cohen's kappa estimates by health facility and the overall estimate from a random-effects model, Uganda 2023\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6603474/v1/1378905cbc30f2bd18f45620.png"},{"id":83124466,"identity":"b2b9d2f8-4f0c-46b0-b1ee-2e18c441676f","added_by":"auto","created_at":"2025-05-20 09:32:20","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":434870,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of factors moderating Cohen’s kappa score from a random-effects model, Uganda 2023\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6603474/v1/a1d528f11469e4cb157e963b.jpeg"},{"id":96104946,"identity":"2f3ca804-00d4-46e8-ae1b-7b99436bcbff","added_by":"auto","created_at":"2025-11-17 16:03:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2553387,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6603474/v1/c23661ce-14b3-4138-9bcf-bb4a55ed1c92.pdf"},{"id":83124443,"identity":"ff863e54-3418-4458-8607-ae024f4cb8db","added_by":"auto","created_at":"2025-05-20 09:32:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14713,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6603474/v1/5e6de915e7442557c175ef02.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accuracy of reporting of malaria rapid diagnostic test results in Uganda","fulltext":[{"header":"Background","content":"\u003cp\u003eOver the past two decades, Uganda has made remarkable progress in malaria control, achieving a 47% decline in malaria incidence and an 81% reduction in mortality between 2000 and 2023 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). One of the major contributors to this success has been improvement in malaria case detection and management, largely driven by the promotion and widespread use of malaria rapid diagnostic tests (RDTs) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite this progress, Uganda remains a significant contributor to the global malaria burden, with recent World Health Organization (WHO) estimates ranking the country third in malaria cases and tenth in malaria-related deaths in 2023 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRDTs are immunochromatographic test strips that detect parasite antigens or enzymes in blood that are either genus- or species-specific (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Since 2011, Uganda adopted the WHO policy of mandatory parasitological confirmation of malaria with either microscopy or RDTs before the prescription of antimalarials (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). At the inception of the policy, microscopy was to remain the \u0026ldquo;gold standard\u0026rdquo; for malaria diagnosis at health centre (HC) III (i.e. health units with laboratory services) and higher-level facilities, while RDTs were limited for use at HC IIs and other facilities whenever microscopy was not possible (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, microscopy is labor-intensive, and its accuracy is dependent on the expertise of the laboratorian, the equipment, and the quality of staining reagents (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Given the high patient load amidst staffing shortages, easier-to-use RDTs have become the preferred test type across all facility types, especially at lower-level health facilities (HCs II and III). Increased use of RDTs has boosted testing rates, resulting in a significant decline in the presumptive treatment of malaria among febrile patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The scale-up of RDTs has also improved the accuracy of reported malaria surveillance data, thereby enabling Uganda to better monitor malaria trends and the effectiveness of interventions (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor malaria RDTs to serve the purpose for which they are intended, test results must be accurate, healthcare workers (HCWs) must prescribe antimalarials based on the test results, and results must be accurately recorded in health facility registers. Studies investigating compliance of HCWs with malaria RDT results have reported varying levels of adherence (\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). A meta-analysis of 14 studies reported an overall compliance with RDT results of 83%, with factors such as patient expectations, HCW cadre, work experience, and perceived test accuracy influencing adherence (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In Uganda, previous studies have documented high proficiency in administering and interpreting malaria RDTs, including among trained community health workers (CHWs) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, none of these studies have assessed the accuracy of malaria RDT data recorded in public health facilities. This study aimed to assess the accuracy of RDT results recorded by HCWs by measuring the level of agreement between RDT results recorded in health facility registers and those of a trained external panel at lower-level public health facilities in Uganda\u0026rsquo;s Busoga and Lango regions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a prospective study conducted as part of a larger multi-country study in sub-Saharan Africa, including Benin, C\u0026ocirc;te d\u0026rsquo;Ivoire and Nigeria. More detailed methods are available in the study overview paper (Lindblade et al.). In brief, between June and November 2023, we captured high-quality images of malaria RDTs performed at health facilities among patients with suspected malaria cases using an AI-powered digital RDT reader smartphone application (HealthPulse, Audere, Seattle, WA USA). These images were sent to an external panel for interpretation. RDT results recorded by HCWs were compared to the interpretation by the external panel to determine the level of agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy sites\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in 16 public health facilities (HC II and III) located in two high-burden regions: Busoga and Lango in Eastern and Northern Uganda, respectively. HC II facilities are defined as those at parish level that provide basic outpatient services and treat common illnesses such as uncomplicated malaria, while HC III facilities offer diagnostic, outpatient, and maternity services at sub-county level. In each region, two districts were purposively selected based on two criteria: 1) the presence of HC II or III public facilities, and 2) the absence of ongoing external interventions supporting malaria testing capacity at the facilities. All lower-level public health facilities in the selected districts were considered eligible if they met the following criteria: 1) three years (2020 to 2022) of complete outpatient malaria data (defined as at least 9 of 12 months each year) reported to the District Health Information System 2 (DHIS-2), which serves as Uganda\u0026rsquo;s health management information system, and 2) a minimum of 50 RDTs performed monthly from 2020 to 2022. Using medians, eligible facilities within each district were grouped into four strata based on patient volume and test positivity rate (TPR). One facility from each stratum was randomly selected, resulting in four study facilities per district (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrained research assistants (RAs) collected RDT images and data using the HealthPulse application on weekdays. At the start of the study, a facility survey was conducted to assess infrastructure and resource capacity for malaria case management. HCWs were surveyed to collect their demographic information and training and experience using RDTs. RAs observed HCWs as they performed an RDT, using a 19-point standardized checklist to assess proficiency (Supplemental file 1).\u003c/p\u003e\n\u003cp\u003eRAs photographed RDTs using the HealthPulse app as soon as practically feasible after the HCW completed interpreting the results, without interfering with or obstructing patient care. The app had an image quality assurance component that immediately flagged images that did not meet quality standards and prompted users to retake the photo when needed. RDT images were matched using barcode labels to corresponding patient data in the outpatient department (OPD) registers, which were captured by RAs using the HealthPulse application.\u003c/p\u003e\n\u003cp\u003eImages were sent to an external panel of trained reviewers who determined whether control and test lines were present in an RDT image. Panelists flagged anomalies in the RDTs and the presence of faint lines. The overview paper [citation] provides further information on the quality control process for image results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCharacteristics of health facilities, HCWs and patients for whom RDTs were performed were presented as proportions disaggregated by region. The latitude and longitude of health facilities were used to calculate \u003cem\u003ePlasmodium falciparum\u0026nbsp;\u003c/em\u003eparasite prevalence for children 2 \u0026ndash; 10 years (PfPR\u003csub\u003e2-10\u003c/sub\u003e) within 5 km using the malariaAtlas package in R (R Foundation for Statistical Computing, Vienna, Austria)\u0026nbsp;(17). HCWs were divided into terciles based on their RDT proficiency scores. Results interpreted by the external panel as positive but recorded as negative by the HCW were termed \u0026lsquo;misrecorded as negative,\u0026rsquo; and those interpreted as negative by the external panel and positive by the HCW were termed \u0026lsquo;misrecorded as positive.\u0026rsquo; The positive predictive value (true positives / [true positives + misrecorded as positive]) and the negative predictive value (true negatives / [true negatives + misrecorded as negative]) were calculated to measure the likelihood that a positive or negative test recorded in a facility register was a true positive or negative, respectively.\u003c/p\u003e\n\u003cp\u003eCohen\u0026rsquo;s kappa was used to measure the level of agreement between malaria RDT results recorded by HCWs in health facility registers and those interpreted by the external panel. To generate an overall estimate of agreement across study facilities, we applied a random-effects meta-analytic approach (16). Specifically, we computed a weighted mean kappa, with each facility-level kappa estimate weighted by the inverse of its variance to account for variation in measurement precision across facilities. In addition to calculating the pooled kappa, we explored potential sources of variability in agreement by including key characteristics of health facilities, HCWs, RDTs and patients on whom RDTs were performed as moderators in the model. All statistical analysis was conducted using R version 4.3.3 (18).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of study health facilities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 16 study health facilities, 10 (62.5%) were HCs II while \u0026nbsp;the remaining six were HCs III (Table 1). The malaria prevalence among children 2 to 10 years old in the 5 km radius around the health facility was \u0026gt;30% in 5 (31.3%) health facilities. Most facilities (10, 62.0%) had at least five staff who performed RDTs. Seven facilities (43.8%) had a laboratory technician present. Lango had fewer HCs II (50.0%) compared to Busoga (75.0%). Additionally, more facilities in Lango had a parasite prevalence \u0026gt;30% (8, 50%) compared to those in Busoga (2, 12.5%).\u003c/p\u003e\n\u003cp\u003eMost RDTs (17,112, 46.2%) were recorded at health facilities in Dokolo district \u0026nbsp;over the study period (Table 1). Health facilities from the high RDT volume stratum performed more RDTs (20,414, 55.1%) than those from the low volume stratum (16,606, 44.9%). Similar numbers of RDTs were performed in facilities with a laboratory technician present (18,441, 49.8%) as without (18,579, 50.2%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of HCWs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 217 health facility staff, 171 (78.8%) reported involvement in the administration, recording, and reporting of malaria RDTs and 169 (98.8%) were interviewed. Over the six months of the study implementation, 93 (55.0%) HCWs contributed at least one RDT recording to the analytical dataset. Of these, 46 (49.5%) were female, and a plurality (36, 38.7%) were aged 30-39 years (Table 2). Nurses constituted the largest cadre of HCWs (49, 52.7%), followed by nonmedical or volunteer staff (12, 12.9%) and medical auxiliary staff (10, 10.8%). Most HCWs (76, 81.7%) had a university degree, while more than half (48, 51.6%) had at least 10 years of experience in their professional roles. Most \u0026nbsp; HCWs reported frequently performing RDTs (60, 64.5%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to Busoga, health facilities in Lango had a higher proportion of male HCWs (63.6% vs. 38.8%) and a greater proportion aged 50\u0026ndash;59 years (15.9% vs. 4.1%) (Table 2). In contrast, fewer HCWs in Lango had 10 or more years of experience (45.5% vs. 57.1%). The proportion of HCWs who reported performing RDTs very often was similar between the two regions. However, a larger proportion of HCWs in Lango had RDT proficiency scores in the upper tercile (46.2% vs. 22.9%).\u003c/p\u003e\n\u003cp\u003eThe distribution of RDTs recorded by HCWs was broadly proportional to the frequency of their demographic and professional characteristics. However, several notable deviations were observed. HCWs under 30 accounted for 15.9% of RDTs recorded, despite representing 23.7% of the HCWs, whereas those aged 50\u0026ndash;59 years recorded 16.5% of RDTs but comprised only 9.7% of HCWs. Nurses and clinical officers recorded a higher-than-expected proportion of RDTs relative to their representation, while other cadres contributed less than expected. HCWs who reported performing RDTs very often made up 64.5% of the total but were responsible for 85.7% of RDTs recorded. Interestingly, HCWs in the middle tercile of RDT proficiency conducted 43.6% of RDTs, compared to 21.1% among those in the highest proficiency tercile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of RDTs observed in this study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study period, a total of 45,838 RDT results was recorded in OPD registers. Of these, 40,049 (87.4%) had images captured in the HealthPulse application. \u0026nbsp;Most missing RDT images were performed on weekends or evenings when RAs were not present. After excluding images without corresponding patient data, 37,020 (92.4%) RDT results were included in the final analysis (Figure 2).\u003c/p\u003e\n\u003cp\u003eThe overall TPR among RDTs recorded in the health facility registers was 61.8% (Table 3). The most common RDT product was Bioline Malaria Pf (Abbott, IL, USA), which accounted for 19,148 (51.7%) cases, while STANDARD Q Malaria Pf (SD Biosensor, Gyeonggi-do, Republic of Korea) and Bioline Malaria Pf/Pan each comprised more than 10% of RDTs performed. A total of 4980 (13.5%) RDTs were tagged as having a faint line. Most RDTs were performed on female patients (24,804, 67.0%) and those aged 15 years or more (15,804, 42.7%). There were more patients recorded in OPD registers with a diagnosis of malaria (24,220, 65.4%), of whom 22,834 (61.7%) received antimalarial prescriptions, than there the number of positive RDTs recorded (22,887, 61.8%). Close to a third of RDT results (11,159, 30.1%) were recorded in the OPD register by the same individual who performed the RDT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRDTs from health\u0026nbsp;facilities in Busoga had a lower TPR than Lango (54.5% vs 63.3%) (Table 3). STANDARD Q Malaria Pf was the main RDT product used in Busoga, while Bioline Malaria Pf was the main product in Lango. The age distribution of patients differed by region, with a lower proportion of patients in Lango aged 0 \u0026ndash; 4 years (17.9%) compared with Busoga (30.7%). RDTs in Busoga were more likely to be performed and recorded by the same person (45.0%) compared to Lango (21.0%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAgreement between healthcare workers\u0026rsquo; and external panel\u0026rsquo;s RDT results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of the 37,020 RDTs reviewed, 33,743 (91.1%) of the HCW results were in agreement with the external panel results, including 20,254 (54.7%) true positives and 13,489 (36.4%) true negatives. Among the disagreements, 2618 (7.1%) were misrecorded as positive and 659 (1.8%) were misrecorded as negative. The positive predictive value was 88.6% and the negative predictive value was 95.3%. While the TPR declined over the study period in line with seasonal patterns (19), the proportion misrecorded as positive or negative did not vary substantially by study week (Figure 3).\u003c/p\u003e\n\u003cp\u003eCohen\u0026apos;s kappa (\u0026kappa;) for the overall level of agreement was 0.82 (95% CI, 0.79, 0.84). There was variability in the estimate of \u0026kappa; by health facility, with individual facility-level varying from a low of 0.73 (95% CI 0.69, 0.77) to a high of 0.92 (95% CI 0.90, 0.94) (Figure 4). Generally, health facilities in Busoga (facilities 1-8) had higher agreement levels compared to those in Lango (facilities 9-16). The level of agreement in Busoga was higher (\u0026kappa; 0.86, 95% CI 0.83, 0.88) compared to Lango (\u0026kappa; 0.78, 95% CI 0.75, 0.81).\u003c/p\u003e\n\u003cp\u003eThere were a few factors other than region found to be associated with the level of agreement. The two districts of Busoga (Buyende and Bugweri) had higher measures of \u0026kappa; than the two districts of Lango (Otuke and Dokolo) (Figure 5). There was no association between either the baseline strata for TPR or RDT volume and the level of agreement. There was a decreasing level of agreement with increasing parasite prevalence, but confidence intervals overlapped. Neither the number of staff who performed RDTs nor the presence of a laboratory technician within the facility had any association with the level of agreement.\u003c/p\u003e\n\u003cp\u003eAmong HCW characteristics, no factor was found to moderate the level of agreement. Several RDT characteristics, however, were associated with \u0026kappa;, including RDT product and the presence of faint lines. The level of agreement was lower for Bioline Malaria Pf (HRP2/pLDH) (\u0026kappa; 0.73, 95% CI 0.56, 0.89), Bioline Malaria Pf/Pan (\u0026kappa; 0.78, 95% CI 0.73, 0.82) and First Response Malaria (pLDH/HRP2) (\u0026kappa; 0.78, 95% CI 0.72, 0.84), all of which have two test lines, compared to other, single test line RDT products. Faint lines were associated with significantly lower levels of agreement (\u0026kappa; 0.16, 95% CI 0.04, 0.28).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we evaluated the accuracy of RDT reporting in Ugandan health facilities by comparing RDT results recorded in facility registers with results interpreted by an external panel reviewing images of the RDTs. Overall, there was a strong level of agreement between HCW\u0026rsquo;s records and the panel\u0026rsquo;s interpretations, indicating that RDT results reported from health facilities are generally reliable. However, 7.1% of results were misrecorded as positive, resulting in an 88.6% probability that a positive result in the register was a true positive. These findings suggest that while RDT reporting is largely accurate, positive results may be over-reported, potentially obscuring declines in TPR over time and masking the impact of malaria control efforts.\u003c/p\u003e \u003cp\u003eNotable regional discrepancies were observed, with lower agreement levels in the Lango region compared to Busoga. This regional discrepancy may be attributed to several factors. First, compared to Busoga, health facilities in Lango reported a higher proportion of older patients, who were associated with lower agreement levels. This aligns with previous studies indicating that patient expectations may influence HCWs\u0026rsquo; adherence to RDT results (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Older patients, including colleagues and community leaders, may exert pressure on HCWs to prescribe antimalarials regardless of test outcomes. In such cases, HCWs might alter recorded RDT results in OPD registers to justify prescriptions, thereby compromising the accuracy of recorded malaria RDT data.\u003c/p\u003e \u003cp\u003eSecond, most health facilities in Lango experienced a higher patient volume but had relatively fewer HCWs responsible for administering and recording RDT results. A low HCW-to-patient ratio can contribute to reduced accuracy in reporting, as increased workloads and burnout are known to elevate the risk of medical errors (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Therefore, efforts to improve data quality and malaria surveillance should prioritize increasing the number of trained HCWs in lower-level health facilities, particularly in high-burden districts to enhance service delivery.\u003c/p\u003e \u003cp\u003eThird, the study also found that RDTs with two test lines were more frequently reported in Lango than in Busoga, and they were associated with lower agreement levels. In Uganda, single test line RDTs are the most commonly used, making them more familiar to HCWs, especially in rural districts. However, in 2020, molecular surveillance studies in Uganda provided evidence of HRP2/3 deletions in certain regions, which prompted a policy decision to provide RDTs that included pLDH in the affected areas (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Newly introduced two test-line RDTs, such as Bioline Malaria Pf/Pan and Bioline Malaria Pf (HRP2/pLDH), require additional training to ensure accurate interpretation. Since the introduction of these tests was not universal across study facilities, it is likely that HCWs did not receive adequate training, leading to misinterpretations. This finding highlights the importance of ensuring that HCWs receive proper training and updated reference materials for newly introduced RDTs, particularly those with different characteristics. To improve the accuracy of malaria RDT reporting, continuous medical education and refresher training should be prioritized, particularly in lower-level facilities. Strengthening HCW capacity and standardizing RDT use will enhance data quality, ensuring reliable malaria surveillance and case management across Uganda.\u003c/p\u003e \u003cp\u003eResults misrecorded as positive were more common than results misrecorded as negative, suggesting a possible systematic bias or set of factors influencing the direction of recording errors When a HCW is in doubt of how to interpret an RDT result or lacks the expertise to confidently explain the cause of fever in the presence of a negative test, they would rather err in the direction of diagnosing malaria to minimize the risk of missing a potentially life-threatening malaria diagnosis. This partly explains the higher prevalence of non-compliance to negative than positive RDT results that have been observed in multiple studies in the region (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Moreover, skilled and experienced HCWs such as nurses and those with access to reference materials were less likely to misrecord RDT results. Though uncommon, results misrecorded as negative could be partially explained by transcription errors introduced in the health facility register during the recording of test results. Moreover, in this study, about 70% of RDT results were recorded in the health facility register by a staff member different from the one who performed the test. This has serious implications for the quality of OPD register data, regardless of whether it was intentional or not. Innovative solutions such as validation of malaria RDT results could be considered, especially in the Lango region, to improve data quality. The other highly plausible explanation for these results could be premature interpretation of RDTs potentially attributed to the overwhelmingly large patient numbers. This was especially common in the Lango region which contributed the majority of RDTs observed in this study. To cope with overwhelming patient numbers, we witnessed HCWs processing RDTs in batches. While batched RDTs were clearly labelled with patient identification information to ensure patients were assigned correct results, this practice could have led to some RDTs being interpreted prematurely, before the recommended manufacturer test time, affecting the accuracy of the final result. Due to the unavoidable time lag between HCW\u0026rsquo;s interpretation and image capture in such scenarios, some tests initially interpreted as negative could havefinished processing, indicating a positive at the time of image capture, resulting in discrepant interpretations by the external panel. The appearance of the test line on an RDT is a time-dependent function. Therefore, premature interpretation of an RDT as negative due to non-appearance of the test line could easily turn out to be a result misrecorded as negative (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIncorrect interpretation of RDTs and subsequent misdiagnosis of malaria may lead to inappropriate patient treatment. Results misinterpreted as negative lead to under-diagnosis and consequently under-treatment of malaria, with the danger of allowing the progression of illness to severe malaria, a life-threatening condition (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Patients incorrectly diagnosed with malaria lead to the wastage of antimalarials and may contribute to mismanagement of nonmalarial causes of fever, potentially contributing to poor outcomes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Furthermore, incorrect records may result in under- or overestimation of malaria cases, affecting resource allocations and policy decisions in affected regions. This highlights the importance of implementing standardized training programs and regular skills assessments to address these gaps and enhance the reliability of RDT results (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study benefited from a large sample size with over 37,000 individual RDT observations. Unlike previous evaluations using digital RDT readers that have compared TPRs between health facility registers and a subset of patients whose results were read by an RDT reader, we conducted comparisons at the individual RDT cassette level. This approach reduced potential bias associated with sampling. Nonetheless, the study had several limitations. First, RDT test results recorded in laboratory registers were not captured, limiting our ability to assess whether these entries were more accurate than those in the facility register. While including these data could have enhanced understanding of within-facility consistency, it is unlikely to have affected the overall conclusions. Second, the external panel interpreted RDT results based on images captured by a smartphone camera. This interpretation could have been affected by the quality of the images submitted limiting visibility of particular details such as faint lines. However, we believe that the study\u0026rsquo;s robust quality control procedures for image interpretation minimized the likelihood of such errors. Finally, although some evidence suggests that misrecording of RDT results, particularly negative results, may be linked to over-treatment and could be intentional, we cannot determine whether these misrecordings were due to deliberate actions, unintentional errors or misinterpretation of the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe level of agreement between RDT results recorded by HCWs and those interpreted by an external panel was strong, suggesting that RDT results reported from health facilities are generally accurate. However, important proportions of RDTs were misrecorded as positive or negative, particularly in the Lango region where factors such as high patient volume, limited HCW staffing, and the use of newer two-line RDTs were more common. To reduce workload and minimize reporting errors, increasing the number of trained HCWs, especially in high-burden districts, should be prioritized. In addition, measures are needed to limit the number of RDT products in circulation, while the government should ensure that HCWs receive standardized training and reference materials. Finally, innovative strategies, such as validation mechanisms for RDT results should be explored to enhance data quality, particularly in settings with high patient volumes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 558px;\"\u003e\n \u003cp\u003eConfidence interval\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 558px;\"\u003e\n \u003cp\u003eHealth centre\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eHCW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 558px;\"\u003e\n \u003cp\u003eHealthcare worker\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eMOH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 558px;\"\u003e\n \u003cp\u003eMinistry of Health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 558px;\"\u003e\n \u003cp\u003eOutpatient department\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ePf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 558px;\"\u003e\n \u003cp\u003e\u003cem\u003ePlasmodium falciparum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 558px;\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 558px;\"\u003e\n \u003cp\u003eRapid diagnostic test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eTPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 558px;\"\u003e\n \u003cp\u003eTest positivity rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 558px;\"\u003e\n \u003cp\u003eWorld Health Organization\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\u003eWe gratefully acknowledge the many healthcare workers and Ministry of Health officials who gave generously of their time to participate in this evaluation. We appreciate the technical support received from staff at the CHDC, including the director, Herbert Muyinda, for his supervisory support. Paul Opira and Martin Omello assisted with data management, Faith Bagatya led technical coordination of ethics and regulatory approvals, Donald Ngarombo managed logistics, and Margaret Nakuya supported financial accountability. The team received support from Timothy Ogwang and Paul Oketch who were the surveillance coordinators in Lango and Busoga regions, respectively. They provided direct supervision to the research assistants, including Jolly Job Odongo, Nelson Opira, Rebecca Adongo, Denis Opira, Zam Fadir, Kevina Nakamya, John Mary Kibirige, Maria Nabuule, Grace Uwimana, Maureen Biira, Joseph Kyabaggu, Julius Nkuma, Catherine, Taima Hamimu and Fred Birungi.\u003c/p\u003e\n\u003cp\u003eWe received excellent research support from Saadjo Sow, Annie Arnzen and Maia Cullen (PMI Insights). Megan Littrell, Kim Vu and Taj Munson provided overall direction and administrative support to the PMI Insights project. Aysu Uygur (Bill \u0026amp; Melinda Gates Foundation) is greatly appreciated for her contributions during the design of the evaluation. We thank the Uganda PMI staff for their support: Edgar Agaba, Grace Appiah, Patrick Condo. Emily Hilton and Natalie Galles (PATH) supported data analysis. Sasha Frade, Sam Smedinghoff, and the Audere development team (USA and South Africa), supported customization of the HealthPulse application and designed dashboards used during the study. We appreciate the engagement of Julia Mwesigwa (PATH) in the initial protocol harmonization meeting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis evaluation was co-funded by PMI Insights and the Bill \u0026amp; Melinda Gates Foundation (Investment # INV-043942). PMI Insights was the global operational research and program evaluation project of the U.S. President\u0026rsquo;s Malaria Initiative (PMI). Funding for this evaluation was made possible by the generous support of the American people through the United States Agency for International Development (USAID) (Cooperative agreement # 7200AA20CA00031). The contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eChild Health and Development Centre, Makerere University, Kampala, Uganda\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNelson Ssewante\u003c/p\u003e\n\u003cp\u003eJane Frances Namuganga\u003c/p\u003e\n\u003cp\u003eAnne Katahoire\u003c/p\u003e\n\u003cp\u003eJenipher Musoke\u003c/p\u003e\n\u003cp\u003eNoel Mutesi\u003c/p\u003e\n\u003cp\u003eArthur Mpimbaza\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eU.S. President\u0026rsquo;s Malaria Initiative, United States Agency for International Development, Washington, DC USA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMichael Humes\u003c/p\u003e\n\u003cp\u003eKevin Griffith\u003c/p\u003e\n\u003cp\u003eRadina Soebiyanto\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAudere, Seattle, WA USA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eShawna Cooper\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePMI Insights Project/PATH, Geneva, Switzerland\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJohn J. Aponte\u003c/p\u003e\n\u003cp\u003eKim A. Lindblade\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNational Malaria Control Division, Ministry of Health, Kampala, Uganda\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBosco Agaba\u003c/p\u003e\n\u003cp\u003eJimmy Opigo\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eKAL, MH\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;KG conceived and designed the evaluation. NS, JFN, AK, JM, NM, AM, and BA oversaw data collection activities. SC oversaw the development of the HealthPulse application used in the study. NS and KAL drafted the manuscript. JJA, RS and KAL analyzed the data. NS, JFN, AK, JM, NM, MH, KG, JJA, RS, SC, BA, JO, KAL and AM critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Nelson Ssewante\u003c/p\u003e\n\u003cp\u003eUpper Mulago Hospital Complex, P.O. Box 6717, Kampala, Uganda\u003c/p\u003e\n\u003cp\u003eChild Health and Development Centre, Makerere University, Kampala, Uganda\u003c/p\u003e\n\u003cp\[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Vector Control Division Research and Ethics Committee\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Ref # VCDREC172) and the Uganda National Council of Science and Technology (Ref # HS2747ES) in Uganda, and the WGC IRB in the USA (Ref # 20231373). All participants provided written, informed consent to participate.\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. World Malaria Report 2024. Geneva, Switzerland: World Health Organization; 2024.\u003c/li\u003e\n\u003cli\u003eKigozi RN, Bwanika J, Goodwin E, Thomas P, Bukoma P, Nabyonga P, et al. Determinants of malaria testing at health facilities: the case of Uganda. Malar J. 2021;20:1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO guidelines for malaria, 3 June 2022. World Health Organization; 2022.\u003c/li\u003e\n\u003cli\u003eKyabayinze DJ, Asiimwe C, Nakanjako D, Nabakooza J, Bajabaite M, Strachan C, et al. Programme level implementation of malaria rapid diagnostic tests (RDTs) use: outcomes and cost of training health workers at lower level health care facilities in Uganda. BMC Public Health. 2012;12:1\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMinistry of Health U. Uganda National Malaria Control Policy 2011. 2011 Jun.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Malaria microscopy quality assurance manual-version 2. World Health Organization; 2016.\u003c/li\u003e\n\u003cli\u003eMpimbaza A, Babikako H, Rutazanna D, Karamagi C, Ndeezi G, Katahoire A, et al. Adherence to malaria management guidelines by health care workers in the Busoga sub-region, eastern Uganda. Malar J. 2022 Jan 25;21(1):25.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Test, treat, track: scaling up diagnostic testing, treatment and surveillance for malaria. World Health Organization; 2012.\u003c/li\u003e\n\u003cli\u003eMbonye AK, Magnussen P, Lal S, Hansen KS, Cundill B, Chandler C, et al. A cluster randomised trial introducing rapid diagnostic tests into registered drug shops in Uganda: impact on appropriate treatment of malaria. PLoS One. 2015;10(7):e0129545.\u003c/li\u003e\n\u003cli\u003eMubi M, Kakoko D, Ngasala B, Premji Z, Peterson S, Bj\u0026ouml;rkman A, et al. Malaria diagnosis and treatment practices following introduction of rapid diagnostic tests in Kibaha District, Coast Region, Tanzania. Malar J. 2013;12:1\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eBisoffi Z, Sirima BS, Angheben A, Lodesani C, Gobbi F, Tinto H, et al. Rapid malaria diagnostic tests vs. clinical management of malaria in rural Burkina Faso: safety and effect on clinical decisions. A randomized trial. Trop Med Int Health. 2009;14(5):491\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eUzochukwu BS, Onwujekwe E, Ezuma NN, Ezeoke OP, Ajuba MO, Sibeudu FT. Improving rational treatment of malaria: perceptions and influence of RDTs on prescribing behaviour of health workers in southeast Nigeria. PLoS One. 2011;6(1):e14627.\u003c/li\u003e\n\u003cli\u003eBatwala V, Magnussen P, Nuwaha F. Comparative feasibility of implementing rapid diagnostic test and microscopy for parasitological diagnosis of malaria in Uganda. Malar J. 2011;10:1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eKabaghe AN, Visser BJ, Spijker R, Phiri KS, Grobusch MP, Van Vugt M. Health workers\u0026rsquo; compliance to rapid diagnostic tests (RDTs) to guide malaria treatment: a systematic review and meta-analysis. Malar J. 2016;15:1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eMiller JS, Mbusa RK, Baguma S, Patel P, Matte M, Ntaro M, et al. Assessing village health workers\u0026rsquo; ability to perform and interpret rapid diagnostic tests for malaria 4 years after initial training: a cross-sectional study. Am J Trop Med Hyg. 2020;104(1):294.\u003c/li\u003e\n\u003cli\u003eBoyce MR, O\u0026rsquo;Meara WP. Use of malaria RDTs in various health contexts across sub-Saharan Africa: a systematic review. BMC Public Health. 2017 May 18;17(1):470.\u003c/li\u003e\n\u003cli\u003ePfeffer DA, Lucas TCD, May D, Harris J, Rozier J, Twohig KA, et al. malariaAtlas: an R interface to global malariometric data hosted by the Malaria Atlas Project. Malar J. 2018 Oct 5;17(1):352.\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2024. Available from: https://www.R-project.org/\u003c/li\u003e\n\u003cli\u003eKigozi SP, Kigozi RN, Sebuguzi CM, Cano J, Rutazaana D, Opigo J, et al. Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019. BMC Public Health. 2020;20:1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eRakotonandrasana DH, Tsukahara T, Yamamoto-Mitani N. Antimalarial drug prescribing by healthcare workers when malaria testing is negative: a qualitative study in Madagascar. Trop Med Health. 2018;46:1\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eAltaras R, Nuwa A, Agaba B, Streat E, Tibenderana JK, Strachan CE. Why do health workers give anti-malarials to patients with negative rapid test results? A qualitative study at rural health facilities in western Uganda. Malar J. 2016 Jan 11;15(1):23.\u003c/li\u003e\n\u003cli\u003eNaseralallah L, Stewart D, Azfar Ali R, Paudyal V. An umbrella review of systematic reviews on contributory factors to medication errors in health-care settings. Expert Opin Drug Saf. 2022;21(11):1379\u0026ndash;99.\u003c/li\u003e\n\u003cli\u003eBosco AB, Anderson K, Gresty K, Prosser C, Smith D, Nankabirwa JI, et al. Molecular surveillance reveals the presence of pfhrp2 and pfhrp3 gene deletions in Plasmodium falciparum parasite populations in Uganda, 2017\u0026ndash;2019. Malar J. 2020;19:1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eMinistry of Health. The Uganda malaria reduction and elimination strategic plan 2021\u0026ndash;2025. 2020th ed. Kampala, Uganda: Ministry of Health;\u003c/li\u003e\n\u003cli\u003eBonful HA, Awua AK, Adjuik M, Tsekpetse D, Adanu RMK, Nortey PA, et al. Extent of inappropriate prescription of artemisinin and anti-malarial injections to febrile outpatients, a cross-sectional analytic survey in the Greater Accra region, Ghana. Malar J. 2019;18:1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eLal S, Ndyomugenyi R, Paintain L, Alexander ND, Hansen KS, Magnussen P, et al. Community health workers adherence to referral guidelines: evidence from studies introducing RDTs in two malaria transmission settings in Uganda. Malar J. 2016;15:1\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eWatson OJ, Sumner KM, Janko M, Goel V, Winskill P, Slater HC, et al. False-negative malaria rapid diagnostic test results and their impact on community-based malaria surveys in sub-Saharan Africa. BMJ Glob Health. 2019 Jul 29;4(4):e001582.\u003c/li\u003e\n\u003cli\u003eKozycki CT, Umulisa N, Rulisa S, Mwikarago EI, Musabyimana JP, Habimana JP, et al. False-negative malaria rapid diagnostic tests in Rwanda: impact of Plasmodium falciparum isolates lacking hrp2 and declining malaria transmission. Malar J. 2017;16:1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eKolekang AS, Afrane Y, Apanga S, Zurovac D, Kwarteng A, Afari-Asiedu S, et al. Challenges with adherence to the \u0026lsquo;test, treat, and track\u0026rsquo;malaria case management guideline among prescribers in Ghana. Malar J. 2022;21(1):332.\u003c/li\u003e\n\u003cli\u003eShelus V, Mumbere N, Masereka A, Masika B, Kiitha J, Nyangoma G, et al. \u0026ldquo;Testing for malaria does not cure any pain\u0026rdquo; A qualitative study exploring low use of malaria rapid diagnostic tests at drug shops in rural Uganda. PLOS Glob Public Health. 2022 Dec 13;2(12):e0001235.\u003c/li\u003e\n\u003cli\u003eParsel SM, Gustafson SA, Friedlander E, Shnyra AA, Adegbulu AJ, Liu Y, et al. Malaria over-diagnosis in Cameroon: diagnostic accuracy of Fluorescence and Staining Technologies (FAST) Malaria Stain and LED microscopy versus Giemsa and bright field microscopy validated by polymerase chain reaction. Infect Dis Poverty. 2017;6(02):20\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eEliades MJ, Wun J, Burnett SM, Alombah F, Amoo-Sakyi F, Chirambo P, et al. Effect of Supportive Supervision on Performance of Malaria Rapid Diagnostic Tests in Sub-Saharan Africa. Am J Trop Med Hyg. 2019 Apr;100(4):876\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eMarti\u0026aacute;\u0026ntilde;ez-Vendrell X, Skjefte M, Sikka R, Gupta H. Factors Affecting the Performance of HRP2-Based Malaria Rapid Diagnostic Tests. Trop Med Infect Dis. 2022 Sep 25;7(10):265.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Characteristics of study health facilities, Uganda 2023\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 233px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=16\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. RDTs recorded\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=37,020\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusoga\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLango\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eDistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eOtuke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5808 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eDokolo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e17,112 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eBuyende\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e5808 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eBugweri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e8292 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eHealth centre level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eLevel II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e10 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e6 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e20,116 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eLevel III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e6 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e2 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e16,904 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eTest positivity rate stratum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e8 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e18,513 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e8 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e18,507 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eRDT volume stratum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e8 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e20,414 (55.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e8 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e16,606 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eParasite prevalence (PfPR\u003csub\u003e2-10\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003e0 \u0026ndash; 9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e2704 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003e10 \u0026ndash; 19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e1855 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003e20 \u0026ndash; 29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e9 (56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e5 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e14,248 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003e30 \u0026ndash; 39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e5 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e18,213 (49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eNumber of HCWs who perform RDTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003e1 \u0026ndash; 2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e2 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e6833 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003e3 \u0026ndash; 4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e3 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e7273 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003e5 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e10 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e6 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e22,914 (61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eLaboratory technician present\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e7 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e3 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e18,441 (49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 233px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e9 (56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e5 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e4 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e18,579 (50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHCWs: healthcare workers; No.: Number; PfPR\u003csub\u003e2-10\u003c/sub\u003e: \u003cem\u003eP. falciparum\u003c/em\u003e prevalence among children 2-10 years old; RDTs: rapid diagnostic tests\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Characteristics of healthcare workers who recorded rapid diagnostic test (RDT) results during the MaCRA study and number of RDTs recorded, Uganda 2023\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=93\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. RDTs recorded\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=37,020\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusoga\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=49\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLango\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=44\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eSex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e46 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e30 (61.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e16 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e16,745 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e47 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e19 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e28 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e20,275 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026lt; 30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e22 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e13 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e9 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5899 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e30 \u0026ndash; 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e36 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e18 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e18 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e16,019 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e40 \u0026ndash; 49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e26 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e16 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e10 (22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e8993 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e50 \u0026ndash; 59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e9 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6109 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eOccupational cadre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eNurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e49 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e27 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e24,598 (66.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eClinical officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e9 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e8179 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eNonmedical or volunteer staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e12 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2527 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eCommunity health worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e8 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e97 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eLab technician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e19 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eMedical auxiliary staff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e10 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1600 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eHighest educational qualification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePrimary or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e3 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e89 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e14 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2270 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e76 (81.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e39 (79.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e37 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e34,661 (93.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eYears of experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e0 \u0026ndash; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e8 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e3 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2754 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e2 \u0026ndash; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e12 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2843 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e5 \u0026ndash; 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e25 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e8 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e17 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e13,439 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003e10 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e48 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e28 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e20 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e17,984 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eFrequency of performing RDTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eVery often (every day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e60 (64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e31 (63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e29 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e31,744 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eOnce in a while to often\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e27 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e16 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e11 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5201 (14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e6 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e4 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e75 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eRDT proficiency score tercile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e29 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e22 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e10,543 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e29 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e15 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e14 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e13,021 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e29 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e11 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e18 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6313 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRDTs: rapid diagnostic tests.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. Characteristics of rapid diagnostic tests observed in the MaCRA study by region, Uganda 2023\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 327px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=37,020\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusoga\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=14,100\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLango\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN=22,920\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eRDT result recorded in OPD register\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e22,872 (61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7686 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e15,186 (66.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e14,148 (38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6414 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7734 (33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eRDT result interpreted by external panel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e20,913 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7124 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e13,789 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e16,107 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6976 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e9131 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eRDT brand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eBioline\u0026nbsp;Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e19,148 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3882 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e15,266 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eSTANDARD Q Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e7289 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7036 (49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e253 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eBioline Malaria Pf/Pan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e4294 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4293 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eFirst Response Malaria Ag (pLDH/HRP2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e3128 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1903 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1225 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eFirst Response Malaria Pf\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2275 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e520 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1755 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eParaHIT Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e741 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e741 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eBioline Malaria Pf (HRP2/pLDH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e128 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e128 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e17 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e17 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eFaint line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4980 (13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1693 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3287 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e32,040 (86.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e12,407 (88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e19,633 (85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003ePatient sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e24,804 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e9044 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e15,760 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e12,194 (33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5047 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7147 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003ePatient age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003e0 \u0026ndash; 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8442 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4333 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4109 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003e5 \u0026ndash; 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e12,772 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4267 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e8505 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003e15 and more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e15,804 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5500 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e10,304 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003ePatient diagnosed with malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e24,220 (65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7691 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e16,529 (72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e12,800 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6409 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6391 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003ePatient prescribed antimalarial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e22,834 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7676 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e15,158 (66.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e14,186 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6424 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7762 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eRDT performed and recorded by the same person\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e11,159 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e6338 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4821 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 327px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e25,861 (69.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e7762 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e18099 (79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHRP2: histidine-rich protein 2; pLDH: parasite lactate dehydrogenase; RDT: rapid diagnostic test\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"malaria-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"malj","sideBox":"Learn more about [Malaria Journal](http://malariajournal.biomedcentral.com/)","snPcode":"12936","submissionUrl":"https://submission.nature.com/new-submission/12936/3","title":"Malaria Journal","twitterHandle":"@malariajournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"malaria, diagnosis, testing, accuracy, agreement","lastPublishedDoi":"10.21203/rs.3.rs-6603474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6603474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eMalaria rapid diagnostic tests (RDTs) have been critical in promoting the rational use of antimalarials and strengthening malaria surveillance. However, the accuracy of routinely reported RDT results in Uganda remains unclear. The study’s objective was to measure the level of agreement between healthcare workers (HCWs) and an external panel’s RDT results among lower-level public health facilities in Busoga and Lango regions, Uganda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A prospective study was conducted in 16 public health facilities in four purposively selected districts in Uganda. At each study site, images of all RDTs were taken as soon as the HCW had finished interpreting the test results and uploaded into HealthPulse (Audere, Seattle, WA USA), a digital RDT reader. Corresponding patient data was captured from the outpatient department (OPD) register, including demographics, RDT test results and prescribed treatment. RDT images were sent to a trained, external panel for interpretation. Cohen’s kappa statistic (κ) was used to determine agreement. The associations between characteristics of health facilities, HCWs and RDTs and the level of agreement were analyzed using meta-analytical approaches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e From June to November 2023, 40,049 RDT images were captured, of which 37,020 (92.4%) were included in the analysis. Overall, the test positivity rate based on OPD records was 61.8%. The overall agreement was strong (κ 0.82, 95% confidence interval [CI] 0.79, 0.84). Where disagreement occurred, HCWs misrecorded more RDT results as positive (7.1%) than negative (1.8%). Agreement was higher in Busoga (κ 0.86, 95% CI 0.83, 0.88) compared to Lango (κ 0.78, 95% CI 0.75, 0.81). Lower agreement levels were also associated with older patients, RDTs with faint lines and RDTs with two test lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe study found a strong level of agreement between HCWs' RDT results and an external panel. However, significant proportions of results were misrecorded as positive or negative, particularly in the Lango region. Targeted interventions, such as RDT validation exercises and tailored refresher training, are recommended to enhance RDT reporting accuracy in Uganda.\u003c/p\u003e","manuscriptTitle":"Accuracy of reporting of malaria rapid diagnostic test results in Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 09:32:14","doi":"10.21203/rs.3.rs-6603474/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-02T07:54:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-28T22:24:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-18T11:08:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49312882971635975323997070944702159477","date":"2025-05-18T10:43:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61622911252087657531735068797663082100","date":"2025-05-15T09:02:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-14T18:48:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-08T05:44:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-08T05:42:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Malaria Journal","date":"2025-05-06T13:14:36+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":"ab2dc042-511f-433e-aef3-967dc691eb64","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T15:59:18+00:00","versionOfRecord":{"articleIdentity":"rs-6603474","link":"https://doi.org/10.1186/s12936-025-05637-7","journal":{"identity":"malaria-journal","isVorOnly":false,"title":"Malaria Journal"},"publishedOn":"2025-11-10 15:57:00","publishedOnDateReadable":"November 10th, 2025"},"versionCreatedAt":"2025-05-20 09:32:14","video":"","vorDoi":"10.1186/s12936-025-05637-7","vorDoiUrl":"https://doi.org/10.1186/s12936-025-05637-7","workflowStages":[]},"version":"v1","identity":"rs-6603474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6603474","identity":"rs-6603474","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}