Low practical knowledge but high willingness to use of rapid diagnostic tests for malaria by healthcare workers in Republic of Congo

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This study assessed these factors to guide malaria case management strategies in the Republic of Congo. Methods : A descriptive cross-sectional study was conducted from November 2024 to February 2025 across nine health districts in Brazzaville. Thirty-six (36) randomly selected public and private primary healthcare facilities participated, with 211 HCWs involved in malaria diagnosis. Data were collected using a semi-structured questionnaire. Knowledge, attitude and pratice levels were classified as good (≥75%), intermediate (50–74.9%), or insufficient (<50%). Results Among participants, 96% reported using mRDTs, though only 36% had received specific training. General malaria knowledge was low, with just 9% scoring good. Most (90%) had poor understanding of target antigens and diagnostic procedures. However, 97% and 59% perceived mRDTs as easy to use and reliable, respectively. In practice, 85.8% used both mRDTs and microscopy, but only 7.6% followed national guidelines. Following a negative mRDT in symptomatic patients, 39% requested microscopy, and 27% prescribed antimalarials based on clinical suspicion. No significant associations were found between knowledge, attitudes or practices and demographic or professional characteristics. Conclusion Although mRDT usage is HCWs in Brazzaville, significant gaps persist in knowledge, training, and compliance with national diagnostic protocols. Enhanced training and policy enforcement are needed to strengthen malaria case management in the Republic of Congo. Malaria Rapid Diagnostic Tests Knowledge Attitudes Practices Healthcare Workers Republic of Congo Background Malaria remains a leading cause of morbidity and mortality in tropical regions. According to the latest estimates from the World Health Organization (WHO), sub-Saharan Africa accounted for 94% of global malaria cases and 95% of malaria-related deaths, with Central Africa alone reporting approximately 60 million cases and 124,000 deaths [ 1 ]. In the Republic of Congo, malaria constitutes the primary cause of medical consultations (59%), hospital admissions (61%), and mortality (22%) [ 2 ]. Effective malaria control relies on timely and accurate diagnosis. The gold standard remains the microscopic examination of Giemsa-stained thick blood smears [ 3 ]. However, the sensitivity of microscopy depends largely on the skill of the microscopist and the quality of the laboratory equipment [ 4 ]. Although molecular techniques such as polymerase chain reaction (PCR) offer superior sensitivity detecting as few as 1 to 5 parasites/µL of blood [ 5 ], they are expensive and require specialized personnel, which limits their routine use in low-resource settings. In this context, malaria rapid diagnostic tests (mRDTs) have emerged as a practical alternative due to their ease of use, minimal infrastructure requirements, and rapid turnaround time. These tests detect specific Plasmodium antigens such as histidine-rich protein 2 (PfHRP2), Plasmodium lactate dehydrogenase (pLDH), or aldolase. However, the persistence of some antigens in the bloodstream after parasite clearance can result in false-positive results [ 6 – 8 ]. Despite this limitation, mRDTs remain essential for malaria diagnosis, particularly in low- and middle-income countries where access to microscopy and stable electricity is limited. In the Republic of Congo, the National Malaria Control Strategy (2022–2023) aimed to ensure quality parasitological diagnosis for 90% of suspected malaria cases nationwide. However, only 72% of suspected cases were tested using microscopy or mRDTs, likely due to a lack of diagnostic tools and gaps in HCWs’ diagnostic capacity [ 2 ]. Previously, the country maintained a sufficient number of well-trained microscopists, but their numbers have declined due to reduced training opportunities and a shortage of functional microscopes in health facilities [ 9 ]. As malaria control and elimination efforts increasingly depend on mRDTs, their effectiveness relies heavily on the attitudes of HCWs, which are shaped by their knowledge and willingness to use these tests. Understanding how knowledge and perception influence diagnostic practices is crucial to identify operational barriers and design targeted interventions to strengthen malaria diagnosis and case management in the Republic of Congo. This study aimed to assess HCWs’ knowledge, perceptions, and willingness to use mRDTs and to identify the key barriers that must be addressed to improve malaria diagnostic practices in the country. Methods Study design A cross-sectional descriptive study was conducted between November 2024 and February 2025. The study used a quantitative approach based on a semi-structured questionnaire administered to HCWs operating in both public and private healthcare facilities. Study setting and sampling strategy The study took place in all 9 health districts of Brazzaville: Makélékélé, Bacongo, Poto-Poto, Moungali, Talangaï, Mfilou, Djiri, Madibou, and Ouenzé. Only first-level (primary) healthcare facilities were included. Facilities had to be operational, accessible, and must have performed mRDTs in the six months preceding the survey. A simple random sampling method was applied to select at least four eligible health facilities per district. A complete list of first-level health facilities was obtained from district health authorities, resulting in the random selection of 36 facilities. Within each selected facility, at least four HCWs involved in malaria diagnosis were recruited using simple random sampling. Eligible participants were HCWs (medical doctors, nurses, midwives, laboratory technicians, and other healthcare personnel) who had been providing malaria diagnosis or treatment services for at least six months at the time of the survey. HCWs were excluded if they were on extended leave, unavailable during the study period, or declined to participate. The recruitment period lasted from 19 November 2024 to 14 February 2025. Data Collection A semi-structured questionnaire was developed to capture sociodemographic information and data related to knowledge, attitudes, and practices (KAP) regarding the use of mRDTs. Prior to implementation, the questionnaire was pretested on a small group of respondents to assess clarity, internal consistency, and the average time required for completion. Based on feedback, modifications were made to improve question wording and structure. The final version was administered in French, the official language of the healthcare system in the Republic of Congo. Data were collected using paper forms during in-person interviews and subsequently digitized using the KoboCollect platform. Each interview lasted between 45 and 60 minutes. Quality control procedures were implemented daily throughout the data collection period to ensure completeness, consistency, and accuracy. Sample size The minimum sample size was calculated using Cochran’s formula adjusted for design effect due to multistage cluster sampling: m = average number of respondents per cluster = 4 ICC = intraclass correlation coefficient = 0.008 Z ₐ = 1.96 for 95% confidence p = assumed proportion using mRDTs = 0.80 q = 1 − p = 0.20 d = desired precision = 0.05 This yielded a design effect (DEFF) of 1.024 and a required sample size of 252. After applying a finite population correction (target population ~1429) and adjusting for a 5% non-response rate, the minimum required sample size was 121. A total of 211 participants were ultimately enrolled. Data analysis Data were analyzed using R statistical software (version 4.2.3). Descriptive statistics, including frequencies and percentages, were used for categorical variables. Chi-square tests (with expected frequencies >5) or Fisher’s exact test was applied to compare proportions between groups. When assumptions for these tests were not met, the Kruskal-Wallis test was used. A p-value < 0.05 was considered statistically significant. Knowledge, perceptions, and practices were scored using predefined criteria S2 Table (supplementary.docx). Each correct response received a score of 1; incorrect or uncertain responses received 0. Scores were converted into percentages. Knowledge, perception, and practice levels were classified as follows: Good: ≥75% Intermediate: 50–74.9% Insufficient: <50% Results Characteristics of the study population. A total of 211 HCWs participated in the study. Nurses comprised the largest group (38%), followed by laboratory technicians (25%) and midwives (19%). Among the nine health districts, Madibou and Ouenzé had the highest representation (25% and 12%, respectively). Most participants (74.4%) worked in public health facilities, and 81% reported no specialization in infectious diseases or diagnostics. The median duration of professional experience was 12 years (Table 1). Table 1: Sociodemographic characteristics of recruited participants. Variables n (%) Profession Health assistant 12 (5.7) Nurse 80 (38.0) Doctor 10 (4.7) Midwife 40 (19.0) Laboratory technician 53 (25.0) Others 16 (7.6) Health district Bacongo 16 (7.5) Djiri 23 (11.0) Madibou 53 (25.0) Makélékélé 21 (10.0) Mfilou 20 (9.5) Moungali 18 (8.5) Ouenze 25 (12.0) Poto-Poto 12 (5.7) Talangaï 23 (11.0) Type of structure Private health 54 (25.6) Public health 157 (74.4) Specialisation in infectious disease or diagnosis No 171 (81.0) Yes 40 (19.0) Years of experience (years), median [Q1, Q3] 12 [3, 18] Knowledge of Malaria and rapid diagnostic tests Overall, only 9% of participants demonstrated good general knowledge of malaria, with most showing only intermediate understanding (67%). Most HCWs (63.5%) correctly defined malaria, but their knowledge of its symptoms was limited, while understanding of transmission modes was moderate. Knowledge of mRDTs was generally poor. Very few participants understood the operating principles, target antigens, or key quality control steps. Detailed distributions are presented in Table 2. Table 2 : Healthcare workers knowledge on malaria and Malaria Rapid Diagnostic Tests. Knowledge n (%) General knowledge of malaria Good 19 (9.0) Intermediate 142 (67.0) Insufficient 50 (24.0) Definition Good 134 (63.5) Intermediate 68 (32.2) Insufficient 9 (4.3) Symptoms Good 24 (11.0) Intermediate 56 (27.0) Insufficient 131 (62.0) Modes of transmission Good 17 (8.1) Intermediate 194 (91.9) Insufficient 0 (0.0) Global knowledge about mRDT Good 0 (0.0) Intermediate 20 (9.5) Insufficient 191 (90.5) Definition Good 46 (22.0) Intermediate 68 (32.0) Insufficient 97 (46.0) How mRDT work Good 0 (0.0) Intermediate 13 (6.2) Insufficient 198 (93.8) Antigens Good 16 (7.6) Intermediate 13 (6.2) Insufficient 182 (86.2) Procedures Good 26 (12.3) Intermediate 62 (29.4) Insufficient 123 (58.3) Critical steps to ensure accurate results Good 31 (15.0) Intermediate 43 (20.0) Insufficient 137 (65.0) Perceptions of healthcare workers regarding mRDTs HCWs’ perceptions about mRDTs were mixed. While many (58.8%) considered them reliable due to their accuracy (45.2%) and speed (23.4%), others expressed doubts to false negatives (35.6%) and lack of precision (37.9%). Most HCWs (68.2%) agreed that not all available mRDTs are equally effective. The most preferred test was SD Bioline Malaria Ag Pf (70.8%), followed by SD Bioline Malaria Ag Pf (HRP2/pLDH) (16%). Accessibility of mRDTs was judged satisfactory by 91.5% of participants. A large majority (97%) found mRDTs easy to use, attributing this to simplicity (50.5%) and speed (43.6%). Additionally, 20.1% stated that no specialized training was needed (Table 3). Table 3: Healthcare worker’s perceptions about Malaria Rapid Diagnostic Tests. Perceptions n(%) RDT is a reliable malaria diagnostic tool (Yes) 124 (58.8) Reasons Speed 29 (23.4) Simple 7 (5.6) accuracy 56 (45.2) quality 13 (10.5) Compliance with procedures 6 (4.8) No reviews 17 (13.7) mRDT is a reliable malaria diagnostic tool (No) 87 (41.2) Reasons False negative 31 (35.6) False positive 4 (4.6) No speed 1 (1.1) Complex 6 (6.9) Not accuracy 33 (37.9) Bad quality 5 (5.7) No reviews 9 (10.3) mRDTs (non-equivalent effectiveness of mRDTs) 144 (68.2) The most effective SD Bioline Malaria Ag Pf 102 (70.8) SD Bioline Malaria Ag Pf (HRP2, pLDH) 23 (16.0) First Response Malaria Ag Pf 4 (2.8) ADV DX Malaria Pf 3 (2.1) Malaria Pf.Pv 1 (0.7) Don't know 10 (7.0) Accessibility and use of mRDTs in health centers Easy access to mRDTs in health facilities 193 (91.5) mRDT is easy to use (Yes) 204 (97) Reasons Speed 89 (43.6) Simplicity 103 (50.5) Without qualification 41 (20.1) No Reviews 12 (5.9) mRDT is Easy to use tool (No) 7 (3.3) Reasons Complexity 3 (42.8) Requires qualification 2 (28.6) No Reviews 0 (0.0) Practices of healthcare workers in using mRDTs Nearly all participants (96%) reported using mRDTs for malaria diagnosis, most commonly the SD Bioline Malaria Ag Pf test (53.5%). However, only about one-third (36%) had received specific training. In practice, microscopy and mRDTs were often used together (86%), and over half of the respondents were unaware of the source of their test supplies. Daily use of mRDTs was frequent (81.5%) of participants. Most performed the test when clinical symptoms were present (70.6%), though only 7.6% reported adherence to national malaria guidelines, and correct interpretation of results was achieved by about two-thirds. Following a positive mRDT result, 73.9% prescribed antimalarial treatment, in case of a negative result, responses varied considerably (Table 4). Table 4 : Knowledge on the use of Malaria Rapid Diagnostic Tests. Practical n (%) Use of mRDTs for malaria diagnosis 203 (96.0) SD Bioline Malaria Ag Pf 113 (53.5) First Response Malaria Ag Pf 13 (6.1) SD Bioline Malaria Ag Pf ( HRP2, pLDH ) 60 (28.4) Paracheck Pf 5 (2.3) ADV DX Malaria Pf 4 (1.8) Malaria Pf.Pv 6 (2.8) Training on the use of mRDTs 75 (36.0) How is malaria diagnosed mRDT 15 (7.1) Microscopy 15 (7.1) Microscopy and mRDT 181 (85.8) mRDTs Suppliers Purchase (Pharmacies or others) 12 (5.7) CAMEPS 47 (22.3) CRS 11 (5.2) PNLP 16 (7.6) DGSSSa 24 (11.4) No idea 112 (53.1) Frequency of use of mRDT Daily 172 (81.5) Weekly 22 (10.4) Monthly 17 (8.1) Situation in which mRDT is used National algorithm 16 (7.6) Automatic 46 (21.8) Symptoms 149 (70.6) Interpretation of mRDT results Good 132 (62.5) Intermediate 17 (8.1) Insufficient 62 (29.4) Actions to take if mRDT result is positive Treatment 156 (73.9) Actions to take if mRDT result is negative Consider other alternative diagnoses 56 (26.5) Additional review 22 (10.4) Microscopy 45 (21.3) Symptomatologic treatment 38 (18.0) Redo mRDT 21 (9.9) Action to take in case of discrepancy between mRDT results and clinical symptoms Microscopy 83 (39.3) Presumptive treatment 57 (27.0) Additional examens (CRP, pregnancy test, CBC,) 62 (29.4) Redo the mRDT 5 (2.4) Refer to clinician 16 (7.6) Don't know 13 (6.2) Association between knowledge and professional or structural characteristics General knowledge of Malaria Analysis of the data presented in S2 Table (supplementary) revealed no statistically significant associations between HCWs’ general knowledge of malaria and any of the assessed variables (p > 0.05). Although nurses represented the largest share of those with good knowledge (36.8%), followed by laboratory technicians (21.0%), physicians were underrepresented (10.5%). Geographically, no significant variations in knowledge levels were observed between districts (p = 0.454), although a notable proportion of participants with intermediate knowledge came from the Madibou district (28.9%). Regarding the type of healthcare facility, public institutions accounted for the majority (89.5%) of participants with good knowledge, but this was not statistically significant (p = 0.065). Neither years of professional experience (p = 0.4) nor specialization in infectious diseases or diagnostics (p = 0.295) was significantly associated with knowledge level. Knowledge of mRDT-targeted antigens There was no significant association (p = 0.586) between professional category and knowledge of mRDT-targeted antigens S3 Table (supplementary). Nurses and laboratory technicians had the highest representation among those with good knowledge (37.5%), followed by midwives (12.5%). No health assistants demonstrated good knowledge. Professional experience (p = 0.596) and specialization in infectious diseases or diagnostics (p = 0.265) were not significantly associated with knowledge level, although 31.3% of those with good knowledge reported having a specialization. Knowledge of mRDT procedures and quality assurance steps None of the variables assessed including profession, health district, type of facility, years of experience, and specialization were significantly associated with knowledge of the mRDT testing procedure (p > 0.05) S4 Table (supplementary). Nurses made up the majority of those with good knowledge (52.2%), followed by laboratory technicians (26.1%) and midwives (13.1%), while no doctors were represented in this category. Most participants with good knowledge came from the Djiri (30.5%) and Ouenzé (26.1%) districts, and from public facilities (65.2%). Regarding knowledge of critical steps to ensure test accuracy, no significant association was found with profession (p = 0.312), years of experience (p = 0.546), or specialization (p = 0.523) S5 Table (supplementary). Interpretation of mRDT results No significant association was observed between profession and ability to interpret mRDT results (p = 0.637), though nurses were most frequently represented among those with poor interpretation skills. Geographic location (p = 0.401), years of experience (p = 0.418), and specialization (p = 0.087) also showed no significant relationship. However, HCWs in public facilities tended to interpret results more accurately than those in private settings (p = 0.061) S6 Table (supplementary). Perceptions of mRDT efficacy and reliability by professional and structural characteristics. Perceived efficacy equivalence of mRDTs The professional category of HCWs was not significantly associated with perceptions regarding the equivalence of mRDT efficacy (p = 0.106). Nurses were the most likely to question the equivalence between different mRDT brands (41.7%), while physicians were least likely to accept the idea of test equivalence (1.5%). Similar trends were observed across health districts (p = 0.830), with HCWs from the Madibou district accounting for the largest share of those expressing scepticism (23.6%). Public sector HCWs more frequently believed in the equivalence of mRDTs (79.1%) compared to their counterparts in private facilities (20.9%), although this difference was not statistically significant (p = 0.313). Neither years of professional experience (p > 0.9) nor specialization in infectious diseases or diagnostics (p = 0.600) was significantly associated with perceived efficacy equivalence of mRDTs S7 Table (supplementary). Perceived Reliability of mRDTs There was no significant association between professional category and perceptions of mRDT reliability (p = 0.349) S8 Table (supplementary). Nurses constituted the largest group among those who perceived mRDTs as reliable (37.1%), followed by midwives (20.2%). Across health districts, most of those who trusted mRDTs were from Madibou (21.8%), Djiri (14.5%), and Ouenzé (12.1%), but these differences were not statistically significant (p = 0.061). Similarly, HCWs in public facilities were more likely to report trust in mRDTs (76.6%) than those in private institutions, although this difference approached but did not reach statistical significance (p = 0.065). No significant associations were found between perceived reliability and years of experience or specialization in infectious diseases or diagnostics. Discussion This study is the first to provide a comprehensive assessment of HCWs' knowledge, perceptions, and practices regarding the use of mRDTs across all 9 health districts of Brazzaville, Republic of Congo. The predominance of nurses among the participants reflects the healthcare system structure, where nurses serve as the primary providers of frontline care particularly in primary healthcare settings. Similar findings have been reported in other sub-Saharan African contexts [ 10 , 11 ]. Despite the widespread use of mRDTs, this study highlights a persistent deficiency in malaria-related knowledge among HCWs. Many providers showed only a partial understanding of the disease’s transmission and clinical manifestations, revealing important gaps in training. These results align with previous studies from Africa showing that many HCWs lack sufficient knowledge about malaria, despite being actively involved in diagnosis and treatment [ 12 , 13 ]. The findings concerning mRDT knowledge are particularly concerning. None of the WCHs demonstrated good overall knowledge, and most were classified as having insufficient knowledge. This gap is critical, as appropriate knowledge is essential to ensure diagnostic accuracy, particularly in resource-limited environments where mRDTs are often the only feasible diagnostic tool [ 14 , 15 ]. Inadequate knowledge can lead to poor diagnostic practices, the continuation of presumptive treatments contrary to WHO guidelines [ 1 , 4 , 16 ]. and even contribute to the emergence of antimalarial drug resistance [ 17 , 18 ]. Moreover, lack of proficiency may erode community trust in health services, which is essential for the success of malaria control programs. The perceptions of mRDTs among HCWs revealed a degree of uncertainty regarding their reliability. Although many participants expressed confidence in these tests, a substantial minority remained doubtful, often citing false-negative results or perceived lack of precision. Such scepticism has also been reported in studies from Nigeria and Kenya [ 19 – 24 ], reflecting technical limitations of mRDTs, including reduced sensitivity at low parasitemia and issues related to pfhrp2 gene deletions [ 25 – 27 ]. Nevertheless, the prevalence of pfhrp2 deletions in Brazzaville appears to be low [ 28 ]. Prior experience, training, and trust in diagnostic tools are known to shape these perceptions, as previously demonstrated by Boyce et al. and Kyabayinze et al . [ 29 , 30 ]. A large proportion of HCWs believed that not all mRDT brands are equally effective, with SD Bioline Malaria Ag Pf being the preferred option. This preference is consistent with previous performance evaluations showing superior accuracy of this brand [ 31 – 33 ]. Furthermore, the majority of participants found mRDTs easy to use citing simplicity and speed as key advantages. Similar perceptions have been reported in Nigeria, where ease of use was a critical factor in promoting test uptake [ 34 ]. However, despite this favourable perception, the limited proportion of HCWs reported having received formal training on mRDT use. This lack of training raises concerns about proper implementation and the risk of diagnostic errors due to procedural mistakes or misinterpretation [ 35 , 36 ]. Moreover, very few participants reported following national malaria diagnostic guidelines. This suggests a gap either in guideline dissemination or in the ability of health systems to reinforce their implementation. These findings are consistent with previous research showing that compliance with guidelines is influenced by tool availability, work environment, and institutional support [ 37 , 38 ]. When faced with negative mRDT results, HCWs demonstrated considerable variability in their clinical decision-making. Some tented to rely on alternative diagnoses or supplementary microscopy, while others resorted to symptomatic or presumptive treatment. This diversity of responses highlights the ongoing tension between test results and clinical judgement, particularly in contexts where confidence in diagnostic tools remains limited. Similar observations have been reported in Tanzania, where deviation from protocols was linked to perceived test unreliability [ 39 ]. The routine use of confirmatory microscopy following negative mRDTs, though sometimes necessary, also places a financial burden on the health system. Interestingly, no significant associations were found between knowledge, perception, or practices and professional or sociodemographic characteristics. This may reflect a systemic gap affecting the entire health workforce, rather than one limited to specific categories or regions. Similar uniformity has been reported in Tanzania [ 39 ], although it contrasts with findings from Nigeria, where specialization in infectious diseases was associated with better knowledge of mRDTs [ 23 ]. One strength of this study lies in its broad coverage, with representation from all health districts and a range of professional categories. This enhances the validity and generalizability of the findings. These data highlight the need for systematic and sustainable policy actions to strengthen malaria diagnostic practices. In particular, the implementation of structured and periodic refresher training programs on the use of mRDTs for healthcare professionals in both the public and private sectors is essential. Such training should emphasize the correct interpretation of test results, adherence to national diagnostic guidelines, and appropriate management of patients with negative test results. This study presents several strengths as well as certain limitations that should be acknowledged. One of its main strengths lies in the comprehensive design, which included all health districts of Brazzaville and covered several primary healthcare facilities, with representation of different categories of healthcare professionals from both public and private facilities. This wide coverage enhances the generalizability of the findings. However, some limitations should be acknowledged. The use of self-reported data may have introduced social desirability bias, with participants potentially overestimating their knowledge or reporting ideal rather than actual practices. Conclusion This study reveals significant deficiencies in the knowledge and practices related to mRDTs among HCWs in Brazzaville, Republic of Congo. Although the use of mRDTs is widespread, understanding of their principles, procedures, and targeted antigens remains alarmingly low. Furthermore, adherence to national malaria diagnostic guidelines is poor, with only a small fraction of HCWs applying the protocols correctly in routine practice. These findings underscore the urgent need for comprehensive and continuous training programs targeting both urban and rural HCWs. Strengthening the dissemination and implementation of national guidelines, along with supervision and quality control mechanisms, is essential to improve diagnostic accuracy and the rational use of antimalarial treatments. Addressing these systemic gaps will be critical to achieving effective malaria case management and supporting national efforts toward malaria control and elimination in the Republic of Congo Abbreviations CAMEPS : Centrale d'achat des Médicaments Essentiels et des Produits de Santé DDSSA : Direction Générale des Services de la Santé DEFF : Design effect FCRM : Fondation Congolaise pour la Recherche Médicale HCW: Healthcare Worker mRDT : Malaria Rapid Diagnosis Test PCR: polymerase chain reaction PfHRP2: Plasmodium falciparum Histidine-Rich Protein 2 pLDH: Plasmodium lactate dehydrogenase Declarations Ethics approval and consent to participate The study received ethical approval from the institutional ethics committee of the Fondation Congolaise pour la Recherche Médicale (Approval No. 057/CEI/FCRM/2024) and administrative authorization from the Ministry of Public Health (Ref. 0752/MSP/CAB.24). Written informed consent was obtained from all participants prior to inclusion in the study. Acknowledgements Our sincere gratitude to the healthcare professionals who actively participated in this study, generously sharing their time and knowledge. We thank Emerode Borhel Nkounga-kounga for his help with data entry. We would like to thank Dr Jean Claude Djontu and Dr Adrian J. F. Luty for their valuable contribution to the critical review of the manuscript. Data Availability Statement The raw datasets generated and/ analyzed for this study will be made available by the authors on request. Conflict of interest The authors declare that they have no competing interest. Funding This work was funded by Abbott Rapid Diagnostics (Pty) Ltd and by Alexander von Humboldt Foundation through the Central Africa Research Hub (HR-Coca) led by FN. Additional support came from the CATCR project (Reference Project No.101145698) funded by Global Health EDCTP3 Joint Undertaking which supported FN, AMM, and CV. ND was supported by SOCOSAM. The funders did not play a role in the design of the study, collection, analysis, and interpretation of data, as well as the writing of the manuscript. Authors' contributions The study was designed, coordinated and supervised by AMM and FN. MTB; SDK and ND conducted the field survey. MTB, SDK, CV and ND designed and developed data analysis methods. CV carried out the data analysis. MTB wrote the first draft of the manuscript. MTB, SDK, CV, ND, AMM and FN participated in the critical reading and revision of the manuscript. All authors read and approved the final version of the manuscript. References WHO: World malaria report 2024: addressing inequity in the global malaria response . In . Geneva: World Health Organization; 2024. PNLP: Rapport d'activités du Programme National de Lutte contre le Paludisme (PNLP), Brazzaville : République du Congo. 2024 . In . ; 2024. 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Zongo S, Farquet V, Ridde V: A qualitative study of health professionals' uptake and perceptions of malaria rapid diagnostic tests in Burkina Faso . Malar J 2016, 15 :190. Bird C, Hayward GN, Turner PJ, Merrick V, Lyttle MD, Mullen N, Fanshawe TR: A Diagnostic Accuracy Study to Evaluate Standard Rapid Diagnostic Test (RDT) Alone to Safely Rule Out Imported Malaria in Children Presenting to UK Emergency Departments . J Pediatric Infect Dis Soc 2023, 12 (5):290-297. Hofer LM, Kweyamba PA, Sayi RM, Chabo MS, Maitra SL, Moore SJ, Tambwe MM: Malaria rapid diagnostic tests reliably detect asymptomatic Plasmodium falciparum infections in school-aged children that are infectious to mosquitoes . Parasites & Vectors 2023, 16 (1):217. Obi IF, Sabitu K, Olorukooba A, Adebowale AS, Usman R, Nwokoro U, Ajumobi O, Idris S, Nwankwo L, Ajayi IO: Health workers' perception of malaria rapid diagnostic test and factors influencing compliance with test results in Ebonyi state, Nigeria . PLoS One 2019, 14 (10):e0223869. Diggle E, Asgary R, Gore-Langton G, Nahashon E, Mungai J, Harrison R, Abagira A, Eves K, Grigoryan Z, Soti D et al : Perceptions of malaria and acceptance of rapid diagnostic tests and related treatment practises among community members and health care providers in Greater Garissa, North Eastern Province, Kenya . Malaria Journal 2014, 13 (1):502. Ranadive N, Kunene S, Darteh S, Ntshalintshali N, Nhlabathi N, Dlamini N, Chitundu S, Saini M, Murphy M, Soble A et al : Limitations of Rapid Diagnostic Testing in Patients with Suspected Malaria: A Diagnostic Accuracy Evaluation from Swaziland, a Low-Endemicity Country Aiming for Malaria Elimination . Clin Infect Dis 2017, 64 (9):1221-1227. Bosco AB, Nankabirwa JI, Yeka A, Nsobya S, Gresty K, Anderson K, Mbaka P, Prosser C, Smith D, Opigo J: Limitations of rapid diagnostic tests in malaria surveys in areas with varied transmission intensity in Uganda 2017-2019: Implications for selection and use of HRP2 RDTs . PloS one 2020, 15 (12):e0244457. 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, 7 (10). Krueger T, Ikegbunam M, Lissom A, Sandri TL, Ntabi JDM, Djontu JC, Baina MT, Lontchi RAL, Maloum M, Ella GZ et al : Low Prevalence of Plasmodium falciparum Histidine-Rich Protein 2 and 3 Gene Deletions-A Multiregional Study in Central and West Africa . Pathogens 2023, 12 (3). Boyce MR, Menya D, Turner EL, Laktabai J, Prudhomme-O'Meara W: Evaluation of malaria rapid diagnostic test (RDT) use by community health workers: a longitudinal study in western Kenya . Malar J 2018, 17 (1):206. Kyabayinze DJ, Asiimwe C, Nakanjako D, Nabakooza J, Bajabaite M, Strachan C, Tibenderana JK, Van Geetruyden JP: 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):291. Simon IN, Malau MB, David MY, Emile NJ: Performance of Four Malaria Rapid Diagnostic Tests (RDTs) in the Diagnosis of Malaria in North Central Nigeria . Int J Infect Dis Ther [Internet] 2020, 5 (4):106-111. Manjurano A, Omolo JJ, Lyimo E, Miyaye D, Kishamawe C, Matemba LE, Massaga JJ, Changalucha J, Kazyoba PE: Performance evaluation of the highly sensitive histidine‐rich protein 2 rapid test for Plasmodium falciparum malaria in North-West Tanzania . Malaria Journal 2021, 20 (1):58. Orimadegun AE, Dada-Adegbola HO, Michael OS, Adepoju AA, Funwei RI, Olusola FI, Ajayi IO, Ogunkunle OO, Ademowo OG, Jegede AS et al : SD-Bioline malaria rapid diagnostic test performance and time to become negative after treatment of malaria infection in Southwest Nigerian Children . Ann Afr Med 2023, 22 (4):470-480. Weigl BH, Boyle DS, de los Santos T, Peck RB, Steele MS: Simplicity of use: a critical feature for widespread adoption of diagnostic technologies in low-resource settings . Expert Rev Med Devices 2009, 6 (5):461-464. Rennie W, Phetsouvanh R, Lupisan S, Vanisaveth V, Hongvanthong B, Phompida S, Alday P, Fulache M, Lumagui R, Jorgensen P et al : Minimising human error in malaria rapid diagnosis: clarity of written instructions and health worker performance . Trans R Soc Trop Med Hyg 2007, 101 (1):9-18. Mbanefo A, Kumar N: Evaluation of Malaria Diagnostic Methods as a Key for Successful Control and Elimination Programs . Trop Med Infect Dis 2020, 5 (2). Kabaghe AN, Visser BJ, Spijker R, Phiri KS, Grobusch MP, van Vugt M: Health workers’ compliance to rapid diagnostic tests (RDTs) to guide malaria treatment: a systematic review and meta-analysis . Malaria Journal 2016, 15 (1):163. Amboko B, Stepniewska K, Machini B, Bejon P, Snow RW, Zurovac D: Factors influencing health workers’ compliance with outpatient malaria ‘test and treat’ guidelines during the plateauing performance phase in Kenya, 2014–2016 . Malaria Journal 2022, 21 (1):68. Bohle LF, Abdallah AK, Galli F, Canavan R, Molesworth K: Knowledge, attitudes and practices towards malaria diagnostics among healthcare providers and healthcare-seekers in Kondoa district, Tanzania: a multi-methodological situation analysis . Malar J 2022, 21 (1):224. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablesS1S8.docx Suplementary: S1 Table: definition of scores; S2 Table: General knowledge about malaria according to healthcare worker characteristics; S3 Table: Knowledge antigens detected by mRDTs according to healthcare worker characteristics; S4 Table: Knowledge of testing procedures according to healthcare worker characteristics S5 Table: Knowledge of critical steps to ensure accurate malaria test results according to healthcare workers characteristics; S6 Table: Interpretation of mRDT results according to healthcare workers characteristics; S7 Table:Perception about mRDT equivalency of efficacity according to healthcare worker characteristics, and S8 Table: Perception about mRDT reliability according to healthcare workers characteristics Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 May, 2026 Reviews received at journal 23 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor invited by journal 06 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 02 Mar, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9006175","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616607208,"identity":"9be57f1f-d93e-4c49-b600-2a939b3ae14c","order_by":0,"name":"Marcel Tapsou BAINA","email":"","orcid":"","institution":"Fondation Congolaise Pour La Recherche Médicale","correspondingAuthor":false,"prefix":"","firstName":"Marcel","middleName":"Tapsou","lastName":"BAINA","suffix":""},{"id":616607209,"identity":"f8b2c69a-a0c2-4b05-aae0-ae45f76ac8df","order_by":1,"name":"Alain Maxime MOUANGA","email":"","orcid":"","institution":"Société Congolaise de Santé Mentale","correspondingAuthor":false,"prefix":"","firstName":"Alain","middleName":"Maxime","lastName":"MOUANGA","suffix":""},{"id":616607210,"identity":"aaa6469c-f659-4310-aecb-bfc0689813e1","order_by":2,"name":"Christevy VOUVOUNGUI","email":"","orcid":"","institution":"Fondation Congolaise Pour La Recherche Médicale","correspondingAuthor":false,"prefix":"","firstName":"Christevy","middleName":"","lastName":"VOUVOUNGUI","suffix":""},{"id":616607211,"identity":"e084e71a-8a8d-4661-99c2-1b56ac9e9334","order_by":3,"name":"Steve DIAFOUKA-KIETELA","email":"","orcid":"","institution":"Fondation Congolaise Pour La Recherche Médicale","correspondingAuthor":false,"prefix":"","firstName":"Steve","middleName":"","lastName":"DIAFOUKA-KIETELA","suffix":""},{"id":616607212,"identity":"e1fd851c-c18b-4d92-862a-60818894d324","order_by":4,"name":"Marliti NGAMBOU NGUISSALIKI","email":"","orcid":"","institution":"Ministry of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Marliti","middleName":"NGAMBOU","lastName":"NGUISSALIKI","suffix":""},{"id":616607213,"identity":"13227389-dcbc-411b-b6fe-f941bf9b11b1","order_by":5,"name":"Nafissatou DIALLO","email":"","orcid":"","institution":"Fondation Congolaise Pour La Recherche Médicale","correspondingAuthor":false,"prefix":"","firstName":"Nafissatou","middleName":"","lastName":"DIALLO","suffix":""},{"id":616607214,"identity":"581b2831-8228-42cd-8c57-b96fc301811d","order_by":6,"name":"Francine NTOUMI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBAC9gYwJSEHZRABeA4wg7UY8xwgUQtDYg/xWqT7Dz7mzbFI72E/+3QDQ41Nnrx7A+OHjzl4tMgcZjbm3SaR28OTbnaD4VhaseGZA8ySM7fh1mIvkcwmDdKyX4KN7fbfhsOJG2cksDHz4tHCA9WSzgPUcoORFC0JcC3zJQhrMTacu03CsIcnjQ3kl8QNPAeb8fqFRyLx4YO32+rkediPAbXU2CTOb28++OEjHi2YwOAAYwMp6oFAnlQNo2AUjIJRMOwBAMHMR9pTpOx2AAAAAElFTkSuQmCC","orcid":"","institution":"University of Tübingen","correspondingAuthor":true,"prefix":"","firstName":"Francine","middleName":"","lastName":"NTOUMI","suffix":""}],"badges":[],"createdAt":"2026-03-02 06:09:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9006175/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9006175/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106725886,"identity":"d8bd7917-c2df-402c-8ae4-73bbdbec5c03","added_by":"auto","created_at":"2026-04-12 18:34:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3341320,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9006175/v1/222c905d-d7ec-461c-b750-e95d921f3ddc.pdf"},{"id":106436813,"identity":"4d3362f4-99c0-40d0-acef-77cb27e6594a","added_by":"auto","created_at":"2026-04-08 14:04:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSuplementary\u003c/strong\u003e: \u003cstrong\u003eS1 Table\u003c/strong\u003e: definition of scores; \u003cstrong\u003eS2 Table\u003c/strong\u003e: General knowledge about malaria according to healthcare worker characteristics; \u003cstrong\u003eS3 Table\u003c/strong\u003e: Knowledge antigens detected by mRDTs according to healthcare worker characteristics; \u003cstrong\u003eS4 Table\u003c/strong\u003e: Knowledge of testing procedures according to healthcare worker characteristics \u003cstrong\u003eS5 Table\u003c/strong\u003e: Knowledge of critical steps to ensure accurate malaria test results according to healthcare workers characteristics; \u003cstrong\u003eS6 Table\u003c/strong\u003e: Interpretation of mRDT results according to healthcare workers characteristics; \u003cstrong\u003eS7 Table\u003c/strong\u003e:Perception about mRDT equivalency of efficacity according to healthcare worker characteristics, \u0026nbsp;and \u003cstrong\u003eS8 Table\u003c/strong\u003e: Perception about mRDT reliability according to healthcare workers characteristics\u003c/p\u003e","description":"","filename":"SupplementaryTablesS1S8.docx","url":"https://assets-eu.researchsquare.com/files/rs-9006175/v1/3612fc2d672f073d1643a48f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Low practical knowledge but high willingness to use of rapid diagnostic tests for malaria by healthcare workers in Republic of Congo","fulltext":[{"header":"Background","content":"\u003cp\u003eMalaria remains a leading cause of morbidity and mortality in tropical regions. According to the latest estimates from the World Health Organization (WHO), sub-Saharan Africa accounted for 94% of global malaria cases and 95% of malaria-related deaths, with Central Africa alone reporting approximately 60\u0026nbsp;million cases and 124,000 deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the Republic of Congo, malaria constitutes the primary cause of medical consultations (59%), hospital admissions (61%), and mortality (22%) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEffective malaria control relies on timely and accurate diagnosis. The gold standard remains the microscopic examination of Giemsa-stained thick blood smears [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the sensitivity of microscopy depends largely on the skill of the microscopist and the quality of the laboratory equipment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although molecular techniques such as polymerase chain reaction (PCR) offer superior sensitivity detecting as few as 1 to 5 parasites/\u0026micro;L of blood [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], they are expensive and require specialized personnel, which limits their routine use in low-resource settings.\u003c/p\u003e \u003cp\u003eIn this context, malaria rapid diagnostic tests (mRDTs) have emerged as a practical alternative due to their ease of use, minimal infrastructure requirements, and rapid turnaround time. These tests detect specific \u003cem\u003ePlasmodium\u003c/em\u003e antigens such as histidine-rich protein 2 (PfHRP2), \u003cem\u003ePlasmodium\u003c/em\u003e lactate dehydrogenase (pLDH), or aldolase. However, the persistence of some antigens in the bloodstream after parasite clearance can result in false-positive results [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite this limitation, mRDTs remain essential for malaria diagnosis, particularly in low- and middle-income countries where access to microscopy and stable electricity is limited.\u003c/p\u003e \u003cp\u003eIn the Republic of Congo, the National Malaria Control Strategy (2022\u0026ndash;2023) aimed to ensure quality parasitological diagnosis for 90% of suspected malaria cases nationwide. However, only 72% of suspected cases were tested using microscopy or mRDTs, likely due to a lack of diagnostic tools and gaps in HCWs\u0026rsquo; diagnostic capacity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Previously, the country maintained a sufficient number of well-trained microscopists, but their numbers have declined due to reduced training opportunities and a shortage of functional microscopes in health facilities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As malaria control and elimination efforts increasingly depend on mRDTs, their effectiveness relies heavily on the attitudes of HCWs, which are shaped by their knowledge and willingness to use these tests. Understanding how knowledge and perception influence diagnostic practices is crucial to identify operational barriers and design targeted interventions to strengthen malaria diagnosis and case management in the Republic of Congo.\u003c/p\u003e \u003cp\u003eThis study aimed to assess HCWs\u0026rsquo; knowledge, perceptions, and willingness to use mRDTs and to identify the key barriers that must be addressed to improve malaria diagnostic practices in the country.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional descriptive study was conducted between November 2024 and February 2025. The study used a quantitative approach based on a semi-structured questionnaire administered to HCWs operating in both public and private healthcare facilities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy setting and sampling strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study took place in all 9 health districts of Brazzaville: Mak\u0026eacute;l\u0026eacute;k\u0026eacute;l\u0026eacute;, Bacongo, Poto-Poto, Moungali, Talanga\u0026iuml;, Mfilou, Djiri, Madibou, and Ouenz\u0026eacute;. Only first-level (primary) healthcare facilities were included. Facilities had to be operational, accessible, and must have performed mRDTs in the six months preceding the survey.\u003c/p\u003e\n\u003cp\u003eA simple random sampling method was applied to select at least four eligible health facilities per district. A complete list of first-level health facilities was obtained from district health authorities, resulting in the random selection of 36 facilities. Within each selected facility, at least four HCWs involved in malaria diagnosis were recruited using simple random sampling. Eligible participants were HCWs (medical doctors, nurses, midwives, laboratory technicians, and other healthcare personnel) who had been providing malaria diagnosis or treatment services for at least six months at the time of the survey. HCWs were excluded if they were on extended leave, unavailable during the study period, or declined to participate. The recruitment period lasted from 19 November 2024 to 14 February 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA semi-structured questionnaire was developed to capture sociodemographic information and data related to knowledge, attitudes, and practices (KAP) regarding the use of mRDTs. Prior to implementation, the questionnaire was pretested on a small group of respondents to assess clarity, internal consistency, and the average time required for completion. Based on feedback, modifications were made to improve question wording and structure.\u003c/p\u003e\n\u003cp\u003eThe final version was administered in French, the official language of the healthcare system in the Republic of Congo. Data were collected using paper forms during in-person interviews and subsequently digitized using the KoboCollect platform. Each interview lasted between 45 and 60 minutes. Quality control procedures were implemented daily throughout the data collection period to ensure completeness, consistency, and accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe minimum sample size was calculated using Cochran\u0026rsquo;s formula adjusted for design effect due to multistage cluster sampling:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cem\u003em\u003c/em\u003e = average number of respondents per cluster = 4\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eICC\u003c/em\u003e = intraclass correlation coefficient = 0.008\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eZ\u003c/em\u003eₐ = 1.96 for 95% confidence\u003c/li\u003e\n \u003cli\u003e\u003cem\u003ep\u003c/em\u003e = assumed proportion using mRDTs = 0.80\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eq\u003c/em\u003e = 1 \u0026minus; \u003cem\u003ep\u003c/em\u003e = 0.20\u003c/li\u003e\n \u003cli\u003e\u003cem\u003ed\u003c/em\u003e = desired precision = 0.05\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis yielded a design effect (DEFF) of 1.024 and a required sample size of 252. After applying a finite population correction (target population ~1429) and adjusting for a 5% non-response rate, the minimum required sample size was 121. A total of 211 participants were ultimately enrolled.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using R statistical software (version 4.2.3). Descriptive statistics, including frequencies and percentages, were used for categorical variables. Chi-square tests (with expected frequencies \u0026gt;5) or Fisher\u0026rsquo;s exact test was applied to compare proportions between groups. When assumptions for these tests were not met, the Kruskal-Wallis test was used. A p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003eKnowledge, perceptions, and practices were scored using predefined criteria S2 Table (supplementary.docx). Each correct response received a score of 1; incorrect or uncertain responses received 0. Scores were converted into percentages. Knowledge, perception, and practice levels were classified as follows:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eGood: \u0026ge;75%\u003c/li\u003e\n \u003cli\u003eIntermediate: 50\u0026ndash;74.9%\u003c/li\u003e\n \u003cli\u003eInsufficient: \u0026lt;50%\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of the study population.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 211 HCWs participated in the study. Nurses comprised the largest group (38%), followed by laboratory technicians (25%) and midwives (19%). Among the nine health districts, Madibou and Ouenz\u0026eacute; had the highest representation (25% and 12%, respectively). Most participants (74.4%) worked in public health facilities, and 81% reported no specialization in infectious diseases or diagnostics. The median duration of professional experience was 12 years (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eSociodemographic characteristics of recruited participants.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"583\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProfession\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eHealth assistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e12 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eNurse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e80 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eDoctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e10 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eMidwife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e40 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eLaboratory technician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e53 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e16 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth district\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eBacongo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e16 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eDjiri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e23 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eMadibou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e53 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eMak\u0026eacute;l\u0026eacute;k\u0026eacute;l\u0026eacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e21 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eMfilou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e20 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eMoungali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e18 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eOuenze\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e25 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003ePoto-Poto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e12 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eTalanga\u0026iuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e23 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of structure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003ePrivate health\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e54 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003ePublic health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e157 (74.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialisation in infectious disease or diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e171 (81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e40 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 466px;\"\u003e\n \u003cp\u003eYears of experience (years), median [Q1, Q3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e12 [3, 18]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eKnowledge of Malaria and rapid diagnostic tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, only 9% of participants demonstrated good general knowledge of malaria, with most showing only intermediate understanding (67%). Most HCWs (63.5%) correctly defined malaria, but their knowledge of its symptoms was limited, while understanding of transmission modes was moderate.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Knowledge of mRDTs was generally poor. Very few participants understood the operating principles, target antigens, or key quality control steps. Detailed distributions are presented in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Healthcare workers knowledge on malaria and Malaria Rapid Diagnostic Tests.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKnowledge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\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: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral knowledge of malaria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e19 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Intermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e142 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Insufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e50 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Definition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e134 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e68 (32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e9 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e24 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e56 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e131 (62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Modes of transmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e17 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e194 (91.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal knowledge about mRDT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Intermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e20 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Insufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e191 (90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Definition\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e46 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e68 (32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e97 (46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;How mRDT work\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e13 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e198 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Antigens\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e16 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e13 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e182 (86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Procedures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e26 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e62 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e123 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Critical steps to ensure accurate results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e31 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e43 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 491px;\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e137 (65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePerceptions of healthcare workers regarding mRDTs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHCWs\u0026rsquo; perceptions about mRDTs were mixed. While many (58.8%) considered them reliable due to their accuracy (45.2%) and speed (23.4%), others expressed doubts to false negatives (35.6%) and lack of precision (37.9%). Most HCWs (68.2%) agreed that not all available mRDTs are equally effective. The most preferred test was SD Bioline Malaria Ag Pf (70.8%), followed by SD Bioline Malaria Ag Pf (HRP2/pLDH) (16%). Accessibility of mRDTs was judged satisfactory by 91.5% of participants. A large majority (97%) found mRDTs easy to use, attributing this to simplicity (50.5%) and speed (43.6%). Additionally, 20.1% stated that no specialized training was needed (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Healthcare worker\u0026rsquo;s perceptions about Malaria Rapid Diagnostic Tests.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"649\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceptions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\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: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRDT is a reliable malaria diagnostic tool (Yes)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e124 (58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eReasons\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eSpeed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e29 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eSimple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e7 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eaccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e56 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003equality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e13 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eCompliance with procedures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e6 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eNo reviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e17 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003emRDT is a reliable malaria diagnostic tool\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e87 (41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eReasons\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eFalse negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e31 (35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eFalse positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e4 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eNo speed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eComplex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e6 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eNot accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e33 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eBad quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e5 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eNo reviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e9 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emRDTs (non-equivalent effectiveness of mRDTs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e144 (68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe most effective\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eSD Bioline Malaria Ag Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e102 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eSD Bioline Malaria Ag Pf (HRP2, pLDH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e23 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eFirst Response Malaria Ag Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e4 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eADV DX Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e3 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eMalaria Pf.Pv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e1 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eDon\u0026apos;t know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e10 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccessibility and use of mRDTs in health centers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eEasy access to mRDTs in health facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e193 (91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emRDT is easy to use (Yes)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e204 (97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eReasons\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eSpeed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e89 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eSimplicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e103 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eWithout qualification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e41 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eNo Reviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e12 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emRDT is Easy to use tool (No)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e7 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eReasons\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e3 (42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eRequires qualification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e2 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 483px;\"\u003e\n \u003cp\u003eNo Reviews\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePractices of healthcare workers in using mRDTs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNearly all participants (96%)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ereported using mRDTs for malaria diagnosis, most commonly the SD Bioline Malaria Ag Pf test (53.5%). However, only about one-third (36%) had received specific training.\u003c/p\u003e\n\u003cp\u003eIn practice, microscopy and mRDTs were often used together (86%), and over half of the respondents were unaware of the source of their test supplies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDaily use of mRDTs was frequent (81.5%) of participants. Most performed the test when clinical symptoms were present (70.6%), though only 7.6% reported adherence to national malaria guidelines, and correct interpretation of results was achieved by about two-thirds.\u003c/p\u003e\n\u003cp\u003eFollowing a positive mRDT result, 73.9% prescribed antimalarial treatment, in case of a negative result, responses varied considerably (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e: Knowledge on the use of Malaria Rapid Diagnostic Tests.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePractical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\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: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of mRDTs for malaria diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e203 (96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; SD Bioline Malaria Ag Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e113 (53.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eFirst Response Malaria Ag Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e13 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eSD Bioline Malaria Ag Pf (\u003cem\u003eHRP2, pLDH\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e60 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eParacheck Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eADV DX Malaria Pf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eMalaria Pf.Pv\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining on the use of mRDTs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e75 (36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow is malaria diagnosed\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; mRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e15 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Microscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e15 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Microscopy and mRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e181 (85.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emRDTs Suppliers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003ePurchase (Pharmacies or others)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eCAMEPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e47 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eCRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e11 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003ePNLP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e16 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eDGSSSa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e24 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eNo idea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e112 (53.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency of use of mRDT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Daily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e172 (81.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Weekly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Monthly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSituation in which mRDT is used\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; National algorithm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e16 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Automatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e46 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e149 (70.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterpretation of mRDT results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e132 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Intermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e17 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Insufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e62 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActions to take if mRDT result is positive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e156 (73.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eActions to take if mRDT result is negative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Consider other alternative diagnoses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e56 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Additional review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Microscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e45 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Symptomatologic treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e38 (18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Redo mRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e21 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAction to take in case of discrepancy between mRDT results and clinical symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eMicroscopy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e83 (39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003ePresumptive treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e57 (27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eAdditional examens (CRP, pregnancy test, CBC,)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e62 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eRedo the mRDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003eRefer to clinician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e16 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 444px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Don\u0026apos;t know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e13 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between knowledge and professional or structural characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral knowledge of Malaria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of the data presented in S2 Table (supplementary) revealed no statistically significant associations between HCWs\u0026rsquo; general knowledge of malaria and any of the assessed variables (p \u0026gt; 0.05). Although nurses represented the largest share of those with good knowledge (36.8%), followed by laboratory technicians (21.0%), physicians were underrepresented (10.5%).\u003c/p\u003e\n\u003cp\u003eGeographically, no significant variations in knowledge levels were observed between districts (p = 0.454), although a notable proportion of participants with intermediate knowledge came from the Madibou district (28.9%). Regarding the type of healthcare facility, public institutions accounted for the majority (89.5%) of participants with good knowledge, but this was not statistically significant (p = 0.065). Neither years of professional experience (p = 0.4) nor specialization in infectious diseases or diagnostics (p = 0.295) was significantly associated with knowledge level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKnowledge of mRDT-targeted antigens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no significant association (p = 0.586) between professional category and knowledge of mRDT-targeted antigens S3 Table (supplementary). Nurses and laboratory technicians had the highest representation among those with good knowledge (37.5%), followed by midwives (12.5%). No health assistants demonstrated good knowledge.\u003c/p\u003e\n\u003cp\u003eProfessional experience (p = 0.596) and specialization in infectious diseases or diagnostics (p = 0.265) were not significantly associated with knowledge level, although 31.3% of those with good knowledge reported having a specialization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKnowledge of mRDT procedures and quality assurance steps\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the variables assessed including profession, health district, type of facility, years of experience, and specialization were significantly associated with knowledge of the mRDT testing procedure (p \u0026gt; 0.05) S4 Table (supplementary).\u003c/p\u003e\n\u003cp\u003eNurses made up the majority of those with good knowledge (52.2%), followed by laboratory technicians (26.1%) and midwives (13.1%), while no doctors were represented in this category. Most participants with good knowledge came from the Djiri (30.5%) and Ouenz\u0026eacute; (26.1%) districts, and from public facilities (65.2%).\u003c/p\u003e\n\u003cp\u003eRegarding knowledge of critical steps to ensure test accuracy, no significant association was found with profession (p = 0.312), years of experience (p = 0.546), or specialization (p = 0.523) S5 Table (supplementary).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation of mRDT results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo significant association was observed between profession and ability to interpret mRDT results (p = 0.637), though nurses were most frequently represented among those with poor interpretation skills. Geographic location (p = 0.401), years of experience (p = 0.418), and specialization (p = 0.087) also showed no significant relationship. However, HCWs in public facilities tended to interpret results more accurately than those in private settings (p = 0.061) S6 Table (supplementary).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceptions of mRDT efficacy and reliability by professional and structural characteristics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived efficacy equivalence of mRDTs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe professional category of HCWs was not significantly associated with perceptions regarding the equivalence of mRDT efficacy (p = 0.106). Nurses were the most likely to question the equivalence between different mRDT brands (41.7%), while physicians were least likely to accept the idea of test equivalence (1.5%).\u003c/p\u003e\n\u003cp\u003eSimilar trends were observed across health districts (p = 0.830), with HCWs from the Madibou district accounting for the largest share of those expressing scepticism (23.6%). Public sector HCWs more frequently believed in the equivalence of mRDTs (79.1%) compared to their counterparts in private facilities (20.9%), although this difference was not statistically significant (p = 0.313).\u003c/p\u003e\n\u003cp\u003eNeither years of professional experience (p \u0026gt; 0.9) nor specialization in infectious diseases or diagnostics (p = 0.600) was significantly associated with perceived efficacy equivalence of mRDTs S7 Table (supplementary).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived Reliability of mRDTs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no significant association between professional category and perceptions of mRDT reliability (p = 0.349) S8 Table (supplementary). Nurses constituted the largest group among those who perceived mRDTs as reliable (37.1%), followed by midwives (20.2%).\u003c/p\u003e\n\u003cp\u003eAcross health districts, most of those who trusted mRDTs were from Madibou (21.8%), Djiri (14.5%), and Ouenz\u0026eacute; (12.1%), but these differences were not statistically significant (p = 0.061). Similarly, HCWs in public facilities were more likely to report trust in mRDTs (76.6%) than those in private institutions, although this difference approached but did not reach statistical significance (p = 0.065).\u003c/p\u003e\n\u003cp\u003eNo significant associations were found between perceived reliability and years of experience or specialization in infectious diseases or diagnostics.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to provide a comprehensive assessment of HCWs' knowledge, perceptions, and practices regarding the use of mRDTs across all 9 health districts of Brazzaville, Republic of Congo. The predominance of nurses among the participants reflects the healthcare system structure, where nurses serve as the primary providers of frontline care particularly in primary healthcare settings. Similar findings have been reported in other sub-Saharan African contexts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite the widespread use of mRDTs, this study highlights a persistent deficiency in malaria-related knowledge among HCWs. Many providers showed only a partial understanding of the disease\u0026rsquo;s transmission and clinical manifestations, revealing important gaps in training. These results align with previous studies from Africa showing that many HCWs lack sufficient knowledge about malaria, despite being actively involved in diagnosis and treatment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings concerning mRDT knowledge are particularly concerning. None of the WCHs demonstrated good overall knowledge, and most were classified as having insufficient knowledge. This gap is critical, as appropriate knowledge is essential to ensure diagnostic accuracy, particularly in resource-limited environments where mRDTs are often the only feasible diagnostic tool [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Inadequate knowledge can lead to poor diagnostic practices, the continuation of presumptive treatments contrary to WHO guidelines [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. and even contribute to the emergence of antimalarial drug resistance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, lack of proficiency may erode community trust in health services, which is essential for the success of malaria control programs.\u003c/p\u003e \u003cp\u003eThe perceptions of mRDTs among HCWs revealed a degree of uncertainty regarding their reliability. Although many participants expressed confidence in these tests, a substantial minority remained doubtful, often citing false-negative results or perceived lack of precision. Such scepticism has also been reported in studies from Nigeria and Kenya [\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], reflecting technical limitations of mRDTs, including reduced sensitivity at low parasitemia and issues related to \u003cem\u003epfhrp2\u003c/em\u003e gene deletions [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Nevertheless, the prevalence of \u003cem\u003epfhrp2\u003c/em\u003e deletions in Brazzaville appears to be low [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Prior experience, training, and trust in diagnostic tools are known to shape these perceptions, as previously demonstrated by \u003cem\u003eBoyce et al. and Kyabayinze et al\u003c/em\u003e. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA large proportion of HCWs believed that not all mRDT brands are equally effective, with SD Bioline Malaria Ag Pf being the preferred option. This preference is consistent with previous performance evaluations showing superior accuracy of this brand [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, the majority of participants found mRDTs easy to use citing simplicity and speed as key advantages. Similar perceptions have been reported in Nigeria, where ease of use was a critical factor in promoting test uptake [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, despite this favourable perception, the limited proportion of HCWs reported having received formal training on mRDT use. This lack of training raises concerns about proper implementation and the risk of diagnostic errors due to procedural mistakes or misinterpretation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Moreover, very few participants reported following national malaria diagnostic guidelines. This suggests a gap either in guideline dissemination or in the ability of health systems to reinforce their implementation. These findings are consistent with previous research showing that compliance with guidelines is influenced by tool availability, work environment, and institutional support [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhen faced with negative mRDT results, HCWs demonstrated considerable variability in their clinical decision-making. Some tented to rely on alternative diagnoses or supplementary microscopy, while others resorted to symptomatic or presumptive treatment. This diversity of responses highlights the ongoing tension between test results and clinical judgement, particularly in contexts where confidence in diagnostic tools remains limited. Similar observations have been reported in Tanzania, where deviation from protocols was linked to perceived test unreliability [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The routine use of confirmatory microscopy following negative mRDTs, though sometimes necessary, also places a financial burden on the health system.\u003c/p\u003e \u003cp\u003eInterestingly, no significant associations were found between knowledge, perception, or practices and professional or sociodemographic characteristics. This may reflect a systemic gap affecting the entire health workforce, rather than one limited to specific categories or regions. Similar uniformity has been reported in Tanzania [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], although it contrasts with findings from Nigeria, where specialization in infectious diseases was associated with better knowledge of mRDTs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. One strength of this study lies in its broad coverage, with representation from all health districts and a range of professional categories. This enhances the validity and generalizability of the findings. These data highlight the need for systematic and sustainable policy actions to strengthen malaria diagnostic practices. In particular, the implementation of structured and periodic refresher training programs on the use of mRDTs for healthcare professionals in both the public and private sectors is essential. Such training should emphasize the correct interpretation of test results, adherence to national diagnostic guidelines, and appropriate management of patients with negative test results. This study presents several strengths as well as certain limitations that should be acknowledged. One of its main strengths lies in the comprehensive design, which included all health districts of Brazzaville and covered several primary healthcare facilities, with representation of different categories of healthcare professionals from both public and private facilities. This wide coverage enhances the generalizability of the findings. However, some limitations should be acknowledged. The use of self-reported data may have introduced social desirability bias, with participants potentially overestimating their knowledge or reporting ideal rather than actual practices.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals significant deficiencies in the knowledge and practices related to mRDTs among HCWs in Brazzaville, Republic of Congo. Although the use of mRDTs is widespread, understanding of their principles, procedures, and targeted antigens remains alarmingly low. Furthermore, adherence to national malaria diagnostic guidelines is poor, with only a small fraction of HCWs applying the protocols correctly in routine practice.\u003c/p\u003e \u003cp\u003eThese findings underscore the urgent need for comprehensive and continuous training programs targeting both urban and rural HCWs. Strengthening the dissemination and implementation of national guidelines, along with supervision and quality control mechanisms, is essential to improve diagnostic accuracy and the rational use of antimalarial treatments.\u003c/p\u003e \u003cp\u003eAddressing these systemic gaps will be critical to achieving effective malaria case management and supporting national efforts toward malaria control and elimination in the Republic of Congo\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eCAMEPS\u0026nbsp;\u003c/strong\u003e: Centrale d\u0026apos;achat des M\u0026eacute;dicaments Essentiels et des Produits de Sant\u0026eacute;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDDSSA\u0026nbsp;\u003c/strong\u003e: Direction G\u0026eacute;n\u0026eacute;rale des Services de la Sant\u0026eacute;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEFF :\u003c/strong\u003e Design effect\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFCRM :\u003c/strong\u003e Fondation Congolaise pour la Recherche M\u0026eacute;dicale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHCW:\u003c/strong\u003e Healthcare Worker\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emRDT\u003c/strong\u003e: Malaria Rapid Diagnosis Test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCR:\u0026nbsp;\u003c/strong\u003epolymerase chain reaction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePfHRP2:\u0026nbsp;\u003c/strong\u003e\u003cem\u003ePlasmodium falciparum\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eHistidine-Rich Protein 2\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003epLDH:\u0026nbsp;\u003c/strong\u003e\u003cem\u003ePlasmodium\u003c/em\u003e lactate dehydrogenase\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study received ethical approval from the institutional ethics committee of the Fondation Congolaise pour la Recherche M\u0026eacute;dicale (Approval No. 057/CEI/FCRM/2024) and administrative authorization from the Ministry of Public Health (Ref. 0752/MSP/CAB.24). Written informed consent was obtained from all participants prior to inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur sincere gratitude to the healthcare professionals who actively participated in this study, generously sharing their time and knowledge. We thank Emerode Borhel Nkounga-kounga for his help with data entry. We would like to thank Dr Jean Claude Djontu and Dr Adrian J. F. Luty for\u0026nbsp;\u003cstrong\u003etheir\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003evaluable contribution to the critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw datasets generated and/ analyzed for this study will be made available by the authors on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by Abbott Rapid Diagnostics (Pty) Ltd\u0026nbsp;and by Alexander von Humboldt Foundation through the Central Africa Research Hub (HR-Coca) led by FN. Additional support came from the CATCR project (Reference Project No.101145698) funded by Global Health EDCTP3 Joint Undertaking which supported FN, AMM, and CV. ND was supported by SOCOSAM. The funders did not play a role in the design of the study, collection, analysis, and interpretation of data, as well as the writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was designed, coordinated and supervised by AMM and FN. MTB; SDK and ND conducted the field survey. MTB, SDK, CV and ND designed and developed data analysis methods. CV carried out the data analysis. MTB wrote the first draft of the manuscript. MTB, SDK, CV, ND, AMM and FN participated in the critical reading and revision of the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\n\u003col\u003e\n\u003cli\u003eWHO: \u003cstrong\u003eWorld malaria report 2024: addressing inequity in the global malaria response\u003c/strong\u003e. In\u003cem\u003e.\u003c/em\u003e Geneva: World Health Organization; 2024.\u003c/li\u003e\n\u003cli\u003ePNLP: \u003cstrong\u003eRapport d\u0026apos;activit\u0026eacute;s du Programme National de Lutte contre le Paludisme (PNLP), Brazzaville : R\u0026eacute;publique du Congo. 2024\u003c/strong\u003e. 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\u003c/em\u003e2022, \u003cstrong\u003e21\u003c/strong\u003e(1):68.\u003c/li\u003e\n\u003cli\u003eBohle LF, Abdallah AK, Galli F, Canavan R, Molesworth K: \u003cstrong\u003eKnowledge, attitudes and practices towards malaria diagnostics among healthcare providers and healthcare-seekers in Kondoa district, Tanzania: a multi-methodological situation analysis\u003c/strong\u003e. \u003cem\u003eMalar J \u003c/em\u003e2022, \u003cstrong\u003e21\u003c/strong\u003e(1):224.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria, Rapid Diagnostic Tests, Knowledge, Attitudes, Practices, Healthcare Workers, Republic of Congo","lastPublishedDoi":"10.21203/rs.3.rs-9006175/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9006175/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Malaria rapid diagnostic tests (mRDTs) are essential for early detection and effective management of malaria, but their success depends on healthcare workers’ (HCWs) knowledge, attitudes, and willingness to use them. This study assessed these factors to guide malaria case management strategies in the Republic of Congo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A descriptive cross-sectional study was conducted from November 2024 to February 2025 across nine health districts in Brazzaville. Thirty-six (36) randomly selected public and private primary healthcare facilities participated, with 211 HCWs involved in malaria diagnosis. Data were collected using a semi-structured questionnaire. Knowledge, attitude and pratice levels were classified as good (≥75%), intermediate (50–74.9%), or insufficient (\u0026lt;50%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nAmong participants, 96% reported using mRDTs, though only 36% had received specific training. General malaria knowledge was low, with just 9% scoring good. Most (90%) had poor understanding of target antigens and diagnostic procedures. However, 97% and 59% perceived mRDTs as easy to use and reliable, respectively. In practice, 85.8% used both mRDTs and microscopy, but only 7.6% followed national guidelines. Following a negative mRDT in symptomatic patients, 39% requested microscopy, and 27% prescribed antimalarials based on clinical suspicion. No significant associations were found between knowledge, attitudes or practices and demographic or professional characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003cbr\u003e\nAlthough mRDT usage is HCWs in Brazzaville, significant gaps persist in knowledge, training, and compliance with national diagnostic protocols. Enhanced training and policy enforcement are needed to strengthen malaria case management in the Republic of Congo.\u003c/p\u003e","manuscriptTitle":"Low practical knowledge but high willingness to use of rapid diagnostic tests for malaria by healthcare workers in Republic of Congo","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 14:04:36","doi":"10.21203/rs.3.rs-9006175/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-07T15:48:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T10:14:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T18:05:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124647738760012458347441896496240181172","date":"2026-04-14T08:15:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T17:18:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154659896217040122659662255683849729244","date":"2026-04-12T20:59:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117225223634977986696539111004344318960","date":"2026-04-08T06:28:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66090222191173329651147634319583006601","date":"2026-04-02T10:29:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157570660513835694649616180613609680873","date":"2026-04-02T09:44:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15063634529123912727223355625951949533","date":"2026-04-02T08:53:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T08:44:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-06T18:20:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T13:29:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-05T13:24:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-03-02T06:03:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2480739d-b836-4345-ae61-f15908c89a17","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-07T15:48:10+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T19:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 14:04:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9006175","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9006175","identity":"rs-9006175","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}