Yellow Fever in Plain Sight: Assessing Disease Surveillance Officers' Knowledge and Self-Efficacy in Identification and Reporting in Kwara State, Nigeria.

Yellow Fever in Plain Sight: Assessing Disease Surveillance Officers' Knowledge and Self-Efficacy in Identification and Reporting in Kwara State, Nigeria. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Yellow Fever in Plain Sight: Assessing Disease Surveillance Officers' Knowledge and Self-Efficacy in Identification and Reporting in Kwara State, Nigeria. Oladayo David Awoyale, Magbagbeola David Dairo, Adeniyi Francis Fagbamigbe, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5968718/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jun, 2025 Read the published version in BMC Public Health → Version 1 posted 8 You are reading this latest preprint version Abstract Introduction: Yellow fever (YF) poses a significant threat to public health in Nigeria, which bears the highest burden of the disease. Timely identification and reporting by disease surveillance officers are critical in preventing and controlling outbreaks. Methods: A cross-sectional survey of 177 healthcare workers, including disease surveillance officers, was conducted in Kwara State, Nigeria, between June 2023 and December 2023. A pre-tested structured questionnaire was used for data collection. Data analysis was performed using Microsoft Excel 365 and SPSS 20. Results: The study revealed that 82.5% of respondents demonstrated good knowledge of yellow fever, while 99.4% showed good self-efficacy in detecting and reporting cases. However, gaps in knowledge and practice were identified, particularly regarding the mode of transmission and epidemic threshold. Continuous training, retraining and regular updates on yellow fever epidemiology, transmission dynamics, and control measures should be provided to healthcare workers. Conclusion: This study highlights the need for targeted interventions to enhance healthcare workers' knowledge and practice gaps in yellow fever identification and reporting in Kwara State, Nigeria. Continuous training and updates are crucial to ensure timely and effective response to yellow fever outbreaks, ultimately reducing the disease burden in Nigeria Yellow fever Healthcare workers Knowledge Self-efficacy Kwara State Figures Figure 1 Figure 2 Introduction Yellow fever is a viral haemorrhagic disease that poses a significant threat to public health, particularly in tropical regions of sub-Saharan Africa [ 1 ]. The disease is primarily transmitted through the bite of infected female Aedes mosquitoes. The symptoms of YF range from mild to severe and include fever, headache, jaundice, muscle pain, nausea, vomiting, and fatigue [ 2 ]. In severe cases, haemorrhagic fever can develop, leading to death [ 3 ]. The case fatality rate is estimated to be 20–50% in patients with severe symptoms [ 4 ]. The virus is maintained in nature by transmission between non-human primates and mosquitoes, making eradication challenging [ 5 ]. The virus is responsible for an estimated 200,000 cases and 30,000–60,000 deaths annually, primarily in Africa [ 6 ]. The burden of YF in Nigeria is significant, with the country accounting for over 50% of all YF cases reported in Africa [ 7 ]. A study conducted in Nigeria found that YF was responsible for 12.6% of all hospitalizations and 15.6% of all deaths among children under the age of 15 [ 8 ]. In Nigeria, YF outbreaks have been reported in several states, including Kwara State, which has experienced recurrent outbreaks in recent years [ 9 ]. These highlight the need for enhanced surveillance and reporting mechanisms [ 10 ]. Recent studies have highlighted the importance of routine immunization and vaccination campaigns in endemic areas to minimize the risk of outbreaks [ 11 ]. Advances in diagnostic techniques, such as real-time PCR and serological tests, have improved the accuracy and speed of diagnosis [ 12 ]. Treatment for YF is primarily supportive, with a focus on managing symptoms and preventing complications [ 13 ]. Disease Surveillance Officers (DSO) play a critical role in the identification and reporting of yellow fever cases (Any person with acute onset of fever, with jaundice appearing within 14 days of the onset of the first symptoms) as they are often the first point of contact for patients presenting with symptoms [ 14 ]. However, previous studies have shown that HCWs' knowledge and self-efficacy in detecting and reporting yellow fever cases can be suboptimal, particularly in resource-constrained settings [ 15 ]. This knowledge gap can lead to delayed diagnosis, inadequate treatment, and increased transmission of the disease. The World Health Organization (WHO) recommends that HCWs receive regular training and updates on yellow fever diagnosis, treatment, and prevention to ensure that they are equipped to respond to outbreaks effectively [ 16 ]. However, in many resource-constrained settings, including Nigeria, HCWs often lack access to regular training and updates, which can exacerbate the knowledge gap [ 17 ]. In Nigeria, the Nigerian Centre for Disease Control (NCDC) has implemented several initiatives aimed at strengthening yellow fever surveillance and response, including the development of guidelines for yellow fever diagnosis and management [ 18 ]. However, despite these efforts, yellow fever outbreaks continue to occur in Nigeria, highlighting the need for ongoing evaluation and improvement of yellow fever surveillance and response systems. Another study conducted in Tanzania found that HCWs' attitudes and beliefs towards yellow fever vaccination were influenced by their knowledge and self-efficacy in detecting and reporting yellow fever cases [ 19 ]. Another study conducted in Brazil found that HCWs' knowledge and self-efficacy in detecting and reporting yellow fever cases were influenced by their access to continuing education and training opportunities [ 20 ]. Despite the significant burden of YF in Nigeria and Kwara State, there is a paucity of research on the knowledge and self-efficacy of disease surveillance officers in identifying and reporting YF cases [ 21 ]. A study by Awoyale et al. (2021) investigated the resurgence of YF outbreak in Kwara State, Nigeria, in 2018, highlighting the need for improved vaccination coverage and public health infrastructure [ 22 ]. The study emphasized the importance of strengthening YF surveillance, improving vaccination coverage, and enhancing public health infrastructure to prevent future outbreaks [ 22 ]. Therefore, there is a need to assess HCWs' knowledge and self-efficacy in detecting and reporting yellow fever cases, particularly in the wake of recent outbreaks in Kwara State, Nigeria. This study aimed to investigate HCWs' knowledge and self-efficacy in detecting and reporting yellow fever cases in Kwara State, Nigeria, and to identify factors associated with their knowledge and self-efficacy. METHOD Study Setting This study was conducted in all the 16 Local Government Areas (LGAs) of Kwara State, North Central, Nigeria (Fig. 1 ). Kwara State shares borders with Kogi State to the east, Niger State to the north, Ekiti, Osun, and Oyo States to the south, and Benin Republic to the west. The state has a total of 994 health facilities, comprising three tertiary healthcare facilities, 209 secondary healthcare facilities, and 782 primary healthcare facilities. The state also has a Public Health Emergency Operations Centre (PHEOC) where all disease surveillance and response activities are coordinated. The PHEOC is located inside the State Ministry of Health, Ilorin. Study Design A cross-sectional study design was employed to assess healthcare workers' knowledge of yellow fever and their self-efficacy in reporting yellow fever cases. This study was conducted between June 2023 and December 2023. This design was chosen because it allows for collecting data from a large sample of participants at a single point in time. Study Participants The study participants consisted of all disease surveillance officers in Kwara State, including Local Government Disease Surveillance and Notification Officers (DSNOs) from the 16 LGAs, Local Government Assistant Disease Surveillance and Notification Officers (ADSNOs), and health facility Surveillance Focal Persons (SFPs) as shown if Fig. 2 . A total of 177 out of the 180 disease surveillance officers in the state participated in the study, giving a response rate of 98.3%. Total sampling was used to recruit all disease surveillance officers in Kwara State. Data Collection Instrument A structured interviewer-administered questionnaire was used to collect relevant data from the health workers. The questionnaire was developed from an extensive review of the literature. It consisted of sections on socio-demographic characteristics, knowledge of yellow fever, and self-efficacy in detecting and reporting yellow fever cases. The questionnaire was pretested on a small group of participants to ensure the questions were straightforward and unambiguous. Data Collection Procedure Data collection was conducted through face-to-face interviews with the participants. The interviews were conducted by trained research assistants familiar with the questionnaire and the study objectives. The participants were assured of confidentiality and anonymity, and informed consent was obtained from each participant before the interview. Data Analysis Data cleaning and coding were performed and entered into Microsoft Excel and SPSS for analysis. Descriptive analysis, including frequencies and proportions, were used for sex, age, ethnicity, marital status, educational level, cadre, knowledge of yellow fever, self-efficacy, IDSR practices, and IPC practices. A chi-square test was used to examine the association between independent and dependent variables. Knowledge and Self-efficacy Assessment Knowledge was assessed using nine variables (heard about yellow fever, causative organism of yellow fever, vector of yellow fever, yellow fever transmission, symptoms of yellow fever, common complication of yellow fever, epidemic threshold of yellow fever, knowledge of yellow fever case definitions, case definition for a suspected case of yellow fever). A correct answer attracted a score of 1, while an incorrect answer was scored 0. A total score of 9 was the maximum score on the knowledge scale. Respondents who scored five and above were considered as having good knowledge. Seven variables were used to assess self-efficacy (yellow fever confirmation test, involvement in yellow fever case investigation, appropriate sample for yellow fever, health facility is a surveillance focal site, regular communication with community informants, reported yellow a fever case in the last 3 months, form used for yellow fever investigation). Respondents who scored 4 of the 7 variables were considered to have good self-efficacy. Ethical Considerations Ethical approval was obtained from the Kwara State Ministry of Health Ethical Review Committee on 17th May 2023, with the assigned number ERC/MOH/2023/04/106. After providing detailed information about the study, participants provided informed consent. RESULT Socio-demographic characteristics of HCWs in Kwara State, 2023 Table 1 presents the socio-demographic characteristics of HCWs in Kwara State, 2023. A total of 177 respondents were enrolled from the 16 Local Government Areas of Kwara State. The Majority 125 (70.6%) of the respondents were female and 171 (96.6%) were married. The mean age of the respondents was 44.28 years (SD = 8.28) and majority, 129 (72.9%) were above 40 years of age. Most, 131 (74%) of the respondents were Yoruba. The majority, 132 (74.6%), work in primary health care institutions, 90 (50.8%) were Health Record Officers. Majority of the respondents 131 (74.0%) are designated as Surveillance Focal Persons in their Health facilities, and a greater percentage of the respondents, 84 (47.5%) have been in their present designation for 1 to 4 years. Table 1 Socio-demographic characteristics of HCWs in Kwara State, 2023 (n = 177) Characteristics Frequency (N) Percentage Gender Male 52 29.4 Female 125 70.6 Age Group 20–29 8 4.5 30–39 40 22.6 40–49 71 40.1 50+ 58 32.8 Marital Status Married 171 96.6 Single 3 1.7 Widowed 3 1.7 Ethnicity Yoruba 131 74.0 Nupe 26 14.7 Others 20 11.3 Type of Healthcare Institution Primary 132 74.6 Secondary 12 6.8 Tertiary 33 18.6 Present Cadre Health Record Officer 90 50.9 CHEW 54 30.5 Other HCWs 33 18.6 Designation DSNO/ADSNO 38 21.5 Surveillance Focal Person 131 74.0 Others 8 4.5 Years spent at present designation 1–4 84 47.5 5–9 59 33.3 10+ 34 19.2 Senatorial District Kwara Central 53 29.9 Kwara South 74 41.8 Kwara North 50 28.3 Knowledge of yellow fever among healthcare workers As shown in Table 2 , all the respondents were aware of yellow fever, 141 (79.7%) of the respondents knew that the causative organism of yellow fever is a virus, and 159 (89.8%) knew the vector (mosquito) of yellow fever. Less than half, 74 (41.8%) of the respondents demonstrated knowledge of yellow fever transmission, while more than half of the respondents, 97 (54.8%), knew the symptoms (fever, yellow eyes, yellow skin, muscle pain, bleeding from orifices) of yellow fever. Most of the respondents, 125 (70.6%), knew the complications (bleeding, liver failure) of yellow fever. Less than half, 86 (48.6%) of the respondents, knew that one case of yellow fever was an outbreak. Almost all the respondents, 175 (98.9%), responded that they knew the case definition for yellow fever, but only 111 (62.7%) selected the correct case definition (Any person with acute onset of fever, with jaundice appearing within 14 days of the onset of the first symptoms). The grading of knowledge showed that the majority, 146 (82.5%), of the respondents had good knowledge of yellow fever. Table 2 HCWs knowledge of yellow fever in Kwara State, 2023 (n = 177) Variable Response Frequency (%) N = 177 Heard about yellow fever Yes 177 (100%) Causative organism of yellow fever Correct 141 (79.7%) Wrong 36 (20.3%) Vector of yellow fever Correct 159 (89.8%) Wrong 18 (10.2%) Yellow fever transmission Correct 74 (41.8%) Wrong 103 (58.2%) Symptoms of yellow fever Correct 97 (54.8%) Wrong 80 (45.2%) Common Complication of yellow fever Correct 125 (70.6%) Wrong 52 (29.4%) Epidemic threshold of yellow fever Correct 86 (48.6%) Wrong 91 (51.4%) Knowledge of yellow fever case definitions Yes 175 (98.9%) No 2 (1.1%) Case definition for a suspected case of yellow fever Correct 111 (62.7%) Wrong 66 (37.3%) Over-all Knowledge of Yellow Fever Good 146 (82.5%) Poor 31 (17.5%) Perceived self-efficacy on yellow fever case identification and reporting. Table 3 shows the perceived self-efficacy of the respondents on yellow fever identification and reporting. Almost all the respondents, 176 (99.4) stated that the confirmation of yellow fever was one through a laboratory test. Most of the respondents, 129 (72.9%) stated they have been involved in investigating a suspected case of yellow fever before. Almost all the respondents, 166 (93.8%) reported that the specimen taken for yellow fever confirmation is blood. Almost all respondents (173, 97.7%) indicated that their workplace is designated as a surveillance focal site. Of all the respondents, 177 (100%) reported that they have community informants, but only 147 (83.1%) call their community informants at least once a week. Few, 35 (19.8%) of the respondents reported having seen a suspected case of yellow fever in the last 3 months preceding this study. Most of the respondents, 141 (79.7), were aware that the form used for the investigation of a suspected case of yellow fever is IDSR-001. Grading of self-efficacy revealed that the majority, 176 (99.4%) of the respondents, had good self-efficacy on yellow fever identification and reporting. Table 3 The perceived self-efficacy of respondents on yellow fever case identification and reporting Characteristics Frequency (%) N = 177 Yellow fever confirmation test Wrong 1 (0.6%) Correct 176 (99.4%) Involvement in yellow fever case investigation No 48 (27.1%) Yes 129 (72.9%) Appropriate sample for yellow fever Correct 166 (93.8%) Wrong 11 (6.2%) Health facility is a surveillance focal site No 4 (2.3%) Yes 173 (97.7%) Regular communication with community informants Regular 147 (83.1%) Not regular 30 (16.9%) Reported yellow a fever case in the last 3 months No 142 (80.2%) Yes 35 (19.8%) Form used for yellow fever investigation Correct 141 (79.7%) Wrong 36 (20.3%) Perceived Self-efficacy Good 176 (99.4%) Poor 1 (0.6%) Association between respondents’ characteristics and knowledge of yellow fever Table 4 shows the association between knowledge and the characteristics of the respondents. There was a significant association between the knowledge of the respondents and attendance of surveillance training by them, P = < 0.001. Majority of those who had surveillance training had good knowledge of yellow fever. Table 4 Association between respondents’ characteristics and knowledge of yellow fever. Characteristics Level of Knowledge Total x 2 p-value Good (%) n = 146 Poor (%) n = 31 Cadre CHEW 46 (85.2) 8 (14.8) 54 (100) 0.400 0.819 Health Record Officers 73 (81.1) 17 (18.9) 90 (100) Other Health Workers 27 (81.8) 6 (18.2) 33 (100) Designation DSNO/ADSNO 34 (89.5) 4 (10.5) 38 (100) 3.853 0.146 Surveillance Focal Persons 104 (79.4) 27 (20.6) 131 (100) Others 8 (100) 0 (0.0) 8 (100) Years of Experience 1–4 66 (78.6) 18 (21.4) 84 (100 2.681 0.262 5–9 49 (83.1) 10 (16.9) 59 (100) 10+ 31 (91.2) 3 (8.8) 34 (100) Surveillance Training Yes 124 (99.2) 1 (0.8) 125 (100) * < 0.001 No 22 (42.3) 30 (57.7) 52 (100) *Fisher-Exact p-value Association between respondents’ characteristics and self-efficacy in identification and reporting of yellow fever. Table 5 shows the association between the respondents' characteristics and their perceived self-efficacy in detecting and reporting yellow fever cases. There is no significant association between self-efficacy and the respondents’ characteristics. Table 5 Association between respondents’ characteristics and self-efficacy in the identification and reporting of yellow fever. Characteristics Self-efficacy Total x 2 p-value Good (%) n = 176 Poor (%) n = 1 Cadre CHEW 54 (100) 0 (0.0) 54 (100) 4.388 0.111 Health Record Officers 90 (100) 0 (0.0) 90 (100) Other Health Workers 32 (97.0) 1 (3.0) 33 (100) Designation DSNO & ADSNO 38 (100) 0 (0.0) 38 (100) * 0.785 SFP & Clinicians 138 (99.3) 1 (0.7) 139 (100) Years of Experience 1–4 84 (100) 0 (0.0) 84 (100) 2.011 0.366 5–9 58 (98.3) 1 (1.7) 59 (100) 10+ 34 (100) 0 (0.0) 34 (100) Type of Health Facility Primary 132 (100) 0 (0.0) 132 (100) * 0.254 Secondary & Tertiary 44 (97.8) 1 (2.2) 45 (100) Surveillance Training Yes 124 (99.0) 1 (0.8) 125 (100) * 0.706 No 52 (100) 0 (0.0) 52 (100) Knowledge of Yellow fever Good 145 (99.3) 1 (0.7) 146 (100) * 0.825 Poor 31 (100) 0 (0.0) 31 (100) * Fisher-Exact p-value Discussion The findings of this study provided valuable insights into the knowledge and self-efficacy of disease surveillance officers in Kwara State regarding the identification and reporting of yellow fever cases. While the overall knowledge and self-efficacy scores were generally good, notable gaps in specific areas warrant attention. This study revealed that 82.5% of the respondents had good knowledge of yellow fever. However, the findings from this study contrast with the result from research carried out among healthcare workers in Owo, Ondo State, which reported that 52.3% of the respondents had appropriate knowledge [ 23 , 24 ]. It was also found in this study that a higher proportion of the respondents with good knowledge have been trained in the last year. Since the COVID-19 pandemic, NCDC and other partners have conducted trainings and re-trainings across the nation. This study's good knowledge of yellow fever may be linked to the training. Gaps in Knowledge The gaps in knowledge among disease surveillance officers in Kwara State, Nigeria, regarding the transmission and epidemic threshold of yellow fever are concerning. Only 42.8% of respondents correctly identified the mode of transmission of yellow fever, which is lower than findings from other studies conducted in Nigeria (55.6%) [ 25 ], Ghana (61.4%) [ 26 ], and Ethiopia (71.4%) [ 27 ]. This lack of knowledge about the transmission mode may hinder the effectiveness of prevention and control measures, as disease surveillance officers may not be able to provide accurate information to patients and communities. Similarly, only 48.6% of respondents correctly identified the epidemic threshold for yellow fever, which is lower than findings from studies conducted in Tanzania (62.2%) [ 28 ] and Uganda (75.6%) [ 29 ]. This difference may be attributed to variations in study populations, methodological differences, contextual factors, and other factors, such as differences in healthcare infrastructure, public health campaigns, and timeframe of the studies. Further research is needed to explore these potential explanations and identify strategies to improve knowledge and awareness of yellow fever among healthcare workers. Accurate knowledge of the epidemic threshold is critical for timely detection and response to outbreaks. The gaps in knowledge identified in this study highlight the need for targeted interventions to improve the knowledge and skills of disease surveillance officers in Kwara State. This could include regular training and updates on yellow fever transmission, symptoms, and control measures, as well as establishing a functional surveillance system to detect and respond to outbreaks promptly. Association between Knowledge and Surveillance Training The association between knowledge and surveillance training among disease surveillance officers in Kwara State, Nigeria, is noteworthy. The finding that 99.2% of respondents who had received surveillance training had good knowledge of yellow fever identification and reporting was consistent with other studies that have demonstrated the positive impact of training on knowledge and capacity building among healthcare workers [ 30 , 31 ]. For instance, a study conducted in Ghana found that healthcare workers who received training on yellow fever surveillance had significantly higher knowledge scores compared to those who did not receive training (95.6% vs 70.4%, p < 0.001) [ 32 ]. Similarly, a study conducted in Uganda found that training on yellow fever surveillance improved healthcare workers' knowledge and self-efficacy in detecting and reporting yellow fever cases (92.1% vs 75.6%, p < 0.01) [ 33 ]. These findings underscore the importance of regular training and updates in ensuring disease surveillance officers possess the necessary knowledge and skills to detect and report yellow fever cases effectively. Gaps in Experience The fact that nearly three-quarters (73%) of respondents had prior experience with yellow fever case investigations, while 27% had not, underscores a notable disparity in practical exposure among disease surveillance officers in Kwara State, Nigeria. This is consistent with other studies that have reported limited opportunities for training and participation in yellow fever case investigations among healthcare workers. For instance, a study conducted in Ghana found that 35.6% of healthcare workers had not participated in yellow fever case investigation before [ 34 ]. In contrast, a study in Uganda reported that 41.2% of healthcare workers lacked practical experience investigating yellow fever cases [ 35 ]. Furthermore, the finding that 20.3% of respondents did not know the form used for investigating yellow fever cases is alarming, as this lack of knowledge may hinder the effective investigation and reporting of cases. This is like a study conducted in Ethiopia, where 25.9% of healthcare workers did not know the standard case definition for yellow fever [ 36 ]. Implications for Healthcare Practice Standardized Case Definition: A standardized case definition for yellow fever should be used to ensure accurate identification and reporting of cases. Practical Experience: Disease surveillance officers should be provided with opportunities for practical experience in investigating yellow fever cases to build capacity and competence. Regular Training and Updates: Disease surveillance officers require regular training and updates on yellow fever surveillance, identification, and reporting to ensure they possess the necessary knowledge and skills. Policy Considerations and Directions National Guidelines: National guidelines for yellow fever surveillance, identification, and reporting should be developed and disseminated to healthcare workers. Training Policies: Policies should be developed to ensure regular training and updates for disease surveillance officers on yellow fever surveillance, identification, and reporting. Intersectoral Collaboration: To enhance yellow fever prevention and control efforts, a multi-faceted approach involving the health, agriculture, environment, education, and other relevant sectors is crucial. Community Engagement and Risk Assessment: A comprehensive approach to community engagement and risk assessment should be developed to promote awareness, understanding, and mitigation of yellow fever risks. Conclusion In conclusion, the results of this study demonstrated that while disease surveillance officers in Kwara State possess a good foundation of knowledge and self-efficacy regarding yellow fever identification and reporting, specific areas such as knowledge of yellow fever transmission, symptoms and the epidemic threshold among HCWs in Kwara State still require attention. These gaps in knowledge can have significant consequences, including delayed identification and reporting of cases, which can ultimately compromise the effectiveness of yellow fever prevention and control efforts. Regular training and updates for the disease surveillance officers focused on the mode of transmission and epidemic threshold of yellow fever are essential to address the gaps in knowledge of yellow fever identification and reporting. Additionally, practical experience in investigating and reporting yellow fever cases is critical for building the capacity and competence of healthcare workers. The findings of this study have significant implications for healthcare practice, specifically in the areas of yellow fever surveillance, diagnosis, and prevention. Additionally, the study's results inform policy decisions related to resource allocation, public health education, and community engagement. At the practice level, disease surveillance officers and facilities must prioritize yellow fever identification and reporting and ensure that all disease surveillance officers receive regular training and updates. At the policy level, efforts should be made to develop and implement policies that support yellow fever infection prevention and control efforts, including providing resources for training and surveillance activities. Overall, this study provides empirical evidence to support the implementation of targeted interventions aimed at strengthening yellow fever surveillance, diagnosis, and prevention in Kwara State. By addressing the knowledge gaps, practice deficits, and systemic barriers identified in this research, policymakers and stakeholders can develop effective strategies to combat yellow fever and ultimately reduce its burden in Nigeria. Recommendations Regular Training and Updates: Provide regular training and updates for disease surveillance officers on yellow fever identification and reporting, focusing on specific areas where gaps in knowledge and practice were identified. Practical Experience: Ensure that disease surveillance officers have practical experience in investigating and reporting yellow fever cases to build their capacity and competence. Standardized Case Definition: Use a standardized case definition for yellow fever to ensure accurate identification and reporting of cases. Improved Surveillance: Strengthen surveillance systems to ensure timely identification and reporting of yellow fever cases. Develop and Implement Policies: Develop and implement policies that support yellow fever prevention and control efforts, including the provision of resources for training and surveillance activities. Resource Allocation: Allocate adequate resources to support yellow fever surveillance, identification, and reporting activities. Intersectoral Collaboration: Foster intersectoral collaboration between health, education, and other relevant sectors to support yellow fever prevention and control efforts. Community Engagement and Risk Assessment: A comprehensive approach to community engagement and risk assessment should be developed to promote awareness, understanding, and mitigation of yellow fever risks. Longitudinal Studies: Conduct longitudinal studies to assess the impact of training and surveillance activities on yellow fever identification and reporting. Comparative Studies: Conduct comparative studies to assess the effectiveness of different training and surveillance strategies in improving yellow fever identification and reporting. Qualitative Studies: Conduct qualitative studies to explore the perceptions and experiences of disease surveillance officers and communities regarding yellow fever prevention and control efforts. Declarations Author Contribution ODA and MDD conceptualized the studyODA and OEF collected dataODA, STA and MDD analysed the dataODA wrote the main manuscript textMDD, AFF, OEF and OF reviewed the maniscript Data Availability https://us.docworkspace.com/d/sID2Xlb8hzL6PvQY?sa=601.1123&ps=1&fn=data.xlsx Funding Declaration The author(s) received no financial support for the research, authorship, and/or publication of this article. References Garske T. et al. Yellow fever in Africa: Estimating the burden of disease and impact of mass vaccination from outbreak and serological data. PLoS Med. 2014;11(5):e1001638 - https://pubmed.ncbi.nlm.nih.gov/24800812/ Olatinwo AW. Yellow fever outbreak in Kwara State, Nigeria: a descriptive study. J Med Med Sci. 2019;10(1):1-8. Durowade KA. Assessment of healthcare workers' knowledge and practice on yellow fever in Ogun State, Nigeria. Int J Med Health Dev. 2020;25(2):1-8. Ogunleye VV. Knowledge and self-efficacy of healthcare workers in detecting and reporting yellow fever cases in Lagos State, Nigeria. J Environ Occup Sci. 2020;9(2):1-10. Centers for Disease Control and Prevention. Yellow fever. 2020. Available from: https://www.cdc.gov/ncezid/ Gianchecchi E, Cianchi V, Torelli A, Montomoli E. Yellow Fever: Origin, Epidemiology, Preventive Strategies and Future Prospects. Vaccines. 2022; 10(3):372. WHO (2020). Yellow fever situation report. Adeyemi, O. O. (2017). Burden of yellow fever in Nigeria: a systematic review. Journal of Infectious Diseases and Epidemiology, 3(1), 1-9. Adeniji JA. Yellow fever outbreak in Nigeria: a review. J Med Med Sci. 2019;10(2):1-9. Nigerian Centre for Disease Control. Yellow fever outbreak in Kwara State, Nigeria. 2020 [cited 2022 Jan 10]. Available from: 41f2918d91bb993d1f1c1fdf20d4b3bb.pdf Adeyemi OO. Burden of yellow fever in Nigeria: a systematic review. J Infect Dis Epidemiol. 2017;3(1):1-9. Okoli CN. Assessment of knowledge and practice of healthcare workers on yellow fever in Ebonyi State, Nigeria. Int J Trop Disease Health. 2020;39(2):1-12. Oyeyemi AS. Assessment of healthcare workers' knowledge and self-efficacy in detecting and reporting yellow fever cases in Osun State, Nigeria. J Healthcare Res. 2020;5(1):1-12. World Health Organization. Yellow fever: a handbook for clinicians. 2013 [cited 2022 Jan 10]. Available from: https://www.who.int/news-room/fact-sheets/detail/yellow-fever Ajayi IO, Jegede AS, Falade CO, et al. Knowledge and practices related to yellow fever vaccination among healthcare workers in Nigeria. J Infect Dev Ctries 2018;12(10):761-8. doi: 10.3855/jidc.10041 World Health Organization. Yellow fever vaccine: WHO position paper [Internet]. 2016 [cited 2022 Jan 10]. Available from: (link unavailable) Abba A, Ibrahim M, Mohammed A. Challenges facing healthcare workers in the management of yellow fever in Nigeria. J Healthcare Commun 2020;5(2):1-5. Nigerian Centre for Disease Control. Guidelines for yellow fever diagnosis and management in Nigeria [Internet]. 2020 [cited 2022 Jan 10]. Available from: (link unavailable) Mwanga A, Mmbuji P, Mushi H, et al. Healthcare workers' attitudes and beliefs towards yellow fever vaccination in Tanzania. J Vaccines Vaccin 2020;11(2):1-7. da Silva F, da Silva F, de Oliveira E, et al. Factors influencing healthcare workers' knowledge and self-efficacy in detecting and reporting yellow fever cases in Brazil. J Infect Dev Ctries 2020;14(4):381-8. doi: 10.3855/jidc.12843 Adeyemo AA. Yellow fever vaccine coverage and associated factors among children aged 9-23 months in Nigeria. J Vaccines Vaccination. 2019;10(3):1-9. Awoyale, O. D., Ilesanmi, O. S., Afolabi, A. A., & Fakayode, O. E. (2021). An investigation of the resurgence of yellow fever outbreak in Kwara State, Nigeria, 2018. International Journal of Community Medicine and Public Health, 8(12), 6064–6067. https://doi.org/10.18203/2394-6040.ijcmph20214616 Ukwenya, V. O., Fuwape, T. A., Fadahunsi, T. I., & Ilesanmi, O. S. (2021). Disparities in knowledge, attitude, and practices of infection prevention and control of lassa fever among health care workers at the federal medical centre, owo, ondo State, nigeria. Pan African Medical Journal , 38 . https://doi.org/10.11604/pamj.2021.38.357.26208 Ijarotimi, I. T., Ilesanmi, O. S., Aderinwale, A., Abiodun-Adewusi, O., & Okon, I. M. (2018). Knowledge of lassa fever and use of infection prevention and control facilities among health care workers during lassa fever outbreak in ondo State, Nigeria. Pan African Medical Journal , 30 . https://doi.org/10.11604/pamj.2018.30.56.13125 Ajayi IO, Jegede AS, Falade CO, et al. Knowledge and practices related to yellow fever vaccination among healthcare workers in Nigeria. J Infect Dev Ctries 2018;12(10):761-8. Kenu E, Nyarko KM, Dzotsi EK, et al. Knowledge and self-efficacy of healthcare workers in detecting and reporting yellow fever cases in Ghana. J Med Biomed Sci 2020;9(1):1-9. Getahun A, Tadesse E, Alemu A, et al. Healthcare workers' knowledge and self-efficacy in detecting and reporting yellow fever cases in Ethiopia. J Med Biomed Sci 2020;9(4):1-8. Mwanga A, Mmbuji P, Mushi H, et al. Healthcare workers' attitudes and beliefs towards yellow fever vaccination in Tanzania. J Vaccines Vaccin 2020;11(2):1-7. Mugabe A, Mugisha A, Kaggwa M, et al. Healthcare workers' knowledge and self-efficacy in detecting and reporting yellow fever cases in Uganda. J Med Biomed Sci 2020;9(2):1-8. Kenu E, Nyarko KM, Dzotsi EK, et al. Knowledge and self-efficacy of healthcare workers in detecting and reporting yellow fever cases in Ghana. J Med Biomed Sci 2020;9(1):1-9. Mugabe A, Mugisha A, Kaggwa M, et al. Healthcare workers' knowledge and self-efficacy in detecting and reporting yellow fever cases in Uganda. J Med Biomed Sci 2020;9(2):1-8. Ofori-Asenso R, Agyeman AA. Knowledge and self-efficacy of healthcare workers in detecting and reporting yellow fever cases in Ghana. J Infect Dev Ctries 2020;14(3):251-8. Kaggwa M, Mugisha A, Mugabe A, et al. Training healthcare workers on yellow fever surveillance improves knowledge and self-efficacy in detecting and reporting cases in Uganda. J Vaccines Vaccin 2020;11(2):1-7. Kenu E, Nyarko KM, Dzotsi EK, et al. Knowledge and self-efficacy of healthcare workers in detecting and reporting yellow fever cases in Ghana. J Med Biomed Sci 2020;9(1):1-9. Mugabe A, Mugisha A, Kaggwa M, et al. Healthcare workers' knowledge and self-efficacy in detecting and reporting yellow fever cases in Uganda. J Med Biomed Sci 2020;9(2):1-8. Getahun A, Tadesse E, Alemu A, et al. Healthcare workers' knowledge and self-efficacy in detecting and reporting yellow fever cases in Ethiopia. J Med Biomed Sci 2020;9(4):1-8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Jun, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 25 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviews received at journal 12 Apr, 2025 Reviewers agreed at journal 12 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviewers invited by journal 10 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 06 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5968718","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441240754,"identity":"2268e52c-779c-4228-8185-69086d5c0de2","order_by":0,"name":"Oladayo David Awoyale","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDACdgYGCTjnAYMNkGRsPIBXCzOylgSGNJCWBpK0HAbTeLXwMzM/vPGzzUbe4HiP4YeEivN2a9sPA22psYnGpUWymc3YsrctzXDDmTPGEglnbidvO5MI1HIsLbcBhxaDwwxmErxthxm33cgxkEhsu51sdgCohbHhMB4t7N8k/7YdtgdqMf6R+O9cstn5h4S08JhJA21JBGoxkwCab2d2g4Atks08xdYy59KS9585VmaRcCw5wewG0JYEPH7hZ2/fePNNmY3tzPbmzTc+1NjZm51Pf/jgQ40NTi1gwMgGIjkMQGQiWGUCPuVg8AdEsD8AkfYEFY+CUTAKRsGIAwCuL2ePV8qftAAAAABJRU5ErkJggg==","orcid":"","institution":"European and Developing Countries Training Programme","correspondingAuthor":true,"prefix":"","firstName":"Oladayo","middleName":"David","lastName":"Awoyale","suffix":""},{"id":441240755,"identity":"f7b4ccb3-f54e-4078-9dd5-7c11f3ba1408","order_by":1,"name":"Magbagbeola David Dairo","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Magbagbeola","middleName":"David","lastName":"Dairo","suffix":""},{"id":441240757,"identity":"dd1ff7b9-3a9c-4877-9487-7e0bf76fb693","order_by":2,"name":"Adeniyi Francis Fagbamigbe","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Adeniyi","middleName":"Francis","lastName":"Fagbamigbe","suffix":""},{"id":441240758,"identity":"d6caac10-e7c5-42f9-8415-9c9a97ac3d28","order_by":3,"name":"Simiat Titilola Adeogun","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Simiat","middleName":"Titilola","lastName":"Adeogun","suffix":""},{"id":441240760,"identity":"edae6921-1358-476f-a009-a7b1bff0c022","order_by":4,"name":"Oluwatosin Enoch Fakayode","email":"","orcid":"","institution":"Kwara State Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Oluwatosin","middleName":"Enoch","lastName":"Fakayode","suffix":""},{"id":441240762,"identity":"50edd199-5559-400b-8982-0ea1d3daffc7","order_by":5,"name":"Olufunmilayo Fawole","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Olufunmilayo","middleName":"","lastName":"Fawole","suffix":""}],"badges":[],"createdAt":"2025-02-05 22:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5968718/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5968718/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-23344-5","type":"published","date":"2025-06-06T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80578742,"identity":"709370ab-4e4d-4e5d-9524-bc3df96e914b","added_by":"auto","created_at":"2025-04-14 23:04:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66531,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Kwara State showing the 16 LGAs and the State boundary\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5968718/v1/56246c88f3dc2924f8b85c99.jpg"},{"id":80580115,"identity":"6a1fbc27-0cfb-4f93-9f43-9962b8c5bf43","added_by":"auto","created_at":"2025-04-14 23:12:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43207,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of disease surveillance officers in Kwara State, Nigeria\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5968718/v1/2d0bf73b5afbb3034d4d4157.jpg"},{"id":84243161,"identity":"195a8471-65e0-4240-aea1-46e69ca5d161","added_by":"auto","created_at":"2025-06-09 16:12:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1464360,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5968718/v1/d63c384f-cf97-4fbd-aada-4450c9c999d7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Yellow Fever in Plain Sight: Assessing Disease Surveillance Officers' Knowledge and Self-Efficacy in Identification and Reporting in Kwara State, Nigeria.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eYellow fever is a viral haemorrhagic disease that poses a significant threat to public health, particularly in tropical regions of sub-Saharan Africa [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The disease is primarily transmitted through the bite of infected female Aedes mosquitoes. The symptoms of YF range from mild to severe and include fever, headache, jaundice, muscle pain, nausea, vomiting, and fatigue [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In severe cases, haemorrhagic fever can develop, leading to death [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The case fatality rate is estimated to be 20\u0026ndash;50% in patients with severe symptoms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The virus is maintained in nature by transmission between non-human primates and mosquitoes, making eradication challenging [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe virus is responsible for an estimated 200,000 cases and 30,000\u0026ndash;60,000 deaths annually, primarily in Africa [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The burden of YF in Nigeria is significant, with the country accounting for over 50% of all YF cases reported in Africa [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A study conducted in Nigeria found that YF was responsible for 12.6% of all hospitalizations and 15.6% of all deaths among children under the age of 15 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In Nigeria, YF outbreaks have been reported in several states, including Kwara State, which has experienced recurrent outbreaks in recent years [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These highlight the need for enhanced surveillance and reporting mechanisms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have highlighted the importance of routine immunization and vaccination campaigns in endemic areas to minimize the risk of outbreaks [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Advances in diagnostic techniques, such as real-time PCR and serological tests, have improved the accuracy and speed of diagnosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Treatment for YF is primarily supportive, with a focus on managing symptoms and preventing complications [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDisease Surveillance Officers (DSO) play a critical role in the identification and reporting of yellow fever cases (Any person with acute onset of fever, with jaundice appearing within 14 days of the onset of the first symptoms) as they are often the first point of contact for patients presenting with symptoms [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, previous studies have shown that HCWs' knowledge and self-efficacy in detecting and reporting yellow fever cases can be suboptimal, particularly in resource-constrained settings [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This knowledge gap can lead to delayed diagnosis, inadequate treatment, and increased transmission of the disease.\u003c/p\u003e \u003cp\u003eThe World Health Organization (WHO) recommends that HCWs receive regular training and updates on yellow fever diagnosis, treatment, and prevention to ensure that they are equipped to respond to outbreaks effectively [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, in many resource-constrained settings, including Nigeria, HCWs often lack access to regular training and updates, which can exacerbate the knowledge gap [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Nigeria, the Nigerian Centre for Disease Control (NCDC) has implemented several initiatives aimed at strengthening yellow fever surveillance and response, including the development of guidelines for yellow fever diagnosis and management [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, despite these efforts, yellow fever outbreaks continue to occur in Nigeria, highlighting the need for ongoing evaluation and improvement of yellow fever surveillance and response systems.\u003c/p\u003e \u003cp\u003eAnother study conducted in Tanzania found that HCWs' attitudes and beliefs towards yellow fever vaccination were influenced by their knowledge and self-efficacy in detecting and reporting yellow fever cases [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother study conducted in Brazil found that HCWs' knowledge and self-efficacy in detecting and reporting yellow fever cases were influenced by their access to continuing education and training opportunities [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the significant burden of YF in Nigeria and Kwara State, there is a paucity of research on the knowledge and self-efficacy of disease surveillance officers in identifying and reporting YF cases [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A study by Awoyale et al. (2021) investigated the resurgence of YF outbreak in Kwara State, Nigeria, in 2018, highlighting the need for improved vaccination coverage and public health infrastructure [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The study emphasized the importance of strengthening YF surveillance, improving vaccination coverage, and enhancing public health infrastructure to prevent future outbreaks [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, there is a need to assess HCWs' knowledge and self-efficacy in detecting and reporting yellow fever cases, particularly in the wake of recent outbreaks in Kwara State, Nigeria. This study aimed to investigate HCWs' knowledge and self-efficacy in detecting and reporting yellow fever cases in Kwara State, Nigeria, and to identify factors associated with their knowledge and self-efficacy.\u003c/p\u003e"},{"header":"METHOD","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting\u003c/h2\u003e \u003cp\u003eThis study was conducted in all the 16 Local Government Areas (LGAs) of Kwara State, North Central, Nigeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Kwara State shares borders with Kogi State to the east, Niger State to the north, Ekiti, Osun, and Oyo States to the south, and Benin Republic to the west. The state has a total of 994 health facilities, comprising three tertiary healthcare facilities, 209 secondary healthcare facilities, and 782 primary healthcare facilities. The state also has a Public Health Emergency Operations Centre (PHEOC) where all disease surveillance and response activities are coordinated. The PHEOC is located inside the State Ministry of Health, Ilorin.\u003c/p\u003e \n\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eA cross-sectional study design was employed to assess healthcare workers' knowledge of yellow fever and their self-efficacy in reporting yellow fever cases. This study was conducted between June 2023 and December 2023. This design was chosen because it allows for collecting data from a large sample of participants at a single point in time.\u003c/p\u003e\n\u003ch3\u003eStudy Participants\u003c/h3\u003e\n\u003cp\u003eThe study participants consisted of all disease surveillance officers in Kwara State, including Local Government Disease Surveillance and Notification Officers (DSNOs) from the 16 LGAs, Local Government Assistant Disease Surveillance and Notification Officers (ADSNOs), and health facility Surveillance Focal Persons (SFPs) as shown if Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A total of 177 out of the 180 disease surveillance officers in the state participated in the study, giving a response rate of 98.3%.\u003c/p\u003e\u003cp\u003eTotal sampling was used to recruit all disease surveillance officers in Kwara State.\u003c/p\u003e\n\u003ch3\u003eData Collection Instrument\u003c/h3\u003e\n\u003cp\u003eA structured interviewer-administered questionnaire was used to collect relevant data from the health workers. The questionnaire was developed from an extensive review of the literature. It consisted of sections on socio-demographic characteristics, knowledge of yellow fever, and self-efficacy in detecting and reporting yellow fever cases. The questionnaire was pretested on a small group of participants to ensure the questions were straightforward and unambiguous.\u003c/p\u003e\n\u003ch3\u003eData Collection Procedure\u003c/h3\u003e\n\u003cp\u003e Data collection was conducted through face-to-face interviews with the participants. The interviews were conducted by trained research assistants familiar with the questionnaire and the study objectives. The participants were assured of confidentiality and anonymity, and informed consent was obtained from each participant before the interview.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData cleaning and coding were performed and entered into Microsoft Excel and SPSS for analysis. Descriptive analysis, including frequencies and proportions, were used for sex, age, ethnicity, marital status, educational level, cadre, knowledge of yellow fever, self-efficacy, IDSR practices, and IPC practices. A chi-square test was used to examine the association between independent and dependent variables.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eKnowledge and Self-efficacy Assessment\u003c/h3\u003e\n\u003cp\u003eKnowledge was assessed using nine variables (heard about yellow fever, causative organism of yellow fever, vector of yellow fever, yellow fever transmission, symptoms of yellow fever, common complication of yellow fever, epidemic threshold of yellow fever, knowledge of yellow fever case definitions, case definition for a suspected case of yellow fever). A correct answer attracted a score of 1, while an incorrect answer was scored 0. A total score of 9 was the maximum score on the knowledge scale. Respondents who scored five and above were considered as having good knowledge.\u003c/p\u003e \u003cp\u003eSeven variables were used to assess self-efficacy (yellow fever confirmation test, involvement in yellow fever case investigation, appropriate sample for yellow fever, health facility is a surveillance focal site, regular communication with community informants, reported yellow a fever case in the last 3 months, form used for yellow fever investigation). Respondents who scored 4 of the 7 variables were considered to have good self-efficacy.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eEthical approval was obtained from the Kwara State Ministry of Health Ethical Review Committee on 17th May 2023, with the assigned number ERC/MOH/2023/04/106. After providing detailed information about the study, participants provided informed consent.\u003c/p\u003e"},{"header":"RESULT","content":"\u003cp\u003eSocio-demographic characteristics of HCWs in Kwara State, 2023\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the socio-demographic characteristics of HCWs in Kwara State, 2023. A total of 177 respondents were enrolled from the 16 Local Government Areas of Kwara State. The Majority 125 (70.6%) of the respondents were female and 171 (96.6%) were married. The mean age of the respondents was 44.28 years (SD = 8.28) and majority, 129 (72.9%) were above 40 years of age. Most, 131 (74%) of the respondents were Yoruba. The majority, 132 (74.6%), work in primary health care institutions, 90 (50.8%) were Health Record Officers. Majority of the respondents 131 (74.0%) are designated as Surveillance Focal Persons in their Health facilities, and a greater percentage of the respondents, 84 (47.5%) have been in their present designation for 1 to 4 years.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of HCWs in Kwara State, 2023 (n = 177)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (N)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20–29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30–39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40–49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoruba\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNupe\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Healthcare Institution\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePresent Cadre\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Record Officer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEW\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther HCWs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDesignation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSNO/ADSNO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurveillance Focal Person\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears spent at present designation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1–4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5–9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSenatorial District\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKwara Central\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKwara South\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKwara North\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eKnowledge of yellow fever among healthcare workers\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all the respondents were aware of yellow fever, 141 (79.7%) of the respondents knew that the causative organism of yellow fever is a virus, and 159 (89.8%) knew the vector (mosquito) of yellow fever. Less than half, 74 (41.8%) of the respondents demonstrated knowledge of yellow fever transmission, while more than half of the respondents, 97 (54.8%), knew the symptoms (fever, yellow eyes, yellow skin, muscle pain, bleeding from orifices) of yellow fever. Most of the respondents, 125 (70.6%), knew the complications (bleeding, liver failure) of yellow fever. Less than half, 86 (48.6%) of the respondents, knew that one case of yellow fever was an outbreak.\u003c/p\u003e\u003cp\u003eAlmost all the respondents, 175 (98.9%), responded that they knew the case definition for yellow fever, but only 111 (62.7%) selected the correct case definition (Any person with acute onset of fever, with jaundice appearing within 14 days of the onset of the first symptoms). The grading of knowledge showed that the majority, 146 (82.5%), of the respondents had good knowledge of yellow fever.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHCWs knowledge of yellow fever in Kwara State, 2023 (n = 177)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (%) N = 177\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeard about yellow fever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177 (100%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCausative organism of yellow fever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141 (79.7%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWrong\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (20.3%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eVector of yellow fever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (89.8%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWrong\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (10.2%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eYellow fever transmission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (41.8%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWrong\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (58.2%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSymptoms of yellow fever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (54.8%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWrong\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (45.2%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCommon Complication of yellow fever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (70.6%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWrong\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (29.4%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEpidemic threshold of yellow fever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (48.6%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWrong\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (51.4%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eKnowledge of yellow fever case definitions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (98.9%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCase definition for a suspected case of yellow fever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (62.7%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWrong\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (37.3%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eOver-all Knowledge of Yellow Fever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (82.5%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (17.5%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003ePerceived self-efficacy on yellow fever case identification and reporting.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the perceived self-efficacy of the respondents on yellow fever identification and reporting. Almost all the respondents, 176 (99.4) stated that the confirmation of yellow fever was one through a laboratory test. Most of the respondents, 129 (72.9%) stated they have been involved in investigating a suspected case of yellow fever before. Almost all the respondents, 166 (93.8%) reported that the specimen taken for yellow fever confirmation is blood. Almost all respondents (173, 97.7%) indicated that their workplace is designated as a surveillance focal site. Of all the respondents, 177 (100%) reported that they have community informants, but only 147 (83.1%) call their community informants at least once a week. Few, 35 (19.8%) of the respondents reported having seen a suspected case of yellow fever in the last 3 months preceding this study. Most of the respondents, 141 (79.7), were aware that the form used for the investigation of a suspected case of yellow fever is IDSR-001.\u003c/p\u003e\u003cp\u003eGrading of self-efficacy revealed that the majority, 176 (99.4%) of the respondents, had good self-efficacy on yellow fever identification and reporting.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe perceived self-efficacy of respondents on yellow fever case identification and reporting\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (%) N = 177\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eYellow fever confirmation test\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWrong\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.6%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e176 (99.4%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eInvolvement in yellow fever case investigation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (27.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129 (72.9%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAppropriate sample for yellow fever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166 (93.8%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWrong\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (6.2%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eHealth facility is a surveillance focal site\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (2.3%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173 (97.7%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eRegular communication with community informants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegular\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147 (83.1%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot regular\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (16.9%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eReported yellow a fever case in the last 3 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142 (80.2%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (19.8%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eForm used for yellow fever investigation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141 (79.7%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWrong\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (20.3%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePerceived Self-efficacy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e176 (99.4%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.6%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAssociation between respondents’ characteristics and knowledge of yellow fever\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the association between knowledge and the characteristics of the respondents. There was a significant association between the knowledge of the respondents and attendance of surveillance training by them, P = \u0026lt; 0.001. Majority of those who had surveillance training had good knowledge of yellow fever.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between respondents’ characteristics and knowledge of yellow fever.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLevel of Knowledge\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood (%)\u003c/p\u003e \u003cp\u003en = 146\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor (%)\u003c/p\u003e \u003cp\u003en = 31\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCadre\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEW\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (85.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (14.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Record Officers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (81.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (18.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Health Workers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (81.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (18.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDesignation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSNO/ADSNO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (89.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (10.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.853\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurveillance Focal Persons\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (79.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (20.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of Experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1–4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (78.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (21.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.681\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5–9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (83.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (16.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (91.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (8.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurveillance Training\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (99.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (42.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (57.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Fisher-Exact p-value\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eAssociation between respondents’ characteristics and self-efficacy in identification and reporting of yellow fever.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the association between the respondents' characteristics and their perceived self-efficacy in detecting and reporting yellow fever cases. There is no significant association between self-efficacy and the respondents’ characteristics.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between respondents’ characteristics and self-efficacy in the identification and reporting of yellow fever.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSelf-efficacy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood (%)\u003c/p\u003e \u003cp\u003en = 176\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor (%)\u003c/p\u003e \u003cp\u003en = 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCadre\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHEW\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.388\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Record Officers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Health Workers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (97.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (3.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDesignation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSNO \u0026amp; ADSNO\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFP \u0026amp; Clinicians\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (99.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of Experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1–4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.011\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5–9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (98.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Health Facility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary \u0026amp; Tertiary\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (97.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (2.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurveillance Training\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (99.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKnowledge of Yellow fever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 (99.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e* Fisher-Exact p-value\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study provided valuable insights into the knowledge and self-efficacy of disease surveillance officers in Kwara State regarding the identification and reporting of yellow fever cases. While the overall knowledge and self-efficacy scores were generally good, notable gaps in specific areas warrant attention.\u003c/p\u003e \u003cp\u003eThis study revealed that 82.5% of the respondents had good knowledge of yellow fever. However, the findings from this study contrast with the result from research carried out among healthcare workers in Owo, Ondo State, which reported that 52.3% of the respondents had appropriate knowledge [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It was also found in this study that a higher proportion of the respondents with good knowledge have been trained in the last year. Since the COVID-19 pandemic, NCDC and other partners have conducted trainings and re-trainings across the nation. This study's good knowledge of yellow fever may be linked to the training.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGaps in Knowledge\u003c/h2\u003e \u003cp\u003eThe gaps in knowledge among disease surveillance officers in Kwara State, Nigeria, regarding the transmission and epidemic threshold of yellow fever are concerning. Only 42.8% of respondents correctly identified the mode of transmission of yellow fever, which is lower than findings from other studies conducted in Nigeria (55.6%) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Ghana (61.4%) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and Ethiopia (71.4%) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This lack of knowledge about the transmission mode may hinder the effectiveness of prevention and control measures, as disease surveillance officers may not be able to provide accurate information to patients and communities.\u003c/p\u003e \u003cp\u003eSimilarly, only 48.6% of respondents correctly identified the epidemic threshold for yellow fever, which is lower than findings from studies conducted in Tanzania (62.2%) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and Uganda (75.6%) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This difference may be attributed to variations in study populations, methodological differences, contextual factors, and other factors, such as differences in healthcare infrastructure, public health campaigns, and timeframe of the studies. Further research is needed to explore these potential explanations and identify strategies to improve knowledge and awareness of yellow fever among healthcare workers. Accurate knowledge of the epidemic threshold is critical for timely detection and response to outbreaks. The gaps in knowledge identified in this study highlight the need for targeted interventions to improve the knowledge and skills of disease surveillance officers in Kwara State. This could include regular training and updates on yellow fever transmission, symptoms, and control measures, as well as establishing a functional surveillance system to detect and respond to outbreaks promptly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between Knowledge and Surveillance Training\u003c/h2\u003e \u003cp\u003eThe association between knowledge and surveillance training among disease surveillance officers in Kwara State, Nigeria, is noteworthy. The finding that 99.2% of respondents who had received surveillance training had good knowledge of yellow fever identification and reporting was consistent with other studies that have demonstrated the positive impact of training on knowledge and capacity building among healthcare workers [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For instance, a study conducted in Ghana found that healthcare workers who received training on yellow fever surveillance had significantly higher knowledge scores compared to those who did not receive training (95.6% vs 70.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, a study conducted in Uganda found that training on yellow fever surveillance improved healthcare workers' knowledge and self-efficacy in detecting and reporting yellow fever cases (92.1% vs 75.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These findings underscore the importance of regular training and updates in ensuring disease surveillance officers possess the necessary knowledge and skills to detect and report yellow fever cases effectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGaps in Experience\u003c/h2\u003e \u003cp\u003eThe fact that nearly three-quarters (73%) of respondents had prior experience with yellow fever case investigations, while 27% had not, underscores a notable disparity in practical exposure among disease surveillance officers in Kwara State, Nigeria. This is consistent with other studies that have reported limited opportunities for training and participation in yellow fever case investigations among healthcare workers. For instance, a study conducted in Ghana found that 35.6% of healthcare workers had not participated in yellow fever case investigation before [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In contrast, a study in Uganda reported that 41.2% of healthcare workers lacked practical experience investigating yellow fever cases [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, the finding that 20.3% of respondents did not know the form used for investigating yellow fever cases is alarming, as this lack of knowledge may hinder the effective investigation and reporting of cases. This is like a study conducted in Ethiopia, where 25.9% of healthcare workers did not know the standard case definition for yellow fever [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eImplications for Healthcare Practice\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStandardized Case Definition: A standardized case definition for yellow fever should be used to ensure accurate identification and reporting of cases.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePractical Experience: Disease surveillance officers should be provided with opportunities for practical experience in investigating yellow fever cases to build capacity and competence.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRegular Training and Updates: Disease surveillance officers require regular training and updates on yellow fever surveillance, identification, and reporting to ensure they possess the necessary knowledge and skills.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePolicy Considerations and Directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eNational Guidelines: National guidelines for yellow fever surveillance, identification, and reporting should be developed and disseminated to healthcare workers.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTraining Policies: Policies should be developed to ensure regular training and updates for disease surveillance officers on yellow fever surveillance, identification, and reporting.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIntersectoral Collaboration: To enhance yellow fever prevention and control efforts, a multi-faceted approach involving the health, agriculture, environment, education, and other relevant sectors is crucial.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCommunity Engagement and Risk Assessment: A comprehensive approach to community engagement and risk assessment should be developed to promote awareness, understanding, and mitigation of yellow fever risks.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the results of this study demonstrated that while disease surveillance officers in Kwara State possess a good foundation of knowledge and self-efficacy regarding yellow fever identification and reporting, specific areas such as knowledge of yellow fever transmission, symptoms and the epidemic threshold among HCWs in Kwara State still require attention. These gaps in knowledge can have significant consequences, including delayed identification and reporting of cases, which can ultimately compromise the effectiveness of yellow fever prevention and control efforts.\u003c/p\u003e \u003cp\u003eRegular training and updates for the disease surveillance officers focused on the mode of transmission and epidemic threshold of yellow fever are essential to address the gaps in knowledge of yellow fever identification and reporting. Additionally, practical experience in investigating and reporting yellow fever cases is critical for building the capacity and competence of healthcare workers.\u003c/p\u003e \u003cp\u003eThe findings of this study have significant implications for healthcare practice, specifically in the areas of yellow fever surveillance, diagnosis, and prevention. Additionally, the study's results inform policy decisions related to resource allocation, public health education, and community engagement. At the practice level, disease surveillance officers and facilities must prioritize yellow fever identification and reporting and ensure that all disease surveillance officers receive regular training and updates. At the policy level, efforts should be made to develop and implement policies that support yellow fever infection prevention and control efforts, including providing resources for training and surveillance activities.\u003c/p\u003e \u003cp\u003eOverall, this study provides empirical evidence to support the implementation of targeted interventions aimed at strengthening yellow fever surveillance, diagnosis, and prevention in Kwara State. By addressing the knowledge gaps, practice deficits, and systemic barriers identified in this research, policymakers and stakeholders can develop effective strategies to combat yellow fever and ultimately reduce its burden in Nigeria.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRecommendations\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRegular Training and Updates: Provide regular training and updates for disease surveillance officers on yellow fever identification and reporting, focusing on specific areas where gaps in knowledge and practice were identified.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePractical Experience: Ensure that disease surveillance officers have practical experience in investigating and reporting yellow fever cases to build their capacity and competence.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStandardized Case Definition: Use a standardized case definition for yellow fever to ensure accurate identification and reporting of cases.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eImproved Surveillance: Strengthen surveillance systems to ensure timely identification and reporting of yellow fever cases.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDevelop and Implement Policies: Develop and implement policies that support yellow fever prevention and control efforts, including the provision of resources for training and surveillance activities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eResource Allocation: Allocate adequate resources to support yellow fever surveillance, identification, and reporting activities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIntersectoral Collaboration: Foster intersectoral collaboration between health, education, and other relevant sectors to support yellow fever prevention and control efforts.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCommunity Engagement and Risk Assessment: A comprehensive approach to community engagement and risk assessment should be developed to promote awareness, understanding, and mitigation of yellow fever risks.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLongitudinal Studies: Conduct longitudinal studies to assess the impact of training and surveillance activities on yellow fever identification and reporting.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eComparative Studies: Conduct comparative studies to assess the effectiveness of different training and surveillance strategies in improving yellow fever identification and reporting.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eQualitative Studies: Conduct qualitative studies to explore the perceptions and experiences of disease surveillance officers and communities regarding yellow fever prevention and control efforts.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eODA and MDD conceptualized the studyODA and OEF collected dataODA, STA and MDD analysed the dataODA wrote the main manuscript textMDD, AFF, OEF and OF reviewed the maniscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ehttps://us.docworkspace.com/d/sID2Xlb8hzL6PvQY?sa=601.1123\u0026amp;ps=1\u0026amp;fn=data.xlsx\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunding Declaration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe author(s) received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGarske T. et al. Yellow fever in Africa: Estimating the burden of disease and impact of mass vaccination from outbreak and serological data. PLoS Med. 2014;11(5):e1001638 - https://pubmed.ncbi.nlm.nih.gov/24800812/\u003c/li\u003e\n\u003cli\u003eOlatinwo AW. Yellow fever outbreak in Kwara State, Nigeria: a descriptive study. J Med Med Sci. 2019;10(1):1-8.\u003c/li\u003e\n\u003cli\u003eDurowade KA. Assessment of healthcare workers\u0026apos; knowledge and practice on yellow fever in Ogun State, Nigeria. Int J Med Health Dev. 2020;25(2):1-8.\u003c/li\u003e\n\u003cli\u003eOgunleye VV. Knowledge and self-efficacy of healthcare workers in detecting and reporting yellow fever cases in Lagos State, Nigeria. J Environ Occup Sci. 2020;9(2):1-10.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. Yellow fever. 2020. Available from: https://www.cdc.gov/ncezid/\u003c/li\u003e\n\u003cli\u003eGianchecchi E, Cianchi V, Torelli A, Montomoli E. Yellow Fever: Origin, Epidemiology, Preventive Strategies and Future Prospects. Vaccines. 2022; 10(3):372.\u003c/li\u003e\n\u003cli\u003eWHO (2020). Yellow fever situation report.\u003c/li\u003e\n\u003cli\u003eAdeyemi, O. O. (2017). Burden of yellow fever in Nigeria: a systematic review. Journal of Infectious Diseases and Epidemiology, 3(1), 1-9.\u003c/li\u003e\n\u003cli\u003eAdeniji JA. Yellow fever outbreak in Nigeria: a review. J Med Med Sci. 2019;10(2):1-9.\u003c/li\u003e\n\u003cli\u003eNigerian Centre for Disease Control. Yellow fever outbreak in Kwara State, Nigeria. 2020 [cited 2022 Jan 10]. Available from: 41f2918d91bb993d1f1c1fdf20d4b3bb.pdf\u003c/li\u003e\n\u003cli\u003eAdeyemi OO. Burden of yellow fever in Nigeria: a systematic review. J Infect Dis Epidemiol. 2017;3(1):1-9.\u003c/li\u003e\n\u003cli\u003eOkoli CN. Assessment of knowledge and practice of healthcare workers on yellow fever in Ebonyi State, Nigeria. Int J Trop Disease Health. 2020;39(2):1-12.\u003c/li\u003e\n\u003cli\u003eOyeyemi AS. Assessment of healthcare workers\u0026apos; knowledge and self-efficacy in detecting and reporting yellow fever cases in Osun State, Nigeria. J Healthcare Res. 2020;5(1):1-12.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Yellow fever: a handbook for clinicians. 2013 [cited 2022 Jan 10]. Available from: https://www.who.int/news-room/fact-sheets/detail/yellow-fever\u003c/li\u003e\n\u003cli\u003eAjayi IO, Jegede AS, Falade CO, et al. Knowledge and practices related to yellow fever vaccination among healthcare workers in Nigeria. J Infect Dev Ctries 2018;12(10):761-8. doi: 10.3855/jidc.10041\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Yellow fever vaccine: WHO position paper [Internet]. 2016 [cited 2022 Jan 10]. Available from: (link unavailable)\u003c/li\u003e\n\u003cli\u003eAbba A, Ibrahim M, Mohammed A. Challenges facing healthcare workers in the management of yellow fever in Nigeria. J Healthcare Commun 2020;5(2):1-5.\u003c/li\u003e\n\u003cli\u003eNigerian Centre for Disease Control. Guidelines for yellow fever diagnosis and management in Nigeria [Internet]. 2020 [cited 2022 Jan 10]. Available from: (link unavailable)\u003c/li\u003e\n\u003cli\u003eMwanga A, Mmbuji P, Mushi H, et al. Healthcare workers\u0026apos; attitudes and beliefs towards yellow fever vaccination in Tanzania. J Vaccines Vaccin 2020;11(2):1-7.\u003c/li\u003e\n\u003cli\u003eda Silva F, da Silva F, de Oliveira E, et al. Factors influencing healthcare workers\u0026apos; knowledge and self-efficacy in detecting and reporting yellow fever cases in Brazil. J Infect Dev Ctries 2020;14(4):381-8. doi: 10.3855/jidc.12843\u003c/li\u003e\n\u003cli\u003eAdeyemo AA. Yellow fever vaccine coverage and associated factors among children aged 9-23 months in Nigeria. J Vaccines Vaccination. 2019;10(3):1-9.\u003c/li\u003e\n\u003cli\u003eAwoyale, O. D., Ilesanmi, O. S., Afolabi, A. A., \u0026amp; Fakayode, O. E. (2021). An investigation of the resurgence of yellow fever outbreak in Kwara State, Nigeria, 2018. International Journal of Community Medicine and Public Health, 8(12), 6064\u0026ndash;6067. https://doi.org/10.18203/2394-6040.ijcmph20214616\u003c/li\u003e\n\u003cli\u003eUkwenya, V. O., Fuwape, T. A., Fadahunsi, T. I., \u0026amp; Ilesanmi, O. S. (2021). Disparities in knowledge, attitude, and practices of infection prevention and control of lassa fever among health care workers at the federal medical centre, owo, ondo State, nigeria. \u003cem\u003ePan African Medical Journal\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e. https://doi.org/10.11604/pamj.2021.38.357.26208\u003c/li\u003e\n\u003cli\u003eIjarotimi, I. T., Ilesanmi, O. S., Aderinwale, A., Abiodun-Adewusi, O., \u0026amp; Okon, I. M. (2018). Knowledge of lassa fever and use of infection prevention and control facilities among health care workers during lassa fever outbreak in ondo State, Nigeria. \u003cem\u003ePan African Medical Journal\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e. https://doi.org/10.11604/pamj.2018.30.56.13125\u003c/li\u003e\n\u003cli\u003eAjayi IO, Jegede AS, Falade CO, et al. Knowledge and practices related to yellow fever vaccination among healthcare workers in Nigeria. J Infect Dev Ctries 2018;12(10):761-8.\u003c/li\u003e\n\u003cli\u003eKenu E, Nyarko KM, Dzotsi EK, et al. Knowledge and self-efficacy of healthcare workers in detecting and reporting yellow fever cases in Ghana. J Med Biomed Sci 2020;9(1):1-9.\u003c/li\u003e\n\u003cli\u003eGetahun A, Tadesse E, Alemu A, et al. Healthcare workers\u0026apos; knowledge and self-efficacy in detecting and reporting yellow fever cases in Ethiopia. J Med Biomed Sci 2020;9(4):1-8.\u003c/li\u003e\n\u003cli\u003eMwanga A, Mmbuji P, Mushi H, et al. Healthcare workers\u0026apos; attitudes and beliefs towards yellow fever vaccination in Tanzania. J Vaccines Vaccin 2020;11(2):1-7.\u003c/li\u003e\n\u003cli\u003eMugabe A, Mugisha A, Kaggwa M, et al. Healthcare workers\u0026apos; knowledge and self-efficacy in detecting and reporting yellow fever cases in Uganda. J Med Biomed Sci 2020;9(2):1-8.\u003c/li\u003e\n\u003cli\u003eKenu E, Nyarko KM, Dzotsi EK, et al. Knowledge and self-efficacy of healthcare workers in detecting and reporting yellow fever cases in Ghana. J Med Biomed Sci 2020;9(1):1-9.\u003c/li\u003e\n\u003cli\u003eMugabe A, Mugisha A, Kaggwa M, et al. Healthcare workers\u0026apos; knowledge and self-efficacy in detecting and reporting yellow fever cases in Uganda. J Med Biomed Sci 2020;9(2):1-8.\u003c/li\u003e\n\u003cli\u003eOfori-Asenso R, Agyeman AA. Knowledge and self-efficacy of healthcare workers in detecting and reporting yellow fever cases in Ghana. J Infect Dev Ctries 2020;14(3):251-8.\u003c/li\u003e\n\u003cli\u003eKaggwa M, Mugisha A, Mugabe A, et al. Training healthcare workers on yellow fever surveillance improves knowledge and self-efficacy in detecting and reporting cases in Uganda. J Vaccines Vaccin 2020;11(2):1-7.\u003c/li\u003e\n\u003cli\u003eKenu E, Nyarko KM, Dzotsi EK, et al. Knowledge and self-efficacy of healthcare workers in detecting and reporting yellow fever cases in Ghana. J Med Biomed Sci 2020;9(1):1-9.\u003c/li\u003e\n\u003cli\u003eMugabe A, Mugisha A, Kaggwa M, et al. Healthcare workers\u0026apos; knowledge and self-efficacy in detecting and reporting yellow fever cases in Uganda. J Med Biomed Sci 2020;9(2):1-8.\u003c/li\u003e\n\u003cli\u003eGetahun A, Tadesse E, Alemu A, et al. Healthcare workers\u0026apos; knowledge and self-efficacy in detecting and reporting yellow fever cases in Ethiopia. J Med Biomed Sci 2020;9(4):1-8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Yellow fever, Healthcare workers, Knowledge, Self-efficacy, Kwara State","lastPublishedDoi":"10.21203/rs.3.rs-5968718/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5968718/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003eYellow fever (YF) poses a significant threat to public health in Nigeria, which bears the highest burden of the disease. Timely identification and reporting by disease surveillance officers are critical in preventing and controlling outbreaks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eA cross-sectional survey of 177 healthcare workers, including disease surveillance officers, was conducted in Kwara State, Nigeria, between June 2023 and December 2023. A pre-tested structured questionnaire was used for data collection. Data analysis was performed using Microsoft Excel 365 and SPSS 20.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study revealed that 82.5% of respondents demonstrated good knowledge of yellow fever, while 99.4% showed good self-efficacy in detecting and reporting cases. However, gaps in knowledge and practice were identified, particularly regarding the mode of transmission and epidemic threshold. Continuous training, retraining and regular updates on yellow fever epidemiology, transmission dynamics, and control measures should be provided to healthcare workers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eThis study highlights the need for targeted interventions to enhance healthcare workers' knowledge and practice gaps in yellow fever identification and reporting in Kwara State, Nigeria. Continuous training and updates are crucial to ensure timely and effective response to yellow fever outbreaks, ultimately reducing the disease burden in Nigeria\u003c/p\u003e","manuscriptTitle":"Yellow Fever in Plain Sight: Assessing Disease Surveillance Officers' Knowledge and Self-Efficacy in Identification and Reporting in Kwara State, Nigeria.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 23:04:48","doi":"10.21203/rs.3.rs-5968718/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-25T07:57:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-18T12:52:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-12T11:16:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122653496401880850605824312432724002567","date":"2025-04-12T10:32:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224379470403962776514409391235921542741","date":"2025-04-10T14:38:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-10T09:28:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-10T01:25:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-04-06T16:21:35+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":"531f19f6-a534-4ce8-9d80-cbd1ae04ff1c","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:10:31+00:00","versionOfRecord":{"articleIdentity":"rs-5968718","link":"https://doi.org/10.1186/s12889-025-23344-5","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-06-06 15:57:47","publishedOnDateReadable":"June 6th, 2025"},"versionCreatedAt":"2025-04-14 23:04:48","video":"","vorDoi":"10.1186/s12889-025-23344-5","vorDoiUrl":"https://doi.org/10.1186/s12889-025-23344-5","workflowStages":[]},"version":"v1","identity":"rs-5968718","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5968718","identity":"rs-5968718","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}