Assessment and exploratory analysis of Indicator Based Surveillance (IBS) system in Rwanda

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Since 2002, Rwanda has implemented indicator-based surveillance through the Integrated Disease Surveillance and Response (IDSR) framework. This study aimed to explore the completeness and timeliness of reporting and use of the tool in Rwanda. Method : In this study, we used a cross-sectional descriptive approach using a structured questionnaire for IDSR focal persons and data managers in 46 public hospitals accompanied by a secondary data analysis of monthly records from the national HMIS for the period 2018–2021 from 564 public health facilities (HFs) and 283 private HFs. Exploratory analysis and correlation assessments complemented dataset analysis. Results : This study reveals that public HF consistently achieves or surpasses the 80% completeness (96.7%) and timeliness (80.8%) targets. In contrast, private HF demonstrates 42.8% and 25.3%, completeness and timeliness, respectively. Eight-seven percent (87%) of interviewees reported to have received feedback from the central level, with varying frequencies. Hospitals provide feedback to HFs in their catchment area (91%), but frequencies differ. Regarding data accuracy, 95.7% possess standard case definitions, and 87% regularly refer to them. Two-thirds (67.6%) report they monitor weekly trends, but only 34.9% produce and share weekly reports promptly. Challenges identified include internet issues (30%), other competing duties (30%), and forgetting to report (26%). 84.8% of HFs used the system to detect outbreaks in their catchment areas; 71.7% of these were responded to according to national guidelines. Furthermore, 92.3% of all HFs have used the eIDSR system for planning purposes. Conclusion : The IDSR system was highly useful by providing evidence for decision-making in early detection and response to outbreaks as well as for hospital program planning. Maintaining timely reporting, enhancing data quality and timely use, and improving health workers' knowledge and practices are vital for a surveillance system user to detect outbreaks early. More focus should be put on Private Health Facilities. Indicator-Based Surveillance Integrated Diseases Surveillance in Rwanda Rwanda exploratory analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION According to the World Health Organization (WHO), infectious diseases are the leading cause of death worldwide [ 4 ]. The emergence and re-emergence of infectious diseases is a global challenge that affects all countries including Rwanda [ 1 ]. Factors that contribute to the emergence and re-emergence of infectious diseases include virus mutations and recombination, population growth, urbanization, human population movement, hunting and pasture practices, agricultural practices and deforestation, globalization of commerce, human social behavior, and environmental factors such as global warming [ 2 , 3 ]. The International Health Regulations (IHR) 2005 is a legally binding instrument whose purpose and scope are to prevent, protect against, control, and provide a public health response to the international spread of disease. It requires countries to have the capacity to detect, assess, notify, and report any events through an early warning, alert, and response (EWAR) function of a national surveillance system [ 4 ]. Effective surveillance systems are a vital component of a comprehensive public health infrastructure, as they act as the watchful eyes and attentive ears of disease control efforts. These systems systematically gather, analyze, and interpret data to offer essential insights that guide health policies and strategies. The importance of surveillance systems in the field of healthcare cannot be overstated. They provide timely information about disease trends and health-related events, which helps in responding quickly to public health threats. Additionally, they help in evaluating the effectiveness of public health interventions and monitoring the progress of health programs. As a result, they contribute significantly to global health security. The effectiveness of a health system in controlling disease and maintaining population health is largely dependent on the performance of its surveillance system. [ 6 , 7 ] There are two main approaches to health surveillance systems: Indicator-Based Surveillance (IBS) and Event-Based Surveillance (EBS). IBS is a traditional method that involves collecting data on disease occurrence regularly. Healthcare providers or labs often report specific diseases or conditions to track long-term trends, allocate resources, and measure performance. In contrast, EBS collects information about events that could indicate a disease outbreak or public health risk. These events are often detected from sources outside traditional health reporting channels, such as news reports or community rumors. EBS is more sensitive and timelier, allowing for quicker responses to potential health threats. Many countries pursue a balanced approach to IBS and EBS, leveraging the strengths of both systems while mitigating their respective limitations. [ 8 – 9 ]. Rwanda faces a continuous risk of epidemic disease transmission. Factors contributing to this risk include its high population density (525 per km^2), location in a region prone to epidemic diseases, easy domestic and international travels or population movements including tourism promotion, healthcare infrastructure, climate, and various socioeconomic factors. Rwanda is a landlocked country located in East-Central Africa. It is bordered by Uganda to the north, Tanzania to the east, Burundi to the south, and the Democratic Republic of the Congo to the west. In the near past, the region has been affected by Ebola outbreak (DRC and Uganda), Marburg outbreak (Tanzania), Cholera (Burundi), Polio (Burundi and DRC), RVF (Rwanda and Tanzania), and some other listed transmissible diseases [ 13 , 14 , 15 , 16 ] In Rwanda, the history of surveillance systems is closely tied to the country's progress in public health. After the genocide in 1994, Rwanda's health infrastructure had to be reconstructed from scratch. Despite this challenge, Rwanda achieved significant progress, including the introduction of an effective Indicator-Based Surveillance (IBS) system. With help from international partners such as the WHO, Rwanda launched an electronic Integrated Disease Surveillance and Response (eIDSR) in 2012 that formed the foundation of the IBS system. The IBS is an intricate framework integrated into the broader DHIS-2 network, linking all health facilities across the country. It consistently gathers, consolidates, and examines data from numerous public health surveillance indicators, which aid public health planning, monitoring, and evaluation. Although the IBS is well-established in Rwanda, the Event-Based Surveillance (EBS) is still in its early stages. However, recognizing the importance of a comprehensive surveillance approach, there have been efforts to establish an EBS system to complement the existing IBS. By adopting EBS, the country's ability to detect and respond to new and emerging health threats in real-time will be significantly improved. This will provide a strong early warning system for disease outbreaks. While Rwanda has commendably implemented a comprehensive Indicator-Based Surveillance (IBS) system, the public health landscape calls for constant evolution and adaptability. Despite the strengths of IBS, its limitations may potentially hinder the country's ability to promptly detect and respond to new and emerging health threats. There is a growing recognition that for a surveillance system to be optimally effective, IBS should be complemented with Event-Based Surveillance (EBS). However, in Rwanda, EBS is yet to be fully implemented and integrated into the existing health surveillance framework. The purpose of this study, therefore, was to undertake a robust evaluation of Rwanda's current IBS system based on key attributes such as completeness, timeliness, acceptability, simplicity, flexibility, representativeness, data accuracy, data use, usefulness, and feedback. By highlighting the challenges and gaps in the IBS system, this paper aims to underscore the need for a more balanced surveillance approach that integrates and strengthens EBS within the health system. Through such a blend of IBS and EBS, Rwanda can ensure a more responsive, comprehensive, and efficient surveillance system, better prepared to safeguard public health in an ever-changing disease landscape. METHODS Rwanda is a country located in East Central Africa with high population density. The population is around 14 million with an area of 26,338 km 2 . Rwanda has 4 provinces and the city of Kigali. The provinces are divided into districts and districts into sectors, sectors into cells, and cells into villages. Rwanda's health system is designed in tiers and is built on Community health workers, Health posts, Health centers, District hospitals, provincial hospitals, referral hospitals, and University hospitals. Operational-wise, health centers are a point of referral to health posts. District hospitals are a point of referral for health centers. The provincial hospital plays a pivotal role as a referral center for the district hospital and serves as the ultimate referral hospital for the entire province. Rwanda boasts a total of 60 hospitals, with 8 of them privately owned. The Rwanda Ministry of Health's performance report for 2021–2022 reveals that 90% of outpatients visit public facilities for medical care, while 10% prefer private facilities. Throughout our research, we have thoroughly examined all 46 district and provincial hospitals. In order to gain insights from those who use the system most frequently, we carefully selected two health workers from each hospital for interviews. Specifically, we chose IDSR focal persons and data managers, as they are the primary users of the system. IDSR focal persons are critical in identifying and relaying key information, while data managers are responsible for managing, analyzing, and utilizing the collected data. In the realm of public health, 'completeness' refers to the measure of all expected reports from reporting sites that have been received, irrespective of their time of submission. This attribute is crucial as an incomplete report cannot accurately describe the problem and can lead to missed opportunities to respond to public health challenges. Timeliness in public health refers to the speed between steps in a public health surveillance system. The single most important measure of timeliness is whether data are submitted in time to begin investigations and implement control measures. An effective and timely surveillance system increases the possibility of detecting a problem and conducting a prompt response. In Rwanda's Indicator-Based Surveillance (IBS) system, completeness is evaluated based on the proportion of public and private health facilities that submit their surveillance reports. For the period between 2018 and 2021, the evaluation indicates varying degrees of completeness in reporting. Timeliness in Rwanda's IBS system is assessed based on the submission of weekly reports by Monday, not later than noon, following standards developed by The Ministry of Health/Rwanda Biomedical Centre by the timeliness set by WHO AFRO. Acceptability reflects the willingness of individuals and organizations to participate in the surveillance system. Acceptability is closely connected to the completeness and timeliness of the reported data. To evaluate acceptability, we examined (1) how frequently the system was used, (2) the importance of the user's contribution to the surveillance system, and (3) the opinion of key individuals on the usefulness of the system. We conducted a cross-sectional survey to evaluate IDSR in Rwanda. In order to gather all information reflecting all indicators. Using a structured questionnaire, trained assistant conducted interviews with the selected health workers at hospitals for some indicators like…. [ 5 , 10 ]. We also extracted data from the national health information management system HMIS/eIDSR for some indicators such as completeness and timeliness. We have considered data gathered for the period of 2018 to 2021 from 564 public health facilities (HFs) and 283 private HFs. Data were collected through national health surveillance data entry tools (RedCap) and were extracted in an Excel spreadsheet, cleaned and analyzed R. Based on different literature reviews [ 4 , 5 ], we generally considered the following factors that reflect the core functions of IDSR: 1) Completeness, 2) Timeliness, 3) Acceptability, 4) Data accuracy, 5) Data use, 6) Usefulness, 7) Simplicity, 8) Knowledge attitude and practice, 9) Feedback, 10) Representativeness, 11) Involvement of private hospitals. The main method we adopt to analyze is exploratory analysis and correlation assessments. We have described each variable using both graphical representation such as frequency distribution and numerical measures and performed the relationship analysis between some variables. RESULTS Completeness and Timeliness in the period of 2018 to 2021 Public health facilities achieved an impressive overall completeness of 96.7%, exceeding the target of 80%. All districts achieved completeness greater than the target, indicating high performance. On the other hand, private health facilities' overall completeness was considerably lower at 42.8%. Only three districts exceeded the 80% target (Burera, Musanze, and Ngoma), while the remaining districts showed low performance, and four had a zero-completion rate. An evaluation of the period from 2018 to 2021 reveals that public health facilities' overall timeliness was 80.8%, matching the target of 80%. However, about 37% of the districts fell short of the target. In contrast, private health facilities reported a significantly lower overall timeliness of 25.3%. Only one district met the target (Burera), while most districts showed very low performance. A closer look at the trend in completeness and timeliness from 2018 to 2021 for public health facilities reveals an increase in both attributes from 2018 to 2019, followed by a decrease in timeliness in 2020 and a decrease in both completeness and timeliness in 2021. In private health facilities, although the completeness and timeliness were low throughout the four years, they increased from 2018 to 2020 and subsequently decreased in 2021. Acceptability The effectiveness of a surveillance system relies heavily on whether it leads to actionable public health measures. A successful public health system aids in the prevention and control of adverse health-related events. This is evidenced by decision-making and policy implementation based on the reported data. When focal persons (users) can see the impact of their contributions to the surveillance, they feel more invested in the process. Additionally, surveillance data can be useful in measuring performance, further motivating users to utilize the system. The system's frequency of use is also a key indicator of its usefulness. Research has shown that if users do not find a surveillance system acceptable, their productivity will suffer. Based on our research, it was discovered that the second edition of e IDSR effectively detected variations in the trend of cases. A significant 82.6% of hospitals relied on data generated by the system to stay informed of weekly changes in reportable diseases. Furthermore, our study revealed that 84.8% of health facilities used the system to identify outbreaks within their areas of coverage. In addition, the eIDSR system has been utilized by 92.3% of health facilities for planning purposes, as indicated by recent research. The system has proven to be extremely beneficial in providing statistical evidence to support decision-making processes, particularly in the areas of outbreak detection and response, as well as the planning of hospital programs. However, it was found that only 71.7% of outbreaks were handled by guidelines, highlighting the need to reinforce the use of IDSR technical guidelines in response to outbreaks. Frequency of use is a key indicator of a system's ability to fulfill its primary function. If users delay reporting, early detection and response will be compromised. The IDSR system is made up of both immediate and weekly disease reporting components. However, assessments have revealed that most focal persons only enter data every week, which poses a challenge given the immediate disease-reporting aspect of the system. Additionally, some focal persons only access the system on a monthly or less frequent basis. Figure 3 summarizes the respondent's answers on (1) the User’s opinion on IBS usefulness, (2) the IBS system usage frequency, and (3) the User's perceived value of the contribution. Feedback According to the results of this study, many participants (87%) stated that they received feedback from the central level, although the frequency of feedback varied significantly. Slightly over half of the respondents reported receiving feedback weekly, while the remaining participants received feedback either immediately, monthly, or quarterly. On the hospital side, it was found that 91% of them provide feedback to health centers in their catchment area. However, the frequency of feedback provided by hospitals is inconsistent, with approximately 40% of them providing weekly feedback, 30% providing immediate feedback, and 25% providing monthly feedback. Accuracy The correct use of Standard Case Definitions is key to accurate data. Data Accuracy is a measure of how well a piece of information reflects reality and is key to appropriate decision-making, especially for public health. The assessment has shown that 95.7% of health facilities possess Standard Case definitions and 87% of all health facilities refer to them regularly in case detection and reporting. Though there is a considerable portion of health facilities that use accurate detection and reporting in IDSR, there is a need to improve the accuracy of data by fully providing Standard Case Definitions and sensitization about the importance of using Standard Case Definitions for detection and reporting in the improvement of data accuracy. Data use To make informed decisions, it is essential to base them on evidence that is grounded in reliable data. Data use not only serves as evidence to support decision-making but also plays a critical role in identifying potential data quality issues and providing feedback to reporting entities for improvement. Furthermore, the use of data can shed light on emerging trends in disease, while also facilitating the modeling of various diseases, thereby advancing scientific discovery. Based on the assessment, it was found that 67.6% of healthcare facilities use data to monitor reportable diseases every week and share the results. However, only 34.9% of these facilities produce and share the weekly trend of cases promptly compared to the set threshold. It was also observed that the use of weekly reports to monitor potential outbreaks is still low at the hospital level, despite it being the primary level of detection for outbreaks at the district level. Reasons for poor reporting According to the results, there are several main reasons for poor reporting. The assessment found that 30% of respondents reported experiencing internet problems while trying to complete their reporting duties. Additionally, 30% of respondents reported that they struggle to balance their IDSR activities with other provided duties, leading to incomplete or delayed reporting. Forgetting to report was also identified as a significant issue, with 26% of respondents indicating that this was a problem for them. 13% presented other reasons for not reporting. Table 1 Summary of results on data accuracy, data use, and usefulness Key (Attributes) Variables Categories Counts % Data Accuracy Possession of SCD Yes 44 95,7 No 2 4,30 Use of SCD Yes 40 87,0 No 4 8,70 Not Applicable 2 4,30 Data use Production of the weekly trend of cases compared to a threshold Yes 32 69,6 No 14 30,4 Dissemination of weekly trend Yes 31 67,4 No 1 2,20 Not Applicable 14 30,4 Frequency of dissemination of weekly trend weekly 15 34,9 monthly 12 27,9 quarterly 2 4,70 Not Applicable 14 30,4 Missing 3 Use of the system Analysis 2 4,30 Both 29 63,0 Reporting 5 10,9 Missing 10 21,7 Usefulness Detect any change in data trend Yes 38 82,6 No 8 17,4 Detect outbreak Yes 39 84,8 No 6 13,0 Missing 1 2,20 Response according to guideline Yes 33 71,7 No 12 26,1 missing 1 2,20 Use for planning Yes 42 91,3 No 2 4,30 Missing 2 4,30 Knowledge Attitude and Practice (KAP) Score The significance of KAP lies in its ability to gauge the scope of a given situation, verify or refute a hypothesis, and determine what is already known and being done regarding different health-related matters with a focus on IDSR. For our research, we have formulated six queries to evaluate KAP. These inquiries pertain to the eIDSR platform, medical conditions, data utilization, and reporting. The KAP score, which we determined by adding up the points from all six questions, serves as a cumulative measure of one's KAP. Summary Statistics of KAP score. Max 100.0 Min 50.0 Mean 74.8 Median 66.7 Mode 66.7 Based on the findings, 51% fall within the 70–100 range, and 49% fall within the 50–69 range. This may be due to some personnel who are committed to the IDSR process not fully understanding when they need to report (either immediately or weekly), particularly about the latest indicator/data elements within the system. Simplicity In the context of Indicator Based Surveillance, the significance of simplicity cannot be overstated. By keeping things simple, all users can easily comprehend and retain the information for future use. This approach ensures that the surveillance process remains accessible and user-friendly. According to the graph above, a significant percentage of respondents (33%) strongly agreed that IDSR tools are simple to use, whereas 48% of respondents reported that they agreed with this sentiment. A small percentage of respondents (7%) were neutral, while 9% disagreed and 4% strongly disagreed. This may be due to some FPs taking on new responsibilities without being adequately trained in that area, as well as some institutions being new to using the IDSR (especially those who started using it midway through 2022). As such, there may be a need for refresher training to ensure that all personnel are well-equipped to use the IDSR effectively. Moreover, 96% of data managers found the system to be highly user-friendly. However, 4% of hospitals that had recently adopted the system reported facing some difficulties in using it. Representativeness We examined the coverage of the population and regions included in the disease surveillance system, as well as any illnesses that may not have been detected by our surveillance measures. According to the survey results, 76% of those who responded said that the disease surveillance system covers the entire population in their area, while 24% reported that certain groups within their catchment area are not covered. Additionally, 78% of respondents stated that the surveillance system fully covers their catchment area, but 22% reported that some areas are not covered. Lastly, 91% expressed satisfaction with the current list of priority diseases under surveillance, but 9% recommended that non-communicable diseases, Scabies, and Chickenpox/Varicella be added to the list. Figure 7: Representativeness in the aspect of population, area, and diseases The disease surveillance system of Rwanda generally covers most of the population, the minor part of the population that is not covered might be attributed to the population visiting traditional healers and some people using private clinics that do not report in routine disease surveillance. Furthermore, certain individuals opt to treat themselves at home based on their cultural or religious convictions. Unfortunately, these groups are not identified through disease surveillance efforts. While all regions of the country are reachable, there may be a handful of areas that pose challenges due to inadequate infrastructure, remote dwellings, and other factors. This may account for any gaps in the disease surveillance system's coverage. The list of prioritized diseases is determined by several factors, such as the public health significance of the condition. However, it is important to note that certain diseases may have a greater impact on specific regions, which may require adjustments to the list of priority diseases. Respondents have indicated a need to expand the list of diseases under surveillance. To address this, the Rwanda Biomedical Center's PHS&EPR Division should conduct further investigations to identify clusters of populations and regions not currently covered by the disease surveillance system. An assessment and review should be conducted to update the list of priority diseases. Additionally, an early warning system that extends beyond health facilities, focusing on the community level, should be implemented to ensure that no diseases or events go unnoticed in uncovered clusters of populations and regions. Involvement of private health facilities Private health facilities play a vital role in delivering healthcare services throughout the nation, and their significance has only been increasing in recent years. In Rwanda, a considerable portion of healthcare services are provided by private health facilities, which may bear a substantial burden of diseases. To address this, disease surveillance has been extended to private clinics through the introduction of IDSR. Nonetheless, despite their crucial role in healthcare delivery, the extent of participation of private health facilities in the disease surveillance system remains poor. According to the survey, 61% of those polled acknowledged their obligation to supervise private clinics within their designated catchment areas. Meanwhile, 39% believed that private clinics were not within their purview of responsibility. Of the 28 respondents who considered private clinics to be their responsibility, 39% admitted to never reviewing surveillance reports, while the remaining 61% claimed to regularly check such reports from private clinics. In addition, 46% of the respondents reported that they never conduct inspections of private clinics in their catchment areas. 21% of respondents conduct monthly and quarterly supervision, and 7% and 3% of the respondents reported conducting bi-annually and annual supervision to private clinics respectively. Regarding the reasons driving to not conduct supervision among private clinics, from the 28 respondents accepting their responsibilities of private clinics in their catchment areas, 14% of the respondents reported financial constraints as the main reason for limiting supervision among private clinics, 11% indicated that high workload and being busy at work as another reason and 39% showed non-specific reasons limiting them to conduct supervisions in private clinics. Despite the significant contribution that private health facilities make to health service delivery in the country in general, their participation in disease surveillance and notification systems is generally poor. The study's findings in Nigeria ( https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320761/ ) align with this study's observation of low compliance with disease surveillance. The low number of disease notifications from private clinics in the IDSR system may be due to a lack of coordination and consistency in disease surveillance in public health facilities. Additionally, private clinics may not receive regular supervision, which can lead to the surveillance system missing events, conditions, or diseases that occur in the community through these clinics. Moreover, it is imperative to perform a comprehensive evaluation of the risk factors linked to inadequate adherence to disease surveillance protocols in private clinics across Rwanda. This should be coupled with heightened awareness-raising efforts among surveillance officers stationed in various districts to bolster disease surveillance activities in private clinics under their purview. Regular audits of data quality, along with funding drives to support supervision and mentorship initiatives, should be prioritized, with a specific focus on private clinics. This will ensure that the Integrated Disease Surveillance and Response (IDSR) is not solely the responsibility of data managers, but also focal persons and others occupying positions at various levels of decision-making. DISCUSSION This in-depth assessment of Rwanda's IBS system offers important information about how well the nationwide public health surveillance program is working, including its strengths and weaknesses. IBS is part of the IDSR framework, which helps identify and monitor critical infectious diseases and supports prompt public health interventions. An important finding of this study is the high level of surveillance completeness and timeliness demonstrated in the public health sector, with overall rates of 96.7% and 80.8% respectively over the 4-year study period. This indicates a robust system for prompt case reporting and exceeds rates observed in some other African nations implementing IDSR [ 11 ]. The increasing trends in these attributes until 2019 also reflect improving surveillance infrastructure in Rwanda, aligned with previous research. [ 12 ] Public health authorities' concerted efforts to build a comprehensive IBS system connecting all health facilities nationwide have yielded positive outcomes regarding these key data quality attributes. However, the declines in completeness and timeliness among public facilities in 2021 highlight potential vulnerabilities in sustaining data quality, especially during public health emergencies like the COVID-19 pandemic. This underscores the importance of continued capacity building and resource allocation to maintain optimal surveillance operations even amidst a crisis. Adoption of digital tools and alternative reporting arrangements during emergencies may help safeguard data completeness and timeliness. A major gap revealed by this evaluation is the limited engagement of private health facilities within the IBS system, as evidenced by low completeness (42.8%) and timeliness (25.3%) of reporting in this sector. With private facilities' growing role in Rwanda's healthcare landscape, this poses a serious risk of missing early warning signs of outbreaks or clusters of illness in the community. Lack of coordination and oversight from district surveillance teams may contribute to this gap, as evidenced by nearly 40% of hospital focal persons not checking private facilities' reports. Previous studies in Africa have noted similar challenges in engaging private providers in national surveillance systems [ 11 ]. Strengthening participation and data quality from the private health sector should, therefore, be a top priority. Strategies could include designing appropriate incentives, enforcing mandatory IBS reporting through licensing policies, leveraging digital tools for easier reporting, and tasking district hospital teams with more rigorous monitoring and mentoring of private facility staff on IBS procedures [ 11 ]. Beyond data completeness and timeliness, the study also assessed the accuracy and usage of IBS data, both being critical for optimal surveillance utility. The widespread availability and application of standard case definitions are positive, although continued sensitization on their importance could enhance data accuracy. More concerning, however, is the sub-optimal level of data usage for epidemic monitoring and dissemination to guide outbreak preparedness and health planning. For example, only 35% of facilities regularly generated and shared weekly trends of priority diseases. This indicates missed opportunities for proactively detecting anomalous disease spikes using IBS data. Promoting practices and building capacities for applied epidemiology and data usage should be part of further IBS strengthening. Hospitals also need adequate personnel to take on data management and analytics functions. Additional training and mentorship programs focused on data analysis skills for hospital teams could help address this gap. From a health system development perspective, investments in data use competencies bring manifold dividends by catalyzing evidence-based planning and resource allocation. Regarding attributes, IBS was found to have high acceptability and flexibility based on user perspectives. The majority affirmed the system's benefits for outbreak monitoring and health facility planning. However, users also cited internet connectivity problems as a barrier to timely reporting. Investments in digital infrastructure and platforms can help overcome this challenge across Rwanda, especially in rural areas. While IBS's flexibility to accommodate new diseases seems adequate, the varying Knowledge, Attitude, and Practice scores indicate a need to strengthen and standardize training procedures for hospital surveillance focal persons and data managers. Refresher training should consider staff turnover and new appointments to continuously expand the pool of competent IBS users. The representativeness attribute of disease surveillance measures the system's accuracy in reflecting health events in terms of time, place, and person. To generalize the effectiveness of the disease surveillance system, the surveillance system should reflect most of the population and region characteristics and list of diseases under surveillance which is very key to the goals and objectives of the system. An important result of evaluating the representativeness of a surveillance system is the identification of population subgroups that may be systematically excluded from the reporting system. This process allows appropriate modification of data collection and a more accurate projection of the incidence of the health event in the target population. The evaluation suggests reasonably good representativeness of the IBS system in terms of populations and diseases under surveillance. However, some limitations were reported in terms of reaching certain communities and tracking some illnesses. This is consistent with findings from Tanzania and other African countries implementing IDSR [ 10 ]. Expanding the network of community health workers could help improve population coverage and early warning for unusual health events. Regarding new diseases, balancing comprehensive surveillance with feasibility considerations is important. However, IBS systems should have enough flexibility to incorporate emerging and re-emerging infections. CONCLUSION The IDSR system serves as a fundamental pillar for public health surveillance and outbreak response in Rwanda. In this study, we have evaluated the performance of IBS to achieve its core function using 12 indicators. According to the findings of this study, surveillance in public facilities in Rwanda is functioning effectively. However, private facilities' surveillance is lacking in timeliness and completeness. This poses a potential challenge, as public health threats may infiltrate these facilities unnoticed. Inadequate surveillance in certain facilities may lead to delayed detection and response, putting numerous lives at risk. Therefore, it is crucial to enhance and strengthen the surveillance in private health facilities to ensure better public health outcomes. The findings of this study reveal that there is still a low level of data utilization. It is also evident that regular capacity-building sessions are necessary for the focal group to thoroughly comprehend their tasks. To keep them informed, it is recommended to distribute updated posters containing standard case definitions, including newly added disease cases, which they can refer to at any given time. Regular monitoring visits to assist and empower the team including those who recently joined or were tasked with new responsibilities of IDSR focal persons on the use of IDSR. An exhaustive early system that goes beyond health facilities mainly targeting the community level should be introduced to avoid any disease or event that should be missed from the uncovered cluster of population and regions. Rwanda Biomedical Center through the PHS&EPR Division should conduct more investigations to elaborate clusters of population and regions that are not covered by the disease surveillance system and to conduct an assessment and review aimed at updating the list of priority diseases under the surveillance system. The system exhibits significant strengths in its completeness, acceptability, specificity, and representativeness. However, there is room for improvement in certain areas, specifically with regard to data timeliness, data utilization, and the standardization of case definitions. Ensuring consistent and timely reporting, promoting greater utilization of data, and implementing standardized case definitions will be crucial in enhancing the system's performance. It is evident that ensuring consistent and timely reporting, increasing utilization of data for decision-making, and improving the knowledge, attitudes, and practices of health workers are critical for enhancing the surveillance system's efficiency. In addition, efforts should be made to address existing gaps in surveillance coverage, particularly among communities with lower access to public health facilities. This study provides crucial insights for decision-makers and stakeholders in the health sector to understand the system's strengths and weaknesses better, ultimately guiding interventions to optimize IDSR performance in Rwanda. Further research is also recommended to explore innovative strategies, including leveraging information technology and e-health solutions, to improve the timeliness and accuracy of data transmission. This study has focused on the human health sector. However, In the future the assessment of collaborative surveillance in Rwanda to optimize the evaluation and establish the gap and area to improve in all sectors of integrated disease surveillance. The lessons from this study can also serve as a reference point for other countries in the region, working towards strengthening their disease surveillance systems. Declarations Ethics approval and consent to participate This study was conducted under the approval of the Rwanda Biomedical Center (RBC) as part of an evaluation of the performance of disease surveillance in Rwanda. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available due to confidentiality restrictions but are available from the corresponding author upon reasonable request. The questionnaire used in the study is available in the manuscript. Competing interests The authors declare that they have no competing interests. Funding This paper was made possible with the support of a CDC foundation grant to support Evidence-Based Strategies (EBS) in Rwanda. The grant played a pivotal role in enabling the epidemiologic and surveillance activities associated with the Event-based surveillance (EBS) system and associated alert and response operations (ARO) at the Rwanda Biomedical Centre (RBC) in the Public Health Surveillance and Epidemic Preparedness and Response Division. The contents do not necessarily reflect the views of the CDC Foundation or the United States Government. Authors' contributions PN, ON, LR, ST, MM, AZ, MRK, SU, II, AK, ER, and CMM contributed to the study conception and design. Data collection was performed by PN, ON, LR, ST, MM, and AZ. Data analysis was done by PN, ON, and LR. The first draft was written by ON and PN with input from all authors. All authors reviewed, edited, and approved the final version. Acknowledgments We thank the study participants from the hospitals for their time and cooperation. We acknowledge the support of the Rwanda Biomedical Center/Public Health Surveillance & Emergency Preparedness and Response Division. References Fenollar F, Mediannikov O. (2018), Emerging infectious diseases in Africa in the 21st century. New Microbes and New Infectious, Vol 26, pages S10-S18. World Economic Forum. These are the 10 biggest global health threats of the decade, https://www.weforum.org/agenda/2020/02/who-healthcare-challenges-2020s-climate-conflict-epidemics/ (2020, accessed 10 July 2023). Tambo E, Ugwu EC, Ngogang Jy. (2014) Need of surveillance response systems to combat Ebola outbreaks and other emerging infectious diseases in African countries. Infect Dis Poverty, 3, v 29. World Health Organization. INTERNATIONAL HEALTH REGULATIONS 2005 THIRD EDITION, https://apps.who.int/iris/bitstream/handle/10665/246107/9789241580496-eng.pdf (accessed 10 July 2023). CDC. Introduction to Public Health Surveillance, https://www.cdc.gov/training/publichealth101/surveillance.html (2018, accessed 10 July 2023). Nsubuga P, White ME, Thacker SB et al. Public Health Surveillance: A Tool for Targeting and Monitoring Interventions ‘What gets measured gets done.’-Anonymous, . Mcnabb SJ, Chungong S, Ryan M et al. Conceptual framework of public health surveillance and action and its application in health sector reform, http://www.biomedcentral.com/1471-2458/2/2 (2002). Organization WH, TECHNICAL GUIDELINES FOR INTEGRATED DISEASE SURVEILLANCE, AND RESPONSE IN THE WHO AFRICAN REGION BOOKLET TWO. : SECTIONS 1, 2 AND 3 M A R C H 2 0 1 9. Norzin T, Ghiasbeglou H, Patricio M, et al. Event-based surveillance: Providing early warning for communicable disease threats. Can Commun Dis Rep. 2023;49:29–34. Mghamba JM, Mboera LE, Krekamoo W, Shayo EH. (2018). Challenges of implementing the Integrated Disease Surveillance and Response Strategy using the current health management information system in Tanzania. Tanzan Health Res Bull, 20(1). Phalkey RK, Yamamoto S, Awate P, Marx M. Challenges with the implementation of an Integrated Disease Surveillance and Response (IDSR) system: a systematic review of the lessons learned. Health Policy Plann. 2015;30(1):131–43. Bulimbe DB, Masunga DS, Paul IK, Kassim GH, Bahati PB, Thomas JA, Mwakisole C, Nazir A, Uwishema O. (2023). Marburg virus disease outbreak in Tanzania: current efforts and recommendations–a short communication. Annals of Medicine and Surgery , 85 (8), p.4190. Rwagasore E, Nsekuye O, El-Khatib Z, Kabeja A, Mucunguzi VH, Nizeyimana P, Ruseesa E, Ruyange L, Teta IB, Uwamahoro S, Twahirwa S. Lessons Learned from Sudan Ebola Virus Disease (SUDV) Preparedness in Rwanda: A Comprehensive Review and Way Forward. J Epidemiol Global Health 2023 Jun 28:1–1. Debes AK, Shaffer AM, Ndikumana T, Liesse I, Ribaira E, Djumo C, Ali M, Sack DA. 2021. Cholera hot-spots and contextual factors in Burundi, planning for elimination. Tropical Medicine and Infectious Disease , 6 (2), p.76. Ahmed A, Makame J, Robert F, Julius K, Mecky M. Sero-prevalence and spatial distribution of Rift Valley fever infection among agro-pastoral and pastoral communities during Interepidemic period in the Serengeti ecosystem, northern Tanzania. BMC Infect Dis. 2018;18(1):1–8. Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4288889","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294047535,"identity":"77a58f9f-fba8-41c9-a1c8-b5b6ad8b223d","order_by":0,"name":"Pacifique Nizeyimana","email":"","orcid":"","institution":"Jhpiego","correspondingAuthor":false,"prefix":"","firstName":"Pacifique","middleName":"","lastName":"Nizeyimana","suffix":""},{"id":294047536,"identity":"0f0e612a-fa3d-4669-9f8a-31fe953f582b","order_by":1,"name":"Olivier Nsekuye","email":"data:image/png;base64,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","orcid":"","institution":"Rwanda Biomedical Centre (RBC)","correspondingAuthor":true,"prefix":"","firstName":"Olivier","middleName":"","lastName":"Nsekuye","suffix":""},{"id":294047537,"identity":"cd0f84b6-2a21-451b-888c-e9421425e4bb","order_by":2,"name":"Laurent Ruyange","email":"","orcid":"","institution":"Rwanda Biomedical Centre (RBC)","correspondingAuthor":false,"prefix":"","firstName":"Laurent","middleName":"","lastName":"Ruyange","suffix":""},{"id":294047538,"identity":"64dc4338-fa18-4c0c-bcb5-7ea5a04dbf80","order_by":3,"name":"Solange Twahirwa","email":"","orcid":"","institution":"Rwanda Biomedical Centre (RBC)","correspondingAuthor":false,"prefix":"","firstName":"Solange","middleName":"","lastName":"Twahirwa","suffix":""},{"id":294047539,"identity":"48fc4297-e35a-4b92-aaa1-6a65659f497f","order_by":4,"name":"Marcel Manariyo","email":"","orcid":"","institution":"Jhpiego","correspondingAuthor":false,"prefix":"","firstName":"Marcel","middleName":"","lastName":"Manariyo","suffix":""},{"id":294047540,"identity":"1ddf5fb9-fa92-4418-b162-36af8e1887d4","order_by":5,"name":"Alain 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(RBC)","correspondingAuthor":false,"prefix":"","firstName":"Ines","middleName":"","lastName":"Itanga","suffix":""},{"id":294047547,"identity":"252f9332-1113-4c67-a2de-be552849ba6c","order_by":9,"name":"Adeline Kabeja","email":"","orcid":"","institution":"Rwanda Biomedical Centre (RBC)","correspondingAuthor":false,"prefix":"","firstName":"Adeline","middleName":"","lastName":"Kabeja","suffix":""},{"id":294047549,"identity":"6a5ba543-9101-45d2-aa9f-4d0c0e494cb1","order_by":10,"name":"Edson Rwagasore","email":"","orcid":"","institution":"Rwanda Biomedical Centre (RBC)","correspondingAuthor":false,"prefix":"","firstName":"Edson","middleName":"","lastName":"Rwagasore","suffix":""},{"id":294047552,"identity":"45c465ed-acfe-4117-82c3-c0b52f6a191f","order_by":11,"name":"Claude Mambo Muvunyi","email":"","orcid":"","institution":"Rwanda Biomedical Centre (RBC)","correspondingAuthor":false,"prefix":"","firstName":"Claude","middleName":"Mambo","lastName":"Muvunyi","suffix":""}],"badges":[],"createdAt":"2024-04-18 15:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4288889/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4288889/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55526074,"identity":"9fdeaa71-b9b3-4987-9e49-55912e50e2c3","added_by":"auto","created_at":"2024-04-29 14:50:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":153682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCompleteness and timeliness distribution of completeness and timeliness by District among public and private health facilities, 2018-2021\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4288889/v1/52e5e52aa3b1e22523f7d07e.png"},{"id":55525337,"identity":"d77c2ad4-34ad-4e64-b3b0-b2e3b3f1957d","added_by":"auto","created_at":"2024-04-29 14:42:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe trend of completeness and timeliness in public and private health facilities,2018-2021\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4288889/v1/626d51c038fa34089acbfce4.jpg"},{"id":55526070,"identity":"6bc2eb03-a167-4aee-82c7-55b4b01b2154","added_by":"auto","created_at":"2024-04-29 14:50:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSummary of respondents, on acceptability. From the right, the bar chart summarizes the opinion of the respondent on the system's usefulness, in the middle the bar chart summarizes the assessment of how often users visit the system, and on the right, the bar chart \u003c/em\u003erepresents the opinion of users on whether their contribution is valuable.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4288889/v1/653a0051e356565355973e66.png"},{"id":55527806,"identity":"d9169d24-9920-4df4-9802-be48a9cf0354","added_by":"auto","created_at":"2024-04-29 15:06:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70621,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFeedback from the central level and from the hospital to lower facilities\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4288889/v1/98578d44836fb27c88421ddf.png"},{"id":55525336,"identity":"53e0bf3f-87cc-4df8-a517-bd7db6e05f50","added_by":"auto","created_at":"2024-04-29 14:42:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":37510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOn the left, we have the feedback status at health centers, and on the right, we have the frequency of feedback to health centers.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4288889/v1/bccfc8c2b6201d203bd9458c.png"},{"id":55526941,"identity":"0e543ace-735c-48ea-8dc7-61bb783a01e6","added_by":"auto","created_at":"2024-04-29 14:58:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerception of respondents on the simplicity of the system\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4288889/v1/cabc6c89f87cce05d3ac2cfc.png"},{"id":55525341,"identity":"c2314d95-f785-4e8e-a8f3-834c6740b176","added_by":"auto","created_at":"2024-04-29 14:42:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":62248,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRepresentativeness in the aspect of population, area, and diseases\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4288889/v1/504ec31cfd6837f1443e8abf.png"},{"id":55525343,"identity":"bca061d9-f1ed-4f8d-86bb-efc2b50498d0","added_by":"auto","created_at":"2024-04-29 14:42:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":217600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInvolvement of private facilities\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4288889/v1/b5ea9524ce17e9bf2a12a8f6.png"},{"id":55528537,"identity":"1140aa4f-89b9-4b8b-acc4-03cd07ba17da","added_by":"auto","created_at":"2024-04-29 15:14:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1085470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4288889/v1/d7d82405-2f1c-4222-8725-1bcf0d0156ea.pdf"},{"id":55525335,"identity":"6d49254c-34ad-4fcf-8312-7b98907e3657","added_by":"auto","created_at":"2024-04-29 14:42:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18989,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4288889/v1/593ef7501db78af094a952c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment and exploratory analysis of Indicator Based Surveillance (IBS) system in Rwanda","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAccording to the World Health Organization (WHO), infectious diseases are the leading cause of death worldwide [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The emergence and re-emergence of infectious diseases is a global challenge that affects all countries including Rwanda [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Factors that contribute to the emergence and re-emergence of infectious diseases include virus mutations and recombination, population growth, urbanization, human population movement, hunting and pasture practices, agricultural practices and deforestation, globalization of commerce, human social behavior, and environmental factors such as global warming [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The International Health Regulations (IHR) 2005 is a legally binding instrument whose purpose and scope are to prevent, protect against, control, and provide a public health response to the international spread of disease. It requires countries to have the capacity to detect, assess, notify, and report any events through an early warning, alert, and response (EWAR) function of a national surveillance system [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEffective surveillance systems are a vital component of a comprehensive public health infrastructure, as they act as the watchful eyes and attentive ears of disease control efforts. These systems systematically gather, analyze, and interpret data to offer essential insights that guide health policies and strategies. The importance of surveillance systems in the field of healthcare cannot be overstated. They provide timely information about disease trends and health-related events, which helps in responding quickly to public health threats. Additionally, they help in evaluating the effectiveness of public health interventions and monitoring the progress of health programs. As a result, they contribute significantly to global health security. The effectiveness of a health system in controlling disease and maintaining population health is largely dependent on the performance of its surveillance system. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThere are two main approaches to health surveillance systems: Indicator-Based Surveillance (IBS) and Event-Based Surveillance (EBS). IBS is a traditional method that involves collecting data on disease occurrence regularly. Healthcare providers or labs often report specific diseases or conditions to track long-term trends, allocate resources, and measure performance. In contrast, EBS collects information about events that could indicate a disease outbreak or public health risk. These events are often detected from sources outside traditional health reporting channels, such as news reports or community rumors. EBS is more sensitive and timelier, allowing for quicker responses to potential health threats. Many countries pursue a balanced approach to IBS and EBS, leveraging the strengths of both systems while mitigating their respective limitations. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRwanda faces a continuous risk of epidemic disease transmission. Factors contributing to this risk include its high population density (525 per km^2), location in a region prone to epidemic diseases, easy domestic and international travels or population movements including tourism promotion, healthcare infrastructure, climate, and various socioeconomic factors. Rwanda is a landlocked country located in East-Central Africa. It is bordered by Uganda to the north, Tanzania to the east, Burundi to the south, and the Democratic Republic of the Congo to the west. In the near past, the region has been affected by Ebola outbreak (DRC and Uganda), Marburg outbreak (Tanzania), Cholera (Burundi), Polio (Burundi and DRC), RVF (Rwanda and Tanzania), and some other listed transmissible diseases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn Rwanda, the history of surveillance systems is closely tied to the country's progress in public health. After the genocide in 1994, Rwanda's health infrastructure had to be reconstructed from scratch. Despite this challenge, Rwanda achieved significant progress, including the introduction of an effective Indicator-Based Surveillance (IBS) system. With help from international partners such as the WHO, Rwanda launched an electronic Integrated Disease Surveillance and Response (eIDSR) in 2012 that formed the foundation of the IBS system. The IBS is an intricate framework integrated into the broader DHIS-2 network, linking all health facilities across the country. It consistently gathers, consolidates, and examines data from numerous public health surveillance indicators, which aid public health planning, monitoring, and evaluation.\u003c/p\u003e \u003cp\u003eAlthough the IBS is well-established in Rwanda, the Event-Based Surveillance (EBS) is still in its early stages. However, recognizing the importance of a comprehensive surveillance approach, there have been efforts to establish an EBS system to complement the existing IBS. By adopting EBS, the country's ability to detect and respond to new and emerging health threats in real-time will be significantly improved. This will provide a strong early warning system for disease outbreaks.\u003c/p\u003e \u003cp\u003eWhile Rwanda has commendably implemented a comprehensive Indicator-Based Surveillance (IBS) system, the public health landscape calls for constant evolution and adaptability. Despite the strengths of IBS, its limitations may potentially hinder the country's ability to promptly detect and respond to new and emerging health threats. There is a growing recognition that for a surveillance system to be optimally effective, IBS should be complemented with Event-Based Surveillance (EBS). However, in Rwanda, EBS is yet to be fully implemented and integrated into the existing health surveillance framework.\u003c/p\u003e \u003cp\u003eThe purpose of this study, therefore, was to undertake a robust evaluation of Rwanda's current IBS system based on key attributes such as completeness, timeliness, acceptability, simplicity, flexibility, representativeness, data accuracy, data use, usefulness, and feedback. By highlighting the challenges and gaps in the IBS system, this paper aims to underscore the need for a more balanced surveillance approach that integrates and strengthens EBS within the health system. Through such a blend of IBS and EBS, Rwanda can ensure a more responsive, comprehensive, and efficient surveillance system, better prepared to safeguard public health in an ever-changing disease landscape.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eRwanda is a country located in East Central Africa with high population density. The population is around 14\u0026nbsp;million with an area of 26,338 km\u003csup\u003e2\u003c/sup\u003e. Rwanda has 4 provinces and the city of Kigali. The provinces are divided into districts and districts into sectors, sectors into cells, and cells into villages. Rwanda's health system is designed in tiers and is built on Community health workers, Health posts, Health centers, District hospitals, provincial hospitals, referral hospitals, and University hospitals. Operational-wise, health centers are a point of referral to health posts. District hospitals are a point of referral for health centers. The provincial hospital plays a pivotal role as a referral center for the district hospital and serves as the ultimate referral hospital for the entire province. Rwanda boasts a total of 60 hospitals, with 8 of them privately owned. The Rwanda Ministry of Health's performance report for 2021\u0026ndash;2022 reveals that 90% of outpatients visit public facilities for medical care, while 10% prefer private facilities. Throughout our research, we have thoroughly examined all 46 district and provincial hospitals. In order to gain insights from those who use the system most frequently, we carefully selected two health workers from each hospital for interviews. Specifically, we chose IDSR focal persons and data managers, as they are the primary users of the system. IDSR focal persons are critical in identifying and relaying key information, while data managers are responsible for managing, analyzing, and utilizing the collected data.\u003c/p\u003e \u003cp\u003eIn the realm of public health, 'completeness' refers to the measure of all expected reports from reporting sites that have been received, irrespective of their time of submission. This attribute is crucial as an incomplete report cannot accurately describe the problem and can lead to missed opportunities to respond to public health challenges. Timeliness in public health refers to the speed between steps in a public health surveillance system. The single most important measure of timeliness is whether data are submitted in time to begin investigations and implement control measures. An effective and timely surveillance system increases the possibility of detecting a problem and conducting a prompt response.\u003c/p\u003e \u003cp\u003eIn Rwanda's Indicator-Based Surveillance (IBS) system, completeness is evaluated based on the proportion of public and private health facilities that submit their surveillance reports. For the period between 2018 and 2021, the evaluation indicates varying degrees of completeness in reporting. Timeliness in Rwanda's IBS system is assessed based on the submission of weekly reports by Monday, not later than noon, following standards developed by The Ministry of Health/Rwanda Biomedical Centre by the timeliness set by WHO AFRO.\u003c/p\u003e \u003cp\u003eAcceptability reflects the willingness of individuals and organizations to participate in the surveillance system. Acceptability is closely connected to the completeness and timeliness of the reported data. To evaluate acceptability, we examined (1) how frequently the system was used, (2) the importance of the user's contribution to the surveillance system, and (3) the opinion of key individuals on the usefulness of the system.\u003c/p\u003e \u003cp\u003eWe conducted a cross-sectional survey to evaluate IDSR in Rwanda. In order to gather all information reflecting all indicators. Using a structured questionnaire, trained assistant conducted interviews with the selected health workers at hospitals for some indicators like\u0026hellip;. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. We also extracted data from the national health information management system HMIS/eIDSR for some indicators such as completeness and timeliness. We have considered data gathered for the period of 2018 to 2021 from 564 public health facilities (HFs) and 283 private HFs.\u003c/p\u003e \u003cp\u003eData were collected through national health surveillance data entry tools (RedCap) and were extracted in an Excel spreadsheet, cleaned and analyzed R. Based on different literature reviews [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], we generally considered the following factors that reflect the core functions of IDSR: 1) Completeness, 2) Timeliness, 3) Acceptability, 4) Data accuracy, 5) Data use, 6) Usefulness, 7) Simplicity, 8) Knowledge attitude and practice, 9) Feedback, 10) Representativeness, 11) Involvement of private hospitals.\u003c/p\u003e \u003cp\u003eThe main method we adopt to analyze is exploratory analysis and correlation assessments. We have described each variable using both graphical representation such as frequency distribution and numerical measures and performed the relationship analysis between some variables.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCompleteness and Timeliness in the period of 2018 to 2021\u003c/h2\u003e \u003cp\u003ePublic health facilities achieved an impressive overall completeness of 96.7%, exceeding the target of 80%. All districts achieved completeness greater than the target, indicating high performance. On the other hand, private health facilities' overall completeness was considerably lower at 42.8%. Only three districts exceeded the 80% target (Burera, Musanze, and Ngoma), while the remaining districts showed low performance, and four had a zero-completion rate.\u003c/p\u003e \u003cp\u003eAn evaluation of the period from 2018 to 2021 reveals that public health facilities' overall timeliness was 80.8%, matching the target of 80%. However, about 37% of the districts fell short of the target. In contrast, private health facilities reported a significantly lower overall timeliness of 25.3%. Only one district met the target (Burera), while most districts showed very low performance.\u003c/p\u003e \u003cp\u003e A closer look at the trend in completeness and timeliness from 2018 to 2021 for public health facilities reveals an increase in both attributes from 2018 to 2019, followed by a decrease in timeliness in 2020 and a decrease in both completeness and timeliness in 2021. In private health facilities, although the completeness and timeliness were low throughout the four years, they increased from 2018 to 2020 and subsequently decreased in 2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAcceptability\u003c/h2\u003e \u003cp\u003eThe effectiveness of a surveillance system relies heavily on whether it leads to actionable public health measures. A successful public health system aids in the prevention and control of adverse health-related events. This is evidenced by decision-making and policy implementation based on the reported data. When focal persons (users) can see the impact of their contributions to the surveillance, they feel more invested in the process. Additionally, surveillance data can be useful in measuring performance, further motivating users to utilize the system. The system's frequency of use is also a key indicator of its usefulness. Research has shown that if users do not find a surveillance system acceptable, their productivity will suffer.\u003c/p\u003e \u003cp\u003eBased on our research, it was discovered that the second edition of e IDSR effectively detected variations in the trend of cases. A significant 82.6% of hospitals relied on data generated by the system to stay informed of weekly changes in reportable diseases. Furthermore, our study revealed that 84.8% of health facilities used the system to identify outbreaks within their areas of coverage. In addition, the eIDSR system has been utilized by 92.3% of health facilities for planning purposes, as indicated by recent research. The system has proven to be extremely beneficial in providing statistical evidence to support decision-making processes, particularly in the areas of outbreak detection and response, as well as the planning of hospital programs. However, it was found that only 71.7% of outbreaks were handled by guidelines, highlighting the need to reinforce the use of IDSR technical guidelines in response to outbreaks.\u003c/p\u003e \u003cp\u003eFrequency of use is a key indicator of a system's ability to fulfill its primary function. If users delay reporting, early detection and response will be compromised. The IDSR system is made up of both immediate and weekly disease reporting components. However, assessments have revealed that most focal persons only enter data every week, which poses a challenge given the immediate disease-reporting aspect of the system. Additionally, some focal persons only access the system on a monthly or less frequent basis. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the respondent's answers on (1) the User\u0026rsquo;s opinion on IBS usefulness, (2) the IBS system usage frequency, and (3) the User's perceived value of the contribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFeedback\u003c/h2\u003e \u003cp\u003eAccording to the results of this study, many participants (87%) stated that they received feedback from the central level, although the frequency of feedback varied significantly. Slightly over half of the respondents reported receiving feedback weekly, while the remaining participants received feedback either immediately, monthly, or quarterly. On the hospital side, it was found that 91% of them provide feedback to health centers in their catchment area. However, the frequency of feedback provided by hospitals is inconsistent, with approximately 40% of them providing weekly feedback, 30% providing immediate feedback, and 25% providing monthly feedback.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAccuracy\u003c/h2\u003e \u003cp\u003eThe correct use of Standard Case Definitions is key to accurate data. Data Accuracy is a measure of how well a piece of information reflects reality and is key to appropriate decision-making, especially for public health. The assessment has shown that 95.7% of health facilities possess Standard Case definitions and 87% of all health facilities refer to them regularly in case detection and reporting. Though there is a considerable portion of health facilities that use accurate detection and reporting in IDSR, there is a need to improve the accuracy of data by fully providing Standard Case Definitions and sensitization about the importance of using Standard Case Definitions for detection and reporting in the improvement of data accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData use\u003c/h2\u003e \u003cp\u003eTo make informed decisions, it is essential to base them on evidence that is grounded in reliable data. Data use not only serves as evidence to support decision-making but also plays a critical role in identifying potential data quality issues and providing feedback to reporting entities for improvement. Furthermore, the use of data can shed light on emerging trends in disease, while also facilitating the modeling of various diseases, thereby advancing scientific discovery. Based on the assessment, it was found that 67.6% of healthcare facilities use data to monitor reportable diseases every week and share the results. However, only 34.9% of these facilities produce and share the weekly trend of cases promptly compared to the set threshold. It was also observed that the use of weekly reports to monitor potential outbreaks is still low at the hospital level, despite it being the primary level of detection for outbreaks at the district level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eReasons for poor reporting\u003c/h2\u003e \u003cp\u003eAccording to the results, there are several main reasons for poor reporting. The assessment found that 30% of respondents reported experiencing internet problems while trying to complete their reporting duties. Additionally, 30% of respondents reported that they struggle to balance their IDSR activities with other provided duties, leading to incomplete or delayed reporting. Forgetting to report was also identified as a significant issue, with 26% of respondents indicating that this was a problem for them. 13% presented other reasons for not reporting.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of results on data accuracy, data use, and usefulness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKey (Attributes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCounts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eData Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePossession of SCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUse of SCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8,70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eData use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProduction of the weekly trend of cases compared to a threshold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDissemination of weekly trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFrequency of dissemination of weekly trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003equarterly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUse of the system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReporting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eUsefulness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDetect any change in data trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDetect outbreak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eResponse according to guideline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUse for planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eKnowledge Attitude and Practice (KAP) Score\u003c/h2\u003e \u003cp\u003eThe significance of KAP lies in its ability to gauge the scope of a given situation, verify or refute a hypothesis, and determine what is already known and being done regarding different health-related matters with a focus on IDSR. For our research, we have formulated six queries to evaluate KAP. These inquiries pertain to the eIDSR platform, medical conditions, data utilization, and reporting. The KAP score, which we determined by adding up the points from all six questions, serves as a cumulative measure of one's KAP.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSummary Statistics of KAP score.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the findings, 51% fall within the 70\u0026ndash;100 range, and 49% fall within the 50\u0026ndash;69 range. This may be due to some personnel who are committed to the IDSR process not fully understanding when they need to report (either immediately or weekly), particularly about the latest indicator/data elements within the system.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSimplicity\u003c/h2\u003e \u003cp\u003eIn the context of Indicator Based Surveillance, the significance of simplicity cannot be overstated. By keeping things simple, all users can easily comprehend and retain the information for future use. This approach ensures that the surveillance process remains accessible and user-friendly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the graph above, a significant percentage of respondents (33%) strongly agreed that IDSR tools are simple to use, whereas 48% of respondents reported that they agreed with this sentiment. A small percentage of respondents (7%) were neutral, while 9% disagreed and 4% strongly disagreed. This may be due to some FPs taking on new responsibilities without being adequately trained in that area, as well as some institutions being new to using the IDSR (especially those who started using it midway through 2022). As such, there may be a need for refresher training to ensure that all personnel are well-equipped to use the IDSR effectively.\u003c/p\u003e \u003cp\u003eMoreover, 96% of data managers found the system to be highly user-friendly. However, 4% of hospitals that had recently adopted the system reported facing some difficulties in using it.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRepresentativeness\u003c/h2\u003e \u003cp\u003eWe examined the coverage of the population and regions included in the disease surveillance system, as well as any illnesses that may not have been detected by our surveillance measures.\u003c/p\u003e \u003cp\u003eAccording to the survey results, 76% of those who responded said that the disease surveillance system covers the entire population in their area, while 24% reported that certain groups within their catchment area are not covered. Additionally, 78% of respondents stated that the surveillance system fully covers their catchment area, but 22% reported that some areas are not covered. Lastly, 91% expressed satisfaction with the current list of priority diseases under surveillance, but 9% recommended that non-communicable diseases, Scabies, and Chickenpox/Varicella be added to the list. \u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 7: Representativeness in the aspect of population, area, and diseases\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe disease surveillance system of Rwanda generally covers most of the population, the minor part of the population that is not covered might be attributed to the population visiting traditional healers and some people using private clinics that do not report in routine disease surveillance. Furthermore, certain individuals opt to treat themselves at home based on their cultural or religious convictions. Unfortunately, these groups are not identified through disease surveillance efforts. While all regions of the country are reachable, there may be a handful of areas that pose challenges due to inadequate infrastructure, remote dwellings, and other factors. This may account for any gaps in the disease surveillance system's coverage. The list of prioritized diseases is determined by several factors, such as the public health significance of the condition. However, it is important to note that certain diseases may have a greater impact on specific regions, which may require adjustments to the list of priority diseases. Respondents have indicated a need to expand the list of diseases under surveillance. To address this, the Rwanda Biomedical Center's PHS\u0026amp;EPR Division should conduct further investigations to identify clusters of populations and regions not currently covered by the disease surveillance system. An assessment and review should be conducted to update the list of priority diseases. Additionally, an early warning system that extends beyond health facilities, focusing on the community level, should be implemented to ensure that no diseases or events go unnoticed in uncovered clusters of populations and regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInvolvement of private health facilities\u003c/h2\u003e \u003cp\u003ePrivate health facilities play a vital role in delivering healthcare services throughout the nation, and their significance has only been increasing in recent years. In Rwanda, a considerable portion of healthcare services are provided by private health facilities, which may bear a substantial burden of diseases. To address this, disease surveillance has been extended to private clinics through the introduction of IDSR. Nonetheless, despite their crucial role in healthcare delivery, the extent of participation of private health facilities in the disease surveillance system remains poor.\u003c/p\u003e \u003cp\u003eAccording to the survey, 61% of those polled acknowledged their obligation to supervise private clinics within their designated catchment areas. Meanwhile, 39% believed that private clinics were not within their purview of responsibility. Of the 28 respondents who considered private clinics to be their responsibility, 39% admitted to never reviewing surveillance reports, while the remaining 61% claimed to regularly check such reports from private clinics.\u003c/p\u003e \u003cp\u003eIn addition, 46% of the respondents reported that they never conduct inspections of private clinics in their catchment areas. 21% of respondents conduct monthly and quarterly supervision, and 7% and 3% of the respondents reported conducting bi-annually and annual supervision to private clinics respectively. Regarding the reasons driving to not conduct supervision among private clinics, from the 28 respondents accepting their responsibilities of private clinics in their catchment areas, 14% of the respondents reported financial constraints as the main reason for limiting supervision among private clinics, 11% indicated that high workload and being busy at work as another reason and 39% showed non-specific reasons limiting them to conduct supervisions in private clinics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite the significant contribution that private health facilities make to health service delivery in the country in general, their participation in disease surveillance and notification systems is generally poor. The study's findings in Nigeria (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320761/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320761/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) align with this study's observation of low compliance with disease surveillance. The low number of disease notifications from private clinics in the IDSR system may be due to a lack of coordination and consistency in disease surveillance in public health facilities. Additionally, private clinics may not receive regular supervision, which can lead to the surveillance system missing events, conditions, or diseases that occur in the community through these clinics. Moreover, it is imperative to perform a comprehensive evaluation of the risk factors linked to inadequate adherence to disease surveillance protocols in private clinics across Rwanda. This should be coupled with heightened awareness-raising efforts among surveillance officers stationed in various districts to bolster disease surveillance activities in private clinics under their purview. Regular audits of data quality, along with funding drives to support supervision and mentorship initiatives, should be prioritized, with a specific focus on private clinics. This will ensure that the Integrated Disease Surveillance and Response (IDSR) is not solely the responsibility of data managers, but also focal persons and others occupying positions at various levels of decision-making.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis in-depth assessment of Rwanda's IBS system offers important information about how well the nationwide public health surveillance program is working, including its strengths and weaknesses. IBS is part of the IDSR framework, which helps identify and monitor critical infectious diseases and supports prompt public health interventions.\u003c/p\u003e \u003cp\u003eAn important finding of this study is the high level of surveillance completeness and timeliness demonstrated in the public health sector, with overall rates of 96.7% and 80.8% respectively over the 4-year study period. This indicates a robust system for prompt case reporting and exceeds rates observed in some other African nations implementing IDSR [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The increasing trends in these attributes until 2019 also reflect improving surveillance infrastructure in Rwanda, aligned with previous research. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Public health authorities' concerted efforts to build a comprehensive IBS system connecting all health facilities nationwide have yielded positive outcomes regarding these key data quality attributes.\u003c/p\u003e \u003cp\u003eHowever, the declines in completeness and timeliness among public facilities in 2021 highlight potential vulnerabilities in sustaining data quality, especially during public health emergencies like the COVID-19 pandemic. This underscores the importance of continued capacity building and resource allocation to maintain optimal surveillance operations even amidst a crisis. Adoption of digital tools and alternative reporting arrangements during emergencies may help safeguard data completeness and timeliness.\u003c/p\u003e \u003cp\u003eA major gap revealed by this evaluation is the limited engagement of private health facilities within the IBS system, as evidenced by low completeness (42.8%) and timeliness (25.3%) of reporting in this sector. With private facilities' growing role in Rwanda's healthcare landscape, this poses a serious risk of missing early warning signs of outbreaks or clusters of illness in the community. Lack of coordination and oversight from district surveillance teams may contribute to this gap, as evidenced by nearly 40% of hospital focal persons not checking private facilities' reports. Previous studies in Africa have noted similar challenges in engaging private providers in national surveillance systems [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Strengthening participation and data quality from the private health sector should, therefore, be a top priority. Strategies could include designing appropriate incentives, enforcing mandatory IBS reporting through licensing policies, leveraging digital tools for easier reporting, and tasking district hospital teams with more rigorous monitoring and mentoring of private facility staff on IBS procedures [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond data completeness and timeliness, the study also assessed the accuracy and usage of IBS data, both being critical for optimal surveillance utility. The widespread availability and application of standard case definitions are positive, although continued sensitization on their importance could enhance data accuracy. More concerning, however, is the sub-optimal level of data usage for epidemic monitoring and dissemination to guide outbreak preparedness and health planning. For example, only 35% of facilities regularly generated and shared weekly trends of priority diseases. This indicates missed opportunities for proactively detecting anomalous disease spikes using IBS data. Promoting practices and building capacities for applied epidemiology and data usage should be part of further IBS strengthening. Hospitals also need adequate personnel to take on data management and analytics functions. Additional training and mentorship programs focused on data analysis skills for hospital teams could help address this gap. From a health system development perspective, investments in data use competencies bring manifold dividends by catalyzing evidence-based planning and resource allocation.\u003c/p\u003e \u003cp\u003eRegarding attributes, IBS was found to have high acceptability and flexibility based on user perspectives. The majority affirmed the system's benefits for outbreak monitoring and health facility planning. However, users also cited internet connectivity problems as a barrier to timely reporting. Investments in digital infrastructure and platforms can help overcome this challenge across Rwanda, especially in rural areas. While IBS's flexibility to accommodate new diseases seems adequate, the varying Knowledge, Attitude, and Practice scores indicate a need to strengthen and standardize training procedures for hospital surveillance focal persons and data managers. Refresher training should consider staff turnover and new appointments to continuously expand the pool of competent IBS users.\u003c/p\u003e \u003cp\u003eThe representativeness attribute of disease surveillance measures the system's accuracy in reflecting health events in terms of time, place, and person. To generalize the effectiveness of the disease surveillance system, the surveillance system should reflect most of the population and region characteristics and list of diseases under surveillance which is very key to the goals and objectives of the system. An important result of evaluating the representativeness of a surveillance system is the identification of population subgroups that may be systematically excluded from the reporting system. This process allows appropriate modification of data collection and a more accurate projection of the incidence of the health event in the target population.\u003c/p\u003e \u003cp\u003eThe evaluation suggests reasonably good representativeness of the IBS system in terms of populations and diseases under surveillance. However, some limitations were reported in terms of reaching certain communities and tracking some illnesses. This is consistent with findings from Tanzania and other African countries implementing IDSR [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Expanding the network of community health workers could help improve population coverage and early warning for unusual health events. Regarding new diseases, balancing comprehensive surveillance with feasibility considerations is important. However, IBS systems should have enough flexibility to incorporate emerging and re-emerging infections.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe IDSR system serves as a fundamental pillar for public health surveillance and outbreak response in Rwanda. In this study, we have evaluated the performance of IBS to achieve its core function using 12 indicators. According to the findings of this study, surveillance in public facilities in Rwanda is functioning effectively. However, private facilities' surveillance is lacking in timeliness and completeness. This poses a potential challenge, as public health threats may infiltrate these facilities unnoticed. Inadequate surveillance in certain facilities may lead to delayed detection and response, putting numerous lives at risk. Therefore, it is crucial to enhance and strengthen the surveillance in private health facilities to ensure better public health outcomes. The findings of this study reveal that there is still a low level of data utilization.\u003c/p\u003e \u003cp\u003eIt is also evident that regular capacity-building sessions are necessary for the focal group to thoroughly comprehend their tasks. To keep them informed, it is recommended to distribute updated posters containing standard case definitions, including newly added disease cases, which they can refer to at any given time. Regular monitoring visits to assist and empower the team including those who recently joined or were tasked with new responsibilities of IDSR focal persons on the use of IDSR. An exhaustive early system that goes beyond health facilities mainly targeting the community level should be introduced to avoid any disease or event that should be missed from the uncovered cluster of population and regions. Rwanda Biomedical Center through the PHS\u0026amp;EPR Division should conduct more investigations to elaborate clusters of population and regions that are not covered by the disease surveillance system and to conduct an assessment and review aimed at updating the list of priority diseases under the surveillance system.\u003c/p\u003e \u003cp\u003eThe system exhibits significant strengths in its completeness, acceptability, specificity, and representativeness. However, there is room for improvement in certain areas, specifically with regard to data timeliness, data utilization, and the standardization of case definitions. Ensuring consistent and timely reporting, promoting greater utilization of data, and implementing standardized case definitions will be crucial in enhancing the system's performance.\u003c/p\u003e \u003cp\u003eIt is evident that ensuring consistent and timely reporting, increasing utilization of data for decision-making, and improving the knowledge, attitudes, and practices of health workers are critical for enhancing the surveillance system's efficiency. In addition, efforts should be made to address existing gaps in surveillance coverage, particularly among communities with lower access to public health facilities.\u003c/p\u003e \u003cp\u003eThis study provides crucial insights for decision-makers and stakeholders in the health sector to understand the system's strengths and weaknesses better, ultimately guiding interventions to optimize IDSR performance in Rwanda. Further research is also recommended to explore innovative strategies, including leveraging information technology and e-health solutions, to improve the timeliness and accuracy of data transmission. This study has focused on the human health sector. However, In the future the assessment of collaborative surveillance in Rwanda to optimize the evaluation and establish the gap and area to improve in all sectors of integrated disease surveillance. The lessons from this study can also serve as a reference point for other countries in the region, working towards strengthening their disease surveillance systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted under the approval of the Rwanda Biomedical Center (RBC) as part of an evaluation of the performance of disease surveillance in Rwanda.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to confidentiality restrictions but are available from the corresponding author upon reasonable request. The questionnaire used in the study is available in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper was made possible with the support of a CDC foundation grant to support Evidence-Based Strategies (EBS) in Rwanda. The grant played a pivotal role in enabling the epidemiologic and surveillance activities associated with the Event-based surveillance (EBS) system and associated alert and response operations (ARO) at the Rwanda Biomedical Centre (RBC) in the Public Health Surveillance and Epidemic Preparedness and Response Division. The contents do not necessarily reflect the views of the CDC Foundation or the United States Government.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePN, ON, LR, ST, MM, AZ, MRK, SU, II, AK, ER, and CMM contributed to the study conception and design. Data collection was performed by PN, ON, LR, ST, MM, and AZ. Data analysis was done by PN, ON, and LR. The first draft was written by ON and PN with input from all authors. All authors reviewed, edited, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the study participants from the hospitals for their time and cooperation. We acknowledge the support of the Rwanda Biomedical Center/Public Health Surveillance \u0026amp; Emergency Preparedness and Response Division.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFenollar F, Mediannikov O. (2018), Emerging infectious diseases in Africa in the 21st century. New Microbes and New Infectious, Vol 26, pages S10-S18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Economic Forum. 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Cholera hot-spots and contextual factors in Burundi, planning for elimination. \u003cem\u003eTropical Medicine and Infectious Disease\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2), p.76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed A, Makame J, Robert F, Julius K, Mecky M. Sero-prevalence and spatial distribution of Rift Valley fever infection among agro-pastoral and pastoral communities during Interepidemic period in the Serengeti ecosystem, northern Tanzania. BMC Infect Dis. 2018;18(1):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Indicator-Based Surveillance, Integrated Diseases Surveillance in Rwanda, Rwanda, exploratory analysis","lastPublishedDoi":"10.21203/rs.3.rs-4288889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4288889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eIntroduction\u003c/b\u003e: Rwanda, like other countries, is facing emerging and re-emerging infectious diseases. Since 2002, Rwanda has implemented indicator-based surveillance through the Integrated Disease Surveillance and Response (IDSR) framework. This study aimed to explore the completeness and timeliness of reporting and use of the tool in Rwanda.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethod\u003c/b\u003e: In this study, we used a cross-sectional descriptive approach using a structured questionnaire for IDSR focal persons and data managers in 46 public hospitals accompanied by a secondary data analysis of monthly records from the national HMIS for the period 2018\u0026ndash;2021 from 564 public health facilities (HFs) and 283 private HFs. Exploratory analysis and correlation assessments complemented dataset analysis.\u003c/p\u003e \u003cp\u003e\u003cb\u003eResults\u003c/b\u003e: This study reveals that public HF consistently achieves or surpasses the 80% completeness (96.7%) and timeliness (80.8%) targets. In contrast, private HF demonstrates 42.8% and 25.3%, completeness and timeliness, respectively. Eight-seven percent (87%) of interviewees reported to have received feedback from the central level, with varying frequencies. Hospitals provide feedback to HFs in their catchment area (91%), but frequencies differ. Regarding data accuracy, 95.7% possess standard case definitions, and 87% regularly refer to them. Two-thirds (67.6%) report they monitor weekly trends, but only 34.9% produce and share weekly reports promptly. Challenges identified include internet issues (30%), other competing duties (30%), and forgetting to report (26%). 84.8% of HFs used the system to detect outbreaks in their catchment areas; 71.7% of these were responded to according to national guidelines. Furthermore, 92.3% of all HFs have used the eIDSR system for planning purposes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e: The IDSR system was highly useful by providing evidence for decision-making in early detection and response to outbreaks as well as for hospital program planning. Maintaining timely reporting, enhancing data quality and timely use, and improving health workers' knowledge and practices are vital for a surveillance system user to detect outbreaks early. More focus should be put on Private Health Facilities.\u003c/p\u003e","manuscriptTitle":"Assessment and exploratory analysis of Indicator Based Surveillance (IBS) system in Rwanda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-29 14:42:38","doi":"10.21203/rs.3.rs-4288889/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58db512f-e894-467b-ad13-57428513cce9","owner":[],"postedDate":"April 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-29T14:42:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-29 14:42:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4288889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4288889","identity":"rs-4288889","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}