Assessment and exploratory analysis of the 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 of 2018–2021 from 564 public health facilities (HFs) and 283 private HFs. Exploratory analysis and correlation assessments complemented the dataset analysis. Results : This study revealed 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 the interviewees reported having received feedback from the central level, with varying frequencies. Hospitals provide feedback to HFs in their catchment area (91%), but the frequencies differ. Regarding the data accuracy, 95.7% of the respondents possessed standard case definitions, and 87% regularly referred to them. Two-thirds (67.6%) reported that they monitored weekly trends, but only 34.9% produced and shared weekly reports promptly. The challenges identified included internet issues (30%), other competing duties (30%), and forgetting to report (26%). A total of 84.8% of HFs used the system to detect outbreaks in their catchment areas; 71.7% of these HFs responded to the system according to national guidelines. Furthermore, 92.3% of all HFs used the eIDSR system for planning purposes. Conclusion : The IDSR system was highly useful because it provided 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 placed 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 the 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 watchful eyes and attentive ears of disease control efforts. These systems systematically gather, analyse, and interpret data to offer essential insights that can 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 individuals respond 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 regularly collecting data on disease occurrence. 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 rumours. The 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 km2), location in a region prone to epidemic diseases, and easy domestic and international travel 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 the Ebola outbreak (DRC and Uganda), Marburg outbreak (Tanzania), cholera (Burundi), polio (Burundi and DRC), RVF (Rwanda and Tanzania), and 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 in public health planning, monitoring, and evaluation. Although IBS is well established in Rwanda, 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 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 has yet to be fully implemented and integrated into the existing health surveillance framework. Therefore, the purpose of this study was to perform 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 that is better prepared to safeguard public health in an ever-changing disease landscape. The approval to conduct the study was obtained from the Rwanda Biodemical Centre as part disease surveillance evaluation. Informed consent was obtained from all subjects involved in the study. Data was de-identified to maintain confidentiality. The study adhered to principles of respect for persons, beneficence, and justice outlined in the Belmont Report. METHODS Rwanda is a country located in East Central Africa with high population density. The population is approximately 14 million, with an area of 26,338 km 2 . Rwanda has 4 provinces and is located in 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. Operationally, 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, 8 of which are privately owned. The Rwanda Ministry of Health's performance report for 2021–2022 revealed 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. To gain insights from those who used the system most frequently, we carefully selected two health workers from each hospital for interviews. Specifically, we chose IDSR focal persons and data managers because 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, analysing, 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 because incomplete reports 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 the 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. All the information reflecting all the indicators was collected. Using a structured questionnaire, trained assistants conducted interviews with the selected health workers at hospitals for indicators such as…. [ 5 , 10 ]. We also extracted data from the national health information management system HMIS/eIDSR for several indicators, such as completeness and timeliness. We considered data gathered for the period from 2018 to 2021 from 564 public health facilities (HFs) and 283 private HFs. The data were collected through the National Health Surveillance Data Entry Tool (RedCap) and were extracted in an Excel spreadsheet, cleaned and analysed by 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 attitudes and practices, 9) feedback, 10) representativeness, and 11) the involvement of private hospitals. The main methods we adopted for analysis were exploratory analysis and correlation assessments. We described each variable using graphical representations, such as frequency distribution and numerical measures, and performed a relationship analysis between several variables. RESULTS Completeness and timeliness in the period from 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, the overall completeness of private health facilities was considerably lower, at 42.8%. Only three districts exceeded the 80% target (Burera, Musanze, and Ngoma); the remaining districts exhibited low performance, and four exhibited a zero-completion rate. An evaluation of the period from 2018 to 2021 revealed that the overall timeliness of public health facilities was 80.8%, matching the target of 80%. However, approximately 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 exhibited 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 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. Our research revealed that the second edition of the eIDSR effectively detected variations in patient trends. 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, only 71.7% of outbreaks were handled by guidelines, highlighting the need to reinforce the use of IDSR technical guidelines in response to outbreaks. The frequency of use is a key indicator of a system's ability to fulfil its primary function. If users delay reporting, early detection and response will be compromised. The IDSR system is composed 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 respondents’ 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 more than half of the respondents reported receiving feedback weekly, while the remaining participants received feedback either immediately, monthly, or quarterly. On the hospital side, 91% of them provided feedback to health centers in their catchment area. However, the frequency of feedback provided by hospitals is inconsistent, with approximately 40% providing weekly feedback, 30% providing immediate feedback, and 25% providing monthly feedback. Accuracy The correct use of standard case definitions is key to obtaining 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 showed 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. Although there is a considerable portion of health facilities that use accurate detection and reporting in the IDSR, there is a need to improve the accuracy of the data by fully providing standard case definitions and sensitization to the importance of using standard case definitions for detection and reporting to improve the accuracy of the data. 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 research while also facilitating the modelling of various diseases, thereby advancing scientific discovery. The assessment revealed 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 revealed 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. Thirteen percent 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 formulated six queries to evaluate the 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 scores. Max 100.0 Min 50.0 Mean 74.8 Median 66.7 Mode 66.7 Based on the findings, 51% fell within the 70–100 range, and 49% fell 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 the 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 covered their catchment area, but 22% reported that some areas were not covered. Finally, 91% expressed satisfaction with the current list of priority diseases under surveillance, but 9% recommended that noncommunicable diseases, scabies, and Chickenpox/Varicella be added to the list. Figure 7: Representativeness of population, area, and disease The disease surveillance system of Rwanda generally covers most of the population, and 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. The respondents indicated a need to expand the list of diseases under surveillance. To address this issue, 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 disease. To address this issue, 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 conducted inspections of private clinics in their catchment areas. Twenty-one percent of respondents conducted monthly and quarterly supervision, and 7% and 3% of the respondents reported conducting biaannual and annual supervision of private clinics, respectively. Regarding the reasons for not conducting supervision among private clinics, of the 28 respondents who accepted the 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 were other reasons, and 39% reported nonspecific reasons limiting them from conducting supervision 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 missing events, conditions, or diseases 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. 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 that focal persons and others occupy 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 improvements in the surveillance infrastructure in Rwanda, which is consistent 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 such as the COVID-19 pandemic. This underscores the importance of continued capacity building and resource allocation to maintain optimal surveillance operations even amidst a crisis. The 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 the low completeness (42.8%) and timeliness (25.3%) of reporting in this sector. With the growing role of private facilities in Rwanda's healthcare landscape, there is a serious risk of missing early warning signs of outbreaks or clusters of illness in the community. A 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 ]. Therefore, strengthening participation and data quality from the private health sector should 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 of which are critical for optimal surveillance utility. The widespread availability and application of standard case definitions are positive, although continued sensitization to their importance could enhance data accuracy. More concerning, however, is the suboptimal 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 perform 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. 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 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 suggested 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 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 effective. However, private facility surveillance lacks 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 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 included 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. Through the PHS&EPR Division, the Rwanda Biomedical Center should conduct more investigations to elaborate on clusters of populations 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 the utilization of data for decision-making, and improving the knowledge, attitudes, and practices of health workers are critical for enhancing the efficiency of the surveillance system. 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 better understand the system's strengths and weaknesses, 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 focused on the human health sector. However, in the future, collaborative surveillance in Rwanda should be assessed to optimize the evaluation and establish gaps and areas for improvement 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 CDC ION# 90106405 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. PN, ON, LR, ST, MM, and AZ collected the data. PN, ON, and LR performed the data analysis. The first draft was written by ON and PN with input from all the authors. All authors reviewed, edited, and approved the final version. Acknowledgements 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, Infectious Diseases of 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, www.who.int/csr/ihr/howtheywork/faq/en/#draft. 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 2 0 1 9. Norzin T, Ghiasbeglou H, Patricio M, et al. Event-based surveillance: Providing early warning for communicable disease threats. Canada Communicable Disease Report 2023; 49: 29–34. Mghamba, J. M., Mboera, L. E., Krekamoo, W., & Shayo, E. H. (2018). Challenges of implementing the Integrated Disease Surveillance and Response Strategy using the current health management information system in Tanzania. Tanzania Health Research Bulletin, 20(1). Phalkey, R. K., Yamamoto, S., Awate, P., & Marx, M. (2015). Challenges with the implementation of an Integrated Disease Surveillance and Response (IDSR) system: a systematic review of the lessons learned. Health policy and planning, 30(1), 131-143. Ngamije, D., Belay, T., Karema, C., Umubyeyi, A., Mbanjumucyo, G., Perrin, J. M., ... & Ndejuru, R. (2021). Implementing the International Health Regulations in Rwanda progresses and lessons learned. BMC Public Health, 21(1), 1-13. Bulimbe, D.B., Masunga, D.S., Paul, I.K., Kassim, G.H., Bahati, P.B., Thomas, J.A., Mwakisole, C., Nazir, A. and 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. Journal of Epidemiology and Global Health. 2023 Jun 28:1-1. Debes, A.K., Shaffer, A.M., Ndikumana, T., Liesse, I., Ribaira, E., Djumo, C., Ali, M. and Sack, D.A., 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. and Mecky, M., 2018. 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 infectious diseases , 18 (1), pp.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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4316514","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308772850,"identity":"b33b2e09-6f7e-4297-8a08-b245b873406f","order_by":0,"name":"Pacifique Nizeyimana","email":"","orcid":"","institution":"Jhpiego","correspondingAuthor":false,"prefix":"","firstName":"Pacifique","middleName":"","lastName":"Nizeyimana","suffix":""},{"id":308772852,"identity":"694573df-2183-44f2-a53e-ffb365f79dd6","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":308772856,"identity":"8a97d79d-ef1a-40a9-a6b9-19b837c0a4e1","order_by":2,"name":"Laurent Ruyange","email":"","orcid":"","institution":"Rwanda Biomedical Centre (RBC)","correspondingAuthor":false,"prefix":"","firstName":"Laurent","middleName":"","lastName":"Ruyange","suffix":""},{"id":308772857,"identity":"cbc27ad4-861a-4840-9f89-6de2eb04c4ed","order_by":3,"name":"Solange Twahirwa","email":"","orcid":"","institution":"Rwanda Biomedical Centre (RBC)","correspondingAuthor":false,"prefix":"","firstName":"Solange","middleName":"","lastName":"Twahirwa","suffix":""},{"id":308772861,"identity":"5d7424eb-f786-46dd-9df3-e86aaa92bd5d","order_by":4,"name":"Marcel Manariyo","email":"","orcid":"","institution":"Jhpiego","correspondingAuthor":false,"prefix":"","firstName":"Marcel","middleName":"","lastName":"Manariyo","suffix":""},{"id":308772862,"identity":"45d4d263-c367-4381-9038-c723adb2f63f","order_by":5,"name":"Alain 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(RBC)","correspondingAuthor":false,"prefix":"","firstName":"Claude","middleName":"Mambo","lastName":"Muvunyi","suffix":""}],"badges":[],"createdAt":"2024-04-24 08:14:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4316514/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4316514/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57729000,"identity":"8b32b915-94e0-4ba5-96ef-78c45d41af8b","added_by":"auto","created_at":"2024-06-04 21:50:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCompleteness and timeliness distribution of completeness and timeliness by districtamong 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":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4316514/v1/5077657f0b80bf59162582f2.png"},{"id":57729014,"identity":"c114cdc1-af21-44fc-9e36-34620f2387d5","added_by":"auto","created_at":"2024-06-04 21:50:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117249,"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":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4316514/v1/6c37eb1e064fc014801750d3.png"},{"id":57729001,"identity":"898450e7-3d45-4031-807a-ec0475c4ab7d","added_by":"auto","created_at":"2024-06-04 21:50:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72885,"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":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4316514/v1/44324d3ad2bf962c356d5824.png"},{"id":57729018,"identity":"d74e405a-1148-433c-900a-ad118de135f0","added_by":"auto","created_at":"2024-06-04 21:50:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55640,"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":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4316514/v1/5349c77e5c2779f54dcca2c9.png"},{"id":57730160,"identity":"a6dd66a3-2953-435c-9e7c-b9bd0a26e213","added_by":"auto","created_at":"2024-06-04 21:58:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39849,"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":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4316514/v1/41e72db284e2aff9f7ba6552.png"},{"id":57729015,"identity":"b779fb90-936b-4314-b715-4a546116dde5","added_by":"auto","created_at":"2024-06-04 21:50:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11311,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRespondents’ perceptions of the simplicity of the system\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4316514/v1/7c839f7a1e8b67ea6fb9f447.png"},{"id":57729017,"identity":"1c8d28da-0af3-414c-8192-e9f1e4aeaa86","added_by":"auto","created_at":"2024-06-04 21:50:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":54095,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRepresentativeness of population, area, and disease\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4316514/v1/6996d69e8c68611fd917e571.png"},{"id":57729012,"identity":"36ff97c1-d687-41ac-87ee-46e42fb6fc07","added_by":"auto","created_at":"2024-06-04 21:50:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":185411,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInvolvement of private facilities\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4316514/v1/38b822503ffebce73100847d.png"},{"id":64053973,"identity":"683d13c3-d474-44fa-90bd-885f4f6c4136","added_by":"auto","created_at":"2024-09-05 17:48:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1172409,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4316514/v1/7d88d8d8-d8c0-46b9-888a-630a41676845.pdf"},{"id":57730161,"identity":"a8bdb2ec-8ede-4514-9f27-ab4b9c82c6b0","added_by":"auto","created_at":"2024-06-04 21:58:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24287,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4316514/v1/9420155287a03c5bac99378d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment and exploratory analysis of the 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 the 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 watchful eyes and attentive ears of disease control efforts. These systems systematically gather, analyse, and interpret data to offer essential insights that can 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 individuals respond 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 regularly collecting data on disease occurrence. 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 rumours. The 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 km2), location in a region prone to epidemic diseases, and easy domestic and international travel 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 the Ebola outbreak (DRC and Uganda), Marburg outbreak (Tanzania), cholera (Burundi), polio (Burundi and DRC), RVF (Rwanda and Tanzania), and 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 in public health planning, monitoring, and evaluation.\u003c/p\u003e \u003cp\u003eAlthough IBS is well established in Rwanda, 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 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 has yet to be fully implemented and integrated into the existing health surveillance framework.\u003c/p\u003e \u003cp\u003eTherefore, the purpose of this study was to perform 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 that is better prepared to safeguard public health in an ever-changing disease landscape.\u003c/p\u003e \u003cp\u003eThe approval to conduct the study was obtained from the Rwanda Biodemical Centre as part disease surveillance evaluation. Informed consent was obtained from all subjects involved in the study. Data was de-identified to maintain confidentiality. The study adhered to principles of respect for persons, beneficence, and justice outlined in the Belmont Report.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eRwanda is a country located in East Central Africa with high population density. The population is approximately 14\u0026nbsp;million, with an area of 26,338 km\u003csup\u003e2\u003c/sup\u003e. Rwanda has 4 provinces and is located in 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. Operationally, 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, 8 of which are privately owned. The Rwanda Ministry of Health's performance report for 2021\u0026ndash;2022 revealed 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. To gain insights from those who used the system most frequently, we carefully selected two health workers from each hospital for interviews. Specifically, we chose IDSR focal persons and data managers because 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, analysing, 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 because incomplete reports 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 the 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. All the information reflecting all the indicators was collected. Using a structured questionnaire, trained assistants conducted interviews with the selected health workers at hospitals for indicators such as\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 several indicators, such as completeness and timeliness. We considered data gathered for the period from 2018 to 2021 from 564 public health facilities (HFs) and 283 private HFs.\u003c/p\u003e \u003cp\u003eThe data were collected through the National Health Surveillance Data Entry Tool (RedCap) and were extracted in an Excel spreadsheet, cleaned and analysed by 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 attitudes and practices, 9) feedback, 10) representativeness, and 11) the involvement of private hospitals.\u003c/p\u003e \u003cp\u003eThe main methods we adopted for analysis were exploratory analysis and correlation assessments. We described each variable using graphical representations, such as frequency distribution and numerical measures, and performed a relationship analysis between several variables.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCompleteness and timeliness in the period from 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, the overall completeness of private health facilities was considerably lower, at 42.8%. Only three districts exceeded the 80% target (Burera, Musanze, and Ngoma); the remaining districts exhibited low performance, and four exhibited a zero-completion rate.\u003c/p\u003e \u003cp\u003eAn evaluation of the period from 2018 to 2021 revealed that the overall timeliness of public health facilities was 80.8%, matching the target of 80%. However, approximately 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 exhibited 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 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\u003eOur research revealed that the second edition of the eIDSR effectively detected variations in patient trends. 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, 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\u003eThe frequency of use is a key indicator of a system's ability to fulfil its primary function. If users delay reporting, early detection and response will be compromised. The IDSR system is composed 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 respondents\u0026rsquo; 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 more than half of the respondents reported receiving feedback weekly, while the remaining participants received feedback either immediately, monthly, or quarterly. On the hospital side, 91% of them provided feedback to health centers in their catchment area. However, the frequency of feedback provided by hospitals is inconsistent, with approximately 40% 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 obtaining 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 showed 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. Although there is a considerable portion of health facilities that use accurate detection and reporting in the IDSR, there is a need to improve the accuracy of the data by fully providing standard case definitions and sensitization to the importance of using standard case definitions for detection and reporting to improve the accuracy of the data.\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 research while also facilitating the modelling of various diseases, thereby advancing scientific discovery. The assessment revealed 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 revealed 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. Thirteen percent 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 \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\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 formulated six queries to evaluate the 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 scores.\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% fell within the 70\u0026ndash;100 range, and 49% fell 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 \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 the 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 covered their catchment area, but 22% reported that some areas were not covered. Finally, 91% expressed satisfaction with the current list of priority diseases under surveillance, but 9% recommended that noncommunicable diseases, scabies, and Chickenpox/Varicella be added to the list.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 7: Representativeness of population, area, and disease\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe disease surveillance system of Rwanda generally covers most of the population, and 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. The respondents indicated a need to expand the list of diseases under surveillance. To address this issue, 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 disease. To address this issue, 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 conducted inspections of private clinics in their catchment areas. Twenty-one percent of respondents conducted monthly and quarterly supervision, and 7% and 3% of the respondents reported conducting biaannual and annual supervision of private clinics, respectively. Regarding the reasons for not conducting supervision among private clinics, of the 28 respondents who accepted the 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 were other reasons, and 39% reported nonspecific reasons limiting them from conducting supervision 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 missing events, conditions, or diseases 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. 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 that focal persons and others occupy 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 improvements in the surveillance infrastructure in Rwanda, which is consistent 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 such as the COVID-19 pandemic. This underscores the importance of continued capacity building and resource allocation to maintain optimal surveillance operations even amidst a crisis. The 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 the low completeness (42.8%) and timeliness (25.3%) of reporting in this sector. With the growing role of private facilities in Rwanda's healthcare landscape, there is a serious risk of missing early warning signs of outbreaks or clusters of illness in the community. A 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]. Therefore, strengthening participation and data quality from the private health sector should 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 of which are critical for optimal surveillance utility. The widespread availability and application of standard case definitions are positive, although continued sensitization to their importance could enhance data accuracy. More concerning, however, is the suboptimal 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 perform 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\u003eIBS 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 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 suggested 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 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 effective. However, private facility surveillance lacks 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 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 included 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. Through the PHS\u0026amp;EPR Division, the Rwanda Biomedical Center should conduct more investigations to elaborate on clusters of populations 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 the utilization of data for decision-making, and improving the knowledge, attitudes, and practices of health workers are critical for enhancing the efficiency of the surveillance system. 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 better understand the system's strengths and weaknesses, 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 focused on the human health sector. However, in the future, collaborative surveillance in Rwanda should be assessed to optimize the evaluation and establish gaps and areas for improvement 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.\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 CDC ION# 90106405 to support\u0026nbsp;evidence-based strategies\u0026nbsp;(EBS) in Rwanda. The grant played a pivotal role in enabling the epidemiologic and surveillance activities associated with the\u0026nbsp;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. PN, ON, LR, ST, MM, and AZ\u0026nbsp;collected the data.\u0026nbsp;PN, ON, and LR\u0026nbsp;performed the data analysis. The first draft was written by ON and PN with input from all\u0026nbsp;the\u0026nbsp;authors. All authors reviewed, edited, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\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\n\u003cli\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/li\u003e\n\u003cli\u003eWorld 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).\u003c/li\u003e\n\u003cli\u003eTambo E., Ugwu EC, Ngogang Jy, (2014) Need of surveillance response systems to combat Ebola outbreaks and other emerging infectious diseases in African countries, Infectious Diseases of Poverty, 3, v 29,\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. INTERNATIONAL HEALTH REGULATIONS 2005 THIRD EDITION, https://apps.who.int/iris/bitstream/handle/10665/246107/9789241580496-eng.pdf (accessed 10 July 2023).\u003c/li\u003e\n\u003cli\u003eCDC. Introduction to Public Health Surveillance, https://www.cdc.gov/training/publichealth101/surveillance.html (2018, accessed 10 July 2023).\u003c/li\u003e\n\u003cli\u003eNsubuga P, White ME, Thacker SB, et al. Public Health Surveillance: A Tool for Targeting and Monitoring Interventions \u0026lsquo;What gets measured gets done.\u0026rsquo;-Anonymous, www.who.int/csr/ihr/howtheywork/faq/en/#draft.\u003c/li\u003e\n\u003cli\u003eMcnabb 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).\u003c/li\u003e\n\u003cli\u003eOrganization 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 2 0 1 9.\u003c/li\u003e\n\u003cli\u003eNorzin T, Ghiasbeglou H, Patricio M, et al. Event-based surveillance: Providing early warning for communicable disease threats. Canada Communicable Disease Report 2023; 49: 29\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eMghamba, J. M., Mboera, L. E., Krekamoo, W., \u0026amp; Shayo, E. H. (2018). Challenges of implementing the Integrated Disease Surveillance and Response Strategy using the current health management information system in Tanzania. Tanzania Health Research Bulletin, 20(1).\u003c/li\u003e\n\u003cli\u003ePhalkey, R. K., Yamamoto, S., Awate, P., \u0026amp; Marx, M. (2015). Challenges with the implementation of an Integrated Disease Surveillance and Response (IDSR) system: a systematic review of the lessons learned. Health policy and planning, 30(1), 131-143.\u003c/li\u003e\n\u003cli\u003eNgamije, D., Belay, T., Karema, C., Umubyeyi, A., Mbanjumucyo, G., Perrin, J. M., ... \u0026amp; Ndejuru, R. (2021). Implementing the International Health Regulations in Rwanda progresses and lessons learned. BMC Public Health, 21(1), 1-13.\u003c/li\u003e\n\u003cli\u003eBulimbe, D.B., Masunga, D.S., Paul, I.K., Kassim, G.H., Bahati, P.B., Thomas, J.A., Mwakisole, C., Nazir, A. and Uwishema, O., (2023). Marburg virus disease outbreak in Tanzania: current efforts and recommendations\u0026ndash;a short communication. \u003cem\u003eAnnals of Medicine and Surgery\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e(8), p.4190.\u003c/li\u003e\n\u003cli\u003eRwagasore 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. Journal of Epidemiology and Global Health. 2023 Jun 28:1-1.\u003c/li\u003e\n\u003cli\u003eDebes, A.K., Shaffer, A.M., Ndikumana, T., Liesse, I., Ribaira, E., Djumo, C., Ali, M. and Sack, D.A., 2021. 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/li\u003e\n\u003cli\u003eAhmed, A., Makame, J., Robert, F., Julius, K. and Mecky, M., 2018. 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. \u003cem\u003eBMC infectious diseases\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), pp.1-8.\u003c/li\u003e\n\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-4316514/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4316514/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 of 2018\u0026ndash;2021 from 564 public health facilities (HFs) and 283 private HFs. Exploratory analysis and correlation assessments complemented the dataset analysis.\u003c/p\u003e \u003cp\u003e\u003cb\u003eResults\u003c/b\u003e: This study revealed 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 the interviewees reported having received feedback from the central level, with varying frequencies. Hospitals provide feedback to HFs in their catchment area (91%), but the frequencies differ. Regarding the data accuracy, 95.7% of the respondents possessed standard case definitions, and 87% regularly referred to them. Two-thirds (67.6%) reported that they monitored weekly trends, but only 34.9% produced and shared weekly reports promptly. The challenges identified included internet issues (30%), other competing duties (30%), and forgetting to report (26%). A total of 84.8% of HFs used the system to detect outbreaks in their catchment areas; 71.7% of these HFs responded to the system according to national guidelines. Furthermore, 92.3% of all HFs used the eIDSR system for planning purposes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e: The IDSR system was highly useful because it provided 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 placed on private health facilities.\u003c/p\u003e","manuscriptTitle":"Assessment and exploratory analysis of the indicator-based surveillance (IBS) system in Rwanda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 21:50:27","doi":"10.21203/rs.3.rs-4316514/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":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-05T17:40:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-04 21:50:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4316514","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4316514","identity":"rs-4316514","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}