Evaluation of the Contribution of Care Systems for Epidemic-Prone Diseases in Strengthening the Health System in Guinea

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Method: A descriptive cross-sectional evaluation study involving 356 response actors from the eight administrative regions of Guinea. Data were collected using structured questionnaires organized around three main components: Structure, Process, and Outcomes. A strengths and weaknesses analysis was also conducted. Results: Among the 356 respondents, 76.40% were female, while 23.60% were male. The predominant age group was 40-60 years, representing 51% of participants, while 89.9% had a university education. The Structure component scored an overall 61%, indicating an average contribution. Sub-components included: human resources (69%), logistics and health products (81%), and health financing (32%). The Process component scored 67%, also reflecting an average contribution. The supports included: care delivery (83%), coordination (81%), epidemiological surveillance (81%), awareness-raising (82%), human resources (58%), logistics and health products (49%), development of normative documents (31%), and health information systems (69%). Finally, the Outcomes component achieved an 88% contribution, reflecting a generally good impact, characterized by improved care indicators for epidemic-prone diseases (89%) and surveillance (96%), beneficiary satisfaction (96%), and improved laboratory indicators (78%). The overall contribution of care systems was estimated at 72%, corresponding to an average contribution. Conclusion: The study highlights an average contribution of epidemic-prone disease care systems in Guinea despite notable advances. An integrated approach is necessary to strengthen system resilience and improve preparedness for future epidemics. Epidemiology Evaluation contribution care systems epidemic Guinea Introduction West Africa has been a region affected by multiple epidemics and outbreaks of infectious diseases, leading to high morbidity and mortality, with negative consequences for the health systems of these countries [1]⁠. Among these, we can cite Ebola virus disease, yellow fever, meningitis, and COVID-19 in Guinea; dengue fever in Benin and Côte d'Ivoire; yellow fever in Nigeria; meningitis in Ghana, Niger, and Togo; as well as measles in Guinea, Niger, and Sierra Leone; and cholera in Benin, Liberia, Nigeria, and Sierra Leone. Lassa fever is endemic in West Africa and has been reported in Sierra Leone, Guinea, Liberia, and Nigeria [2]. The Republic of Guinea has long faced numerous health crises, worsened by the fragility of its health system. Among the most significant challenges the country has had to face, the 2014-2016 Ebola virus disease (EVD) outbreak remains a major event. This health crisis exposed the many shortcomings of the Guinean health system, including limited geographical coverage, insufficient response capacity, and reduced access to care. Before the Ebola outbreak, only 38.9% of the population had access to healthcare services, and their utilization remained limited to 18.6% [3,4]. In response to this situation, and as part of the post-Ebola recovery efforts, Guinea has undertaken ambitious reforms to strengthen its health system. These reforms were implemented to improve the management of epidemic-prone diseases (EPDs), such as yellow fever, Lassa fever, measles, meningitis, COVID-19, and more recently, diphtheria. These diseases pose a constant threat to the Guinean population due to their potential for rapid transmission and the severity of the complications they cause. The country has therefore established several mechanisms aimed at improving patient care and strengthening the capacity to respond to epidemics [3–7]. Key measures include the establishment of Epidemic Treatment Centers (CTEs) and Community Transit Centers (CTCom) for the isolation and treatment of suspected or confirmed cases. Furthermore, strengthening human resources through specialized training and improving healthcare infrastructure have helped enhance the epidemic response framework. The epidemiological surveillance system has also been improved with the creation of regional and prefectural alert and response teams (ERARE and EPARE) at decentralized levels [3,7,8]. Today, epidemic treatment centers are staffed with regularly trained personnel, ensuring the care of patients suspected or confirmed of having an epidemic-prone disease or a priority zoonosis, in accordance with the national protocols in place [9]. However, despite these advances, regular evaluation of these mechanisms is crucial to understand their actual contribution to improving the Guinean health system and their effectiveness in epidemic management. Therefore, this study aims to assess the contribution of the management mechanisms for epidemic-prone diseases (EPDs) in strengthening the Guinean health system. Study Methods Study Framework The Republic of Guinea is a coastal country located in the western part of the African continent, halfway between the Equator and the Tropic of Cancer (7°30' and 12°30' North latitude, and 8° and 15° West longitude). Covering an area of 245,857 km², it is bordered to the west by Guinea-Bissau and the Atlantic Ocean, to the north by Senegal and Mali, to the east by Côte d'Ivoire, and to the south by Sierra Leone and Liberia [10]. The country has a population of approximately eleven million people, with 52% women and 16% children under the age of five [11]. From a geo-ecological perspective, Guinea is divided into four distinct natural regions that are internally homogeneous: Guinea Maritime, Middle Guinea, Upper Guinea, and Forest Guinea [10,12]. The country owes this uniqueness to its natural environment, characterized by climatic contrasts, mountain barriers, and the orientation of the reliefs, which combine to give each region its own specificities in terms of climate, soil, vegetation, and the way of life of the populations. Administratively, Guinea is subdivided into seven administrative regions (Boké, Faranah, Kindia, Labé, Mamou, Kankan, and N'Zérékoré), with the city of Conakry, the capital, enjoying a special status as a special zone. Each administrative region consists of prefectures, with the number varying. In total, there are 33 prefectures, 38 urban communes (CU), five of which are in Conakry, and 303 rural development communities (CRD) [10,12]. It was one of the countries in West Africa most severely affected by the Ebola virus disease epidemic [11]. Infant and maternal mortality rates are estimated at 123‰ and 724 per 100,000 live births, respectively. The country faces a serious shortage of human resources in health, with only 98 health workers per 100,000 inhabitants. These human resources are unevenly distributed across the country, with nearly 52% of health workers residing in Conakry and its surroundings, serving only 15% of the population [11]. Study site The study site will encompass all three national directorates (National Agency for Health Security, National Directorate of Epidemiology and Disease Control, National Directorate of Public and Private Hospital Establishments), as well as the thirty-eight (38) health districts of Guinea. The eight (08) regional health inspections, the thirty-eight (38) prefectural health directorates, and the thirty-eight (38) epidemic treatment centers play a crucial role in the epidemic response in the Republic of Guinea. This response is coordinated at the central level, represented by the Ministry of Health, the National Agency for Health Security, and the National Directorate of Epidemiology and Disease Control. Study type : This was a cross-sectional evaluative study conducted from April 4 to October 30, 2024. It was a normative evaluation, using the theoretical framework of the WHO health system [13]. Study Population It will consist of all the actors involved in the epidemic response in Guinea, including those from the technical departments of the Ministry of Health, the regional health inspections, the prefectural health directorates, as well as the epidemic treatment centers. Sampling The sampling method used for this study was non-probabilistic, based on the convenience sampling technique. It involved selecting actors from the epidemic response by health district in Guinea to answer our survey questions. We also used purposive sampling to select the heads of departments from the Ministry of Health, regional health inspections, prefectural health directorates, and epidemic treatment centers. The sample size is 356 participants. Variables à l’étude The Donabedian evaluation model, based on three components (structure, process, and outcomes) [14,15], Using the theoretical framework of the WHO health system will be applied [16]. The following variables were used : Demographic Information: Age, marital status, education level, position held, residence, response commission; Structure/Resources Component: Existence of a functional isolation site, presence of eight (8) care personnel per isolation site, existence of trained personnel for epidemic disease care (PEC), existence of trained laboratory personnel, availability of non-expired PEC medications, existence of a functional epidemic disease diagnostic laboratory, availability of non-expired diagnostic kits, availability of non-expired vaccines, availability of functional rolling logistics, existence of financial support, presence of a generator for the isolation site, existence of a functional borehole at the isolation site, availability of infection prevention control (IPC) kits at the isolation site, availability of personal protective equipment (PPE) at the isolation site, existence of a waste management area at the isolation site, existence of a prescription system at the isolation site, existence of a printer at the isolation site; Process Component: Participation in the training of actors on epidemic disease care (PEC), participation in the development of PEC protocols, participation in the development of infection prevention control (IPC) manuals, participation in the development of the PEC guide for epidemic diseases, participation in the development of diagnostic protocols for epidemic diseases, participation in the development of continuity of care plans for complicated cases, participation in the development of supervision plans for PEC actors, participation in the development of supervision plans for PEC actors, participation in the development of supply plans for medications and other inputs for isolation sites, participation in the development of rehabilitation plans for isolation sites, participation in the development of patient transfer protocols, participation in the functionality of case reporting mechanisms, regular participation in PEC coordination meetings during epidemics, participation in investigations, participation in community awareness campaigns, participation in vaccination campaigns, participation in the management of epidemic-prone diseases (MAPI), participation in the management of other non-epidemic diseases (dog and snake bites), participation in actor training, participation in crisis meetings, participation in regional/prefectural coordination meetings, participation in needs assessment for response efforts, participation in epidemic disease surveillance, participation in the development of national/regional/prefectural response plans, participation in contact tracing follow-up, participation in the supervision of actors ; Results Component: Improvement of epidemic disease care (MPE) indicators, improvement of epidemic disease surveillance indicators, satisfaction with epidemic disease care, satisfaction with the management of dog and snake bites, and diagnosis of epidemic disease cases within 48 hours of suspicion. Data Collection Techniques and Tools Data were collected through observation, document review, and questionnaire surveys, using an observation grid, a data sheet, and a questionnaire, respectively. Some health workers from the National Agency for Health Security were recruited to facilitate data collection from the target population. The data collection tools were pre-tested in the meeting room of the National Agency for Health Security. Data Processing and Analysis The collected data were verified and validated as the investigators progressed in the field. They were entered using a data entry template developed with KoboToolbox. Data analysis was conducted using Epi-Info version 7 and Stata version 13. The descriptive analysis part involved describing the study sample through a detailed description of the variables. Qualitative variables were described in terms of absolute and relative frequency, while quantitative variables were described by the mean ± standard deviation if the distribution was normal, and by the median and quartiles (Q1, Q3) if the distribution was not normal. The contribution of the systems was evaluated based on the average scores obtained for their three components (structure, process, and result). For each explanatory component, as well as the main component, the total points obtained were expressed as a percentage of the total expected. The average percentages of each component (structure, process, and result) across the eight (08) administrative regions were used to determine the national contribution level of the epidemic-prone disease management systems in the context of improving the health system in Guinea. We used a three-level evaluation based on Varkevisser's measurement scale [17,18] To evaluate the contribution of the systems, the assessment was as follows: "Good": if the percentage obtained is between [80 and 100%]; "Average": if the percentage obtained is between [60 and 80%]; "Poor": if the percentage obtained is between [0 and 60%]. An analysis of strengths and weaknesses was conducted, allowing the evaluation to be structured by highlighting the factors influencing the contribution of the systems either positively or negatively [19,20]. Results Description of the Sample A total of 356 epidemic response actors in Guinea were surveyed, covering the eight (8) administrative regions. Among the participants, the majority were female, accounting for 76.4%, compared to 23.6% male, giving a ratio of 3.23 women for every man. The average age of the participants was 57.53 ± 9.43 years, with the following distribution: 47% were under 40 years old, 51% were between 40 and 60 years old, and 3% were over 60 years old. In terms of education level, the vast majority of the participants (89.89%) held a university degree, while 10.11% had a secondary school level. Regarding positions held, care providers (PEC) represented the majority at 40.45%, followed by disease control doctors (13.76%), data managers (6.74%), and unit heads (5.90%). Leadership positions were less represented, such as general directors (1.40%) and regional health inspectors (0.84%). As for their residence, the majority of participants were from the following regions: Conakry (26.97%), N’Zérékoré (24.43%), and Kankan (22.19%). Regions like Kindia (13.76%), Labé (5.06%), Mamou (3.38%), Faranah (3.37%), and Boké (0.8%) had fewer actors participating in the study. Lastly, in terms of distribution across response commissions, most participants were involved in the care (PEC) commission (49%), followed by the surveillance commission (35%). Other commissions, such as coordination (12%), laboratory (3%), and logistics (1%), were less represented (see Table I). Table I: Distribution of 356 epidemic response actors in Guinea according to their sociodemographic characteristics, from August 1 to November 31, 2024. Sociodemographic Characteristics Frequency (n) Percentage (%) Mean ± Standard Deviation Sex Sex-Ratio (F/M) = 3.23 Male 84 23.60 Female 272 76.40 Age (years) 57.53 ± 9.43 Under 40 years 166 47 40 to 60 years 181 51 Over 60 years 9 3 Education Level University 320 89.89 Secondary 36 10.11 Position Held PEC Agent 144 40.45 Surveillance Officer 15 4.21 Study Officer 30 8.43 Department Head 6 1.69 Unit Head 21 5.90 General Director 5 1.40 Prefectural Health Director 19 5.34 Data Manager 24 6.74 COU-SP Manager 18 5.06 Regional Health Inspector 3 0.84 Disease Management Doctor 49 13.76 Support Partner 10 2.81 Planning, Training, and Research Officer 12 3.37 Residence Conakry Region (Capital) 96 26.97 Kindia Region 49 13.76 Mamou Region 12 3.38 Faranah Region 12 3.37 Labé Region 18 5.06 Boké Region 3 0.84 Kankan Region 79 22.19 N’Zérékoré Region 87 24.43 Response Commission PEC Commission 174 49 Surveillance Commission 125 35 Laboratory Commission 12 3 Logistics Commission 3 1 Coordination Commission 42 12 COU-SP: Health Emergency Operations Center; PEC: Health Care Management Contribution of the "Structure" Component This component is assessed through three key sub-components: 1. Human Resources: With a score of 69%, reflecting an average contribution, this sub-component indicates a moderate availability of trained personnel in the CTEPI (Health Emergency Treatment Centers). While the presence of qualified staff is notable, the shortage of healthcare workers remains a major barrier to improving the systems. 2.Logistics and Health Products: This sub-component achieves a score of 81%, considered good. It highlights the presence of energy and water sources in the CTEPI, as well as the availability of medicines, supplies, PCI kits, and essential equipment, such as diagnostic laboratories and rabies vaccines. However, some weaknesses persist, notably the lack of antivenom serum and insufficient laboratory kits. 3.Health Financing: With a score of 32%, rated as poor, this sub-component reveals a worrying deficit in financial resources, directly affecting the sustainability and effectiveness of the systems.The overall contribution of the "Structure" component is 61%, indicating an average contribution (see Table II). Table II: Characteristics of the "Structure" Component of the Epidemic-Potential Disease Care Systems in Guinea in 2024. Structure Component Scores Achieved Evaluation Human Resources Sub-component 69% Average Logistics and Health Products Sub-component 81% Good Health Financing Sub-component 32% Poor Overall Structure Component Contribution 61% Average Contribution of the "Process" Component The contribution of disease management systems with epidemic potential in Guinea in 2024, through their "Process" component, is assessed at 67%, representing an average contribution. This contribution is particularly notable in support for healthcare delivery (83%), coordination (81%), epidemiological surveillance (81%), and awareness-raising (82%), all of which play a key role in the effectiveness of the response. However, some dimensions limit the overall impact of the system, particularly the strengthening of human resources (58%), support for logistics and health products (49%), and the development of normative documents (31%), whose contributions remain insufficient. The health information system shows a moderate or average contribution (69%) (see Table III). Table III: Characteristics of the "Process" Component of Disease Management Systems with Epidemic Potential in Guinea in 2024. Process Component Achieved Scores Assessment Support for Strengthening Human Resources 58% Poor Support for Logistics and Health Products 49% Poor Support for Healthcare Delivery 83% Good Support for Strengthening the Health Information System 69% Average Support for Coordination 81% Good Support for Epidemiological Surveillance 81% Good Support for Awareness-Raising 82% Good Support for the Development of Normative Documents 31% Poor Contribution of the Process Component 67% Average Contribution of the "Results" Component The contribution of disease management systems with epidemic potential (MPE) in Guinea in 2024, through their "Results" components, is achieved at 88%, reflecting an overall good contribution. This contribution is particularly notable in the improvement of MPE management indicators (89%) and surveillance (96%), demonstrating effective case management and strengthened epidemiological follow-up. Additionally, beneficiary satisfaction with MPE management (96%) and dog and snake bites (83%) confirms a positive impact of the implemented systems. However, the contribution remains average (78%) regarding the improvement of laboratory indicators, suggesting the need for enhanced diagnostic capacity to ensure faster and more reliable detection of epidemic-prone diseases (See Table IV). Table IV: Characteristics of the "Results" Component of Disease Management Systems with Epidemic Potential in Guinea in 2024. Results Component Achieved Scores Assessment Improvement of MPE Management Indicators 89% Good Improvement of MPE Surveillance Indicators 96% Good Satisfaction with MPE Management 96% Good Satisfaction with Dog and Snake Bite Management 83% Good Improvement of Laboratory Indicators 78% Average Contribution of the Results Component 88% Good MPE : Epidemic-Potential Disease Overall Contribution of Disease Management Systems with Epidemic Potential to the Improvement of the Guinean System Table V evaluates the contribution of disease management systems with epidemic potential in Guinea in 2024. The results show that the contribution of the structures is 61%, reflecting an average contribution in terms of infrastructure, personnel, inputs, and equipment. Similarly, the contribution in terms of processes is 67%, indicating an average contribution in case management support, training, investigations, surveillance, and the development of normative documents. In contrast, in terms of results, this contribution is high, with a score of 88%, reflecting a good contribution from the systems in terms of effectiveness and the impact of the actions implemented. Finally, the overall contribution of disease management systems with epidemic potential is 72%, an average contribution that highlights progress but also the need to strengthen certain aspects, particularly the structures and processes. Table V: Performance of Disease Management Systems with Epidemic Potential in Guinea in 2024. Components Achieved Scores Assessment Structure 61% Average Process 67% Average Results 88% Good Overall Contribution of MPE Systems 72% Average Discussion This evaluation has provided us with an overall view of the contribution of disease management systems with epidemic potential in Guinea. The sampling, which is non-probabilistic and based on convenience, was designed taking into account the operational constraints of stakeholders in the 38 prefectures. To reduce the risk of bias, data triangulation was conducted. This approach involved cross-referencing information gathered from agents of epidemic treatment centers with data from other response actors, as well as data from observations and document analysis. Due to limited financial resources, a simulation exercise on epidemic disease management could not be conducted within the planned timeframe, although it would have allowed for direct observation of the contribution of these systems. "Structure" Component With an overall average contribution, this component demonstrates strong logistical points. However, it highlights major weaknesses related to human resources and funding. These weaknesses compromise the sustainability of the systems. A moderate availability of trained staff in CTEPIs was noted. However, the insufficient staff in these centers remains a major obstacle. This issue is similar to what was observed during the Ebola outbreaks in Sierra Leone and the Democratic Republic of Congo, where the lack of qualified personnel hindered response efforts [21,22]. In terms of logistics and health products, the availability of medications, inputs, and IPC kits is an asset. The presence of 38 epidemic treatment centers (CTEPIs) spread across the territory strengthens this component. These results differ from those in the DRC, where the availability of logistical resources was lower (50% and 42% during the tenth and twelfth Ebola outbreaks) [23]. Health financing remains a critical weakness, with budget deficits that compromise the sustainability of the systems, particularly after the Ebola outbreak. The lack of sufficient financial resources limits not only the functioning of infrastructures but also the treatment of epidemic-prone diseases (MPE), thereby hindering the sustainability of management strategies. "Process" Component With an average contribution, this component stands out for its consistent support related to coordination, healthcare delivery in CTEPIs, epidemiological surveillance, investigations, and awareness-raising. This observation has been made by several West African countries affected by epidemics [24–28]. On the other hand, several authors also highlight shortcomings in preparation, coordination, and resource allocation [29,30]. Efforts are still needed regarding the involvement of local actors in the development of protocols and standard operating procedures (SOPs), as well as logistics. The low involvement of local actors in the development of training and supervision plans is a significant challenge. This issue is similar to what was observed in Liberia, where local participation in decision-making was limited [31]. In contrast, during the tenth and twelfth Ebola outbreaks in the DRC, the involvement of local actors had strengthened the effectiveness of the interventions [23]. However, shortcomings persist, particularly in the management of CTEPI supplies, characterized by shortages of antivenom serum and laboratory kits. "Results" Component With a good contribution, this component shows positive results. Several dimensions display remarkable progress, although improvements are still needed. This study highlights the impact of the availability of systems and their implications in preparation and response activities. These impacts remain dominated by improvements in management, surveillance, and laboratory indicators. Patient satisfaction was also noted, and the increase in the recovery rate in CTEPIs is an illustration of this. During the Ebola outbreak, several countries recorded lower recovery rates due to inadequate patient management [32]. In Guinea, the progress confirms the effectiveness of the interventions implemented. The high patient satisfaction also reflects the quality of the care provided. However, dissatisfaction in management was also reported in other studies [33]. The slow diagnosis remains a major obstacle [32]. This issue was also observed during the Ebola outbreak, where it slowed the response to the epidemic in some cases [34,35]. Global Contribution The overall contribution of disease management systems with epidemic potential (MPE) in Guinea is considered average, with the insufficient human resources in CTEPIs remaining a major challenge. Funding for management activities deserves particular attention. The adoption of best practices could consolidate these gains. Greater involvement of local actors in training and supervision is essential. Their participation in the development of plans is also crucial. In other African countries, such as Benin, Tanzania, Ghana, and Madagascar, integrated surveillance and response systems (SMIR) have shown generally low contributions [36–38]. Conclusion This study assessed the contribution of disease management systems with epidemic potential (MPE) in Guinea, revealing an overall average contribution accompanied by significant progress. The CTEPIs suffer from a lack of sufficient human resources, while logistical weaknesses remain significant. Additionally, the lack of involvement of local actors in the development of normative documents and training constitutes a major limitation. To optimize the systems, an integrated approach is necessary. These measures are essential to strengthen the resilience of the management system and ensure better preparedness for future epidemics. Declarations Ethical Approval and Consent to Participate The study received approval from the National Ethics Committee for Health Research; Data collection authorization was granted by the General Directorate of the National Health Security Agency; An information note was provided to participants, and their consent was obtained before administering the questionnaire. Anonymity and confidentiality were ensured throughout the data collection and analysis process. Consent for Publication The manuscript does not contain any identifiable personal data, so no specific consent for publication is required. Availability of Data and Materials The data generated and/or analyzed during this study are available from the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests. Funding No specific funding is mentioned in the article. Authors' contributions KPAMY Dimaï Ouo: Writing the protocol, data collection, manuscript writing, and table presentation; DELAMOU Alexandre, TRAORE Fodé Amara, DOUMBIA Seydou, PETER Winch, TOURE Abdoulaye, CAMARA Alioun, CONDE Sory, CHERIF Fatoumata, KEITA Fatoumata: Review and revision of the protocol and manuscript. Acknowledgements The authors express their gratitude to all the actors involved in the epidemic response in Guinea, particularly the agents of the CTEPIs, the prefectural health directorates, regional health inspections, and partners, for their active contribution to this study. A special thank you is extended to the General Directorate of the National Health Security Agency for their valuable support. References Organisation Ouest Africaine de la Santé. Bulletins épidémiologiques Semaine 18-24 [Internet]. 2019 [cited 2024 Jan 25]. Available from: https://www.wahooas.org/web-ooas/en/publications-et-recherches/bulletins-epidemiologiques?page=13 O. Ogbu, E. 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Available from: https://ruor.uottawa.ca/server/api/core/bitstreams/a1107159-98e2-4bcd-8a4c-69785a2643bd/content Maltais S. La gestion résiliente des crises sanitaires dans les États fragiles : étude de la crise d’Ebola en Guinée. 2019 [cited 2025 Jan 6]; Available from: http://ruor.uottawa.ca/handle/10393/39855 Jean-Bosco Kahindo Mbeva MNP, Edgar Tsongo Musubao BM, Cyrille Ngadjo VK, Pablo Paluku MM, Elizabeth Kahindo GMK, Janvier Kubuya Bonane NSE. Gestion de la douzième épidémie de la maladie à Virus Ebola: Perceptions des acteurs de la province du Nord-Kivu, République démocratique du Congo. 36th ed. 2022;1090–102. Besson C, Chareyre S, Kirouani N, Jean-Jean S, Bretagnolle C, Henry A, et al. Contribution d’une équipe de pharmacie hospitalière à la prise en charge en réanimation des patients infectés par le SARS-CoV-2. Annales Pharmaceutiques Françaises. 2021;79:473–80. Desclaux A, Anoko J. L’anthropologie engagée dans la lutte contre Ebola (2014-2016) : approches, contributions et nouvelles questions: Santé Publique. 2017;Vol. 29:477–85. Groupe d’études PostEboGui, Msellati P, Touré A, Sow MS, Cécé K, Taverne B, et al. (Re)vivre après Ebola : bilan à un an et perspectives d’une étude d’évaluation et accompagnement des patients déclarés guéris d’une infection par le virus Ebola en Guinée (cohorte PostEboGui). Bull Soc Pathol Exot. 2016;109:236–43. Kastler F. La nécessité d’une coordination efficace des actions de R&D en cas de pandémie: Journal du Droit de la Santé et de l’Assurance - Maladie (JDSAM). 2021;N° 29:16–9. Cáceres VM, Sidibe S, Andre M, Traicoff D, Lambert S, King ME, et al. Surveillance Training for Ebola Preparedness in Côte d’Ivoire, Guinea-Bissau, Senegal, and Mali. Emerg Infect Dis [Internet]. 2017 [cited 2025 Jan 6];23. Available from: http://wwwnc.cdc.gov/eid/article/23/13/17-0299_article.htm Khan Y, O’Sullivan T, Brown A, Tracey S, Gibson J, Généreux M, et al. Public health emergency preparedness: a framework to promote resilience. BMC Public Health. 2018;18:1344. Mawardi F, Lestari AS, Randita ABT, Kambey DR, Prijambada ID. Strengthening Primary Health Care: Emergency and Disaster Preparedness in Community with Multidisciplinary Approach. Disaster med public health prep. 2021;15:675–6. the ALERRT-WHO Workshop, Saxena A, Horby P, Amuasi J, Aagaard N, Köhler J, et al. Ethics preparedness: facilitating ethics review during outbreaks - recommendations from an expert panel. BMC Med Ethics. 2019;20:29. Muhindo Kivikyavo I. Vainqueurs d’Ebola et séjour dans les centres de traitement Ebola : de l’annonce de la maladie à la guérison pendant le 10ème épisode en R.D. Congo. 2023; Desclaux A, Malan MS, Egrot M, Akindès F, Sow K. Patients négligés, effets imprévus. L’expérience des cas suspects de maladie à virus Ebola: Santé Publique. 2018;Vol. 30:565–74. Barranca E. Quand la sérologie contredit le vécu de la maladie : Éthique, recherche et annonce à propos d’Ebola en Guinée: Santé Publique. 2023;Vol. 35:65–73. Camara A, Al Et. Evaluation de la performance du test GoldMag SARS-CoV-2 IgG/IgM dans la détection des Immunoglobulines G chez les patients COVID-19 au Centre de Traitement Epidémiologique de Gbessia. Rev Mali Infectiol Microbiol. 2022;17:24–31. Ly M, N’Gbichi J-M, Lippeveld T, Ye Y. Rapport d’évaluation de la performance du Système d’Information Sanitaire de Routine (SISR) et de la Surveillance Intégrée de la Maladie et la Riposte. Available from: https://www.measureevaluation.org/resources/publications/sr-16-129-fr/at_download/document Mongbo V. KA, Glèlè-Ahanhanzo Y. BAMS. Evaluation de la performance de la surveillance intégrée de la maladie et de la riposte dans la zone sanitaire Ouidah-Kpomassè-Tori-Bossito, au Bénin | Revue Africaine de Médecine et de Santé Publique [Internet]. [cited 2024 Sep 2]. Available from: https://www.rams-journal.com/index.php/RAMS/article/view/345 S.F. RUMISHA LEGM, K.P. SENKORO DG, P.K. MMBUJI. Monitoring and evaluation of Integrated Disease Surveillance and Response in selected districts in Tanzania. Tanzania Journal of Health Research. 2007;9. Table 6 Table 6 is available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files TableVI.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. 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09:04:45","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6410041/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6410041/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80285862,"identity":"d6aa827b-aeeb-41c3-a0c7-df40fcc3e941","added_by":"auto","created_at":"2025-04-10 06:50:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1028648,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6410041/v1/67877e3d-5db4-460f-a151-6dd52d1b7454.pdf"},{"id":80285547,"identity":"3d8c70ab-d99b-4a00-9582-bd9bfd05f8b7","added_by":"auto","created_at":"2025-04-10 06:42:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17633,"visible":true,"origin":"","legend":"","description":"","filename":"TableVI.docx","url":"https://assets-eu.researchsquare.com/files/rs-6410041/v1/e22533f8e975cb392abd9af0.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEvaluation of the Contribution of Care Systems for Epidemic-Prone Diseases in Strengthening the Health System in Guinea\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWest Africa has been a region affected by multiple epidemics and outbreaks of infectious diseases, leading to high morbidity and mortality, with negative consequences for the health systems of these countries [1]⁠. Among these, we can cite Ebola virus disease, yellow fever, meningitis, and COVID-19 in Guinea; dengue fever in Benin and C\u0026ocirc;te d\u0026apos;Ivoire; yellow fever in Nigeria; meningitis in Ghana, Niger, and Togo; as well as measles in Guinea, Niger, and Sierra Leone; and cholera in Benin, Liberia, Nigeria, and Sierra Leone. Lassa fever is endemic in West Africa and has been reported in Sierra Leone, Guinea, Liberia, and Nigeria [2].\u003c/p\u003e\n\u003cp\u003eThe Republic of Guinea has long faced numerous health crises, worsened by the fragility of its health system. Among the most significant challenges the country has had to face, the 2014-2016 Ebola virus disease (EVD) outbreak remains a major event. This health crisis exposed the many shortcomings of the Guinean health system, including limited geographical coverage, insufficient response capacity, and reduced access to care. Before the Ebola outbreak, only 38.9% of the population had access to healthcare services, and their utilization remained limited to 18.6% [3,4]. In response to this situation, and as part of the post-Ebola recovery efforts, Guinea has undertaken ambitious reforms to strengthen its health system. These reforms were implemented to improve the management of epidemic-prone diseases (EPDs), such as yellow fever, Lassa fever, measles, meningitis, COVID-19, and more recently, diphtheria. These diseases pose a constant threat to the Guinean population due to their potential for rapid transmission and the severity of the complications they cause. The country has therefore established several mechanisms aimed at improving patient care and strengthening the capacity to respond to epidemics [3\u0026ndash;7]. Key measures include the establishment of Epidemic Treatment Centers (CTEs) and Community Transit Centers (CTCom) for the isolation and treatment of suspected or confirmed cases. Furthermore, strengthening human resources through specialized training and improving healthcare infrastructure have helped enhance the epidemic response framework. The epidemiological surveillance system has also been improved with the creation of regional and prefectural alert and response teams (ERARE and EPARE) at decentralized levels [3,7,8]. Today, epidemic treatment centers are staffed with regularly trained personnel, ensuring the care of patients suspected or confirmed of having an epidemic-prone disease or a priority zoonosis, in accordance with the national protocols in place [9]. However, despite these advances, regular evaluation of these mechanisms is crucial to understand their actual contribution to improving the Guinean health system and their effectiveness in epidemic management. Therefore, this study aims to assess the contribution of the management mechanisms for epidemic-prone diseases (EPDs) in strengthening the Guinean health system. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Study Methods","content":"\u003ch3\u003eStudy Framework\u0026nbsp;\u003c/h3\u003e\n\u003cp skip=\"true\"\u003eThe Republic of Guinea is a coastal country located in the western part of the African continent, halfway between the Equator and the Tropic of Cancer (7\u0026deg;30\u0026apos; and 12\u0026deg;30\u0026apos; North latitude, and 8\u0026deg; and 15\u0026deg; West longitude). Covering an area of 245,857 km\u0026sup2;, it is bordered to the west by Guinea-Bissau and the Atlantic Ocean, to the north by Senegal and Mali, to the east by C\u0026ocirc;te d\u0026apos;Ivoire, and to the south by Sierra Leone and Liberia [10]. The country has a population of approximately eleven million people, with 52% women and 16% children under the age of five [11].\u003c/p\u003e\n\u003cp\u003eFrom a geo-ecological perspective, Guinea is divided into four distinct natural regions that are internally homogeneous: Guinea Maritime, Middle Guinea, Upper Guinea, and Forest Guinea [10,12]. The country owes this uniqueness to its natural environment, characterized by climatic contrasts, mountain barriers, and the orientation of the reliefs, which combine to give each region its own specificities in terms of climate, soil, vegetation, and the way of life of the populations. Administratively, Guinea is subdivided into seven administrative regions (Bok\u0026eacute;, Faranah, Kindia, Lab\u0026eacute;, Mamou, Kankan, and N\u0026apos;Z\u0026eacute;r\u0026eacute;kor\u0026eacute;), with the city of Conakry, the capital, enjoying a special status as a special zone. Each administrative region consists of prefectures, with the number varying. In total, there are 33 prefectures, 38 urban communes (CU), five of which are in Conakry, and 303 rural development communities (CRD) [10,12].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt was one of the countries in West Africa most severely affected by the Ebola virus disease epidemic [11].\u003c/p\u003e\n\u003cp skip=\"true\"\u003eInfant and maternal mortality rates are estimated at 123\u0026permil; and 724 per 100,000 live births, respectively. The country faces a serious shortage of human resources in health, with only 98 health workers per 100,000 inhabitants. These human resources are unevenly distributed across the country, with nearly 52% of health workers residing in Conakry and its surroundings, serving only 15% of the population [11].\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eStudy site\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe study site will encompass all three national directorates (National Agency for Health Security, National Directorate of Epidemiology and Disease Control, National Directorate of Public and Private Hospital Establishments), as well as the thirty-eight (38) health districts of Guinea. The eight (08) regional health inspections, the thirty-eight (38) prefectural health directorates, and the thirty-eight (38) epidemic treatment centers play a crucial role in the epidemic response in the Republic of Guinea. This response is coordinated at the central level, represented by the Ministry of Health, the National Agency for Health Security, and the National Directorate of Epidemiology and Disease Control.\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy type :\u0026nbsp;\u003c/strong\u003eThis was a cross-sectional evaluative study conducted from April 4 to October 30, 2024. It was a normative evaluation, using the theoretical framework of the WHO health system [13].\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eStudy Population \u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eIt will consist of all the actors involved in the epidemic response in Guinea, including those from the technical departments of the Ministry of Health, the regional health inspections, the prefectural health directorates, as well as the epidemic treatment centers.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSampling \u0026nbsp;\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe sampling method used for this study was non-probabilistic, based on the convenience sampling technique. It involved selecting actors from the epidemic response by health district in Guinea to answer our survey questions. We also used purposive sampling to select the heads of departments from the Ministry of Health, regional health inspections, prefectural health directorates, and epidemic treatment centers. The sample size is 356 participants.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eVariables \u0026agrave; l\u0026rsquo;\u0026eacute;tude\u0026nbsp;\u003c/h3\u003e\n\u003cp skip=\"true\"\u003eThe Donabedian evaluation model, based on three components (structure, process, and outcomes) [14,15], Using the theoretical framework of the WHO health system will be applied [16]. The following variables were used :\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eDemographic Information: Age, marital status, education level, position held, residence, response commission;\u003c/li\u003e\n \u003cli\u003eStructure/Resources Component: Existence of a functional isolation site, presence of eight (8) care personnel per isolation site, existence of trained personnel for epidemic disease care (PEC), existence of trained laboratory personnel, availability of non-expired PEC medications, existence of a functional epidemic disease diagnostic laboratory, availability of non-expired diagnostic kits, availability of non-expired vaccines, availability of functional rolling logistics, existence of financial support, presence of a generator for the isolation site, existence of a functional borehole at the isolation site, availability of infection prevention control (IPC) kits at the isolation site, availability of personal protective equipment (PPE) at the isolation site, existence of a waste management area at the isolation site, existence of a prescription system at the isolation site, existence of a printer at the isolation site;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eProcess Component: Participation in the training of actors on epidemic disease care (PEC), participation in the development of PEC protocols, participation in the development of infection prevention control (IPC) manuals, participation in the development of the PEC guide for epidemic diseases, participation in the development of diagnostic protocols for epidemic diseases, participation in the development of continuity of care plans for complicated cases, participation in the development of supervision plans for PEC actors, participation in the development of supervision plans for PEC actors, participation in the development of supply plans for medications and other inputs for isolation sites, participation in the development of rehabilitation plans for isolation sites, participation in the development of patient transfer protocols, participation in the functionality of case reporting mechanisms, regular participation in PEC coordination meetings during epidemics, participation in investigations, participation in community awareness campaigns, participation in vaccination campaigns, participation in the management of epidemic-prone diseases (MAPI), participation in the management of other non-epidemic diseases (dog and snake bites), participation in actor training, participation in crisis meetings, participation in regional/prefectural coordination meetings, participation in needs assessment for response efforts, participation in epidemic disease surveillance, participation in the development of national/regional/prefectural response plans, participation in contact tracing follow-up, participation in the supervision of actors ;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eResults Component: Improvement of epidemic disease care (MPE) indicators, improvement of epidemic disease surveillance indicators, satisfaction with epidemic disease care, satisfaction with the management of dog and snake bites, and diagnosis of epidemic disease cases within 48 hours of suspicion. \u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003eData Collection Techniques and Tools\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eData were collected through observation, document review, and questionnaire surveys, using an observation grid, a data sheet, and a questionnaire, respectively. Some health workers from the National Agency for Health Security were recruited to facilitate data collection from the target population. The data collection tools were pre-tested in the meeting room of the National Agency for Health Security.\u003c/p\u003e\n\u003ch3\u003eData Processing and Analysis\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe collected data were verified and validated as the investigators progressed in the field. They were entered using a data entry template developed with KoboToolbox. Data analysis was conducted using Epi-Info version 7 and Stata version 13.\u003c/p\u003e\n\u003cp\u003eThe descriptive analysis part involved describing the study sample through a detailed description of the variables. Qualitative variables were described in terms of absolute and relative frequency, while quantitative variables were described by the mean \u0026plusmn; standard deviation if the distribution was normal, and by the median and quartiles (Q1, Q3) if the distribution was not normal.\u003c/p\u003e\n\u003cp\u003eThe contribution of the systems was evaluated based on the average scores obtained for their three components (structure, process, and result). For each explanatory component, as well as the main component, the total points obtained were expressed as a percentage of the total expected. The average percentages of each component (structure, process, and result) across the eight (08) administrative regions were used to determine the national contribution level of the epidemic-prone disease management systems in the context of improving the health system in Guinea. We used a three-level evaluation based on Varkevisser\u0026apos;s measurement scale [17,18] To evaluate the contribution of the systems, the assessment was as follows:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u0026quot;Good\u0026quot;: if the percentage obtained is between [80 and 100%];\u003c/li\u003e\n \u003cli\u003e\u0026quot;Average\u0026quot;: if the percentage obtained is between [60 and 80%];\u003c/li\u003e\n \u003cli\u003e\u0026quot;Poor\u0026quot;: if the percentage obtained is between [0 and 60%].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAn analysis of strengths and weaknesses was conducted, allowing the evaluation to be structured by highlighting the factors influencing the contribution of the systems either positively or negatively [19,20].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescription of the Sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 356 epidemic response actors in Guinea were surveyed, covering the eight (8) administrative regions. Among the participants, the majority were female, accounting for 76.4%, compared to 23.6% male, giving a ratio of 3.23 women for every man. The average age of the participants was 57.53 \u0026plusmn; 9.43 years, with the following distribution: 47% were under 40 years old, 51% were between 40 and 60 years old, and 3% were over 60 years old. In terms of education level, the vast majority of the participants (89.89%) held a university degree, while 10.11% had a secondary school level. Regarding positions held, care providers (PEC) represented the majority at 40.45%, followed by disease control doctors (13.76%), data managers (6.74%), and unit heads (5.90%). Leadership positions were less represented, such as general directors (1.40%) and regional health inspectors (0.84%). As for their residence, the majority of participants were from the following regions: Conakry (26.97%), N\u0026rsquo;Z\u0026eacute;r\u0026eacute;kor\u0026eacute; (24.43%), and Kankan (22.19%). Regions like Kindia (13.76%), Lab\u0026eacute; (5.06%), Mamou (3.38%), Faranah (3.37%), and Bok\u0026eacute; (0.8%) had fewer actors participating in the study. Lastly, in terms of distribution across response commissions, most participants were involved in the care (PEC) commission (49%), followed by the surveillance commission (35%). Other commissions, such as coordination (12%), laboratory (3%), and logistics (1%), were less represented (see Table I).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable I:\u0026nbsp;\u003c/strong\u003eDistribution of 356 epidemic response actors in Guinea according to their sociodemographic characteristics, from August 1 to November 31, 2024.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"652\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSociodemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; Standard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\n \u003cp\u003eSex-Ratio (F/M) = 3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e23.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e76.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\n \u003cp\u003e57.53 \u0026plusmn; 9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eUnder 40 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003e40 to 60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eOver 60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e89.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePosition Held\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003ePEC Agent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e40.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eSurveillance Officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eStudy Officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eDepartment Head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eUnit Head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eGeneral Director\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003ePrefectural Health Director\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eData Manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eCOU-SP Manager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eRegional Health Inspector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eDisease Management Doctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e13.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eSupport Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003ePlanning, Training, and Research Officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eConakry Region (Capital)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e26.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eKindia Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e13.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eMamou Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eFaranah Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eLab\u0026eacute; Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eBok\u0026eacute; Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eKankan Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e22.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eN\u0026rsquo;Z\u0026eacute;r\u0026eacute;kor\u0026eacute; Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e24.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponse Commission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003ePEC Commission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eSurveillance Commission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eLaboratory Commission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eLogistics Commission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.7301%;\"\u003e\n \u003cp\u003eCoordination Commission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9509%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2454%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.0736%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCOU-SP:\u0026nbsp;\u003c/strong\u003eHealth Emergency Operations Center;\u003cstrong\u003e\u0026nbsp;PEC:\u0026nbsp;\u003c/strong\u003eHealth Care Management\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eContribution of the \u0026quot;Structure\u0026quot; Component\u003c/h3\u003e\n\u003cp\u003eThis component is assessed through three key sub-components: 1. Human Resources: With a score of 69%, reflecting an average contribution, this sub-component indicates a moderate availability of trained personnel in the CTEPI (Health Emergency Treatment Centers). While the presence of qualified staff is notable, the shortage of healthcare workers remains a major barrier to improving the systems. 2.Logistics and Health Products: This sub-component achieves a score of 81%, considered good. It highlights the presence of energy and water sources in the CTEPI, as well as the availability of medicines, supplies, PCI kits, and essential equipment, such as diagnostic laboratories and rabies vaccines. However, some weaknesses persist, notably the lack of antivenom serum and insufficient laboratory kits. 3.Health Financing: With a score of 32%, rated as poor, this sub-component reveals a worrying deficit in financial resources, directly affecting the sustainability and effectiveness of the systems.The overall contribution of the \u0026quot;Structure\u0026quot; component is 61%, indicating an average contribution (see Table II).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable II:\u0026nbsp;\u003c/strong\u003e\u003cem\u003eCharacteristics of the \u0026quot;Structure\u0026quot; Component of the Epidemic-Potential Disease Care Systems in Guinea in 2024.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.3226%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStructure Component\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0645%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScores Achieved\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6129%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvaluation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.3226%;\"\u003e\n \u003cp\u003eHuman Resources Sub-component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0645%;\"\u003e\n \u003cp\u003e69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6129%;\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.3226%;\"\u003e\n \u003cp\u003eLogistics and Health Products Sub-component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0645%;\"\u003e\n \u003cp\u003e81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6129%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.3226%;\"\u003e\n \u003cp\u003eHealth Financing Sub-component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0645%;\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6129%;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.3226%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Structure Component Contribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0645%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6129%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eContribution of the \u0026quot;Process\u0026quot; Component\u003c/h3\u003e\n\u003cp\u003eThe contribution of disease management systems with epidemic potential in Guinea in 2024, through their \u0026quot;Process\u0026quot; component, is assessed at 67%, representing an average contribution. This contribution is particularly notable in support for healthcare delivery (83%), coordination (81%), epidemiological surveillance (81%), and awareness-raising (82%), all of which play a key role in the effectiveness of the response. However, some dimensions limit the overall impact of the system, particularly the strengthening of human resources (58%), support for logistics and health products (49%), and the development of normative documents (31%), whose contributions remain insufficient. The health information system shows a moderate or average contribution (69%) (see Table III).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable III:\u0026nbsp;\u003c/strong\u003eCharacteristics of the \u0026quot;Process\u0026quot; Component of Disease Management Systems with Epidemic Potential in Guinea in 2024.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55.412%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcess Component\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.1018%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAchieved Scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.4863%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.412%;\"\u003e\n \u003cp\u003eSupport for Strengthening Human Resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1018%;\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4863%;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.412%;\"\u003e\n \u003cp\u003eSupport for Logistics and Health Products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1018%;\"\u003e\n \u003cp\u003e49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4863%;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.412%;\"\u003e\n \u003cp\u003eSupport for Healthcare Delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1018%;\"\u003e\n \u003cp\u003e83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4863%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.412%;\"\u003e\n \u003cp\u003eSupport for Strengthening the Health Information System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1018%;\"\u003e\n \u003cp\u003e69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4863%;\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.412%;\"\u003e\n \u003cp\u003eSupport for Coordination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1018%;\"\u003e\n \u003cp\u003e81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4863%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.412%;\"\u003e\n \u003cp\u003eSupport for Epidemiological Surveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1018%;\"\u003e\n \u003cp\u003e81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4863%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.412%;\"\u003e\n \u003cp\u003eSupport for Awareness-Raising\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1018%;\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4863%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.412%;\"\u003e\n \u003cp\u003eSupport for the Development of Normative Documents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1018%;\"\u003e\n \u003cp\u003e31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4863%;\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.412%;\"\u003e\n \u003cp\u003eContribution of the Process Component\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.1018%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e67%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.4863%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eContribution of the \u0026quot;Results\u0026quot; Component\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe contribution of disease management systems with epidemic potential (MPE) in Guinea in 2024, through their \u0026quot;Results\u0026quot; components, is achieved at 88%, reflecting an overall good contribution. This contribution is particularly notable in the improvement of MPE management indicators (89%) and surveillance (96%), demonstrating effective case management and strengthened epidemiological follow-up. Additionally, beneficiary satisfaction with MPE management (96%) and dog and snake bites (83%) confirms a positive impact of the implemented systems. However, the contribution remains average (78%) regarding the improvement of laboratory indicators, suggesting the need for enhanced diagnostic capacity to ensure faster and more reliable detection of epidemic-prone diseases (See Table IV).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable IV:\u0026nbsp;\u003c/strong\u003eCharacteristics of the \u0026quot;Results\u0026quot; Component of Disease Management Systems with Epidemic Potential in Guinea in 2024.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.0293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults Component\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0326%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Achieved Scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.0293%;\"\u003e\n \u003cp\u003eImprovement of MPE Management Indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0326%;\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9381%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.0293%;\"\u003e\n \u003cp\u003eImprovement of MPE Surveillance Indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0326%;\"\u003e\n \u003cp\u003e96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9381%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.0293%;\"\u003e\n \u003cp\u003eSatisfaction with MPE Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0326%;\"\u003e\n \u003cp\u003e96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9381%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.0293%;\"\u003e\n \u003cp\u003eSatisfaction with Dog and Snake Bite Management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0326%;\"\u003e\n \u003cp\u003e83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9381%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.0293%;\"\u003e\n \u003cp\u003eImprovement of Laboratory Indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0326%;\"\u003e\n \u003cp\u003e78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9381%;\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.0293%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContribution of the Results Component\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0326%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e88%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eMPE\u0026nbsp;:\u003c/strong\u003e Epidemic-Potential Disease\u003c/p\u003e\n\u003ch3\u003eOverall Contribution of Disease Management Systems with Epidemic Potential to the Improvement of the Guinean System\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eTable V evaluates the contribution of disease management systems with epidemic potential in Guinea in 2024. The results show that the contribution of the structures is 61%, reflecting an average contribution in terms of infrastructure, personnel, inputs, and equipment. Similarly, the contribution in terms of processes is 67%, indicating an average contribution in case management support, training, investigations, surveillance, and the development of normative documents. In contrast, in terms of results, this contribution is high, with a score of 88%, reflecting a good contribution from the systems in terms of effectiveness and the impact of the actions implemented. Finally, the overall contribution of disease management systems with epidemic potential is 72%, an average contribution that highlights progress but also the need to strengthen certain aspects, particularly the structures and processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable V:\u0026nbsp;\u003c/strong\u003ePerformance of Disease Management Systems with Epidemic Potential in Guinea in 2024.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"609\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.1117%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.0197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAchieved Scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8686%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.1117%;\"\u003e\n \u003cp\u003eStructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.0197%;\"\u003e\n \u003cp\u003e61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8686%;\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.1117%;\"\u003e\n \u003cp\u003eProcess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.0197%;\"\u003e\n \u003cp\u003e67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8686%;\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.1117%;\"\u003e\n \u003cp\u003eResults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.0197%;\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8686%;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.1117%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Contribution of MPE Systems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.0197%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e72%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.8686%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis evaluation has provided us with an overall view of the contribution of disease management systems with epidemic potential in Guinea. The sampling, which is non-probabilistic and based on convenience, was designed taking into account the operational constraints of stakeholders in the 38 prefectures. To reduce the risk of bias, data triangulation was conducted. This approach involved cross-referencing information gathered from agents of epidemic treatment centers with data from other response actors, as well as data from observations and document analysis. Due to limited financial resources, a simulation exercise on epidemic disease management could not be conducted within the planned timeframe, although it would have allowed for direct observation of the contribution of these systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026quot;Structure\u0026quot; Component\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith an overall average contribution, this component demonstrates strong logistical points. However, it highlights major weaknesses related to human resources and funding. These weaknesses compromise the sustainability of the systems. A moderate availability of trained staff in CTEPIs was noted. However, the insufficient staff in these centers remains a major obstacle. This issue is similar to what was observed during the Ebola outbreaks in Sierra Leone and the Democratic Republic of Congo, where the lack of qualified personnel hindered response efforts\u0026nbsp;[21,22]. In terms of logistics and health products, the availability of medications, inputs, and IPC kits is an asset. The presence of 38 epidemic treatment centers (CTEPIs) spread across the territory strengthens this component. These results differ from those in the DRC, where the availability of logistical resources was lower (50% and 42% during the tenth and twelfth Ebola outbreaks)\u0026nbsp;[23]. Health financing remains a critical weakness, with budget deficits that compromise the sustainability of the systems, particularly after the Ebola outbreak. The lack of sufficient financial resources limits not only the functioning of infrastructures but also the treatment of epidemic-prone diseases (MPE), thereby hindering the sustainability of management strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026quot;Process\u0026quot; Component\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith an average contribution, this component stands out for its consistent support related to coordination, healthcare delivery in CTEPIs, epidemiological surveillance, investigations, and awareness-raising. This observation has been made by several West African countries affected by epidemics\u0026nbsp;[24\u0026ndash;28]. On the other hand, several authors also highlight shortcomings in preparation, coordination, and resource allocation\u0026nbsp;[29,30]. Efforts are still needed regarding the involvement of local actors in the development of protocols and standard operating procedures (SOPs), as well as logistics. The low involvement of local actors in the development of training and supervision plans is a significant challenge. This issue is similar to what was observed in Liberia, where local participation in decision-making was limited\u0026nbsp;[31]. In contrast, during the tenth and twelfth Ebola outbreaks in the DRC, the involvement of local actors had strengthened the effectiveness of the interventions\u0026nbsp;[23]. However, shortcomings persist, particularly in the management of CTEPI supplies, characterized by shortages of antivenom serum and laboratory kits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026quot;Results\u0026quot; Component\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith a good contribution, this component shows positive results. Several dimensions display remarkable progress, although improvements are still needed. This study highlights the impact of the availability of systems and their implications in preparation and response activities. These impacts remain dominated by improvements in management, surveillance, and laboratory indicators. Patient satisfaction was also noted, and the increase in the recovery rate in CTEPIs is an illustration of this. During the Ebola outbreak, several countries recorded lower recovery rates due to inadequate patient management\u0026nbsp;[32]. In Guinea, the progress confirms the effectiveness of the interventions implemented. The high patient satisfaction also reflects the quality of the care provided. However, dissatisfaction in management was also reported in other studies\u0026nbsp;[33]. The slow diagnosis remains a major obstacle\u0026nbsp;[32]. This issue was also observed during the Ebola outbreak, where it slowed the response to the epidemic in some cases\u0026nbsp;[34,35].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall contribution of disease management systems with epidemic potential (MPE) in Guinea is considered average, with the insufficient human resources in CTEPIs remaining a major challenge. Funding for management activities deserves particular attention. The adoption of best practices could consolidate these gains. Greater involvement of local actors in training and supervision is essential. Their participation in the development of plans is also crucial. In other African countries, such as Benin, Tanzania, Ghana, and Madagascar, integrated surveillance and response systems (SMIR) have shown generally low contributions [36\u0026ndash;38].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study assessed the contribution of disease management systems with epidemic potential (MPE) in Guinea, revealing an overall average contribution accompanied by significant progress. The CTEPIs suffer from a lack of sufficient human resources, while logistical weaknesses remain significant. Additionally, the lack of involvement of local actors in the development of normative documents and training constitutes a major limitation. To optimize the systems, an integrated approach is necessary. These measures are essential to strengthen the resilience of the management system and ensure better preparedness for future epidemics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe study received approval from the National Ethics Committee for Health Research;\u003c/li\u003e\n \u003cli\u003eData collection authorization was granted by the General Directorate of the National Health Security Agency;\u003c/li\u003e\n \u003cli\u003eAn information note was provided to participants, and their consent was obtained before administering the questionnaire. Anonymity and confidentiality were ensured throughout the data collection and analysis process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe manuscript does not contain any identifiable personal data, so no specific consent for publication is required.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe data generated and/or analyzed during this study are available from the corresponding author upon reasonable request.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe authors declare that they have no competing interests.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eNo specific funding is mentioned in the article.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eKPAMY Dima\u0026iuml; Ouo: Writing the protocol, data collection, manuscript writing, and table presentation;\u003c/li\u003e\n \u003cli\u003eDELAMOU Alexandre, TRAORE Fod\u0026eacute; Amara, DOUMBIA Seydou, PETER Winch, TOURE Abdoulaye, CAMARA Alioun, CONDE Sory, CHERIF Fatoumata, KEITA Fatoumata: Review and revision of the protocol and manuscript.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe authors express their gratitude to all the actors involved in the epidemic response in Guinea, particularly the agents of the CTEPIs, the prefectural health directorates, regional health inspections, and partners, for their active contribution to this study.\u003c/li\u003e\n \u003cli\u003eA special thank you is extended to the General Directorate of the National Health Security Agency for their valuable support.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOrganisation Ouest Africaine de la Sant\u0026eacute;. 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Can Fam Physician. 2011;57:e241\u0026ndash;3.\u003c/li\u003e\n \u003cli\u003eUtilisation du cadre conceptuel des syst\u0026egrave;mes de sant\u0026eacute; de l\u0026rsquo;OMS pour la RMO [Internet]. TDR Implementation research toolkit. [cited 2024 Jan 24]. Available from: https://adphealth.org/irtoolkit/fr/integrer-la-rmo-dans-le-systeme-de-sante/utilisation-du-cadre-conceptuel-des-systemes-de-sante-de-loms-pour-la-rmo.html\u003c/li\u003e\n \u003cli\u003ePara\u0026iuml;so M, Degbey CC, Ozavino YB, Azandjeme C, Sossa-Jerome C. Sanitary Quality of the Meals Served in the Canteens and Vicinity of the Lagoon Mother and Child University Hospital Centre and National Hospital and University Centre Hubert Koutoukou Maga of Cotonou (Benin). OJPM. 2023;13:183\u0026ndash;97.\u003c/li\u003e\n \u003cli\u003eAgboton B L, Agueh V D, Bodjrenou A S, Vigan J, Ahoui S. Etat des Lieux et \u0026eacute;valuation de la qualit\u0026eacute; de la Th\u0026eacute;rapie Nutritionnelle des Patients Porteurs de Syndrome M\u0026eacute;tabolique au CNHU-HKM de Cotonou. RAFMI. 2017;4:24\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eKossivi Agbelenko Afanvi. Analyse SWOT pour les gestionnaires des organisations et syst\u0026egrave;mes de sant\u0026eacute; [Internet]. Unpublished; 2015 [cited 2025 Jan 15]. Available from: http://rgdoi.net/10.13140/RG.2.1.3822.0246\u003c/li\u003e\n \u003cli\u003eAbsil G. Analyse SWOT-Un outil d\u0026rsquo;analyse et d\u0026rsquo;aide \u0026agrave; la d\u0026eacute;cision. 2011; Available from: https://orbi.uliege.be/bitstream/2268/169629/1/ELE%20MET-DON%20L-10621.pdf\u003c/li\u003e\n \u003cli\u003eAudrey CARON. Ebola en Afrique de L\u0026rsquo;Ouest : L\u0026rsquo;Impact des D\u0026eacute;terminants Sociaux. Revue interdisciplinaire des sciences de la sant\u0026eacute; [Internet]. Available from: https://ruor.uottawa.ca/server/api/core/bitstreams/a1107159-98e2-4bcd-8a4c-69785a2643bd/content\u003c/li\u003e\n \u003cli\u003eMaltais S. La gestion r\u0026eacute;siliente des crises sanitaires dans les \u0026Eacute;tats fragiles : \u0026eacute;tude de la crise d\u0026rsquo;Ebola en Guin\u0026eacute;e. 2019 [cited 2025 Jan 6]; Available from: http://ruor.uottawa.ca/handle/10393/39855\u003c/li\u003e\n \u003cli\u003eJean-Bosco Kahindo Mbeva MNP, Edgar Tsongo Musubao BM, Cyrille Ngadjo VK, Pablo Paluku MM, Elizabeth Kahindo GMK, Janvier Kubuya Bonane NSE. Gestion de la douzi\u0026egrave;me \u0026eacute;pid\u0026eacute;mie de la maladie \u0026agrave; Virus Ebola: Perceptions des acteurs de la province du Nord-Kivu, R\u0026eacute;publique d\u0026eacute;mocratique du Congo. 36th ed. 2022;1090\u0026ndash;102.\u003c/li\u003e\n \u003cli\u003eBesson C, Chareyre S, Kirouani N, Jean-Jean S, Bretagnolle C, Henry A, et al. Contribution d\u0026rsquo;une \u0026eacute;quipe de pharmacie hospitali\u0026egrave;re \u0026agrave; la prise en charge en r\u0026eacute;animation des patients infect\u0026eacute;s par le SARS-CoV-2. Annales Pharmaceutiques Fran\u0026ccedil;aises. 2021;79:473\u0026ndash;80.\u003c/li\u003e\n \u003cli\u003eDesclaux A, Anoko J. L\u0026rsquo;anthropologie engag\u0026eacute;e dans la lutte contre Ebola (2014-2016) : approches, contributions et nouvelles questions: Sant\u0026eacute; Publique. 2017;Vol. 29:477\u0026ndash;85.\u003c/li\u003e\n \u003cli\u003eGroupe d\u0026rsquo;\u0026eacute;tudes PostEboGui, Msellati P, Tour\u0026eacute; A, Sow MS, C\u0026eacute;c\u0026eacute; K, Taverne B, et al. (Re)vivre apr\u0026egrave;s Ebola : bilan \u0026agrave; un an et perspectives d\u0026rsquo;une \u0026eacute;tude d\u0026rsquo;\u0026eacute;valuation et accompagnement des patients d\u0026eacute;clar\u0026eacute;s gu\u0026eacute;ris d\u0026rsquo;une infection par le virus Ebola en Guin\u0026eacute;e (cohorte PostEboGui). Bull Soc Pathol Exot. 2016;109:236\u0026ndash;43.\u003c/li\u003e\n \u003cli\u003eKastler F. La n\u0026eacute;cessit\u0026eacute; d\u0026rsquo;une coordination efficace des actions de R\u0026amp;D en cas de pand\u0026eacute;mie: Journal du Droit de la Sant\u0026eacute; et de l\u0026rsquo;Assurance - Maladie (JDSAM). 2021;N\u0026deg; 29:16\u0026ndash;9.\u003c/li\u003e\n \u003cli\u003eC\u0026aacute;ceres VM, Sidibe S, Andre M, Traicoff D, Lambert S, King ME, et al. Surveillance Training for Ebola Preparedness in C\u0026ocirc;te d\u0026rsquo;Ivoire, Guinea-Bissau, Senegal, and Mali. Emerg Infect Dis [Internet]. 2017 [cited 2025 Jan 6];23. Available from: http://wwwnc.cdc.gov/eid/article/23/13/17-0299_article.htm\u003c/li\u003e\n \u003cli\u003eKhan Y, O\u0026rsquo;Sullivan T, Brown A, Tracey S, Gibson J, G\u0026eacute;n\u0026eacute;reux M, et al. Public health emergency preparedness: a framework to promote resilience. BMC Public Health. 2018;18:1344.\u003c/li\u003e\n \u003cli\u003eMawardi F, Lestari AS, Randita ABT, Kambey DR, Prijambada ID. Strengthening Primary Health Care: Emergency and Disaster Preparedness in Community with Multidisciplinary Approach. Disaster med public health prep. 2021;15:675\u0026ndash;6.\u003c/li\u003e\n \u003cli\u003ethe ALERRT-WHO Workshop, Saxena A, Horby P, Amuasi J, Aagaard N, K\u0026ouml;hler J, et al. Ethics preparedness: facilitating ethics review during outbreaks - recommendations from an expert panel. BMC Med Ethics. 2019;20:29.\u003c/li\u003e\n \u003cli\u003eMuhindo Kivikyavo I. Vainqueurs d\u0026rsquo;Ebola et s\u0026eacute;jour dans les centres de traitement Ebola : de l\u0026rsquo;annonce de la maladie \u0026agrave; la gu\u0026eacute;rison pendant le 10\u0026egrave;me \u0026eacute;pisode en R.D. Congo. 2023;\u003c/li\u003e\n \u003cli\u003eDesclaux A, Malan MS, Egrot M, Akind\u0026egrave;s F, Sow K. Patients n\u0026eacute;glig\u0026eacute;s, effets impr\u0026eacute;vus. L\u0026rsquo;exp\u0026eacute;rience des cas suspects de maladie \u0026agrave; virus Ebola: Sant\u0026eacute; Publique. 2018;Vol. 30:565\u0026ndash;74.\u003c/li\u003e\n \u003cli\u003eBarranca E. Quand la s\u0026eacute;rologie contredit le v\u0026eacute;cu de la maladie : \u0026Eacute;thique, recherche et annonce \u0026agrave; propos d\u0026rsquo;Ebola en Guin\u0026eacute;e: Sant\u0026eacute; Publique. 2023;Vol. 35:65\u0026ndash;73.\u003c/li\u003e\n \u003cli\u003eCamara A, Al Et. Evaluation de la performance du test GoldMag SARS-CoV-2 IgG/IgM dans la d\u0026eacute;tection des Immunoglobulines G chez les patients COVID-19 au Centre de Traitement Epid\u0026eacute;miologique de Gbessia. Rev Mali Infectiol Microbiol. 2022;17:24\u0026ndash;31.\u003c/li\u003e\n \u003cli\u003eLy M, N\u0026rsquo;Gbichi J-M, Lippeveld T, Ye Y. Rapport d\u0026rsquo;\u0026eacute;valuation de la performance du Syst\u0026egrave;me d\u0026rsquo;Information Sanitaire de Routine (SISR) et de la Surveillance Int\u0026eacute;gr\u0026eacute;e de la Maladie et la Riposte. Available from: https://www.measureevaluation.org/resources/publications/sr-16-129-fr/at_download/document\u003c/li\u003e\n \u003cli\u003eMongbo V. KA, Gl\u0026egrave;l\u0026egrave;-Ahanhanzo Y. BAMS. Evaluation de la performance de la surveillance int\u0026eacute;gr\u0026eacute;e de la maladie et de la riposte dans la zone sanitaire Ouidah-Kpomass\u0026egrave;-Tori-Bossito, au B\u0026eacute;nin | Revue Africaine de M\u0026eacute;decine et de Sant\u0026eacute; Publique [Internet]. [cited 2024 Sep 2]. Available from: https://www.rams-journal.com/index.php/RAMS/article/view/345\u003c/li\u003e\n \u003cli\u003eS.F. RUMISHA LEGM, K.P. SENKORO DG, P.K. MMBUJI. Monitoring and evaluation of Integrated Disease Surveillance and Response in selected districts in Tanzania. Tanzania Journal of Health Research. 2007;9.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 6","content":"\u003cp\u003eTable 6 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Gamal Abdel Nasser University of Conakry","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Evaluation, contribution, care systems, epidemic, Guinea","lastPublishedDoi":"10.21203/rs.3.rs-6410041/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6410041/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e This study aimed to evaluate the contribution of care systems for epidemic-prone diseases while identifying their strengths and weaknesses to guide future improvements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e A descriptive cross-sectional evaluation study involving 356 response actors from the eight administrative regions of Guinea. Data were collected using structured questionnaires organized around three main components: Structure, Process, and Outcomes. A strengths and weaknesses analysis was also conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among the 356 respondents, 76.40% were female, while 23.60% were male. The predominant age group was 40-60 years, representing 51% of participants, while 89.9% had a university education. The Structure component scored an overall 61%, indicating an average contribution. Sub-components included: human resources (69%), logistics and health products (81%), and health financing (32%). The Process component scored 67%, also reflecting an average contribution. The supports included: care delivery (83%), coordination (81%), epidemiological surveillance (81%), awareness-raising (82%), human resources (58%), logistics and health products (49%), development of normative documents (31%), and health information systems (69%). Finally, the Outcomes component achieved an 88% contribution, reflecting a generally good impact, characterized by improved care indicators for epidemic-prone diseases (89%) and surveillance (96%), beneficiary satisfaction (96%), and improved laboratory indicators (78%). The overall contribution of care systems was estimated at 72%, corresponding to an average contribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The study highlights an average contribution of epidemic-prone disease care systems in Guinea despite notable advances. An integrated approach is necessary to strengthen system resilience and improve preparedness for future epidemics.\u003c/p\u003e","manuscriptTitle":"Evaluation of the Contribution of Care Systems for Epidemic-Prone Diseases in Strengthening the Health System in Guinea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 06:42:10","doi":"10.21203/rs.3.rs-6410041/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":"804f3279-cbf5-4188-9453-9be12f4e6fbf","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46901633,"name":"Epidemiology"}],"tags":[],"updatedAt":"2025-04-17T15:16:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-10 06:42:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6410041","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6410041","identity":"rs-6410041","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}