Recommendations for an Optimal Model of integrated case detection, referral, and confirmation of Neglected Tropical Diseases: A case study in Bong County, Liberia
العنوان: | Recommendations for an Optimal Model of integrated case detection, referral, and confirmation of Neglected Tropical Diseases: A case study in Bong County, Liberia |
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المؤلفون: | Tiawanlyn G. Godwin-Akpan, Shahreen Chowdhury, Emerson J. Rogers, Karsor K. Kollie, Fasseneh Z. Zaizay, Anna Wickenden, Georgina V.K. Zawolo, Colleen B.M.C. Parker, Laura Dean |
بيانات النشر: | Cold Spring Harbor Laboratory, 2022. |
سنة النشر: | 2022 |
الوصف: | BackgroundPeople affected by Neglected Tropical Diseases (NTDs), specifically leprosy, Buruli ulcer (BU), yaws, and lymphatic filariasis, experience significant delays in accessing health services, often leading to catastrophic physical, psychosocial, and economic consequences. Global health actors have recognized that Sustainable Development Goal 3:3 is only achievable through an integrated inter and intra-sectoral response. This study evaluated existing case detection and referral approaches in Liberia, utilizing the findings to develop and test an Optimal Model for integrated community-based case detection, referral, and confirmation. Finally, this study evaluates the efficacy of implementing the Optimal Model in improving the early diagnosis of NTDs.Methodology/Principal FindingsThe study used mixed methods, including key informant interviews, focus group discussions, participant observation, quantitative analysis, and reflexive sessions to evaluate the implementation of an Optimal Model developed through this study. The quantitative results from the testing of the optimal model are of limited utility. The annual number of cases detected increased in the twelve months of implementation in 2020 compared to 2019 (pre-intervention) but will require observation over a more extended period to be of significance. Qualitative data revealed essential factors that impact the effectiveness of integrated case detection. Data emphasized the gendered dynamics in communities that shape the case identification process, such as men and women preferring to see health workers of the same gender. Furthermore, the qualitative data showed an increase in knowledge of the transmission, signs, symptoms, and management options amongst CHW, which enabled them to dispel misconceptions and stigma associated with NTDs.Conclusion/SignificanceThis study demonstrates the opportunity for greater integration in training, case detection, rereferral, and confirmations. However, the effectiveness of this approach depends on a high level of collaboration, joint planning, and implementation embedded within existing health systems infrastructure. Together, these approaches improve access to health services for NTDs.Author SummaryGlobal health professionals and stakeholders have advocated for integration across diseases and sectors to improve the success of public health interventions. This advocacy has also impacted NTDs programs globally. NTDs interventions are becoming more integrated than disease-specific activities to maximize limited resources, improve coverage and access to healthcare services. However, documentation on the effectiveness of integrated approaches to improve access to healthcare services is minimal. This study evaluated existing case detection and referral approaches in Liberia, utilizing the findings to develop and test an Optimal Model for integrated community-based case detection, referral, and confirmation. Finally, this study evaluates the efficacy of implementing the Optimal Model in improving the early diagnosis of NTDs. The results provide evidence of the benefits of an integrated approach and the programmatic challenges to achieve the goal of improving access to health services for persons affected by NTDs. |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::46f865e1aa52ec454eddf9498801e2a2 https://doi.org/10.1101/2022.02.01.22269709 |
حقوق: | OPEN |
رقم الأكسشن: | edsair.doi...........46f865e1aa52ec454eddf9498801e2a2 |
قاعدة البيانات: | OpenAIRE |
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