Neglected Tropical Diseases (NTDs) are a group of chronic infectious diseases of poverty affecting over one billion people globally. Intersections of NTDs, disability, and mental ill-health are increasingly evidenced but are rarely studied from a mixed-methods perspective. Here, we advance syndemic understandings by further assessing and contextualising the syndemic relationship between NTDs (particularly their associated disability) and mental distress in Liberia.
View Article and Find Full Text PDFIntroduction: The WHO neglected tropical disease (NTD) roadmap stresses the importance of integrating NTDs requiring case management (CM) within the health system. The NTDs programme of Liberia is among the first to implement an integrated approach and evaluate its impact.
Methods: A retrospective study of three of five CM-NTD-endemic counties that implemented the integrated approach was compared with cluster-matched counties with non-integrated CM-NTD.
Background: In 2017, Liberia became one of the first countries in the African region to develop and implement a national strategy for integrated case management of Neglected Tropical Diseases (CM-NTDs), specifically Buruli ulcer, leprosy, lymphatic filariasis morbidities, and yaws. Implementing this plan moves the NTD program from many countries' fragmented (vertical) disease management. This study explores to what extent an integrated approach offers a cost-effective investment for national health systems.
View Article and Find Full Text PDFBackground: People 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.
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