Department of Global Health, RTI International, Washington, DC 20008, USA.
Neglected Tropical Diseases Division, Office of Infectious Disease, Bureau for Global Health, United States Agency for International Development, Washington, DC 20547, USA.
Int Health. 2024 Sep 5;16(5):479-486. doi: 10.1093/inthealth/ihad091.
As neglected tropical disease programs rely on participation in rounds of mass drug administration (MDA), there is concern that individuals who have never been treated could contribute to ongoing transmission, posing a barrier to elimination. Previous research has suggested that the size and characteristics of the never-treated population may be important but have not been sufficiently explored. To address this critical knowledge gap, four meetings were held from December 2020 to May 2021 to compile expert knowledge on never treatment in lymphatic filariasis (LF) MDA programs. The meetings explored four questions: the number and proportion of people never treated, their sociodemographic characteristics, their infection status and the reasons why they were not treated. Meeting discussions noted key issues requiring further exploration, including how to standardize measurement of the never treated, adapt and use existing tools to capture never-treated data and ensure representation of never-treated people in data collection. Recognizing that patterns of never treatment are situation specific, participants noted measurement should be quick, inexpensive and focused on local solutions. Furthermore, programs should use existing data to generate mathematical models to understand what levels of never treatment may compromise LF elimination goals or trigger programmatic action.
由于被忽视热带病规划依赖于参与多轮大规模药物治疗(MDA),人们担心从未接受过治疗的人可能会继续传播疾病,从而成为消除疾病的障碍。先前的研究表明,从未接受过治疗人群的规模和特征可能很重要,但尚未得到充分探讨。为了填补这一关键知识空白,于 2020 年 12 月至 2021 年 5 月举行了四次会议,以汇集淋巴丝虫病(LF)MDA 规划中从未接受过治疗的专家知识。会议探讨了四个问题:从未接受过治疗的人数和比例、其社会人口学特征、感染状况以及未接受治疗的原因。会议讨论指出了需要进一步探讨的关键问题,包括如何对从未接受过治疗的人进行标准化衡量、调整和使用现有工具来收集从未接受过治疗的数据,并确保在数据收集过程中从未接受过治疗的人能够得到代表。与会者认识到,从未接受过治疗的模式因具体情况而异,因此衡量工作应该快速、廉价且侧重于当地的解决方案。此外,规划还应利用现有数据生成数学模型,以了解从未接受过治疗可能会影响 LF 消除目标或触发规划行动的程度。