02418nas a2200373   4500000000100000008004100001260001200042653001400054653001500068653001600083653001100099653001500110653002900125100001200154700001400166700001200180700001100192700001400203700001400217700001300231700002100244700001700265700001300282700001500295700001200310700001900322700001400341245017900355856007400534300001200608490000700620520140300627022001402030       2024                            d  c09/202410aAdherence10aCompliance10aElimination10aEquity10aFilariasis10aMass drug administration1 aBrady M1 aToubali E1 aBaker M1 aLong E1 aWorrell C1 aRamaiah K1 aGraves P1 aHollingsworth DT1 aKelly-Hope L1 aStukel D1 aTripathi B1 aMeans A1 aMatendechero S1 aKrentel A00aPersons 'never treated' in mass drug administration for lymphatic filariasis: identifying programmatic and research needs from a series of research review meetings 2020-2021.  uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11021373/pdf/ihad091.pdf  a479-4860 v163 a<p>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.</p>  a1876-3405