Details

Autor(en) / Beteiligte

• Goldberg, Ellen M
• King, Jonathan D
• Mupfasoni, Denise
• Kwong, Kevin
• Hay, Simon I
• Pigott, David M
• Cromwell, Elizabeth A

Titel

Ecological and Socioeconomic Predictors of Transmission Assessment Survey Failure for Lymphatic Filariasis

Ist Teil von

• The American journal of tropical medicine and hygiene, 2019-01, Vol.101 (1), p.271-278

Ort / Verlag

United States: Institute of Tropical Medicine

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• The transmission assessment survey (TAS) is recommended to determine whether cessation of mass drug administration (MDA) for lymphatic filariasis (LF) is warranted. Ministries of health typically implement TASs in evaluation units (EUs) that have had more than five rounds of annual MDA. Under TAS guidelines, sample size calculations determine a decision value: if the number of individuals testing positive exceeds this threshold, then MDA continues in the EU. The objective of this study was to determine whether fine scale geospatial covariates could be used to identify predictors of TAS failure. We geo-referenced 746 TAS EUs, of which 65 failed and extracted geospatial covariates using R to estimate odds of failure. We implemented stepwise backward elimination to select covariates for inclusion in a logistic regression to estimate the odds of TAS failure. Covariates included environmental predictors (aridity, distance to fresh water, elevation, and enhanced vegetation index), cumulative rounds of MDA, measures of urbanicity and access, LF species, and baseline prevalence. Presence of was significantly associated with TAS failure (odds ratio [OR]: 4.79, 95% CI: 2.52-9.07), as was population density (OR: 2.91, 95% CI: 1.06-7.98). The presence of nighttime lights was highly protective against failure (OR: 0.22, 95% CI: 0.10-0.50), as was an increase in elevation (OR: 0.36, 95% CI: 0.18-0.732). This work identifies predictors associated with TAS failure at the EU areal level, given the data presently available, and also identifies the need for more granular data to conduct a more robust assessment of these predictors.