03828nas a2200445 4500000000100000008004100001260004600042653001000088653001100098653001000109653001700119653001200136653001300148653001300161653002400174653002900198100001700227700002400244700001400268700001500282700001400297700001600311700001300327700001300340700001200353700001500365700002000380700001700400700001300417700001700430700001200447700001400459700001500473245018400488856009900672300001100771490000700782520257900789022001403368 2026 d c09/2026bPublic Library of Science (PLoS)10aBenin10aMalawi10aIndia10aDrug Therapy10aSurveys10aReligion10aCensuses10ahelminthic theraphy10aMass drug administration1 aOborevwori E1 aÁsbjörnsdóttir K1 aGalagan S1 aSharrock K1 aAruldas K1 aKaliapan SP1 aPullan R1 aBailey R1 aKalua K1 aSimwanza J1 aWitek-McManus S1 aIbikounlé M1 aLuty AJF1 aAjjampur SSR1 aMeans A1 aWalson JL1 aHübner MP00aIndividual-level agreement between coverage surveys and treatment registers in mass drug administration: A secondary analysis of the multi-country DeWorm3 cluster randomized trial uhttps://journals.plos.org/plosntds/article/file?id=10.1371/journal.pntd.0014740&type=printable a1 - 150 v203 a

Background

Accurate measurement of treatment coverage is critical for the monitoring and evaluation of mass drug administration (MDA) programs, yet post-treatment coverage surveys are subject to multiple sources of bias and may not accurately reflect true individual-level treatment coverage. Using data from the DeWorm3 study, a multi-country randomized trial assessing the feasibility of interrupting soil-transmitted helminth (STH) transmission, we compared coverage estimates from treatment registers with post-treatment coverage surveys and identified factors associated with discordant treatment reporting.

Methods

The DeWorm3 trial analyzed individual-level treatment data across six rounds of biannual MDA with albendazole for STH in Benin, India, and Malawi (2018–2020). Coverage surveys and treatment registers were linked using unique identifiers. This analytic sample included 12,162 individuals with matched survey and register records and non-missing treatment status from the final round of the intervention-arm. Concordance was summarized using percent discrepancy and Cohen’s kappa, and site-specific logistic regression models assessed factors associated with discrepancy.

Results

While aggregate coverage was high (>95%) across all sites, individual-level discordance at round six occurred in 6.12% of individuals in Benin, 2.00% in India, and 4.39% in Malawi, driven predominantly by higher reporting in coverage surveys relative to register records. Kappa values were interpreted descriptively as treatment coverage was very high. Periurban/urban residence was associated with higher odds of discrepancy in Benin (OR: 3.65, 95% CI: 2.56–5.28) and Malawi (OR: 1.54, 95% CI: 1.11–2.12). In Malawi, male sex was also associated with higher discrepancy, while MDA awareness was associated with lower discrepancy. No covariates were significantly associated with discrepancy in India.

Conclusions

Individual-level discordance between coverage surveys and treatment registers was present across all sites and rounds, with surveys consistently estimating higher levels of treatment. Predictors varied by site, suggesting measurement error is driven by site-level programmatic and contextual factors rather than individual demographic characteristics. Programs should implement systematic validation approaches as high aggregate coverage estimates may mask meaningful individual-level measurement error.

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