Remote evaluation of STH program coverage: Experiences from the DeWorm3 study, India

Abstract

Background The DeWorm3 trial is a multi-country study testing the feasibility of interrupting transmission of soil-transmitted helminths by community-wide mass drug administration (cMDA). Treatment coverage during cMDA delivery was validated by in-person coverage evaluation surveys (CES) after each round of treatment. A mobile phone-based CES was carried out in India when access to households was restricted during the COVID-19 lockdown. Methods Two focus group discussions were conducted with the survey implementers to document their experiences of conducting phone-based CES via mobile-phone voice calls. Principal findings In the phone-based CES, only 56% of sampled households were reached compared to 89% during the in-person CES (89%). This was due to phone numbers being wrongly recorded, or calls being unanswered leading to a higher number of households that had to be sampled in order to achieve the sample size of 2,000 households in phone-based CES compared in-person CES (3,600 and 2,352 respectively). Although the phone-based CES took less time to complete than in person coverage evaluations, the surveyors highlighted the lack of gender representation among phone survey participants as it was mostly men who answered calls and were then interviewed. The surveyors also mentioned that eliciting responses to open-ended questions and confirming treatment compliance from every member of the household was challenging during phone based CES. These observations were confirmed by analysing the survey participation data which showed women’s participation in CES was significantly lower in phone-based CES (66%) compared to in-person CES (94%) (Z =-22.38; p<0.01) and that a significantly higher proportion of households provided proxy responses in phone-based CES (51%) compared to in-person CES (21%) (Z = 20.23; p<0.01). Conclusions The phone-based CES may be a viable option to evaluate treatment coverage but issues such as participation bias, gender inclusion, and quality of responses will need to be addressed to optimize this methodology.