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Development of a structured tracking system to improve retention in a birth cohort in rural Ecuador
Stephanie Montenegro1, Alexis J Handal2, Fadya Orozco1
1Centro de Transferencias y Desarrollo de Tecnologías CTT-USFQ, Universidad San Francisco de Quito USFQ, Quito, Ecuador.
Abstract:
Retention in birth cohort studies varies globally, with rates of over 80% in high-income countries and 30%-75% in low- and middle-income settings due to socioeconomic vulnerability, geographic dispersion, and infrastructural barriers. Despite the importance of participant retention, evidence on structured tracking systems contextualized to these settings remains limited. This paper describes the design, adaptations, and outcomes of a participant tracking system developed for the Study of Environmental Exposure of Mothers and Infants Impacted by Large-Scale Agriculture, a community-based birth cohort that enrolled 409 pregnant participants in a floriculture-intensive rural area of Ecuador. In this setting, retention was challenged by informal employment, variable phone access, and high mobility. The tracking system was implemented from pregnancy through 18 months postpartum, including two final follow-up waves conducted at 12 and 18 months. It comprised a predefined follow-up schedule, a team with defined roles, a real-time digital planner for recording contact attempts, participation status, and activity completion, and a five-tier participation classification. Tracking was conducted through office visits, phone calls, and home visits. Flexible protocols and targeted outreach addressed pandemic-related disruptions and irregular work schedules. Overall retention was 77%, with 84% and 75% at 12 and 18 months, respectively. Partial refusals (27%) were primarily linked to mobility and work demands, while 94 participants fully discontinued. Home visits enabled completion for 154 participants, with an 88% success rate. This experience illustrates how context-adapted tracking systems can sustain high retention in rural settings, offering a scalable model to inform global health research in underserved populations.
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