Publication:

Process evaluation of recruitment and retention strategies in a cohort study: qualitative insights from a rural site in Telangana, India

Background Longitudinal cohort studies are important for generating evidence on disease patterns, risk factors, and outcomes, informing public health policies and interventions. However, participant recruitment and long-term retention pose significant challenges, particularly in rural and resource-limited settings. Maintaining participant engagement over extended periods is essential to ensure data validity in such studies. Effective recruitment and retention strategies, tailored to the study design, population characteristics, and setting are therefore crucial. At our site, a robust recruitment and retention plan contributed to a high retention rate of 99.6%.

Methods This study presents a qualitative process evaluation from a rural site in Telangana, which was part of a multicentric cohort study designed to estimate the incidence of acute febrile illnesses, including dengue, over 12 months. In-depth interviews and focus group discussions were conducted with 20 stakeholders, including program personnel, field staff, community health workers, and participants. A manual thematic analysis was performed based on the transcribed interviews.

Results The study identified key facilitators for recruitment and retention, including strong community engagement, support from CHVs and village leaders, cultural sensitivity, flexibility in follow-ups, and a participant-centered approach. Major challenges included participant fatigue, seasonal migration, reluctance to provide blood samples during illness, limited incentives, and increased documentation burden. Strategies like addressing broader health needs, medical camps, and capacity-building trainings enhanced trust and engagement.

Conclusions The findings from this study offer valuable insights into context-specific strategies that can inform the design and implementation of future cohort studies in similar rural environments, thereby ensuring high retention and data quality with minimal attrition.