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BMJ Publishing Group, Journal of Epidemiology and Community Health, p. jech-2023-220963, 2024

DOI: 10.1136/jech-2023-220963

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Evaluating bias with loss to follow-up in a community-based cohort: empirical investigation from the CARRS Study

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

BackgroundRetention of participants is a challenge in community-based longitudinal cohort studies. We aim to evaluate the factors associated with loss to follow-up and estimate attrition bias.MethodsData are from an ongoing cohort study, Center for cArdiometabolic Risk Reduction in South Asia (CARRS) in India (Delhi and Chennai). Multinomial logistic regression analysis was used to identify sociodemographic factors associated with partial (at least one follow-up) or no follow-up (loss to follow-up). We also examined the impact of participant attrition on the magnitude of observed associations using relative ORs (RORs) of hypertension and diabetes (prevalent cases) with baseline sociodemographic factors.ResultsThere were 12 270 CARRS cohort members enrolled in Chennai and Delhi at baseline in 2010, and subsequently six follow-ups were conducted between 2011 and 2022. The median follow-up time was 9.5 years (IQR: 9.3–9.8) and 1048 deaths occurred. Approximately 3.1% of participants had no follow-up after the baseline visit. Younger (relative risk ratio (RRR): 1.14; 1.04 to 1.24), unmarried participants (RRR: 1.75; 1.45 to 2.11) and those with low household assets (RRR: 1.63; 1.44 to 1.85) had higher odds of being lost to follow-up. The RORs of sociodemographic factors with diabetes and hypertension did not statistically differ between baseline and sixth follow-up, suggesting minimal potential for bias in inference at follow-up.ConclusionIn this representative cohort of urban Indians, we found low attrition and minimal bias due to the loss to follow-up. Our cohort’s inconsistent participation bias shows our retention strategies like open communication, providing health profiles, etc have potential benefits.