Kondal Dimple, Awasthi Ashish, Patel Shivani Anil, Chang Howard H, Ali Mohammed K, Deepa Mohan, Mohan Sailesh, Mohan Viswanathan, Narayan K M Venkat, Tandon Nikhil, Prabhakaran Dorairaj
Centre for Chronic Disease Control, New Delhi, India
Centre for Chronic Disease Control, New Delhi, India.
J Epidemiol Community Health. 2024 Mar 8;78(4):220-227. doi: 10.1136/jech-2023-220963.
Retention 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.
Data 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.
There 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.
In 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.
在基于社区的纵向队列研究中，参与者的留存是一项挑战。我们旨在评估与失访相关的因素，并估计失访偏倚。
数据来自一项正在进行的队列研究，即印度（德里和金奈）的南亚心血管代谢风险降低中心（CARRS）。采用多项逻辑回归分析来确定与部分随访（至少一次随访）或无随访（失访）相关的社会人口学因素。我们还使用高血压和糖尿病（现患病例）与基线社会人口学因素的相对比值比（ROR）来研究参与者失访对观察到的关联强度的影响。
2010年基线时，金奈和德里共有12270名CARRS队列成员入组，随后在2011年至2022年期间进行了6次随访。中位随访时间为9.5年（四分位间距：9.3 - 9.8），发生了1048例死亡。约3.1%的参与者在基线访视后未进行随访。年龄较小者（相对风险比（RRR）：1.14；1.04至1.24）、未婚参与者（RRR：1.75；1.45至2.11）以及家庭资产较低者（RRR：1.63；1.44至1.85）失访的几率更高。糖尿病和高血压的社会人口学因素的ROR在基线和第六次随访之间无统计学差异，表明随访时推断偏倚的可能性极小。
在这个具有代表性的印度城市队列中，我们发现失访率较低且失访导致的偏倚极小。我们队列中不一致的参与偏倚表明我们的留存策略，如开放沟通、提供健康档案等具有潜在益处。
