哥德堡H70出生队列研究中的损耗,1930年队列的18年随访

Frontiers in epidemiology Pub Date : 2023-05-09 eCollection Date: 2023-01-01 DOI:10.3389/fepid.2023.1151519
Lina Rydén, Hanna Wetterberg, Felicia Ahlner, Hanna Falk Erhag, Pia Gudmundsson, Xinxin Guo, Erik Joas, Lena Johansson, Silke Kern, Madeleine Mellqvist Fässberg, Jenna Najar, Mats Ribbe, Therese Rydberg Sterner, Simona Sacuiu, Jessica Samuelsson, Robert Sigström, Johan Skoog, Margda Waern, Anna Zettergren, Ingmar Skoog
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引用次数: 0

摘要

背景纵向研究对于了解衰老过程、疾病的风险因素和后果至关重要,但流失可能会导致选择偏差并影响可推广性。我们描述了1930年哥德堡H70出生队列研究的队列,从70岁随访到88岁,并比较了那些继续参与的人与那些在随访期间因任何原因死亡、拒绝和辍学的人的基线特征。方法以1930年出生的人群为基础,在70岁时进行综合评估(N = 524)。对样本进行随访,并在75岁时扩大样本量(N = 767)。随后在79岁、85岁和88岁时进行了随访。采用Logistic回归分析与随访参与状态相关的基线特征。结果拒绝参加后续检查与文化程度低、血压高、认知测试得分低有关。死亡造成的损耗和总损耗均与男性、较低的教育水平、吸烟、ADL依赖性、几种疾病、肺功能较差、步态较慢、认知测试得分较低、抑郁症状和大量药物有关。死亡造成的损耗也与没有伴侣有关。结论在解释纵向研究的结果时,考虑不同类型的磨损是很重要的,因为不同类型的损耗可能会对代表性和结果产生不同的影响。除了减少参与障碍外,还可以使用插补和加权分析等方法来处理选择偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attrition in the Gothenburg H70 birth cohort studies, an 18-year follow-up of the 1930 cohort.

Background: Longitudinal studies are essential to understand the ageing process, and risk factors and consequences for disorders, but attrition may cause selection bias and impact generalizability. We describe the 1930 cohort of the Gothenburg H70 Birth Cohort Studies, followed from age 70 to 88, and compare baseline characteristics for those who continue participation with those who die, refuse, and drop out for any reason during follow-up.

Methods: A population-based sample born 1930 was examined with comprehensive assessments at age 70 (N = 524). The sample was followed up and extended to increase sample size at age 75 (N = 767). Subsequent follow-ups were conducted at ages 79, 85, and 88. Logistic regression was used to analyze baseline characteristics in relation to participation status at follow-up.

Results: Refusal to participate in subsequent examinations was related to lower educational level, higher blood pressure, and lower scores on cognitive tests. Both attrition due to death and total attrition were associated with male sex, lower educational level, smoking, ADL dependency, several diseases, poorer lung function, slower gait speed, lower scores on cognitive tests, depressive symptoms, and a larger number of medications. Attrition due to death was also associated with not having a partner.

Conclusions: It is important to consider different types of attrition when interpreting results from longitudinal studies, as representativeness and results may be differently affected by different types of attrition. Besides reducing barriers to participation, methods such as imputation and weighted analyses can be used to handle selection bias.

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