Narayanan Martina K, Dodgeon Brian, Katsoulis Michail, Ploubidis George B, Silverwood Richard J
Centre for Longitudinal Studies, UCL Social Research Institute, University College London, London, UK.
MRC Unit for Lifelong Health & Ageing, University College London, London, UK.
Eur J Epidemiol. 2024 Nov;39(11):1221-1227. doi: 10.1007/s10654-024-01164-y. Epub 2024 Nov 20.
Non-response to surveys is a common problem; even more so during the COVID-19 pandemic with social distancing measures challenging data collection. As respondents often differ from non-respondents, this can introduce bias. The goal of the current study was to see if we can reduce bias and restore sample representativeness in a series of COVID-19 surveys embedded within five UK cohort studies by using the rich data available from previous waves of data collection. Three surveys were conducted during the pandemic across five UK cohorts: National Survey of Health and Development (NSHD, born 1946), 1958 National Child Development Study (NCDS), 1970 British Cohort Study (BCS70), Next Steps (born 1989-90) and Millennium Cohort Study (MCS, born 2000-02). Response rates in the COVID-19 surveys were lower compared to previous waves, especially in the younger cohorts. We identified bias due to systematic non-response in several variables, with more respondents in the most advantaged social class and among those with higher childhood cognitive ability. Making use of the rich data available pre-pandemic in these longitudinal studies, the application of non-response weights and multiple imputation was successful in reducing bias in parental social class and childhood cognitive ability, nearly eliminating it for the former. Surveys embedded within existing cohort studies offer a clear advantage over cross-sectional samples collected during the pandemic in terms of their ability to mitigate selection bias. This will enhance the quality and reliability of future research studying the medium and long-term effects of the pandemic.
调查无回应是一个常见问题；在新冠疫情期间更是如此，社交距离措施给数据收集带来了挑战。由于回应者往往与无回应者存在差异，这可能会引入偏差。本研究的目的是探讨能否通过利用此前几轮数据收集所获得的丰富数据，在英国五项队列研究中嵌入的一系列新冠疫情调查中减少偏差并恢复样本代表性。在疫情期间对英国五项队列进行了三项调查：全国健康与发展调查（NSHD，出生于1946年）、1958年全国儿童发展研究（NCDS）、1970年英国队列研究（BCS70）、下一步调查（出生于1989 - 90年）以及千禧队列研究（MCS，出生于2000 - 02年）。与之前几轮相比，新冠疫情调查的回应率较低，尤其是在较年轻的队列中。我们在几个变量中发现了因系统性无回应导致的偏差，最具优势社会阶层以及童年认知能力较高的人群中有更多回应者。利用这些纵向研究中疫情前可得的丰富数据，应用无回应权重和多重插补成功减少了父母社会阶层和童年认知能力方面的偏差，对于前者几乎消除了偏差。在减轻选择偏差的能力方面，嵌入现有队列研究中的调查比疫情期间收集的横断面样本具有明显优势。这将提高研究疫情中长期影响的未来研究的质量和可靠性。
