van Zon Sander K R, Scholtens Salome, Reijneveld Sijmen A, Smidt Nynke, Bültmann Ute
Department of Health Sciences, Community & Occupational Medicine, University Medical Center Groningen, University of Groningen, P.O Box 196, FA 10, Antonius Deusinglaan 1, 9700 AD Groningen, The Netherlands.
LifeLines Cohort Study and Biobank, Bloemsingel 1, 9713 BZ Groningen, Groningen, The Netherlands.
J Clin Epidemiol. 2016 Oct;78:52-62. doi: 10.1016/j.jclinepi.2016.03.009. Epub 2016 Mar 29.
Insight into baseline participation rates and their determinants is crucial for designing future population-based biobank studies. We therefore conducted a systematic review and meta-analysis of baseline participation rates and their determinants in large longitudinal population-based biobank studies.
We screened studies registered within the Public Population Project in Genomics and Society and in the Biobanking and Biomolecular Resources Research Infrastructure catalogues to find potentially eligible studies. We retrieved data with regard to participation rate, biobank design, performed measurements, and specific strategies for improving participation. We calculated weighted pooled proportions for each determinant using random-effects models.
We included 25 studies (participation rates 5-96%). Participation rates were highest for studies involving face-to-face recruitment [82.6%; 95% confidence interval (CI): 72.2%, 90.9%], for studies in which participants were visited for an examination (77.5%; 95% CI: 64.0%, 88.6%) and for studies in which at maximum four measurements were performed (78.2%; 95% CI: 69.7%, 85.7%). Specific strategies to improve participation were not found to be associated with higher participation rates.
Specific choices of recruitment methods and design have consequences for participation rates. These insights may help to increase participation in future studies, thereby enhancing the validity of their findings.
深入了解基线参与率及其决定因素对于设计未来基于人群的生物样本库研究至关重要。因此，我们对大型纵向基于人群的生物样本库研究中的基线参与率及其决定因素进行了系统综述和荟萃分析。
我们筛选了在基因组学与社会公共人口项目以及生物样本库与生物分子资源研究基础设施目录中注册的研究，以找出可能符合条件的研究。我们检索了有关参与率、生物样本库设计、进行的测量以及提高参与率的具体策略的数据。我们使用随机效应模型计算每个决定因素的加权合并比例。
我们纳入了25项研究（参与率为5%-96%）。对于采用面对面招募的研究，参与率最高[82.6%；95%置信区间（CI）：72.2%，90.9%]；对于上门为参与者进行检查的研究，参与率为77.5%（95%CI：64.0%，88.6%）；对于最多进行四项测量的研究，参与率为78.2%（95%CI：69.7%，85.7%）。未发现提高参与率的具体策略与更高的参与率相关。
招募方法和设计的特定选择会对参与率产生影响。这些见解可能有助于提高未来研究的参与率，从而增强研究结果的有效性。
