Ridgeway J L, Han L C, Olson J E, Lackore K A, Koenig B A, Beebe T J, Ziegenfuss J Y
Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN 55905, USA.
Public Health Genomics. 2013;16(3):118-26. doi: 10.1159/000349924. Epub 2013 Apr 12.
Biobanks are an important resource for genetic and epidemiologic research, but bias may be introduced if those who accept the recruitment invitation differ systematically from those who do not in terms of attributes important to health-related investigations. To understand potential bias in a clinic-based biobank of biological samples, including genetic data linked to electronic health record information, we compared patient characteristics and self-reported information among participants, nonresponders and refusers. We also compared reasons for nonparticipation between refusers and nonresponders to elucidate potential pathways to reduce nonparticipation and any uncovered bias.
We mailed recruitment packets to 1,600 adult patients with upcoming appointments at Mayo Clinic (Rochester, Minn., USA) and recorded their participation status. Administrative data were used to compare characteristics across groups. We used phone interviews with 26 nonresponders and 26 refusers to collect self-reported information, including reasons for nonparticipation. Participants were asked to complete a mailed questionnaire.
We achieved 26.2% participation (n=419) with 12.1% refusing (n=193) and 61.8% nonresponse (n=988). In multivariate analyses, sex, age, region of residence, and race/ethnicity were significantly associated with participation. The groups differed in information-seeking behaviors and research experience. Refusers more often cited privacy concerns, while nonresponders more often identified time constraints as the reason for nonparticipation.
For genomic medicine to advance, large, representative biobanks are required. Significant associations between patient characteristics and nonresponse, as well as systematic differences between refusers and nonresponders, could introduce bias. Oversampling or recruitment changes, including heightened attention to privacy protection and participation burden, may be necessary to increase participation among less-represented groups.
生物样本库是遗传和流行病学研究的重要资源,但如果接受招募邀请者在对健康相关调查至关重要的属性方面与未接受者存在系统性差异,可能会引入偏差。为了解基于诊所的生物样本库(包括与电子健康记录信息相关的基因数据)中的潜在偏差,我们比较了参与者、无回应者和拒绝者的患者特征及自我报告信息。我们还比较了拒绝者和无回应者不参与的原因,以阐明减少不参与情况及任何发现的偏差的潜在途径。
我们向美国明尼苏达州罗切斯特市梅奥诊所即将预约就诊的1600名成年患者邮寄了招募资料包,并记录他们的参与状态。使用管理数据比较各组特征。我们对26名无回应者和26名拒绝者进行电话访谈,以收集自我报告信息,包括不参与的原因。要求参与者填写邮寄的问卷。
我们实现了26.2%的参与率(n = 419),12.1%的拒绝率(n = 193)和61.8%的无回应率(n = 988)。在多变量分析中,性别、年龄、居住地区和种族/民族与参与显著相关。各组在信息寻求行为和研究经验方面存在差异。拒绝者更常提及隐私问题,而无回应者更常将时间限制作为不参与的原因。
为推动基因组医学发展,需要大型、具有代表性的生物样本库。患者特征与无回应之间的显著关联以及拒绝者和无回应者之间的系统性差异可能会引入偏差。可能需要进行过采样或招募调整,包括更加关注隐私保护和参与负担,以提高代表性较低群体的参与度。