Motives for withdrawal of participation in biobanking and participants’ willingness to allow linkages of their data Data repositories, like research biobanks, seek to optimise the number of responding participants while simultaneously attempting to increase the amount of data donated per participant. Such efforts aim to increase the repository’s value for its uses in medical research to contribute to improve health care, especially when data linkage is permitted by participants. We investigated individuals’ motives for participating in such projects and potential reasons for their withdrawal from participation in a population-based biobank. In addition, we analysed how these motives were related to various characteristics of the participants and their willingness to permit data linkage to their personal data for research. These questions were explored using a sample of participants in the Dutch Lifelines biobank (n = 2615). Our results indicated that motives for participation and withdrawal were premised on benefits or harm to society and to the individuals themselves. Although general values and trust both played key roles in participation, potential withdrawal and willingness to permit data linkage, they were differentially associated with motives for participation and withdrawal. These findings support and nuance previous findings by highlighting the distinctiveness and complexity of decision making regarding participation in or withdrawal from data donation. We suggest some new directions for improving recruitment, retention and safeguarding strategies in biobanking. In addition, our data provide initial evidence regarding how factors may relate with the probability that individuals will agree to data linkages, when controlling for their unique effects. Future research should further investigate how perceptions of harm and benefits may influence decision making on withdrawal of participation. Continuous and full participation of members of the public in data repositories in the medical field is essential for effective scientific research. In addition, it creates the opportunity to improve health care for individuals by data enhancement [1,2,3]. To understand complex relationships in retaining health or developing disease, a large number of participants contributing large amounts of data is needed to get sufficient statistical power [3, 4]. Analyses on a large-scale, centralised data repository enable researchers to understand specific characteristics and behaviour on an individual level in an unprecedented way, which can impact both research and clinical practice [4]. In addition, advances in information technology create the possibility to analyse vast amounts of genetic (genomic) data, and the potential to understand more about risk factors for developing diseases or the course of the disease. As a result of these new possibilities for analysing and knowledge building, the quest to advance the collection and use of data from both patients and healthy volunteers has become urgent, further increasing the efforts on exponential growth of data repository and international data-sharing or linkage projects [5, 6].Large-scale, centralised data repositories can be large prospective population-based cohort studies and biomedical biobanks. These biomedical cohort studies and biobanks (further referred to as biobanks) store large quantities of biological specimens as well as data extracted from questionnaires and measurements as a resource for research, while being managed by professional standards [7, 8]. Biobanks and similar data repositories seek to optimise the number of responding participants increasing the data repositories’ value for health care and research, while simultaneously attempting to increase the amount of data donated per participant. Yet recruiting participants in biobanking in Europe is a challenge even in ‘willing’ populations, for example, among Finnish people [9]. Achieving high participation rates will most likely become an even larger challenge, considering the development of participation rates in population-based survey studies in the past 35 years [10, 11]. In light of these challenges for recruiting new participants, retainment of current participants should be a growing concern for those responsible for data repositories and for those using them.The establishment of mutually enhancing data linkages between existing data repositories might be an important solution for improving the collection and use of data. Data linkage could provide multiple opportunities, such as providing unique insights in existing data without significant efforts of participants [5], or enabling individually tailored insights from data that might be communicated in return for participation to benefit a participants’ health directly [3]. As a result, data linkage might influence current rates of participation and data density, especially when individually tailored results are returned or accessible as return of an individual benefit.Yet data linkage is challenging when it comes to protection of personal sensitive data, and promising potential individual benefits [1, 5]. The clinical utility of individually tailored insights from research is not straight forward and an ongoing topic of debate, for example, the clinical utility of individuals’ polygenic risk scores for various illnesses [12] or of data from wearables [13]. Research has shown that motives for non-participation and (potential) withdrawal are linked to issues regarding commercialisation, confidentiality and privacy of data [14]. Simultaneous, biobank’s operating procedures, such as restricted data access policies, or its decision to return findings from research are associated with higher participation levels [15, 16].Achieving optimised rates of participation therefore requires a sound understanding of citizens’ decisions to participate or not-participate in biobanks. Especially understanding decisions of withdrawal might help to understand differences between participant and (potential) non-participant. Subsequently, it provides valuable insights in the viability of data sharing as potential solution for optimised rates. Yet information about rates of withdrawal in biobanking is scarce, or not publicly available, which limits current understanding of mechanisms of withdrawal. Although one Swedish study investigating withdrawal rates in biobanking during 2005 and 2006 indicated low withdrawal rates at that time [17], the potential of data and data linkage has rapidly developed, and concerns about data linkage in the last decade became realities [18].So far, studies have pointed to the key roles and interplay of individuals’ characteristics in their participation and non-participation in biobanking. For example, a variety of characteristics on an individual level have been found to be positively associated with participation in biobanks, such as a higher educational attainment [19,20,21], positive attitudes towards research and society [6, 20, 22], a general concern about others [23] and high levels of societal or social trust [6, 21]. In addition, several studies suggest that prosocial attitudes and trust in others are especially important factors that independently relate to positive decisions regarding participation [24, 25].Some of these characteristics may also be positively related to withdrawal of participation, depending on the specific context of the biobank, e.g., studies in Europe, United States and Australia showed that concerns about privacy and confidentiality were robustly related to lower willingness to participation [6, 26, 27]. Specifically these concerns could be triggered in case of data linkage or access, especially when commercial enterprises are involved [28, 29]. An Australian study showed that higher educational attainment was associated with a stronger reduction of trust in a public biobank that allows access to third parties [30]. Other studies found that those concerns about privacy or confidentiality were associated with anxiety for commodification of contributed samples or data [31, 32]. Furthermore, technologies are perceived differently depending on health expectancies, gender or social trust [33]. For example, men perceive fewer risk and more benefits in gene technology than women [34]. Levels of trust in data linkage or commercial enterprises can also inhibit or decrease perceived risk in participation, which might be decisive for continuation or withdrawal [21, 30].Hence, the contextual and procedural characteristics of biobanks can affect participation via withdrawal, depending on the characteristics of (potential) participants. Changes in the data after withdrawal can lead to bias in the data and the analysis of the data from data repositories, especially in biobanks representing populations of both participants and non-participants, which could be reduced by refinement of the data [25, 35]. While previous studies have broadly explored the characteristics and motives of participants and non-participants in biobanks as well as in data sharing [6, 20, 21, 26, 36, 37], further research on the associations between individual participants’ characteristics and their motives for participation and withdrawal would be beneficial [35]. In particular, research on the associations between participants’ motives of withdrawal and their psychological characteristics could yield insights into how contextual biobanking elements could impact on individuals’ continued participation or their withdrawal [24, 25].An investigation of the drivers of participation and withdrawal behaviour is therefore required to reduce potential biases. This study aimed to gain insight in the possibilities to classify individuals who participate in population-based biobanks according to various demographic and psychological key characteristics, and find distinguishing traits between those participants who are more likely to withdraw their participation and those who are not. In addition, we tried to identify factors that will affect the likelihood that participants would accept the linkage of their personal data for research purposes. The study could then yield insights for optimising recruitment and retainment strategies associated with biobanking or similar data repository initiatives, while preventing withdrawal of participation.We administered an online survey in August 2018 within a randomly selected sample of 2615 respondents, who were among the 167,000 participants of Lifelines biobank. Lifelines is a population-based biobank and large, multigenerational prospective cohort study in the Northern part of the Netherlands [7]. All Lifelines participants consented between 2006 and 2013 to participate in the biobank, allowing it to examine 10% of the population of Northern part of the Netherlands. It does not offer incentives for research participation. Lifelines applies a broad range of investigative procedures for assessing biomedical, sociodemographic, behavioural, physical and psychological factors, with a particular focus on multi-morbidity and complex genetics.At the time of data collection for the current study, our respondents were registered as active participant of Lifelines, which implies they had not withdrawn their participation at the time of filling in our survey and were actively participating in the biobank. The response rate for the invitation of this additional study was 22.2%. We stratified our sample to improve its representativeness compared to the representativeness of the Lifelines cohort for the Dutch population [7, 38]. We found that our study sample, stratified by sex (male = 50.5%) and age (M = 56, SD = 15.88), nevertheless has more individuals with a high educational level, registered partner and good self-reported health compared to the Lifelines population [38].MeasuresDemographic characteristics and self-reported healthWe measured demographic characteristics that were previously associated with participation or willingness to participate in a biobank, such as marital status [21], education level [19, 24], religion [16, 36], residential area [38] and self-reported general health (1 = ‘very poor’, 5 = ‘very good’) [19, 39]. Work status was included as an additional indicator of socio-economic status.Prosocial intrapersonal characteristics Prosocial orientation We applied two measures to determine respondents’ general orientations towards other individuals within their behaviour, which we refer to as prosocial orientation. The first was their actual prosocial behaviour reflected in their organ and blood donor status and the frequency of charitable donations. The second was the degree to which respondents cared about other individuals’ outcomes in choices concerning resource allocation, which were measured using six items in the social value orientation (SVO) scale. This scale is designed to measure the magnitude of individuals’ general concern for others [40]. Replicating previous research, our results indicated excellent reliability with Cronbach’s alpha >0.90 [41]. Values We used 11 items based on the Theory of Basic Values from Schwartz to assess motivations of and beyond self-interest as guiding principles in individuals’ life [42]. For example, about the relevance of individual pleasure in life as guiding principle or the relevance of health and health care. Although our primary focus was on motivations of and beyond self-interest in the health care context, we investigated these motivations relating to a wider context, such as natural environmental values [43]. As such, values can provide more information about both magnitude and direction of individuals’ motives of self-interest and beyond. We applied an adapted version of the Environmental Personal Values Questionnaire (E-PVQ) scale [44]. We used two different subscales from the E-PVQ to measure hedonic values associated with (individual pleasure) (e.g., ‘it is important [for him/her] to have fun’) and biospheric values relating to the natural environment (e.g., ‘it is important [for him/her] to protect the natural environment’). In addition, we used a tailored subscale on healthspheric values relating to health and health care concerns (e.g., ‘it is important [for him/her] to live a fit life’). This subscale was designed in collaboration with the developers of the E-PVQ. The scales’ reliability was good, with Cronbach’s alpha >0.80 for all of the subscales, thus confirming reliability [44]. Societal trust We identified trust as a key factor of participation and non-participation in biobanking. This trust can relate to the general trust in society and a domain-specific trust in research, especially in case of data linkage in research. In addition, a previous study distinguished levels of trust in organisations from levels of trust in employees of these organisations [45]. We therefore investigated both the level of individuals’ trust in society, and the level of trust in research organisations, in particular their data management and handling (hereinafter referred to as ‘DM&H’). The latter should automatically address issues about privacy in data management and handling. First, societal trust was measured using the trust-based framework proposed by Mayer et al. [46]. We used six items reflecting trust in the government and in other citizens that wer
https://www.nature.com/articles/s41431-021-00997-5
Motives for withdrawal of participation in biobanking and participants’ willingness to allow linkages of their data
