Although linking survey data with administrative data opens new research opportunities as presented above, such linkage is not without challenges. This section describes the main challenges and considerations that may be encountered in data linkage processes and a number of recommendations for future linkages will be formulated. Table 3 provides a summary of the challenges and considerations and the corresponding recommendations.

Table 3 Table Overview of challenges, considerations and recommendations in linking surveys data with administrative data; HISlink, BelgiumLessons learned from to the linkage processes overallTechnical and operational issues of the linkage

The technical challenges inherent in linking survey data with administrative data are mainly related to the data quality and to the linkage errors [43]. Next to these issues, the proportionality principle, infrastructure and statistical challenges are also important.

The quality of the data sources, i.e., the availability, completeness and discriminatory power of identifiers or key personal variables that can be used to construct the linkage key, is very important and determines the choice of linkage methods.

In some countries, a unique personal number, such as the NRN in Belgium or the personal identity number in Scandinavia, is required for access to almost all administrative services, including healthcare services use for each resident and can be readily used to obtain information about individuals. Such identifiers allow the linkage to be relatively straightforward (deterministic linkage approach), and make it possible to link data from many different administrative sources with marginal error [44]. With regards to HISlink, the use of the NRN as a linkage key was a great asset. Moreover, such a unique identifier increases the linkage rate, although this rate varies between subgroups as shown in Table 1. About 8% of the BHIS2013 and 6% of the BHIS2018 could not be linked. This result could be explained by the fact that the BHIS household composition can deviate from the “official” household composition in the national register, preventing the linkage. In addition, as Table 1 shows, the linkage was not possible for a number of people who are more likely to be from the Brussels-Capital Region and more likely to be EU nationals. This sub-group could probably be people working for EU institutions, other international organizations or posted workers from other EU countries, living and working in Belgium but insured in their country of origin. Therefore no data could be retrieved from the BCHI.

In many other countries however, unique identifiers are not available and this might constitute an important barrier to linking the same person across multiple data sources [18]. In such contexts, linkage often depends on the use of non-unique ‘imperfect’ identifiers such as name, postcode, date of birth or other indirect identifiers. In combination, these variables can make it possible to identify records that belong to the same person, using more complex algorithms (probabilistic linkage approach). The probabilistic linkage method is the most common approach, usually in combination with the deterministic methods [45, 46].

The second challenge when linking survey data to administrative data is the risk of linkage errors, which typically occur where there is no unique identifier across different data sources [47] or in the event of imperfect identifiers. This problem could result in substantially biased results [17, 48]. Linkage errors arise when pairs of records are incorrectly classified. False-matches occur when records from different individuals link erroneously, while missed-matches occur when records from the same individual fail to link [45, 46]. Data analysts should therefore evaluate the quality of linked data by measuring linkage errors before proceeding with any further analysis. The availability of similar information in both data sources or in a reference database will be helpful in this regard. For HISlink, comparing age, sex, region of residence and the prevalence of certain chronic diseases, we detected an error in the previous version of HISlink 2018 data due to the use of the wrong database during the linkage process. This error was corrected by the linkage TTPs afterwards.

Another challenge that researchers face in data linkage is the proportionality principle, which means that only those variables that are relevant to the purpose of the study should be selected to avoid the re-identification of individuals. In this context, researchers should have a thorough knowledge of their data sources. The selection of relevant variables must be done precisely before the linkage process. The more information there is in both data sources, the more difficult this task becomes. However, this approach is not optimal as it is time-consuming and requires an in-depth knowledge of the data sources. In addition, when it is necessary to include new relevant variables or indicators that have been forgotten, the whole process has to be restarted (new IRB opinion, new linkage, etc.). An alternative, perhaps better approach could be too ask for permission to link both datasets completely in a first step. In a second step, each research project demands in a simplified procedure access to the relevant variables of the fully linked dataset in accordance with the proportionality principle. This is basically what is done at Statistics Netherlands [49,50,51].

Further consideration for researchers wishing to link data is the infrastructure needed to store and access the linked data. Some questions need to be answered beforehand: how will data be stored safely? What is the cost for the infrastructure? How will data be protected? How can data be accessed in a safe and easy way [28]?. In the case of HISlink, the linked data was stored on the IMA server and researchers access it securely using a token.

Finally, analysing linked datasets raises a number of additional ‘statistical’ challenges for researchers. Although linked data has several advantages, it is important to bear in mind that the limitations of both data sources remain even after the linkage. Researchers need to be aware of this to understand and interpret the results carefully. In addition, in the event of linkage errors, specific statistical methods need to be applied [35, 46]. Furthermore, with the complexity of administrative data, it is often necessary to involve an expert on this data in the analysis stages as well as when interpreting the results. In our case, the BCHI data is collected for administrative purposes, not for epidemiological research. It is therefore not easy to understand and use. Expert advice is often needed to make good choices when planning the analysis. The IMA’s single point of contact and the many experienced Sciensano researchers are well-qualified to fulfil this requirement.

Ethical, legal and societal aspects

The most important concerns facing data linkage are privacy and confidentiality issues [52]. With the implementation of the GDPR in 2018, new decision-making bodies were established for the authorisation of data linkage, and privacy and confidentiality issues were redefined. Because of these confidentiality issues, institutional review board (IRB) approval is often required to link the data. However, such IRB approval processes are usually complex and time-consuming, especially when the linkage is not consent-based. For both HISlink 2013 and HISlink 2018, it took several months to get the IRB approval. Therefore, to facilitate data linkage and overcome the lengthy negotiation and ad hoc approval processes for each BHIS-BCHI linkage, it would be useful to set up some kind of umbrella agreement protocol for public institutions such as Sciensano, to cover several years and several waves of BHIS-BCHI linkages.

To preserve privacy and prevent the disclosure of sensitive information, data linkage often relies on the separation principle of linkage and analysis processes, meaning that those conducting the linkage (often TTPs) only have access to a set of identifiers, whilst those analysing the linked data only have access to de-identified attribute data [17]. However, this type of approach causes a significant delay in the linkage process due to the administrative steps that take time (e.g. the signature of an official agreement between the parties involved). Furthermore, although this approach reduces the risk of disclosure of sensitive information about individuals, it means that important aspects of the linkage process are obscured, which makes it difficult for researchers to judge the reliability of the resulting linked data for their required purposes [17, 47].

Respecting respondents’ rights and maintaining their trust are further considerations. According to the new EU data Act, trust and altruism are essential in secondary data use [53]. When researchers plan to link data as part of a future survey, citizens must be able to decide whether they want to share their data, they must be informed that their data is being used and by whom. In other words, they need to opt-in through informed consent [1, 9, 54, 55]. Informed consent is required to ensure that respondents are aware of the risks and benefits involved in releasing and linking their personal data for research purposes, even though obtaining the opt-in linkage consent from all respondents is a challenging task. To link historical survey data to administrative data, there are exceptions to the requirement for informed consent, especially if contacting study participants is impossible or unreasonable [1, 9]. The GDPR contains specific exemptions to informed consent as a legal basis for the use of data to escape a ‘consent or anonymise approach’ or a ‘fetishisation of consent’, especially in the case of observational health research [56]. For the BHIS2013 and BHIS2018 linkages, because of the disproportionality to inform and seek consent from all BHIS participants and also because the authorization procedure was implemented prior to the GDPR, we proposed that the acquisition of consent from BHIS participants was obtained by way of a waiver, and this approach was accepted by the IRB. While these exemptions to informed consent are possible for historical data linkages, for any planned future linkages, researchers must seek informed consent from participants during the survey.

Lessons learned related to the outcomes

Without a doubt, the HISlink offers the potential to obtain more comprehensive data on the population’s health, facilitating new research perspectives for public health as demonstrated in this study. The BHIS data are only available every 5 years and some studies require more comprehensive data than the current linked data. The HISlink can be seen as a first step towards more comprehensive data linkages. To ensure that the benefits of data linkage are fully maximised, it is important to consider the inclusion of other administrative data such as hospital discharge data, mortality data, environmental data, primary electronic medical record (EMR), etc. For example, extending linked data to hospital discharge data could help target internal quality improvement efforts for specific patient groups (e.g., preventive care for diabetics) or help assess the determinants of hospitalisation and understand the underlying factors that influence length of hospitalisation. A linkage with the EMR may also be useful for studying appropriate polypharmacy, for example. However, in some countries such as Belgium, there is currently no integrated primary EMR. Only a few sentinel networks exist, such as the Intego database. For the future, consideration needs to be given to establishing a legal framework for such an integrated database.

At international level, the linkage between survey and administrative data has also proven its value. Indeed, such a linkage has been widely used in validation studies [10, 57, 58], but also in addressing specific research questions. For example, using health survey data linked to administrative health services data, the Institute for Clinical and Evaluative Sciences (ICES) researchers in Ontario, Canada, developed and validated an algorithm for population-based prediction of diabetes - the Diabetes Population Risk Tool (DPoRT) that accurately predicts diabetes risk in a population [59]. The linkage of Canadian Community Health Survey (CCHS) with medical claim data, has been used to investigate individual-level characteristics that are associated with community-dwelling high-cost users. They found that high-cost users status was strongly associated with being older, having multiple chronic conditions, and reporting poorer self-perceived health. The authors further found that high-cost users tended to be of lower socio-economic status, former daily smokers, physically inactive, current non-drinkers, and obese [60]. Finally, the linkage of survey and administrative data has been used to address methodological issues such as bias adjustment [61,62,63] or non-response analysis [64].

The BCHI data does not contain clinical information. In addition, there is no information on non-reimbursed care in the BCHI data. Although information is available on vital status, there is no information on cause of death. The absence of such important information prevents some policy-oriented research questions from being answered better. In future, efforts could be made to include more data sources in HISlink, and an initial step would be to include hospital discharge data.

The BCHI data is only available two years after consumption, meaning that the linkage can only be made with a two-year delay which precludes ‘real time’ linkage. Data availability should be accelerated in the short to medium term given the widespread use of electronic billing.

Furthermore, with the limited sample size of the BHIS (about 10,000 participants), subgroup analysis is impossible or yields inaccurate results, for example for rare events or specific subgroups.

Finally, access to linked data is thus far highly restricted due to legal constraints. Only Sciensano researchers that are registered with the IMA as the users of the linked data have access to the data. To take further advantage of the linked data, the data owners, i.e., Sciensano, the IMA and the sponsor (NIHDI) could retain ownership but make the data available to other research studies in line with the primary objective of HISlink, subject to the owners’ approval. One example of such an approach in cancer research is the National Cancer Institute’s (NCI’s) linked Surveillance, Epidemiology and End Results (SEER)-Medicare files where the NCI retains ownership of the data and releases it for approved research studies that guarantee the confidentiality of the patients and providers in the SEER areas [65].

Recommendations for future linkages

This study provides important information with regard to the individual linkage of survey data and health-insurance administrative data that other studies can build on. Based on our experience, there are a number of aspects that need to be taken into account to ensure the success of data linkage in future research. The recommendations related to the ethical, legal and societal aspects, technical, practical challenges, as well as those related to the outcomes are summarized in Table 3, and the main ones are further elaborated below.

Recommendation 1: gain and maintain the citizens trust in secondary use of data and data linkage

With the implementation of the GDPR, the consent form became mandatory for future planned linkages. Researchers need to put in place strategies to gain the trust of and to involve citizens whose data will be linked [66]. The perceived risk to privacy and data confidentiality constitutes one of the primary reasons why respondents decline the linkage request [55]. It is therefore important to emphasise the merits of the research, to stress the importance of altruism (contribution to society) and to address respondents’ privacy and confidentiality concerns by informing them of the safeguards put in place to protect their data.

Recommendation 2: improve the communication with the participant, so there is more willingness to give a consent for linkage

The literature suggests a strong correlation between respondents’ understanding and how likely they are to give consent [55, 67]. To achieve higher consent rates, it is necessary to shed light on respondents’ understanding of the linkage consent. Several approaches have been proposed to improve linkage consent rates. One of these consists of providing key subgroups that are less likely to understand the linkage request, with additional targeted explanatory or informative material. Another approach would be to use tailored messages by asking the consent understanding questions first, then doing a targeted intervention to address any misunderstandings, before administering the linkage request. It is preferable to ask for linkage consent upfront, which yields higher consent rates [9, 45, 50, 51].

Recommendation 3: adapt the need for consent to the context of the linkages

For linkages between datasets that already exist, a clear framework of acceptable practices needs to be developed, which the European Health Dataspace initiative is attempting to do [70]. To maintain population trust in secondary use of data and data linkage, it is imperative that this framework is in line with citizens’ values [66]. A clear distinction should be made between:

1) Routine linkages, which are usually for primary use and where implicit consent can be assumed because it concerns direct clinical care. However, a harmonized framework needs to be developed in order to streamline secure data flows;

2) Necessary linkages, in a public health crisis, as exemplified by the COVID-19 pandemic and where consent should not be required [71]; and.

3) Linkages for public health research and surveillance or other scientific research in the public interest, where the preferred legal basis should not be consent, but an explicit legal and ethical framework that is developed by the national health data authorities, resulting in a federated network of Findable, Accessible, Interoperable and Reusable (FAIR), linkable data sources governed by rules that are trusted both by researchers and citizens.

Recommendation 4: advoid the ‘link and destroy model’

Many challenges remain before this can become a reality, but it would resolve the administrative burden, the need for case-by-case consideration and the overall uncertainty and inefficiency surrounding data linkage [72]. From a broader perspective, it will be useful to have streamlined approval processes for efficient data access. Indeed, some jurisdictions adopt approaches for timely and cost-effective access to linked data (e.g. those in Ontario, Wales and Australia where linkage keys can be held in perpetuity), others such as in Belgium are restricted by the ‘link and destroy’ model, where linked data cannot be reused or are destroyed after a predefined dataretention time. In turn, these impact on the availability and accessibility of data for research and policy development (17).

Recommendation 5: take up initiatives to work towards a better balance between the right to privacy of respondents and society’s right to evidence-based information to improve health

Privacy considerations must strike a balance between the privacy rights of respondents and society’s right to evidence-based information to improve health.

Although the separation principle of linkage and analysis processes (as implemented at: the Data Linkage Branch in Western Australia, the Centre for Health Record Linkage (CHeReL) in New South Wales [73], the Secure Anonymous Information Linkage (SAIL) Databank in Wales [74], the Centre for Data Linkage (CDL) in Australia [75], the Manitoba Centre for Health Policy in Canada [73]) is recognised as good practice for protecting confidentiality, allowing linkage and analysis to take place together provides opportunities for both in-depth evaluation of linkage quality, and methodological advances in linkage techniques [76, 77]. Such an approach is in operation at the Institute for Clinical Evaluative Sciences (ICES) in Ontario. The ICES is legally allowed to receive fully identifiable data in order to perform linkage, to assess data quality and to provide coded data to research staff within the organisation. They operate a hierarchical access policy, which means that only a specific number of people have the highest level of access to all data elements, and most researchers can only access de-identified, coded data relevant to their study [73]. The linkage approach as applied at Statistics Netherlands constitutes a good practice in Europe [49,50,51].

Recommendation 6: optimize the way to deal with ethical and privacy requirements in order to be able to carry out data linkages in a reasonable time

Beside the privacy and confidentiality issues, researchers should be aware of some technical aspects such as the complexity of the linkage process which often results with a delay in the linkage process. Getting the agreement signed between the parties involved was a crucial factor in delaying the process, especially when several parties are involved. Therefore, a formal, pre-established accreditation that negates the need for new signatures at each linkage (ad hoc approval) for institutions that are entitled to request a data linkage, would be a further step towards reducing the delay and facilitating the data linkage process.

Recommendation 7: plan ahead the linkage of survey and administrative data, particularly where there is no unique identifier that can be used as a linkage key

If the linkage cannot rely on a unique identifier, researchers should identify more relevant variables (e.g., age, gender, date of birth, name, etc.) that will allow the construction of an almost perfect identifier for probabilistic linkage. As data linkage often relies on the separation of linkage and analysis processes, researchers should assess the linkage errors and quality of the linked data before conducting any further analysis. Several methods can be used to evaluate linkage quality, including the use of gold standard or reference data, sensitivity analyses, a comparison of the characteristics of linked and unlinked data, or post-linkage data validation [17, 35].

Recommendation 8: apply strategies to improve the linkage rates

Although the use of deterministic linkage methods has resulted in a relatively higher linkage rate, this approach is known to give rise to a number of missed matches (e.g. in the case of even a single digit error in the NRN). Therefore, a combination with subsequent probabilistic methods for unlinked cases to the deterministic linkage step would certainly result in a higher linkage rate. In addition, another explanation why the linkage was not always possible for everyone would be that only the NRN of the reference person was available and the others had to be found on the basis of household composition and socio-demographic characteristics. This approach is probably linked to the BHIS sampling strategy. However, BHIS household composition may differ from BCHI household composition or may change over time. Therefore, including the NRN of all individuals included in the survey, regardless of household composition would probably improve the linkage.

Recommendation 9: demonstrate to funders and policy makers the usefulness of linkages, raise awareness of such initiatives and continue to promote the linkage between databases

The linked data is an important source for population health research. Its use by researchers can bring huge benefits in terms of providing a more complete picture of the population’s health. However, within the context of budgetary constraints, it is important for researchers to demonstrate to funders and policy makers the usefulness of such linkage in order to maintain project funding and sustainability and to raise awareness of such initiatives. From a public health perspective, policy makers should continue to invest in data linkages; and the inclusion of other data sources (such as primary-care data and hospital discharge data) will augment the use of the linked data to expand the evidence base for policy makers and practitioners, which could therefore enrich population-based surveillance and research in the field of public health. However, in that case, there is a need to develop an overarching infrastructure. Since making linkages between multiple datasets would be very challenging, to be really cost-effective, it would be better to have an infrastructure that would allow access to different research institutes.

Recommendation 10: consider substituting HIS information by administrative data as much as appropriate

In view of the current challenges facing surveys, there is need to keep survey questionnaires as short as possible. Hence the more information can be obtained through other sources, the shorter can be the questionnaire. When possible, self-reported items should be replaced by administrative data. This will be the case, for example, for cancer screening, reimbursed healthcare use or reimbursed drug use. However, it is important to keep in mind that the replacement of self-reported information by administrative data can have certain limitations since administrative data have their own shortcomings (e.g., incomplete or missing data, recording errors).