Egenvall et al. Acta Veterinaria Scandinavica 2011, 53(Suppl 1):S4 http://www.actavetscand.com/content/53/S1/S4 possible to merge in steps, starting with the merge with most identifiers and successively determining number of individuals merged in each step. Other issues arise when the merge relates to dates as well, i.e. how close events need to be in time to be considered a true match. This may depend on the research question. Some databases contain unique individual identifiers dating back to the basic construction of the databases, e. g. the genetic databases. The level of detail and quality of this information is closely linked to the original purpose of the database. For example, at breeding evaluation, a unique and exact identity is paramount. Comparing to research on humans in the Nordic countries, where epidemiologic studies have been facilitated by the omni- present personal identity number, research in companion animals and horses is, in general, hampered by less accu- rate identification. The possible exception being genetic studies in meticulously controlled breeds or populations. Identities are likely most accurate (and correctly tran- scribed in all aspects) when recorded electronically and verified each time an individual is encountered or when the very identity is absolutely central to the context. To ensure absolute correct identification of the individual a subcutaneously injected microchip carrying a unique, electronic identification-number is ideal because it actu- ally stays with the animal. However, it seems no system is perfect as, even with the Swedish personal identity num- bers there are problems, e.g. relative to immigrants [27]. It should also be borne in mind that outside the Nordic countries personal-identity information is generally less available and its use more sensitive. A recent example of challenges encountered when merging human databases from the US has been provided [28]. Group-level merging and combining databases The criteria for merging at group-level are less strict regarding individual identification. Therefore, integration at this level is easier to perform and probably more widely applied. However, loss of detail regarding the study units is inherent in the methodology and the research questions asked will not be the same as for individual level merges. If the groups are crude there may be a risk of ecological fallacy, which means that while data are analysed at group level, the researcher or readers may make inferences to a lower level, e.g. indivi- duals, which may or may not be true. Depending on the topic, the strength of evidence gained from combining databases might be lower and this needs to be reflected in the presentation and interpretation of results. Challenges and possibilities Clinical databases for research Use of case-based clinical data from veterinary teaching hospitals or private clinics has always been limited by Page 4 of 6 unknown base populations, as well as other issues rela- tive to the client, animal and clinical situation. With the increasing computerization of veterinary clinics, the potential for combining data from multiple sources has increased. However, such analyses should be viewed as multiple-site studies, with all the concerns of individual site studies as well as issues of diagnostic consistency across sites, type and amount of data recorded, as well as representativeness of the cases to the base popula- tions. Unfortunately, the ease of combining the data may overshadow these concerns and standardization and validation may not be done. Further, the mechanism of referral bias will operate to some extent whenever data from primary and secondary referral sites or local, regional and specialty practices are combined. Referral bias was recently demonstrated in data for the veterinary medical database (VMDB) in the US [29]. VMDB actu- ally combines clinical data from a number of large North-American referral animal hospitals and has been doing so since the 1960s [30]. The potential impact of between-site differences will depend on the type of research question examined. Confidentiality versus data quality Privacy of information is both an ethical and a legal issue. However there is often a trade-off between preser- ving anonymity and achieving data quality simply because mistakes in programming will be much more evident and easy to find when identifiers are still present. Data quality When integrating data, there is risk of further data loss. A trade-off between fully secured internal validity in few individuals and the possibility to generalize to a larger population may take place, and it is important to achieve a reasonable balance. It is likely that databases may differ in quality, i.e. validity and exactness of infor- mation, but this is not a huge problem given that errors are random. However, when merging databases even more attention must be paid to validity problems. If risks of differential misclassification are identified this should be investigated and taken into account in the analyses. The possibility of introducing bias when com- bining databases is evident if many individuals are lost and they are not lost by a random process relative to the research objectives. For example it could be possible to limit a study to some breeds or geographic locations where data validity is shown to be good. Promoting collaboration Sometimes the need of the researchers and the database owners may coincide and therefore enhance data qual- ity. Since the year 2002 each registered dog insured at Agria will have its identity verified directly from the Swedish Kennel Club, resulting in year of birth, name