Egenvall et al. Acta Veterinaria Scandinavica 2011, 53(Suppl 1):S4 http://www.actavetscand.com/content/53/S1/S4 progression, both by conventional and research methods. Some of the databases discussed in this article may have been secondary in the aspect that the data were collected for other purposes, but in order to use them for research the data were entered into a compu- ter database. Why Nordic countries? Relative to the population size the Nordic countries are outstanding in the usage of the secondary data in human medical research. Also the opportunity to use secondary databases for research on health and perfor- mance in animals in the Nordic countries has proven to be substantial, as judged by the volume of publications in this area. There appears to be a culture and tradition in the Nordic countries for both collecting and sharing data, exemplified by the extensive use of the Agria insurance database (Sweden), compared to the much more limited use of data from other companies in Europe or North America [2]. Other Nordic examples of the use of secondary small animal and equine data- bases include studies based on data from tumour regis- tries [3-5], breeding registries [6-13], young horse quality contest results [6,7], competition data [7,8], clinic data [14], prescription data [15] and hunting abil- ity tests [16]. In spite of this extensive use of secondary databases, integration between databases is uncommon. Why integration, what is it and what is needed? A further advancement from using one single database is to integrate information between databases, which facilitates a broader collection of information regarding the study units. Integration can be defined in various ways. The most basic is that information is combined between databases by merging records at the individual animal (or lower, for example limb) level. This will be referred to as merging at the individual level in this manuscript. A special case is when records are merged on a higher level, e.g. aggregated at farm or postal code or breed level, which will be referred to as merging atgroup-level. A separate concept is when data are not merged, but data from different sources are directly compared in order to yield fuller understanding of a context. This concept will also be discussed, using the term combining databases. A pre-requisite for individual (or group-level) integration is that the individual identi- fier(s) or “key” variables (e.g. breed, gender, date of birth, postal codes) must be correct and can be utilized for linking across databases, which might explain why such studies are still rare. Aim The aim of this presentation is to briefly review some key papers that exemplify different ways of utilizing data from multiple sources, to highlight the benefits and Page 2 of 6 limitations of the approaches, to discuss key issues/ challenges that must be addressed in any attempts to integrate data and to suggest future directions. Examples from the literature Combining databases Information on reimbursed cases of dystocia and caesar- ean section in a Swedish animal insurance-database [17] during 1995 to 2002 (n=195,931 insured bitches) were combined with information from the Swedish Kennel Club [18] registrations on litters and puppies (n=81,306 litters) born during the same time. Incidence of dystocia among insured Swedish bitches was calculated using the insured population. Furthermore, the proportion of all whelpings that yielded a claim for dystocia was estimated by combining information from the two databases. No formal merging of the databases was performed, and the combined results should be interpreted with cau- tion. However, the two populations are both considered representative of the Swedish dog population where ~30% of dogs are covered by an insurance plan with Agria and >90% of pure-bred dogs are registered with the SKC [19]. In a Norwegian publication, information from a popu- lation-based tumour registry, the Norwegian Canine Cancer Register, was combined with a census among all owners of dogs from the three breeds boxer, bichon frisé and Bernese mountain dog (n=5992 females at risk) [20]. Several methods were used to find all tumours, e.g. skin lumps, removed from the dogs during this period. For example each clinic in four counties signed a written agreement to submit all these to this specific registry and during this time all pathology examinations were free of charge. Based on these two databases both crude and age- specific incidence rates of mammary tumours for the three breeds were calculated. The return-rate for the census which gave rise to the age-distribution among the bitches was 70%, which must be considered good. Potential problems or bias related to the merging or to the assumptions that were made about rate of death in the population were not discussed in the paper [20]. Integrating databases Merging at group-level Breed-level merging has been performed when estimat- ing the relative risk of selected canine cancers [3]. Histologically verified canine tumours : (n=14,401 tumours) from the Norwegian Canine Cancer Register between 1990 and 1998 were used as numerator, and merged by breed to the number of dogs registered with the Norwegian Kennel Club [21]between 1982 and 1997, which was the denominator. The resulting relative risk was presented for five specific tumours among breeds