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Indirectly identifying data items allow the direct identifiers of individuals to be found using additional, accessible external information.
The dataset does not contain any identifying data items (e.g. name).
For example, name or residential address are directly identifying data items.
Directly identifying data items contains sufficient information to allow individuals to be identified or located easily in the absence of additional information.
Although each Disease Register does not hold any directly or indirectly identifying data items, the information which is held by it may still be potentially re-identifiable.
One approach is to severely limit the identifying data items which are disclosed to the record linker – an approach which is often termed "anonymous record linkage" [ 6].
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Additionally, these findings could be used to identify data items which may be of relevance to particular local populations, additional items such as religion and diet could be added and collected as necessary.
To identify data items that could reasonably be assumed to comprise a typical clinical registry, we used the Minimum Data Set for Clinical Cancer Registration from New South Wales, Australia.[ 14] It is based on clinical registries in the United States, United Kingdom and Canada and it represents a fairly typical minimum clinical data set.
For each identified data item, a new row should be added to the table.
Each identified data item should be mapped to a corresponding concept (UML class) in the concept model.
The fundamental operation at the core of all forms of record linkage is the comparison of partially-identifying data items from two (or more) data sources.
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