Using global unique identifiers to link autism collections.
OBJECTIVE
To propose a centralized method for generating global unique identifiers to link collections of research data and specimens.


METHODS
The work is a collaboration between the Simons Foundation Autism Research Initiative and the National Database for Autism Research. The system is implemented as a web service: an investigator inputs identifying information about a participant into a client application and sends encrypted information to a server application, which returns a generated global unique identifier. The authors evaluated the system using a volume test of one million simulated individuals and a field test on 2000 families (over 8000 individual participants) in an autism study.


METHODS
Inverse probability of hash codes; rate of false identity of two individuals; rate of false split of single individual; percentage of subjects for which identifying information could be collected; percentage of hash codes generated successfully.


RESULTS
Large-volume simulation generated no false splits or false identity. Field testing in the Simons Foundation Autism Research Initiative Simplex Collection produced identifiers for 96% of children in the study and 77% of parents. On average, four out of five hash codes per subject were generated perfectly (only one perfect hash is required for subsequent matching).


CONCLUSIONS
The system must achieve balance among the competing goals of distinguishing individuals, collecting accurate information for matching, and protecting confidentiality. Considerable effort is required to obtain approval from institutional review boards, obtain consent from participants, and to achieve compliance from sites during a multicenter study.


CONCLUSIONS
Generic unique identifiers have the potential to link collections of research data, augment the amount and types of data available for individuals, support detection of overlap between collections, and facilitate replication of research findings.
Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA. sbj2@columbia.edu
Stephen B
SB
Johnson
Glen
G
Whitney
Matthew
M
McAuliffe
Hailong
H
Wang
Evan
E
McCreedy
Leon
L
Rozenblit
Clark C
CC
Evans
D001321:Autistic Disorder; D035843:Biomedical Research; D002648:Child; D003219:Confidentiality; D016208:Databases, Factual; D006801:Humans; D007255:Information Services; D016247:Information Storage and Retrieval; D008498:Medical Record Linkage; D010355:Patient Identification Systems; D014481:United States; D014584:User-Computer Interface