Title |
Evaluating the risk of patient re-identification from adverse drug event reports
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Published in |
BMC Medical Informatics and Decision Making, October 2013
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DOI | 10.1186/1472-6947-13-114 |
Pubmed ID | |
Authors |
Khaled El Emam, Fida K Dankar, Angelica Neisa, Elizabeth Jonker |
Abstract |
Our objective was to develop a model for measuring re-identification risk that more closely mimics the behaviour of an adversary by accounting for repeated attempts at matching and verification of matches, and apply it to evaluate the risk of re-identification for Canada's post-marketing adverse drug event database (ADE).Re-identification is only demonstrably plausible for deaths in ADE. A matching experiment between ADE records and virtual obituaries constructed from Statistics Canada vital statistics was simulated. A new re-identification risk is considered, it assumes that after gathering all the potential matches for a patient record (all records in the obituaries that are potential matches for an ADE record), an adversary tries to verify these potential matches. Two adversary scenarios were considered: (a) a mildly motivated adversary who will stop after one verification attempt, and (b) a highly motivated adversary who will attempt to verify all the potential matches and is only limited by practical or financial considerations. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 2 | 20% |
Canada | 2 | 20% |
United Kingdom | 1 | 10% |
India | 1 | 10% |
Indonesia | 1 | 10% |
Spain | 1 | 10% |
Unknown | 2 | 20% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 6 | 60% |
Scientists | 3 | 30% |
Practitioners (doctors, other healthcare professionals) | 1 | 10% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Finland | 1 | 3% |
Unknown | 32 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 9 | 27% |
Student > Ph. D. Student | 7 | 21% |
Researcher | 6 | 18% |
Student > Bachelor | 2 | 6% |
Student > Doctoral Student | 1 | 3% |
Other | 4 | 12% |
Unknown | 4 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 8 | 24% |
Medicine and Dentistry | 5 | 15% |
Nursing and Health Professions | 3 | 9% |
Business, Management and Accounting | 2 | 6% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
Other | 5 | 15% |
Unknown | 9 | 27% |