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J Biomed Inform. 2014 Dec;52:243-50. doi: 10.1016/j.jbi.2014.07.005. Epub 2014 Jul 16.

Size matters: how population size influences genotype-phenotype association studies in anonymized data.

Author information

1
Department of Biomedical Informatics, School of Medicine, Vanderbilt University, 2525 West End Avenue, Suite 1030, Nashville, TN 37203, USA. Electronic address: r.heatherly@vanderbilt.edu.
2
Department of Biomedical Informatics, School of Medicine, Vanderbilt University, 2525 West End Avenue, Suite 1030, Nashville, TN 37203, USA; Department of Medicine, School of Medicine, Vanderbilt University, 2525 West End Avenue, Suite 1030, Nashville, TN 37203, USA.
3
Department of Epidemiology and Biostatistics, University School of Medicine, Case Western Reserve University, USA.
4
Department of Medicine, School of Medicine, Vanderbilt University, 2525 West End Avenue, Suite 1030, Nashville, TN 37203, USA; Department of Pharmacology, School of Medicine, Vanderbilt University, 2525 West End Avenue, Suite 1030, Nashville, TN 37203, USA.
5
Department of Biomedical Informatics, School of Medicine, Vanderbilt University, 2525 West End Avenue, Suite 1030, Nashville, TN 37203, USA; Department of Electrical Engineering and Computer Science, School of Engineering, Vanderbilt University, 2525 West End Avenue, Suite 1030, Nashville, TN 37203, USA.

Abstract

OBJECTIVE:

Electronic medical records (EMRs) data is increasingly incorporated into genome-phenome association studies. Investigators hope to share data, but there are concerns it may be "re-identified" through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data. We systematically investigate this issue using a large-scale biorepository and EMR system through which we evaluate the ability of researchers to learn from anonymized data for genome-phenome association studies under various conditions.

METHODS:

We use a k-anonymization strategy to simulate a data protection process (on data sets containing clinical codes) for resources of similar size to those found at nine academic medical institutions within the United States. Following the protection process, we replicate an existing genome-phenome association study and compare the discoveries using the protected data and the original data through the correlation (r(2)) of the p-values of association significance.

RESULTS:

Our investigation shows that anonymizing an entire dataset with respect to the population from which it is derived yields significantly more utility than small study-specific datasets anonymized unto themselves. When evaluated using the correlation of genome-phenome association strengths on anonymized data versus original data, all nine simulated sites, results from largest-scale anonymizations (population ∼100,000) retained better utility to those on smaller sizes (population ∼6000-75,000). We observed a general trend of increasing r(2) for larger data set sizes: r(2)=0.9481 for small-sized datasets, r(2)=0.9493 for moderately-sized datasets, r(2)=0.9934 for large-sized datasets.

CONCLUSIONS:

This research implies that regardless of the overall size of an institution's data, there may be significant benefits to anonymization of the entire EMR, even if the institution is planning on releasing only data about a specific cohort of patients.

KEYWORDS:

Anonymization; Clinical codes; Data publishing; Privacy

PMID:
25038554
PMCID:
PMC4260994
DOI:
10.1016/j.jbi.2014.07.005
[Indexed for MEDLINE]
Free PMC Article

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