ArticleJournal of the American Medical Informatics Association : JAMIA
Evaluating predictors of geographic area population size cut-offs to manage re-identification risk.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- The Data Tags Suite (DATS) model for discovering data access and use requirements.GigaScience · 2020Article
- Using mobile location data in biomedical research while preserving privacy.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Anonymisation of geographical distance matrices via Lipschitz embedding.International journal of health geographics · 2016Article
- SHARE: system design and case studies for statistical health information release.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Estimating the re-identification risk of clinical data sets.BMC medical informatics and decision making · 2012Article
- Trends in biomedical informatics: most cited topics from recent years.Journal of the American Medical Informatics Association : JAMIA · 2011Article
- The re-identification risk of Canadians from longitudinal demographics.BMC medical informatics and decision making · 2011Article
- A secure protocol for protecting the identity of providers when disclosing data for disease surveillance.Journal of the American Medical Informatics Association : JAMIA · 2011Article
- Spatial dimensions of research on alcohol and sexual risk: a case example from a Mumbai study.AIDS and behavior · 2010Article
- A method for managing re-identification risk from small geographic areas in Canada.BMC medical informatics and decision making · 2010Article
- Musings on privacy issues in health research involving disaggregate geographic data about individuals.International journal of health geographics · 2009Review
- Protecting count queries in study design.Journal of the American Medical Informatics Association : JAMIAArticle
- Evaluating re-identification risks with respect to the HIPAA privacy rule.Journal of the American Medical Informatics Association : JAMIAArticle
- A globally optimal k-anonymity method for the de-identification of health data.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveIn public health and health services research, the inclusion of geographic information in data sets is critical. Because of concerns over the re-identification of patients, data from small geographic areas are either suppressed or the geographic areas are aggregated into larger ones. Our objective is to estimate the population size cut-off at which a geographic area is sufficiently large so that no data suppression or further aggregation is necessary.
designThe 2001 Canadian census data were used to conduct a simulation to model the relationship between geographic area population size and uniqueness for some common demographic variables. Cut-offs were computed for geographic area population size, and prediction models were developed to estimate the appropriate cut-offs. MEASUREMENTS: Re-identification risk was measured using uniqueness. Geographic area population size cut-offs were estimated using the maximum number of possible values in the data set and a traditional entropy measure.
resultsThe model that predicted population cut-offs using the maximum number of possible values in the data set had R2 values around 0.9, and relative error of prediction less than 0.02 across all regions of Canada. The models were then applied to assess the appropriate geographic area size for the prescription records provided by retail and hospital pharmacies to commercial research and analysis firms.
conclusionsTo manage re-identification risk, the prediction models can be used by public health professionals, health researchers, and research ethics boards to decide when the geographic area population size is sufficiently large.
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