ArticleBMC medical informatics and decision making2011
The re-identification risk of Canadians from longitudinal demographics.
Article in BMC medical informatics and decision making, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
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- A portrait of older adults in naturally occurring retirement communities in Ontario, Canada: A population-based study.Journal of the American Geriatrics Society · 2025Article
- Synthesize high-dimensional longitudinal electronic health records via hierarchical autoregressive language model.Nature communications · 2023Article
- A one-size-fits-all approach to data-sharing will not suffice in lifecourse research: a grounded theory study of data-sharing from the perspective of participants in a 50-year-old lifecourse study about health and development.BMC medical research methodology · 2023Article
- Synthesize Extremely High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model.Research square · 2023Article
- Accurate training of the Cox proportional hazards model on vertically-partitioned data while preserving privacy.BMC medical informatics and decision making · 2022Article
- Generating high-fidelity privacy-conscious synthetic patient data for causal effect estimation with multiple treatments.Frontiers in artificial intelligence · 2022Article
- Deep Convolutional Generative Adversarial Network-Based EMG Data Enhancement for Hand Motion Classification.Frontiers in bioengineering and biotechnology · 2022Article
- Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis.Journal of medical Internet research · 2021Article
- Fully synthetic neuroimaging data for replication and exploration.NeuroImage · 2020Article
- The Experience of Establishing Data Sharing & Linkage Platforms for Administrative, Research and Community-Service Data.International journal of population data science · 2019Article
- Sharing longitudinal, non-biological birth cohort data: a cross-sectional analysis of parent consent preferences.BMC medical informatics and decision making · 2018Article
- Detecting the Presence of an Individual in Phenotypic Summary Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Vulnerability- and Diversity-Aware Anonymization of Personally Identifiable Information for Improving User Privacy and Utility of Publishing Data.Sensors (Basel, Switzerland) · 2017Article
- Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation.BMC medical informatics and decision making · 2017Article
- Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity.BMC medical informatics and decision making · 2016Article
- Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1.Journal of biomedical informatics · 2015Review
- Size matters: how population size influences genotype-phenotype association studies in anonymized data.Journal of biomedical informatics · 2014Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe public is less willing to allow their personal health information to be disclosed for research purposes if they do not trust researchers and how researchers manage their data. However, the public is more comfortable with their data being used for research if the risk of re-identification is low. There are few studies on the risk of re-identification of Canadians from their basic demographics, and no studies on their risk from their longitudinal data. Our objective was to estimate the risk of re-identification from the basic cross-sectional and longitudinal demographics of Canadians.
methodsUniqueness is a common measure of re-identification risk. Demographic data on a 25% random sample of the population of Montreal were analyzed to estimate population uniqueness on postal code, date of birth, and gender as well as their generalizations, for periods ranging from 1 year to 11 years.
resultsAlmost 98% of the population was unique on full postal code, date of birth and gender: these three variables are effectively a unique identifier for Montrealers. Uniqueness increased for longitudinal data. Considerable generalization was required to reach acceptably low uniqueness levels, especially for longitudinal data. Detailed guidelines and disclosure policies on how to ensure that the re-identification risk is low are provided.
conclusionsA large percentage of Montreal residents are unique on basic demographics. For non-longitudinal data sets, the three character postal code, gender, and month/year of birth represent sufficiently low re-identification risk. Data custodians need to generalize their demographic information further for longitudinal data sets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.