ArticleIEEE journal of biomedical and health informatics2018
On the Accuracy and Scalability of Probabilistic Data Linkage Over the Brazilian 114 Million Cohort.
Article in IEEE journal of biomedical and health informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Re-identification risk for common privacy preserving patient matching strategies when shared with de-identified demographics.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- The Minha Casa Minha Vida social housing programme and leprosy in Brazil: an analysis of the 100 Million Brazilian Cohort (2010-2015).BMC public health · 2025Article
- An efficient learning based approach for automatic record deduplication with benchmark datasets.Scientific reports · 2024Article
- Income Segregation, Conditional Cash Transfers, and Breast Cancer Mortality Among Women in Brazil.JAMA network open · 2024Article
- Incidence and risk factors of tuberculosis among 420 854 household contacts of patients with tuberculosis in the 100 Million Brazilian Cohort (2004-18): a cohort study.The Lancet. Infectious diseases · 2024Article
- CIDACS' efforts towards an inclusive and dialogic data governance in Brazil: a focused literature review.International journal of population data science · 2024Review
- Association of Conditional Cash Transfers With Maternal Mortality Using the 100 Million Brazilian Cohort.JAMA network open · 2023Article
- Cohort Profile: The 100 Million Brazilian Cohort.International journal of epidemiology · 2022Article
- Relationship between the Bolsa Família national cash transfer programme and suicide incidence in Brazil: A quasi-experimental study.PLoS medicine · 2022Article
- The effect of primary health care on tuberculosis in a nationwide cohort of 7·3 million Brazilian people: a quasi-experimental study.The Lancet. Global health · 2022Article
- The intergenerational relationship between conditional cash transfers and newborn health.BMC public health · 2022Article
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- Conditional Cash Transfer Program and Leprosy Incidence: Analysis of 12.9 Million Families From the 100 Million Brazilian Cohort.American journal of epidemiology · 2020Article
- CIDACS-RL: a novel indexing search and scoring-based record linkage system for huge datasets with high accuracy and scalability.BMC medical informatics and decision making · 2020Article
- Incidence of and Factors Associated With Leprosy Among Household Contacts of Patients With Leprosy in Brazil.JAMA dermatology · 2020Article
- Effect of a conditional cash transfer programme on leprosy treatment adherence and cure in patients from the nationwide 100 Million Brazilian Cohort: a quasi-experimental study.The Lancet. Infectious diseases · 2020Article
- Assessing data linkage quality in cohort studies.Annals of human biology · 2020Article
- The Centre for Data and Knowledge Integration for Health (CIDACS): Linking Health and Social Data in Brazil.International journal of population data science · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Data linkage refers to the process of identifying and linking records that refer to the same entity across multiple heterogeneous data sources. This method has been widely utilized across scientific domains, including public health where records from clinical, administrative, and other surveillance databases are aggregated and used for research, decision making, and assessment of public policies. When a common set of unique identifiers does not exist across sources, probabilistic linkage approaches are used to link records using a combination of attributes. These methods require a careful choice of comparison attributes as well as similarity metrics and cutoff values to decide if a given pair of records matches or not and for assessing the accuracy of the results. In large, complex datasets, linking and assessing accuracy can be challenging due to the volume and complexity of the data, the absence of a gold standard, and the challenges associated with manually reviewing a very large number of record matches. In this paper, we present AtyImo, a hybrid probabilistic linkage tool optimized for high accuracy and scalability in massive data sets. We describe the implementation details around anonymization, blocking, deterministic and probabilistic linkage, and accuracy assessment. We present results from linking a large population-based cohort of 114 million individuals in Brazil to public health and administrative databases for research. In controlled and real scenarios, we observed high accuracy of results: 93%-97% true matches. In terms of scalability, we present AtyImo's ability to link the entire cohort in less than nine days using Spark and scaling up to 20 million records in less than 12s over heterogeneous (CPU+GPU) architectures.
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