ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
De-identifying Socioeconomic Data at the Census Tract Level for Medical Research Through Constraint-based Clustering.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Algorithms to anonymize structured medical and healthcare data: A systematic review.Frontiers in bioinformatics · 2022Pooled it
- The Costs of Anonymization: Case Study Using Clinical Data.Journal of medical Internet research · 2024Article
- Impact of social disparities on 10 year survival rates in paediatric cancers: a cohort study.Lancet regional health. Americas · 2023Article
- A phenome-wide association study of polygenic scores for attention deficit hyperactivity disorder across two genetic ancestries in electronic health record data.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2022Article
- Assessing metadata privacy in neuroimaging.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Numerous studies have shown that a person's health status is closely related to their socioeconomic status. It is evident that incorporating socioeconomic data associated with a patient's geographic area of residence into clinical datasets will promote medical research. However, most socioeconomic variables are unique in combination and are affiliated with small geographical regions (e.g., census tracts) that are often associated with less than 20,000 people. Thus, sharing such tract-level data can violate the Safe Harbor implementation of de-identification under the Health Insurance Portability and Accountability Act of 1996 (HIPAA). In this paper, we introduce a constraint-based k-means clustering approach to generate census tract-level socioeconomic data that is de-identification compliant. Our experimental analysis with data from the American Community Survey illustrates that the approach generates a protected dataset with high similarity to the unaltered values, and achieves a substantially better data utility than the HIPAA Safe Harbor recommendation of 3-digit ZIP code.
Indexed as
Identifiers
35309009PMC8861681What OpenQuestion holds
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.