ArticleCanadian journal of public health = Revue canadienne de sante publique2026
Synthetic health data in Canada: A scoping review of methods, applications, and data sources.
Article in Canadian journal of public health = Revue canadienne de sante publique, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccess to provincial health-related data for multi-jurisdictional studies in Canada is restricted by privacy laws. Synthetic data (SD), which mimic real data, can facilitate privacy preservation. However, information on SD use in Canadian research is limited.
objectivesTo review characteristics, methods, and applications of published studies generating SD from Canadian health data (HD), including administrative, survey, public health, and clinical sources.
methodsWe conducted a scoping review following Arksey and O'Malley, Joanna Briggs Institute, and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines on studies (to September 2024) generating SD from provincial/national HD. We included English-language peer-reviewed articles and grey literature, identified through PubMed, Scopus, Web of Science, Google, and references. We extracted and descriptively analyzed data on HD types, research purposes, geographic sources, synthesis methods, and quality evaluation. SYNTHESIS: Of 232 identified articles, 31 were reviewed and nine met inclusion criteria; three additional articles were found through references and Google. Eleven articles were peer-reviewed. Topics included data replication, bias mitigation, and privacy-risk assessment. Survey data were most commonly synthesized. SD were generated from national/provincial datasets, including Canadian Community Health Survey and administrative/clinical data from Alberta, Manitoba, British Columbia, and Ontario. Synthesis methods included generative, sampling, and predictive models. Data quality evaluations assessed replicability, privacy risk, and predictive performance.
conclusionSD have mainly been used in single-province studies and national surveys. Broader use in clinical and public HD with methodological consistency could strengthen its role for privacy-protecting, multi-jurisdictional research and surveillance initiatives.
Indexed as
Identifiers
41663859What Socratic 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 Socratic graph.