Evidence map›Paper›PMID 37200084›Full record

SynthesisJournal of medical Internet research2023

Development of Indirect Health Data Linkage on Health Product Use and Care Trajectories in France: Systematic Review.

Florence Ranchon, Sébastien Chanoine, Sophie Lambert-Lacroix, Jean-Luc Bosson, Alexandre Moreau-Gaudry, Pierrick Bedouch

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Florence RanchonHospices Civils de Lyon, Groupement Hospitalier Sud, Unité de pharmacie clinique oncologique, Pierre-Bénite, France.ORCID 0000-0002-9181-4231
Sébastien ChanoineTranslational Innovation in Medicine and Complexity - Unité Mixte de Recherche 5525, Université Grenoble Alpes, Grenoble, France.ORCID 0000-0001-7708-9777
Sophie Lambert-LacroixTranslational Innovation in Medicine and Complexity - Unité Mixte de Recherche 5525, Université Grenoble Alpes, Grenoble, France.ORCID 0009-0003-6111-5303
Jean-Luc BossonTranslational Innovation in Medicine and Complexity - Unité Mixte de Recherche 5525, Université Grenoble Alpes, Grenoble, France.ORCID 0000-0003-0967-6026
Alexandre Moreau-GaudryTranslational Innovation in Medicine and Complexity - Unité Mixte de Recherche 5525, Université Grenoble Alpes, Grenoble, France.ORCID 0009-0009-1402-4920
Pierrick BedouchTranslational Innovation in Medicine and Complexity - Unité Mixte de Recherche 5525, Université Grenoble Alpes, Grenoble, France.ORCID 0000-0003-2245-0160

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEuropean national disparities in the integration of data linkage (ie, being able to match patient data between databases) into routine public health activities were recently highlighted. In France, the claims database covers almost the whole population from birth to death, offering a great research potential for data linkage. As the use of a common unique identifier to directly link personal data is often limited, linkage with a set of indirect key identifiers has been developed, which is associated with the linkage quality challenge to minimize errors in linked data.

objectiveThe aim of this systematic review is to analyze the type and quality of research publications on indirect data linkage on health product use and care trajectories in France.

methodsA comprehensive search for all papers published in PubMed/Medline and Embase databases up to December 31, 2022, involving linked French database focusing on health products use or care trajectories was realized. Only studies based on the use of indirect identifiers were included (ie, without a unique personal identifier available to easily link the databases). A descriptive analysis of data linkage with quality indicators and adherence to the Bohensky framework for evaluating data linkage studies was also realized.

resultsIn total, 16 papers were selected. Data linkage was performed at the national level in 7 (43.8%) cases or at the local level in 9 (56.2%) studies. The number of patients included in the different databases and resulting from data linkage varied greatly, respectively, from 713 to 75,000 patients and from 210 to 31,000 linked patients. The diseases studied were mainly chronic diseases and infections. The objectives of the data linkage were multiple: to estimate the risk of adverse drug reactions (ADRs; n=6, 37.5%), to reconstruct the patient's care trajectory (n=5, 31.3%), to describe therapeutic uses (n=2, 12.5%), to evaluate the benefits of treatments (n=2, 12.5%), and to evaluate treatment adherence (n=1, 6.3%). Registries are the most frequently linked databases with French claims data. No studies have looked at linking with a hospital data warehouse, a clinical trial database, or patient self-reported databases. The linkage approach was deterministic in 7 (43.8%) studies, probabilistic in 4 (25.0%) studies, and not specified in 5 (31.3%) studies. The linkage rate was mainly from 80% to 90% (reported in 11/15, 73.3%, studies). Adherence to the Bohensky framework for evaluating data linkage studies showed that the description of the source databases for the linkage was always performed but that the completion rate and accuracy of the variables to be linked were not systematically described.

conclusionsThis review highlights the growing interest in health data linkage in France. Nevertheless, regulatory, technical, and human constraints remain major obstacles to their deployment. The volume, variety, and validity of the data represent a real challenge, and advanced expertise and skills in statistical analysis and artificial intelligence are required to treat these big data.

Indexed as

Artificial IntelligenceInformation Storage and RetrievalBig DataHospitalsHumansRegistriesbig datacare trajectoriesdata linkagedeterministic approachFrancehealth datahealth databasehealth productsintegrationlinkageprobabilistic approachpublic health activityusability

Identifiers

PMID37200084
PMCPMC10236279

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.