Evidence map›Paper›PMID 34549093›Full record

ArticleInternational journal of population data science2021

A bespoke data linkage of an IVF clinical quality registry to population health datasets; methods and performance.

Georgina M Chambers, Stephanie K Y Choi, Katie Irvine, Christos Venetis, Katie Harris, Alys Havard, Robert J Norman, Kei Lui, William Ledger, Louisa R Jorm

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of population data science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 26% of its field
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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

10 authors at 4 institutions in 1 country.

Georgina M ChambersCentre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Stephanie K Y ChoiCentre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Katie IrvineCentre for Health Record Linkage, Ministry of Health, New South Wales, Australia.
Christos VenetisCentre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Katie HarrisThe George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Alys HavardCentre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Robert J NormanThe Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia.
Kei LuiSchool of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
William LedgerSchool of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
Louisa R JormCentre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
UNSW Sydney · AUNew South Wales Department of Health · AUThe George Institute for Global Health · AUUniversity of Adelaide · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAssisted reproductive technologies (ART), such as in-vitro fertilisation (IVF), have revolutionised the treatment of infertility, with an estimated 8 million babies born worldwide. However, the long-term health outcomes for women and their offspring remain an area of concern. Linking IVF treatment data to long-term health data is the most efficient method for assessing such outcomes.

objectivesTo describe the creation and performance of a bespoke population-based data linkage of an ART clinical quality registry to state-based and national administrative datasets.

methodsThe linked dataset was created by deterministically and probabilistically linking the Australia and New Zealand Assisted Reproduction Database (ANZARD) to New South Wales (NSW) and Australian Capital Territory (ACT) administrative datasets (performed by NSW Centre for Health Record Linkage (CHeReL)) and to national claims datasets (performed by Australian Institute of Health and Welfare (AIHW)). The CHeReL's Master Linkage Key (MLK) was used as a bridge between ANZARD's partially identifiable patient data (statistical linkage key) and NSW and ACT administrative datasets. CHeReL then provided personal identifiers to the AIHW to obtain national content data. The results of the linkage were reported, and concordance between births recorded in ANZARD and perinatal data collections (PDCs) was evaluated.

resultsOf the 62,833 women who had ART treatment in NSW or ACT, 60,419 could be linked to the CHeReL MLK (linkage rate: 96.2%). A reconciliation of ANZARD-recorded births among NSW residents found that 94.2% (95% CI: 93.9-94.4%) of births were also recorded in state/territory-based PDCs. A high concordance was found in plurality status and birth outcome ≥99% agreement rate, Cohen's kappa ranged: 0.78-0.98) between ANZARD and PDCs.

conclusionThe data linkage resource demonstrates that high linkage rates can be achieved with partially identifiable data and that a population spine, such as the CHeReL's MLK, can be successfully used as a bridge between clinical registries and administrative datasets.

Indexed as

Population HealthReproductive Techniques, AssistedAustraliaFemaleFertilization in VitroHumansInformation Storage and RetrievalPregnancyRegistriesassisted reproductive techniquesaustraliadata linkageinfertilitypregnancy outcome

Identifiers

PMID34549093
PMCPMC8436881
OpenAlexW3199281423

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.