Evidence map›Paper›PMID 34095526›Full record

ArticleInternational journal of population data science2019

Record linkage to enhance consented cohort and routinely collected health data from a UK birth cohort.

Karen Susan Tingay, Amrita Bandyopadhyay, Lucy Griffiths, Ashley Akbari, Sinead Brophy, Helen Bedford, Mario Cortina-Borja, Efrosini Setakis, Suzann Walton, Emla Fitzsimons and 2 more

Abstract read
In one paragraph

Article in International journal of population data science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Potential for advances in data linkage and data science to support injury prevention research.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2024
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
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

12 authors.

Karen Susan TingayOffice for National Statistics, Government Buildings, Cardiff Rd, Duffryn, Newport NP10 8XG.
Amrita BandyopadhyaySwansea University, Population Data Science, Medical School, Singleton Campus, Swansea SA2 8PP.
Lucy GriffithsSwansea University, Population Data Science, Medical School, Singleton Campus, Swansea SA2 8PP.
Ashley AkbariHealth Data Research UK, Swansea University, Swansea University, Population Data Science, Medical School, Singleton Campus, Swansea SA2 8PP.
Sinead BrophySwansea University, Population Data Science, Medical School, Singleton Campus, Swansea SA2 8PP.
Helen BedfordUCL, Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK.
Mario Cortina-BorjaUCL, Great Ormond Street Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK.
Efrosini SetakisNHS Digital, 1 Trevelyan Square, Boar Lane, Leeds, LS1 6AE.
Suzann WaltonHertfordshire County Council, County Hall Pegs Lane Hertford SG13 8DQ, UK.
Emla FitzsimonsCentre for Longitudinal Studies (CLS) UCL Institute of Education University College London 20 Bedford Way London WC1H 0AL, UK.
Carol DezateuxCentre for Primary Care and Public Health, Barts.
Ronan A LyonsHealth Data Research UK, Swansea University, Swansea University, Population Data Science, Medical School, Singleton Campus, Swansea SA2 8PP.

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundIn longitudinal health research, combining the richness of cohort data to the extensiveness of routine data opens up new possibilities, providing information not available from one data source alone. In this study, we set out to extend information from a longitudinal birth cohort study by linking to the cohort child's routine primary and secondary health care data. The resulting linked datasets will be used to examine health outcomes and patterns of health service utilisation for a set of common childhood health problems. We describe the experiences and challenges of acquiring and linking electronic health records for participants in a national longitudinal study, the UK Millennium Cohort Study (MCS).

methodWritten parental consent to link routine health data to survey responses of the MCS cohort member, mother and her partner was obtained for 90.7% of respondents when interviews took place at age seven years in the MCS. Probabilistic and deterministic linkage was used to link MCS cohort members to multiple routinely-collected health data sources in Wales and Scotland.

resultsOverall linkage rates for the consented population using country-specific health service data sources were 97.6% for Scotland and 99.9% for Wales. Linkage rates between different health data sources ranged from 65.3% to 99.6%. Issues relating to acquisition and linkage of data sources are discussed.

conclusionsLinking longitudinal cohort participants with routine data sources is becoming increasingly popular in population data research. Our results suggest that this is a valid method to enhance information held in both sources of data.

Identifiers

PMID34095526
PMCPMC8142967

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.