Causal analyses using education-health linked data for England: a case study

Causal analyses using education-health linked data for England: a case study | medRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Causal analyses using education-health linked data for England: a case study View ORCID Profile Bianca L De Stavola , View ORCID Profile Andrea Aparicio Castro , View ORCID Profile Vincent G. Nguyen , View ORCID Profile Kate Lewis , View ORCID Profile Lorraine Dearden , View ORCID Profile Katie Harron , View ORCID Profile Ania Zylbersztejn , View ORCID Profile Julia Shumway , View ORCID Profile Ruth Gilbert the HOPE study team doi: https://doi.org/10.64898/2026.03.13.26348340 Bianca L De Stavola 1 Great Ormond Street Institute of Child Health, University College London , London WC1N 1EH, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bianca L De Stavola For correspondence: b.destavola{at}ucl.ac.uk Andrea Aparicio Castro 1 Great Ormond Street Institute of Child Health, University College London , London WC1N 1EH, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrea Aparicio Castro Vincent G. Nguyen 1 Great Ormond Street Institute of Child Health, University College London , London WC1N 1EH, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vincent G. Nguyen Kate Lewis 1 Great Ormond Street Institute of Child Health, University College London , London WC1N 1EH, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kate Lewis Lorraine Dearden 2 Social Research Institute, University College London , London, WC1H 0AL, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lorraine Dearden Katie Harron 1 Great Ormond Street Institute of Child Health, University College London , London WC1N 1EH, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katie Harron Ania Zylbersztejn 1 Great Ormond Street Institute of Child Health, University College London , London WC1N 1EH, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ania Zylbersztejn Julia Shumway 1 Great Ormond Street Institute of Child Health, University College London , London WC1N 1EH, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julia Shumway Ruth Gilbert 1 Great Ormond Street Institute of Child Health, University College London , London WC1N 1EH, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ruth Gilbert Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Introduction This article summarises lessons learnt from the Health Outcomes for young People throughout Education (HOPE) Study and serves as a real world, transferable application for addressing causal questions using administrative data. The HOPE study applied causal methods to analyses of administrative data in Education and Child Health Insights from Linked Data (ECHILD) aimed at studying the effectiveness of provision for special educational needs and disability (SEND) on health and education outcomes. Methods Defining causal questions regarding the impact of SEND provision required judicious mapping of the question onto the data, leading to the selection of appropriate measures of effect, transparent handling of the data and control of confounding factors to estimate effects. We adopted the target trial emulation framework to guide these steps. Having encountered specific computational challenges in estimating the effects of interest, we simulated data that resembled the HOPE study and used them to practice the implementation of alternative estimation methods and to study impact of some of their assumptions. Results The creation and analysis of the simulated data provided valuable insights. First, we learned the importance of aligning the target of estimation with the causal question at hand. Second, we observed how deviations from assumptions specific to each estimation method can affect results. Third, we highlighted the benefits of employing alternative estimation methods as sensitivity tools that can aid the interpretation of the resulting estimates. Finally, we offer user-friendly code in two programming languages (R and Stata) and accompanying simulated data to facilitate the implementation of these methods for similar causal questions. Conclusion We recommend users of administrative data to fully specify -and possibly revise- the causal questions they wish to address and to carefully examine and compare assumptions, implementation and results obtained using alternative estimation methods. Competing Interest Statement The authors have declared no competing interest. Funding Statement This project is funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number NIHR202025, The HOPE Study). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. ECHILD is supported by ADR UK (Administrative Data Research UK), an Economic and Social Research Council (part of UK Research and Innovation) programme (ES/V000977/1, ES/X003663/1, ES/X000427/1). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Permissions to use linked, de-identified data from HES and NPD were granted by NHS England (DARS-NIC- 381972-Q5F0V-v0.5) and the Department for Education (DR200604.02B). Ethical approval for the ECHILD project was granted by the National Research Ethics Service (17/LO/1494), NHS Health Research Authority Research Ethics Committee (20/EE/0180 and 21/SW/0159). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes ↵ ^ Membership of the HOPE study team is provided in the acknowledgments Data Availability Statement The ECHILD database is made available for free for approved research based in the UK, via the ONS Secure Research Service. Enquiries to access the ECHILD database can be made by emailing ich.echild{at}ucl.ac.uk . Researchers will need to be approved and submit a successful application to the ECHILD Data Access Committee and ONS Research Accreditation Panel to access the data, with strict statistical disclosure controls of all outputs of analyses. Abbreviations: AIPW Augmented inverse probability weighting; ATE Average treatment effect; ATT average treatment effect in the treated; CI confidence interval; CLP left lip and/or palate; CP cerebral palsy; ECHILD Education and Child Health Insights from Linked Data; EYFSP early years foundation stage profile; GMM Generalised method of moments; HOPE Health Outcomes of young People throughout Education; IDACI Income Deprivation Affecting Children Index; IPW Inverse probability weighting; PO potential outcome; PS propensity score model; SEND special education need and disabilities; Y outcome; 2SLS 2 stage-least-squares. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license . Back to top Previous Next Posted March 19, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Causal analyses using education-health linked data for England: a case study Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. 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