ArticleBMJ open2020
Impact of matching error on linked mortality outcome in a data linkage of secondary mental health data with Hospital Episode Statistics (HES) and mortality records in South East London: a cross-sectional study.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Augmenting fact and date of death in electronic health records using internet media sources: a validation study from two large healthcare systems.American journal of epidemiology · 2026Article
- Risk functions with outcome measurement error.Biostatistics (Oxford, England) · 2026Article
- The association between violence exposure and general and cause-specific mortality in people using mental health services: cohort study.BJPsych open · 2026Article
- Transparency in the existence, use, and output of a mental health data resource: a descriptive paper from the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC) Clinical Record Interactive Search (CRIS) Platform.International journal of population data science · 2025Article
- A mixed methods study protocol evaluating early screening, triaging, risk assessment and health optimisation in perioperative pathways.PloS one · 2025Article
- Universal Credit receipt among working-age patients who are accessing specialist mental health services: results from a novel data linkage study.Journal of epidemiology and community health · 2024Article
- Defining severity of personality disorder using electronic health records: short report.BJPsych open · 2023Article
- Linking electronic mental healthcare and benefits records in South London: design, procedure and descriptive outcomes.BMJ open · 2023Article
- Data consistency in the English Hospital Episodes Statistics database.BMJ health & care informatics · 2022Article
- Sociodemographic characteristics associated with parenthood amongst patients with a psychotic diagnosis: a cross-sectional study using patient clinical records.Social psychiatry and psychiatric epidemiology · 2022Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesLinkage of electronic health records (EHRs) to Hospital Episode Statistics (HES)-Office for National Statistics (ONS) mortality data has provided compelling evidence for lower life expectancy in people with severe mental illness. However, linkage error may underestimate these estimates. Using a clinical sample (n=265 300) of individuals accessing mental health services, we examined potential biases introduced through missed matching and examined the impact on the association between clinical disorders and mortality.
settingThe South London and Maudsley NHS Foundation Trust (SLaM) is a secondary mental healthcare provider in London. A deidentified version of SLaM's EHR was available via the Clinical Record Interactive Search system linked to HES-ONS mortality records.
participantsRecords from SLaM for patients active between January 2006 and December 2016. OUTCOME MEASURES: Two sources of death data were available for SLaM participants: accurate and contemporaneous date of death via local batch tracing (gold standard) and date of death via linked HES-ONS mortality data. The effect of linkage error on mortality estimates was evaluated by comparing sociodemographic and clinical risk factor analyses using gold standard death data against HES-ONS mortality records.
resultsOf the total sample, 93.74% were successfully matched to HES-ONS records. We found a number of statistically significant administrative, sociodemographic and clinical differences between matched and unmatched records. Of note, schizophrenia diagnosis showed a significant association with higher mortality using gold standard data (OR 1.08; 95% CI 1.01 to 1.15; p
conclusionsDespite significant clinical and sociodemographic differences between matched and unmatched records, changes in mortality estimates were minimal. However, researchers and policy analysts using HES-ONS linked resources should be aware that administrative linkage processes can introduce error.
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