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Using data linkage to electronic patient records to assess the validity of selected mental health diagnoses in English Hospital Episode Statistics (HES).

ABSTRACT: BACKGROUND:Administrative data can be used to support research, such as in the UK Biobank. Hospital Episode Statistics (HES) are national data for England that include contain ICD-10 diagnoses for inpatient mental healthcare episodes, but the validity of these diagnoses for research purposes has not been assessed. METHODS:250 peoples' HES records were selected based on a HES recorded inpatient stay at the South London and Maudsley NHS Foundation Trust with a diagnosis of schizophrenia, a wider schizophrenia spectrum disorder, bipolar affective disorder or unipolar depression. A gold-standard research diagnosis was made using Clinical Records Interactive Search pseudonymised electronic patient records using, and the OPCRIT+ algorithm. RESULTS:Positive predictive value at the level of lifetime psychiatric disorder was 100%, and at the level of lifetime diagnosis in the four categories of schizophrenia, wider schizophrenia spectrum, bipolar or unipolar depression was 73% (68-79). Agreement varied by diagnosis, with schizophrenia having the highest PPV at 90% (80-96). Each person had an average of five psychiatric HES records. An algorithm that looked at the last recorded psychiatric diagnosis led to greatest overall agreement with the research diagnosis. DISCUSSION:For people who have a HES record from a psychiatric admission with a diagnosis of schizophrenia spectrum disorder, bipolar affective disorder or unipolar depression, HES records appear to be a good indicator of a mental disorder, and can provide a diagnostic category with reasonable certainty. For these diagnoses, HES records can be an effective way of ascertaining psychiatric diagnosis.

SUBMITTER: Davis KAS

PROVIDER: S-EPMC5868851 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Using data linkage to electronic patient records to assess the validity of selected mental health diagnoses in English Hospital Episode Statistics (HES).

Davis Katrina Alice Southworth KAS Bashford Oliver O Jewell Amelia A Shetty Hitesh H Stewart Robert J RJ Sudlow Cathie L M CLM Hotopf Matthew Hugo MH

PloS one 20180326 3

<h4>Background</h4>Administrative data can be used to support research, such as in the UK Biobank. Hospital Episode Statistics (HES) are national data for England that include contain ICD-10 diagnoses for inpatient mental healthcare episodes, but the validity of these diagnoses for research purposes has not been assessed.<h4>Methods</h4>250 peoples' HES records were selected based on a HES recorded inpatient stay at the South London and Maudsley NHS Foundation Trust with a diagnosis of schizophr ...[more]

PMID: 29579109

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Project description: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 measuresTwo 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=0.02) but not in HES-ONS data (OR 1.05; 95% CI 0.98 to 1.13; p=0.16). Otherwise, little change was found in the strength of associated risk factors and mortality after accounting for missed matching bias.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.

Project description:ObjectivesThe creation and evaluation of a national record linkage between substance misuse treatment, and inpatient hospitalisation data in England.DesignA deterministic record linkage using personal identifiers to link the National Drug Treatment Monitoring System (NDTMS) curated by Public Health England (PHE), and Hospital Episode Statistics (HES) Admitted Patient Care curated by National Health Service (NHS) Digital.Setting and participantsAdults accessing substance misuse treatment in England between 1 April 2018 and 31 March 2019 (n=268 251) were linked to inpatient hospitalisation records available since 1 April 1997.Outcome measuresUsing a gold-standard subset, linked using NHS number, we report the overall linkage sensitivity and precision. Predictors for linkage error were identified, and inverse probability weighting was used to interrogate any potential impact on the analysis of length of hospital stay.Results79.7% (n=213 814) people were linked to at least one HES record, with an estimated overall sensitivity of between 82.5% and 83.3%, and a precision of between 90.3% and 96.4%. Individuals were more likely to link if they were women, white and aged between 46 and 60. Linked individuals were more likely to have an average length of hospital stay ≥5 days if they were men, older, had no fixed residential address or had problematic opioid use. These associations did not change substantially after probability weighting, suggesting they were not affected by bias from linkage error.ConclusionsLinkage between substance misuse treatment and hospitalisation records offers a powerful new tool to evaluate the impact of treatment on substance related harm in England. While linkage error can produce misleading results, linkage bias appears to have little effect on the association between substance misuse treatment and length of hospital admission. As subsequent analyses are conducted, potential biases associated with the linkage process should be considered in the interpretation of any findings.

Project description:IntroductionCardiovascular diseases (CVDs) are among the leading causes of death globally. Electronic health records (EHRs) provide a rich data source for research on CVD risk factors, treatments and outcomes. Researchers must be confident in the validity of diagnoses in EHRs, particularly when diagnosis definitions and use of EHRs change over time. Our systematic review provides an up-to-date appraisal of the validity of stroke, acute coronary syndrome (ACS) and heart failure (HF) diagnoses in European primary and secondary care EHRs.Methods and analysisWe will systematically review the published and grey literature to identify studies validating diagnoses of stroke, ACS and HF in European EHRs. MEDLINE, EMBASE, SCOPUS, Web of Science, Cochrane Library, OpenGrey and EThOS will be searched from the dates of inception to April 2019. A prespecified search strategy of subject headings and free-text terms in the title and abstract will be used. Two reviewers will independently screen titles and abstracts to identify eligible studies, followed by full-text review. We require studies to compare clinical codes with a suitable reference standard. Additionally, at least one validation measure (sensitivity, specificity, positive predictive value or negative predictive value) or raw data, for the calculation of a validation measure, is necessary. We will then extract data from the eligible studies using standardised tables and assess risk of bias in individual studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data will be synthesised into a narrative format and heterogeneity assessed. Meta-analysis will be considered when a sufficient number of homogeneous studies are available. The overall quality of evidence will be assessed using the Grading of Recommendations, Assessment, Development and Evaluation tool.Ethics and disseminationThis is a systematic review, so it does not require ethical approval. Our results will be submitted for peer-review publication.Prospero registration numberCRD42019123898.

Dataset Information

Using data linkage to electronic patient records to assess the validity of selected mental health diagnoses in English Hospital Episode Statistics (HES).

Publications

Using data linkage to electronic patient records to assess the validity of selected mental health diagnoses in English Hospital Episode Statistics (HES).

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