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

Objectives Linkage 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. U...

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Main Authors: Ruth Gilbert, Johnny Downs, Robert Stewart, Amelia Jewell, Matthew Broadbent, Richard D Hayes
Format: Article
Language:English
Published: BMJ Publishing Group 2020-07-01
Series:BMJ Open
Online Access:https://bmjopen.bmj.com/content/10/7/e035884.full
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author Ruth Gilbert
Johnny Downs
Robert Stewart
Amelia Jewell
Matthew Broadbent
Richard D Hayes
author_facet Ruth Gilbert
Johnny Downs
Robert Stewart
Amelia Jewell
Matthew Broadbent
Richard D Hayes
author_sort Ruth Gilbert
collection DOAJ
description Objectives Linkage 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.Setting The 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.Participants Records 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.Results Of 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.Conclusions Despite 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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spelling doaj-art-2d240303b23a4f8ca99713cc7f3227ba2024-12-04T16:40:08ZengBMJ Publishing GroupBMJ Open2044-60552020-07-0110710.1136/bmjopen-2019-035884Impact 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 studyRuth Gilbert0Johnny Downs1Robert Stewart2Amelia Jewell3Matthew Broadbent4Richard D Hayes5Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UKNIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UKSouth London and Maudsley NHS Foundation Trust, London, UKNIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UKDepartment of Psychological Medicine, IoPPN, King’s College London, London, UKInstitute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UKObjectives Linkage 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.Setting The 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.Participants Records 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.Results Of 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.Conclusions Despite 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.https://bmjopen.bmj.com/content/10/7/e035884.full
spellingShingle Ruth Gilbert
Johnny Downs
Robert Stewart
Amelia Jewell
Matthew Broadbent
Richard D Hayes
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
BMJ Open
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
url https://bmjopen.bmj.com/content/10/7/e035884.full
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