Dataset Information

Completeness and usability of ethnicity data in UK-based primary care and hospital databases.

ABSTRACT: Ethnicity recording across the National Health Service (NHS) has improved dramatically over the past decade. This study profiles the completeness, consistency and representativeness of routinely collected ethnicity data in both primary care and hospital settings.Completeness and consistency of ethnicity recording was examined in the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES), and the ethnic breakdown of the CPRD was compared with that of the 2011 UK censuses.27.1% of all patients in the CPRD (1990-2012) have ethnicity recorded. This proportion rises to 78.3% for patients registered since April 2006. The ethnic breakdown of the CPRD is comparable to the UK censuses. 79.4% of HES inpatients, 46.8% of outpatients and 26.8% of A&E patients had their ethnicity recorded. Amongst those with ethnicity recorded on >1 occasion, consistency was over 90% in all data sets except for HES inpatients. Combining CPRD and HES increased completeness to 97%, with 85% of patients having the same ethnicity recorded in both databases.Using CPRD ethnicity from 2006 onwards maximizes completeness and comparability with the UK population. High concordance within and across NHS sources suggests these data are of high value when examining the continuum of care. Poor completeness and consistency of A&E and outpatient data render these sources unreliable.

SUBMITTER: Mathur R

PROVIDER: S-EPMC4245896 | biostudies-other | 2014 Dec

REPOSITORIES: biostudies-other

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Completeness and usability of ethnicity data in UK-based primary care and hospital databases.

Mathur Rohini R Bhaskaran Krishnan K Chaturvedi Nish N Leon David A DA vanStaa Tjeerd T Grundy Emily E Smeeth Liam L

Journal of public health (Oxford, England) 20131208 4

<h4>Background</h4>Ethnicity recording across the National Health Service (NHS) has improved dramatically over the past decade. This study profiles the completeness, consistency and representativeness of routinely collected ethnicity data in both primary care and hospital settings.<h4>Methods</h4>Completeness and consistency of ethnicity recording was examined in the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES), and the ethnic breakdown of the CPRD was compare ...[more]

PMID: 24323951

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Project description:ObjectivesTo explore whether UK primary care databases arising from two different software systems can be feasibly combined, by comparing rates of Huntington's disease (HD, which is rare) and 14 common cancers in the two databases, as well as characteristics of people with these conditions.DesignDescriptive study.SettingPrimary care electronic health records from Clinical Practice Research Datalink (CPRD) GOLD and CPRD Aurum databases, with linked hospital admission and death registration data.Participants4986 patients with HD and 1 294 819 with an incident cancer between 1990 and 2019.Primary and secondary outcome measuresIncidence and prevalence of HD by calendar period, age group and region, and annual age-standardised incidence of 14 common cancers in each database, and in a subset of 'overlapping' practices which contributed to both databases. Characteristics of patients with HD or incident cancer: medical history, recent prescribing, healthcare contacts and database follow-up.ResultsIncidence and prevalence of HD were slightly higher in CPRD GOLD than CPRD Aurum, but with similar trends over time. Cancer incidence in the two databases differed between 1990 and 2000, but converged and was very similar thereafter. Participants in each database were most similar in terms of medical history (median standardised difference, MSD 0.03 (IQR 0.01-0.03)), recent prescribing (MSD 0.06 (0.03-0.10)) and demographics and general health variables (MSD 0.05 (0.01-0.09)). Larger differences were seen for healthcare contacts (MSD 0.27 (0.10-0.41)), and database follow-up (MSD 0.39 (0.19-0.56)).ConclusionsDifferences in cancer incidence trends between 1990 and 2000 may relate to use of a practice-level data quality filter (the 'up-to-standard' date) in CPRD GOLD only. As well as the impact of data curation methods, differences in underlying data models can make it more challenging to define exactly equivalent clinical concepts in each database. Researchers should be aware of these potential sources of variability when planning combined database studies and interpreting results.

Project description:BackgroundPatient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research.ObjectiveThis study aimed to transform primary care data into the OMOP CDM format.MethodsWe extracted primary care data from electronic health records at a multidisciplinary health center in Wattrelos, France. We performed structural mapping between the design of our local primary care database and the OMOP CDM tables and fields. Local French vocabularies concepts were mapped to OHDSI standard vocabularies. To validate the implementation of primary care data into the OMOP CDM format, we applied a set of queries. A practical application was achieved through the development of a dashboard.ResultsData from 18,395 patients were implemented into the OMOP CDM, corresponding to 592,226 consultations over a period of 20 years. A total of 18 OMOP CDM tables were implemented. A total of 17 local vocabularies were identified as being related to primary care and corresponded to patient characteristics (sex, location, year of birth, and race), units of measurement, biometric measures, laboratory test results, medical histories, and drug prescriptions. During semantic mapping, 10,221 primary care concepts were mapped to standard OHDSI concepts. Five queries were used to validate the OMOP CDM by comparing the results obtained after the completion of the transformations with the results obtained in the source software. Lastly, a prototype dashboard was developed to visualize the activity of the health center, the laboratory test results, and the drug prescription data.ConclusionsPrimary care data from a French health care facility have been implemented into the OMOP CDM format. Data concerning demographics, units, measurements, and primary care consultation steps were already available in OHDSI vocabularies. Laboratory test results and drug prescription data were mapped to available vocabularies and structured in the final model. A dashboard application provided health care professionals with feedback on their practice.

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Project description:OBJECTIVES:To summarise the definitions and combinations of codes used to identify outcomes of anxiety, depression, fatigue, cognitive dysfunction (including mild cognitive dysfunction and dementia), sexual dysfunction, pain, sleep disorders, and fatal and non-fatal self-harm in studies using electronic health records from primary care databases in the UK. DESIGN:Systematic review. DATA SOURCES:Medline, Embase and lists of publications of the main primary care databases in the UK. ELIGIBILITY CRITERIA:Included data from a UK primary care database and studied outcome(s) of interest. DATA EXTRACTION AND SYNTHESIS:We abstracted information on the outcomes definition and codelists. When necessary, authors were contacted to request codelists. RESULTS:120 studies were eligible. Codelists were available for 17/42 studies of depression; 21/41 studies of fatal and non-fatal self-harm; 17/27 studies of dementia/cognitive dysfunction; 5/12 studies of anxiety; 4/8 studies of pain; 3/6 studies of fatigue and sexual dysfunction; 1/2 studies of sleep disorders. Depression was most often defined using codes for diagnoses (37/42 studies) and/or antidepressants prescriptions (21/42 studies); six studies reported including symptoms in their definition. Anxiety was defined with codes for diagnoses (12/12 studies); four studies also reported including symptoms. Fatal self-harm was ascertained in primary care data linked to the Office for National Statistics mortality database in nine studies. Most studies of cognitive dysfunction included Alzheimer's disease, and vascular and frontotemporal dementia. Fatigue definitions varied little, including chronic fatigue syndrome, neurasthenia and postviral fatigue syndrome. All studies of sexual dysfunction focused on male conditions, principally erectile dysfunction. Sleep disorders included insomnia and hypersomnia. There was substantial variability in the codelists; validation was carried out i21/120 studies. CONCLUSIONS:There is a need for standardised definitions and validated list of codes to assess mental health and quality of life outcomes in primary care databases in the UK.

Dataset Information

Completeness and usability of ethnicity data in UK-based primary care and hospital databases.

Publications

Completeness and usability of ethnicity data in UK-based primary care and hospital databases.

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