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Cohort profile: The East London Health and Care Partnership Data Repository: Using novel integrated data to support commissioning and research

Research output: Contribution to journalArticle

Amy Ronaldson, Evangelos Chandakas, Qiongwen Kang, Katie Brennan, Aminat Akande, Irene Ebyarimpa, Eleanor Wyllie, George Howard, Richard Fradgley, Mark Freestone, Kamaldeep Bhui

Original languageEnglish
Pages (from-to)e037183
JournalBMJ Open
Volume10
Issue number9
DOIs
Published18 Sep 2020

Bibliographical note

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

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Abstract

PURPOSE: The East London Health and Care Partnership (ELHCP) Data Repository was established to support commissioning decisions in London. This dataset comprises routine clinical data for the general practitioner (GP)-registered populations of two London boroughs, Tower Hamlets and City and Hackney, and provides a rich source of demographic, clinical and health service use data of relevance to clinicians, commissioners, researchers and policy makers. This paper describes the dataset in its current form, its representativeness and data completeness.

PARTICIPANTS: There were 351 749 and 344 511 members of the GP-registered population in the two boroughs, respectively, for the financial year 2017/2018. Demographic information and prevalence data were available for 9 mental health and 15 physical health conditions. Prevalence rates from the cohort were compared with local and national data. In order to illustrate the health service use data available in the dataset, emergency department use across mental health conditions was described. Information about data completeness was provided.

FINDINGS TO DATE: The ELHCP Data Repository provides a rich source of information about a relatively young, urban, ethnically diverse, population within areas of socioeconomic deprivation. Prevalence data were in line with local and national statistics with some exceptions. Physical health conditions were more common in those with mental health conditions, reflecting that comorbidities are the norm rather than the exception. This has implications for integrated care. Data completeness for risk factors (eg, blood pressure, cholesterol) was high in patients with long-term conditions.

FUTURE PLANS: The data are being further cleaned and evaluated using imputation, Bayesian and economic methods, principally focusing on specific cohorts, including type II diabetes, depression and personality disorder. Data continue to be collected for the foreseeable future to support commissioning decisions, which will also enable more long-term prospective analysis as data become available at the end of each financial year.

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