Literature DB >> 26213344

Use of demographic and pharmacy data to identify patients included within both the Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN).

Dena M Carbonari1,2, M Elle Saine1,2, Craig W Newcomb1, Betina Blak3, Jason A Roy1,2, Kevin Haynes1,2,4, Jennifer Wood5, Arlene M Gallagher6, Harshvinder Bhullar7, Serena Cardillo8, Sean Hennessy1,2, Brian L Strom1,2,9, Vincent Lo Re1,2,8.   

Abstract

PURPOSE: Pharmacoepidemiology researchers often utilize data from two UK electronic medical record databases, the Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN), and may choose to combine the two in an effort to increase sample size. To minimize duplication of data, previous studies examined the practice-level overlap between these databases. However, the proportion of overlapping patients remains unknown. We developed a method using demographic and pharmacy variables to identify patients included in both CPRD and THIN, and applied this method to measure the proportion of overlapping patients who initiated the oral anti-diabetic drug saxagliptin.
METHODS: We conducted a cross-sectional study among patients initiating saxagliptin in CPRD and THIN between October 2009 and September 2012. Within both databases, we identified patients: (i) ≥18 years, (ii) newly prescribed saxagliptin, and (iii) with ≥180 days enrollment prior to saxagliptin initiation. Demographic data (birth year, sex, patient registration date, family number, and marital status) and prescriptions (including dates) for the first two oral anti-diabetic drugs prescribed within the study period were used to identify matching patients.
RESULTS: Among 4202 CPRD and 3641 THIN patients initiating saxagliptin, 2574 overlapping patients (61% of CPRD saxagliptin initiators; 71% of THIN saxagliptin initiators) were identified. Among these patients, 2474 patients (96%) perfectly matched on all demographic and prescription data.
CONCLUSIONS: Within each database, over 60% of patients initiating saxagliptin were included within both CPRD and THIN. Combined demographic and prescription data can be used to identify patients included in both CPRD and THIN.
Copyright © 2015 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Clinical Practice Research Datalink (CPRD); The Health Improvement Network (THIN); overlap; pharmacoepidemiology

Mesh:

Substances:

Year:  2015        PMID: 26213344     DOI: 10.1002/pds.3844

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  6 in total

1.  Evaluation of methods to estimate missing days' supply within pharmacy data of the Clinical Practice Research Datalink (CPRD) and The Health Improvement Network (THIN).

Authors:  Kirsten J Lum; Craig W Newcomb; Jason A Roy; Dena M Carbonari; M Elle Saine; Serena Cardillo; Harshvinder Bhullar; Arlene M Gallagher; Vincent Lo Re
Journal:  Eur J Clin Pharmacol       Date:  2016-10-27       Impact factor: 2.953

2.  Postauthorization safety study of the DPP-4 inhibitor saxagliptin: a large-scale multinational family of cohort studies of five outcomes.

Authors:  Vincent Lo Re; Dena M Carbonari; M Elle Saine; Craig W Newcomb; Jason A Roy; Qing Liu; Qufei Wu; Serena Cardillo; Kevin Haynes; Stephen E Kimmel; Peter P Reese; David J Margolis; Andrea J Apter; K Rajender Reddy; Sean Hennessy; Harshvinder Bhullar; Arlene M Gallagher; Daina B Esposito; Brian L Strom
Journal:  BMJ Open Diabetes Res Care       Date:  2017-07-31

3.  Spatial distribution of clinical computer systems in primary care in England in 2016 and implications for primary care electronic medical record databases: a cross-sectional population study.

Authors:  Evangelos Kontopantelis; Richard John Stevens; Peter J Helms; Duncan Edwards; Tim Doran; Darren M Ashcroft
Journal:  BMJ Open       Date:  2018-02-28       Impact factor: 2.692

4.  Identifying social factors amongst older individuals in linked electronic health records: An assessment in a population based study.

Authors:  Anu Jain; Albert J van Hoek; Jemma L Walker; Rohini Mathur; Liam Smeeth; Sara L Thomas
Journal:  PLoS One       Date:  2017-11-30       Impact factor: 3.240

5.  Obstructive sleep apnea and the risk of gout: a population-based case-control study.

Authors:  Caroline van Durme; Bart Spaetgens; Johanna Driessen; Johannes Nielen; Manuel Sastry; Annelies Boonen; Frank de Vries
Journal:  Arthritis Res Ther       Date:  2020-04-25       Impact factor: 5.156

6.  Risk of venous thromboembolism in knee, hip and hand osteoarthritis: a general population-based cohort study.

Authors:  Chao Zeng; Kim Bennell; Zidan Yang; Uyen-Sa D T Nguyen; Na Lu; Jie Wei; Guanghua Lei; Yuqing Zhang
Journal:  Ann Rheum Dis       Date:  2020-09-16       Impact factor: 19.103

  6 in total

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