Literature DB >> 10672892

Use of routine healthcare data in safe and cost-effective drug use.

C J Currie1, T M MacDonald.   

Abstract

Routine healthcare data is becoming widely available, usually as a result of administrative systems. Other related data are also often available, such as biochemistry results, mortality data, and sometimes prescribing data. These records are often linked via a common identification system or by probability matching techniques. These data sources offer many opportunities to undertake research, and where prescription data are recorded and linked, the facility to research the outcome of drug use often exists. There are now a number of research agencies around the world that use these large routine data sources to undertake drug safety and outcome studies. The purpose of this commentary is to describe some of the history behind the development of these systems, illustrate some of their uses with respect to postmarketing drug safety and to other healthcare research objectives. The review then describes the data sources necessary to develop a system that would offer an optimal system to undertake a range of studies, including population drug safety surveillance. There are both positive and negative considerations when using routine data. On the positive side, these data come from 'real life' experiences and not from the clinical trial situation. On the other hand, there are important biases to be aware of such as confounding by indication. On the whole, it is argued that large databases originating from routine healthcare procedures have an important role to play in the cost-effective prescription drug use in the postmarketing setting. These systems cannot replace other methods of drug safety evaluation but they do offer an important adjunct to spontaneous reporting systems.

Mesh:

Year:  2000        PMID: 10672892     DOI: 10.2165/00002018-200022020-00002

Source DB:  PubMed          Journal:  Drug Saf        ISSN: 0114-5916            Impact factor:   5.606


  17 in total

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Authors:  H Jick; S S Jick; L E Derby
Journal:  BMJ       Date:  1991-03-30

2.  Use of Medicaid data for pharmacoepidemiology.

Authors:  W A Ray; M R Griffin
Journal:  Am J Epidemiol       Date:  1989-04       Impact factor: 4.897

3.  The diabetes audit and research in Tayside Scotland (DARTS) study: electronic record linkage to create a diabetes register. DARTS/MEMO Collaboration.

Authors:  A D Morris; D I Boyle; R MacAlpine; A Emslie-Smith; R T Jung; R W Newton; T M MacDonald
Journal:  BMJ       Date:  1997-08-30

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Journal:  BMJ       Date:  1992-06-06

5.  A cohort study (with re-sampled comparator groups) to measure the association between new NSAID prescribing and upper gastrointestinal hemorrhage and perforation.

Authors:  A D McMahon; J M Evans; G White; F E Murray; M M McGilchrist; D G McDevitt; T M MacDonald
Journal:  J Clin Epidemiol       Date:  1997-03       Impact factor: 6.437

Review 6.  The role of automated record linkage in the postmarketing surveillance of drug safety: a critique.

Authors:  S Shapiro
Journal:  Clin Pharmacol Ther       Date:  1989-10       Impact factor: 6.875

7.  Epidemiology and costs of acute hospital care for cerebrovascular disease in diabetic and nondiabetic populations.

Authors:  C J Currie; C L Morgan; L Gill; N C Stott; J R Peters
Journal:  Stroke       Date:  1997-06       Impact factor: 7.914

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Journal:  J Chronic Dis       Date:  1979

9.  Adherence to insulin treatment, glycaemic control, and ketoacidosis in insulin-dependent diabetes mellitus. The DARTS/MEMO Collaboration. Diabetes Audit and Research in Tayside Scotland. Medicines Monitoring Unit.

Authors:  A D Morris; D I Boyle; A D McMahon; S A Greene; T M MacDonald; R W Newton
Journal:  Lancet       Date:  1997-11-22       Impact factor: 79.321

10.  Computerisation of primary care in Wales.

Authors:  J R Goves; T Davies; T Reilly
Journal:  BMJ       Date:  1991-07-13
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  5 in total

Review 1.  Methods and systems to detect adverse drug reactions in hospitals.

Authors:  P A Thürmann
Journal:  Drug Saf       Date:  2001       Impact factor: 5.606

2.  Enhancing pharmacosurveillance with systematic collection of treatment indication in electronic prescribing: a validation study in Canada.

Authors:  Tewodros Eguale; Nancy Winslade; James A Hanley; David L Buckeridge; Robyn Tamblyn
Journal:  Drug Saf       Date:  2010-07-01       Impact factor: 5.606

3.  [Adverse drugs reactions: diagnosis and assessment].

Authors:  P A Thürmann
Journal:  Pathologe       Date:  2006-02       Impact factor: 1.011

4.  The influence of primary care prescribing rates for new drugs on spontaneous reporting of adverse drug reactions.

Authors:  Richard C Clark; Simon R J Maxwell; Sheena Kerr; Melinda Cuthbert; Duncan Buchanan; Doug Steinke; David J Webb; Nicholas D Bateman
Journal:  Drug Saf       Date:  2007       Impact factor: 5.606

5.  Detection of adverse drug reactions in a neurological department: comparison between intensified surveillance and a computer-assisted approach.

Authors:  Petra A Thuermann; Roland Windecker; Joachim Steffen; Markus Schaefer; Ute Tenter; Erich Reese; Hermann Menger; Klaus Schmitt
Journal:  Drug Saf       Date:  2002       Impact factor: 5.606

  5 in total

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