Measuring prevalence, reliability and variation in high-risk prescribing in general practice using multilevel modelling of observational data in a population database

Author:

Guthrie Bruce1,Yu Ning23,Murphy Douglas1,Donnan Peter T1,Dreischulte Tobias2

Affiliation:

1. Quality, Safety and Informatics Research Group, Population Health Sciences Division, Medical Research Institute, University of Dundee, Dundee, UK

2. Tayside Medicine Unit, NHS Tayside, Dundee, UK

3. Institute of Epidemiology and Health, Faculty of Population Health Sciences, University College London, London, UK

Abstract

BackgroundHigh-risk primary care prescribing is common and is known to vary considerably between practices, but the extent to which high-risk prescribing varies among individual general practitioners (GPs) is not known.ObjectivesTo create prescribing safety indicators usable in existing electronic clinical data and to examine (1) variation in high-risk prescribing between patients, GPs and practices including reliability of measurement and (2) changes over time in high-risk prescribing prevalence and variation between practices.DesignDescriptive analysis and multilevel logistic regression modelling of routine data.SettingUK general practice using routine electronic medical record data.Participants(1) For analysis of variation and reliability, 398 GPs and 26,539 patients in 38 Scottish practices. (2) For analysis of change in high-risk prescribing, ≈ 300,000 patients particularly vulnerable to adverse drug effects registered with 190 Scottish practices.Main outcome measuresFor the analysis of variation between practices and between GPs, five indicators of high-risk non-steroidal anti-inflammatory drug (NSAID) prescribing. For the analysis of change in high-risk prescribing, 19 previously validated indicators.ResultsMeasurement of high-risk prescribing at GP level was feasible only for newly initiated drugs and for drugs similar to NSAIDs which are usually initiated by GPs. There was moderate variation between practices in total high-risk NSAID prescribing [intraclass correlation coefficient (ICC) 0.034], but this indicator was highly reliable (> 0.8 for all practices) at distinguishing between practices because of the large number of patients being measured. There was moderate variation in initiation of high-risk NSAID prescribing between practices (ICC 0.055) and larger variation between GPs (ICC 0.166), but measurement did not reliably distinguish between practices and had reliability > 0.7 for only half of the GPs in the study. Between quarter (Q)2 2004 and Q1 2009, the percentage of patients exposed to high-risk prescribing measured by 17 indicators that could be examined over the whole period fell from 8.5% to 5.2%, which was largely driven by reductions in high-risk NSAID and antiplatelet use. Variation between practices increased for five indicators and decreased for five, with no relationship between change in the rate of high-risk prescribing and change in variation between practices.ConclusionsHigh-risk prescribing is common and varies moderately between practices. High-risk prescribing at GP level cannot be easily measured routinely because of the difficulties in accurately identifying which GP actually prescribed the drug and because drug initiation is often a shared responsibility with specialists. For NSAID initiation, there was approximately three times greater variation between GPs than between practices. Most GPs with above average high-risk prescribing worked in practices which were not themselves above average. The observed reductions in high-risk prescribing between 2004 and 2009 were largely driven by falls in NSAID and antiplatelet prescribing, and there was no relationship between change in rate and change in variation between practices. These results are consistent with improvement interventions in all practices being more appropriate than interventions targeted on practices or GPs with higher than average high-risk prescribing. There is a need for research to understand why high-risk prescribing varies and to design and evaluate interventions to reduce it.FundingFunding for this study was provided by the Health Services and Delivery Research programme of the National Institute for Health Research.

Funder

Health Services and Delivery Research (HS&DR) Programme

Publisher

National Institute for Health Research

Subject

General Economics, Econometrics and Finance

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