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Predicting Prescribing Costs in General Practice using Practice Demography
Author(s) -
WilsonDavis Keith,
Stevenson William G.,
McGavock Hugh
Publication year - 1997
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/(sici)1099-1557(199705)6:3<189::aid-pds264>3.0.co;2-z
Subject(s) - medical prescription , medicine , audit , demography , clinical practice , primary care , health care , family medicine , variation (astronomy) , actuarial science , nursing , accounting , physics , sociology , astrophysics , economics , business , economic growth
Prescription and dispensing costs form a large part ( c . 56%) of primary care expenditure in the NHS and concern has been expressed at its ever increasing total. Previous predictive models have either failed to account for a high proportion of costs or else have not been able to explain adequately the role practice list demography plays upon costs. Using prescription data and the practice demography, our model accounts for 91.4% of the variation in primary health care prescribing costs in Northern Ireland thus explaining them to a much greater extent than previous models and, in addition, explains a large part of the variation in total monthly consultations and numbers of prescriptions. In addition to comprehensiveness it has a high degree of parsimony, needing only three independent variables for each practice, namely, the number of children aged 0–4 years, the number of persons aged 60+ years and the number of partners in the practice, all of which are immediately comprehensible by GPs and their negotiators. Thus, it could form a valuable addition to the ‘evaluation kit’ of prescribing advisers and others concerned with auditing and containing costs. Previous studies have shown the importance of the age–sex structure of practice lists in relation to prescribing costs but none has been able to develop such a powerful, simple and comprehensible predictive model. © John Wiley & Sons, Ltd.