A model based on age, sex, and morbidity to explain variation in UK general practice prescribing: cohort study

Published: July 14, 2008
Category: Bibliography > Papers
Authors: Islam A, Majeed A, O'Sullivan C, Omar RZ, Petersen I
Countries: United Kingdom
Language: null
Types: Care Management
Settings: Academic

BMJ 337:a238.

Department of Statistical Science, University College London, London, UK

OBJECTIVE: To examine whether patient level morbidity based measure of clinical case mix explains variations in prescribing in general practice.

DESIGN: Retrospective study of a cohort of patients followed for one year.

SETTING: UK General Practice Research Database.

PARTICIPANTS: 129 general practices, with a total list size of 1 032 072.

MAIN OUTCOME MEASURES: Each patient was assigned a morbidity group on the bases of diagnoses, age, and sex using the Johns Hopkins adjusted clinical group case mix system. Multilevel regression models were used to explain variability in prescribing, with age, sex, and morbidity as predictors.

RESULTS: The median number of prescriptions issued annually to a patient is 2 (90% range 0 to 18). The number of prescriptions issued to a patient increases with age and morbidity. Age and sex explained only 10% of the total variation in prescribing compared with 80% after including morbidity. When variation in prescribing was split between practices and within practices, most of the variation was at the practice level. Morbidity explained both variations well.

CONCLUSIONS: Inclusion of a diagnosis based patient morbidity measure in prescribing models can explain a large amount of variability, both between practices and within practices. The use of patient based case mix systems may prove useful in allocation of budgets and therefore should be investigated further when examining prescribing patterns in general practices in the UK, particularly for specific therapeutic areas.

PMID: 18625598
PMCID: PMC2658517

Morbidity Patterns,Resource Allocation,Population Markers,United Kingdom,Adolescent,Adult,Age Distribution,Child,Preschool,Cohort Studies,Gender,Middle Aged,Regression Analysis,Sex Distribution

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