title: Development of sample size models for national general practice surveys.
abstract: The most cost-effective method to measure the morbidity managed and treatments provided in general practice is from records of a cluster of consultations (encounters) from each general practitioner (GP) in a random sample. A cluster sampling method is proposed for future surveys for analysis of encounter-based general practice data. The sample sizes needed to measure the most common problems managed and drugs prescribed were estimated using ratio-estimator models for cluster sample surveys. Morbidity and treatment rates were estimated from the Australian Morbidity and Treatment Survey in General Practice 1990-1991 (AMTS). The 20 most common problems in the AMTS were managed at estimated rates of 1.5 to 9.5 per 100 encounters. The 20 most common drugs were prescribed at estimated rates of 0.7 to 3.6 per 100 problems. These rates were used to determine precision as a percentage of each true value for future surveys, that is, as relative precision. If we want to be 95 per cent confident that these rates will be within 5 per cent of each true rate, sample sizes of 552 to 5675 GPs are needed. If we fix the sample size at 1000 GPs, relative precision lies within 12 per cent of these rates. If the sample size is increased to 1500 GPs, relative precision improves only marginally. The differences in sample size for each of the most frequent morbidity and treatment data are largely due to their variable distributions and relatively infrequent occurrence in general practice. A sample size of 1000 GPs will enable measurement of the most common morbidity and treatments at 95 per cent confidence.
authors.affiliation: Department of General Practice, University of Sydney.
authors.forename: R A
authors.initials: RA
authors.lastname: Meza
authors.forename: M
authors.initials: M
authors.lastname: Angelis
authors.forename: H
authors.initials: H
authors.lastname: Britt
authors.forename: D A
authors.initials: DA
authors.lastname: Miles
authors.forename: E
authors.initials: E
authors.lastname: Seneta
authors.forename: C
authors.initials: C
authors.lastname: Bridges-Webb
mesh_terms: D001315:Australia; D016000:Cluster Analysis; D003362:Cost-Benefit Analysis; D004358:Drug Therapy / Q000706:statistics & numerical data; D005194:Family Practice / Q000191:economics / Q000706:statistics & numerical data*; D006306:Health Surveys; D006801:Humans; D009017:Morbidity; D012494:Sampling Studies