Development of sample size models for national general practice surveys

Area of Science:

• General Practice Research
• Health Services Research
• Epidemiology

Background:

• Measuring morbidity and treatment in general practice is crucial for healthcare planning.
• Existing methods may not be cost-effective for comprehensive data collection.
• Cluster sampling of general practitioner (GP) encounters is a potential solution.

Purpose of the Study:

• To propose and evaluate a cluster sampling method for analyzing encounter-based general practice data.
• To estimate the sample sizes required for future surveys on morbidity and treatment in general practice.
• To determine the precision of estimates based on different sample sizes.

Main Methods:

• Utilized data from the Australian Morbidity and Treatment Survey in General Practice 1990-1991 (AMTS).
• Employed ratio-estimator models for cluster sample surveys to estimate required sample sizes.
• Calculated relative precision for various sample sizes to achieve 95% confidence intervals.

Main Results:

• The 20 most common problems occurred at rates of 1.5 to 9.5 per 100 encounters.
• The 20 most common drugs were prescribed at rates of 0.7 to 3.6 per 100 problems.
• A sample size of 1000 GPs allows measurement of common morbidity and treatments with 95% confidence, achieving approximately 12% relative precision.

Conclusions:

• Cluster sampling of GP encounters is a viable and cost-effective strategy for future general practice surveys.
• A sample size of 1000 GPs provides adequate precision for measuring the most frequent morbidity and treatment data.
• Sample size requirements vary due to the distribution and frequency of specific health issues and treatments.