Observed intra-cluster correlation coefficients in a cluster survey sample of patient encounters in general practice in Australia

Abstract

Background: Cluster sample study designs are cost effective, however cluster samples violate the simple random sample assumption of independence of observations. Failure to account for the intra-cluster correlation of observations when sampling through clusters may lead to an under-powered study. Researchers therefore need estimates of intra-cluster correlation for a range of outcomes to calculate sample size. We report intra-cluster correlation coefficients observed within a large-scale cross-sectional study of general practice in Australia, where the general practitioner (GP) was the primary sampling unit and the patient encounter was the unit of inference.

Methods: Each year the Bettering the Evaluation and Care of Health (BEACH) study recruits a random sample of approximately 1,000 GPs across Australia. Each GP completes details of 100 consecutive patient encounters. Intra-cluster correlation coefficients were estimated for patient demographics, morbidity managed and treatments received. Intra-cluster correlation coefficients were estimated for descriptive outcomes and for associations between outcomes and predictors and were compared across two independent samples of GPs drawn three years apart.

Results: Between April 1999 and March 2000, a random sample of 1,047 Australian general practitioners recorded details of 104,700 patient encounters. Intra-cluster correlation coefficients for patient demographics ranged from 0.055 for patient sex to 0.451 for language spoken at home. Intra-cluster correlations for morbidity variables ranged from 0.005 for the management of eye problems to 0.059 for management of psychological problems. Intra-cluster correlation for the association between two variables was smaller than the descriptive intra-cluster correlation of each variable. When compared with the April 2002 to March 2003 sample (1,008 GPs) the estimated intra-cluster correlation coefficients were found to be consistent across samples.

Conclusions: The demonstrated precision and reliability of the estimated intra-cluster correlations indicate that these coefficients will be useful for calculating sample sizes in future general practice surveys that use the GP as the primary sampling unit.

Figures

Figure 1

Intra-cluster correlation(ICC) and 95% confidence intervals for descriptive and morbidity outcomes in two BEACH samples, April 1999–March 2000 (N = 1047 GPs) and April 2002–March 2003(N = 1008 GPs) * Total problems = the number of problems managed at the current encounter.

Figure 2

Intra-cluster correlation (ICC) and 95% confidence interval for association between morbidity outcomes with health care card status as predictor in two BEACH samples, April 1999–March 2000 (N = 1,047 GPs) and April 2002–March 2003 (N = 1,008 GPs)