Clustering diagnoses: a method of interpreting morbidity data.

Detailed classification of morbidity data provides problems in large-scale surveys in general practice: a balance between precise diagnosis and realistic uncertainty must be maintained, and it can be hard to detect the overall pattern when a large number of rubrics is involved. This paper reports the development of a system of clustered diagnoses in which similar diagnoses are linked together in homogeneous clusters. The system is based on the RCGP codes and is compatible with ICHPPC-2. The aim was not to produce another classification of morbidity but to use the system to apply to data already coded using a specific primary code.