This thesis describes the first large-scale studies in the United Kingdom to adjust for
diagnostic-based morbidity when examining variation in home visits, specialist referrals
and prescribing patterns in general practice. The Johns Hopkins ACG Case-Mix
System was used since each patient’s overall morbidity is a better predictor of health
service resource use than individual diseases.
A literature review showed large variations in resource use measures such as
consultations, referrals and prescribing practice patterns in general practice both in the
UK and elsewhere and highlighted inappropriate use of statistical methodology that has
the potential to produce misleading and erroneous conclusions. The review presents a
strong argument for adjusting for diagnostic based morbidity when comparing variation
in general practice outcomes in the UK.
Multilevel models were used to take account of clustering within general practices and
partition variation in general practice outcomes into between and within practice
variation. Statistical measures for appropriately dealing with the challenging\ud
methodological issues were explored with the aim of producing results that could be
more easily communicated to policy makers, clinicians, and other healthcare
professionals.
The datasets used contained detailed patient demographic, social class and diagnostic
information from the Morbidity Statistics in General Practice Survey and the General
Practice Research Database.
This research shows that a combination of measures is required to quantify the effect of
model covariates on variability between practices. Morbidity explains a small
proportion of total variation between general practices for the home visit and referral
outcomes but substantially more for the prescribing outcome compared to age and sex.
Most of the variation was within rather than between practices
