London, UK: University College London (doctoral dissertation).
University College London, London, UK
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 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.
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