BACKGROUND: The potential of a comprehensive measure of patient morbidity to explain variation in referrals to secondary care has not previously been examined in the UK. AIM: To examine the relative role of age, sex and morbidity as defined by the Johns Hopkins ACG Case-Mix System in explaining variations in specialist referrals in general practice. DESIGN OF STUDY: Retrospective study of a cohort of patients followed for 1 year. SETTING: Two hundred and two general practices, with a total list size of 1,161,892, contributing data to the General Practice Research Database. METHOD: Each patient was assigned an ACG and morbidity group, based on their diagnoses, age and sex. The variability in referrals explained by these factors was examined using multilevel logistic regression models by splitting it into variation between practices and variation between patients within practices. RESULTS: The annual median (range) percentage of patients referred was 14.8% (range = 2.4-24.4%). The percentage of patients referred increased with age and morbidity. Morbidity explained 30.4% of the total variation in referrals (composed of variability between and within practices). Age and sex only explained 5.3% of the total variation. The variation attributable to practices was approximately 5%, thus most of the variation occurred within practices. Morbidity was also identified as a better predictor of referral compared to age and sex. CONCLUSIONS: Morbidity explains almost six times more of the variation in general practice referrals than age and sex, although about two-thirds of the variation remains unexplained. Most of the unexplained variation is due to differences within rather than between practices. The amount of variability in referrals between practices may be less than implied by previous studies based on aggregate information. The implications are that any investigation of specialist referrals from general practice should be interpreted cautiously, even after adjustment for age, sex and morbidity.
BACKGROUND: The potential of a comprehensive measure of patient morbidity to explain variation in referrals to secondary care has not previously been examined in the UK. AIM: To examine the relative role of age, sex and morbidity as defined by the Johns Hopkins ACG Case-Mix System in explaining variations in specialist referrals in general practice. DESIGN OF STUDY: Retrospective study of a cohort of patients followed for 1 year. SETTING: Two hundred and two general practices, with a total list size of 1,161,892, contributing data to the General Practice Research Database. METHOD: Each patient was assigned an ACG and morbidity group, based on their diagnoses, age and sex. The variability in referrals explained by these factors was examined using multilevel logistic regression models by splitting it into variation between practices and variation between patients within practices. RESULTS: The annual median (range) percentage of patients referred was 14.8% (range = 2.4-24.4%). The percentage of patients referred increased with age and morbidity. Morbidity explained 30.4% of the total variation in referrals (composed of variability between and within practices). Age and sex only explained 5.3% of the total variation. The variation attributable to practices was approximately 5%, thus most of the variation occurred within practices. Morbidity was also identified as a better predictor of referral compared to age and sex. CONCLUSIONS: Morbidity explains almost six times more of the variation in general practice referrals than age and sex, although about two-thirds of the variation remains unexplained. Most of the unexplained variation is due to differences within rather than between practices. The amount of variability in referrals between practices may be less than implied by previous studies based on aggregate information. The implications are that any investigation of specialist referrals from general practice should be interpreted cautiously, even after adjustment for age, sex and morbidity.
Authors: Kevin Haynes; Warren B Bilker; Tom R Tenhave; Brian L Strom; James D Lewis Journal: Pharmacoepidemiol Drug Saf Date: 2011-07-13 Impact factor: 2.890