A note on the decomposition of the health concentration index

In recent work, the concentration index has been widely used as a measure of income-related health inequality. The purpose of this note is to illustrate two different methods for decomposing the overall health concentration index using data collected from a Short Form (SF-36) survey of the general Australian population conducted in 1995. For simplicity, we focus on the physical functioning scale of the SF-36. Firstly we examine decomposition 'by component' by separating the concentration index for the physical functioning scale into the ten items on which it is based. The results show that the items contribute differently to the overall inequality measure, i.e. two of the items contributed 13% and 5%, respectively, to the overall measure. Second, to illustrate the 'by subgroup' method we decompose the concentration index by employment status. This involves separating the population into two groups: individuals currently in employment; and individuals not currently employed. We find that the inequality between these groups is about five times greater than the inequality within each group. These methods provide insights into the nature of inequality that can be used to inform policy design to reduce income related health inequalities. Copyright (C) 2002 John Wiley Sons, Ltd.

Quality of diabetes care and coronary heart disease absolute risk in patients with type 2 diabetes mellitus in Australian general practice

Objective: To examine the quality of diabetes care and prevention of cardiovascular disease (CVD) in Australian general practice patients with type 2 diabetes and to investigate its relationship with coronary heart disease absolute risk (CHDAR). Methods: A total of 3286 patient records were extracted from registers of patients with type 2 diabetes held by 16 divisions of general practice (250 practices) across Australia for the year 2002. CHDAR was estimated using the United Kingdom Prospective Diabetes Study algorithm with higher CHDAR set at a 10 year risk of >15%. Multivariate multilevel logistic regression investigated the association between CHDAR and diabetes care. Results: 47.9% of diabetic patient records had glycosylated haemoglobin (HbA1c) >7%, 87.6% had total cholesterol >= 4.0 mmol/l, and 73.8% had blood pressure (BP) >= 130/85 mm Hg. 57.6% of patients were at a higher CHDAR, 76.8% of whom were not on lipid modifying medication and 66.2% were not on antihypertensive medication. After adjusting for clustering at the general practice level and age, lipid modifying medication was negatively related to CHDAR (odds ratio (OR) 0.84) and total cholesterol. Antihypertensive medication was positively related to systolic BP but negatively related to CHDAR (OR 0.88). Referral to ophthalmologists/optometrists and attendance at other health professionals were not related to CHDAR. Conclusions: At the time of the study the diabetes and CVD preventive care in Australian general practice was suboptimal, even after a number of national initiatives. The Australian Pharmaceutical Benefits Scheme (PBS) guidelines need to be modified to improve CVD preventive care in patients with type 2 diabetes.