Predictors of government subsidized pharmaceutical use in patients with diabetes or cardiovascular disease in a primary care setting: evidence from a prospective randomized trial

Nicholas G. Hirst, Jennifer A. Whitty, Robyn L. Synnott, Diann S. Eley, Paul A. Scuffham

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Objectives: This study uses data from a prospective randomized controlled trial to estimate predictors of pharmaceutical expenditure in diabetes (DM) or cardiovascular disease (CVD) patients. Identifying drivers of pharmaceutical use and the extent to which they are modifiable may inform cost-effective policy-making. Methods: The trial followed 260 patients aged >18 years (mean 68) from three general practices for 12 months. Patients had type 2 diabetes (90 patients) or cardiovascular disease (170 patients). Costs for pharmaceuticals prescribed on the Pharmaceutical Benefits Scheme (PBS) were obtained retrospectively at 12 months. Sociodemographic data and health-related quality-of-life (QoL) were recorded from questionnaires. Clinical measures (including body mass index (BMI), blood pressure, high and low density lipoprotein (LDL), and HbA1c) were also collected. Results: Mean pharmaceutical costs for DM patients (AU$4119) was greater than CVD patients (AU$2424). The largest contributor to costs in both groups was pharmaceuticals used for management of conditions other than CVD or DM. QoL (EQ5D) and BMI were significant predictors of costs in both groups. A history of cardiac events, HbA1c, age, and unemployment were significant predictors of costs in the DM group. A diagnosis of heart failure, frequency of hospital admissions, and LDL levels were significant predictors of costs in the CVD group. Roughly one third of total variation of costs can be explained by the regressors in both models. Limitations: Generalizability will be limited as data was derived from a trial and the study was not powered for this post-hoc analysis. Missing data imputation and self-reporting bias may also impact on results. Conclusions: Factors such as QoL BMI, HbA1c levels, and a history of cardiac events are significant predictors of costs. The results suggest there may be a place for interventions that improve quality-of-life and concurrently reduce pharmaceutical costs in patients with CVD or DM.
Original languageEnglish
Pages (from-to)698-704
Number of pages7
JournalJournal of Medical Economics
Issue number6
Early online date5 Sep 2011
Publication statusPublished - 2011


  • Drug costs
  • Drug utilization
  • Diabetes mellitus
  • Cardiovascular diseases

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