Socioeconomic disparities in the management of coronary heart disease in 438 general practices in Australia
journal contributionposted on 01.04.2021, 00:00 authored by G Mnatzaganian, C M Y Lee, S Robinson, F Sitas, C K Chow, M Woodward, Rachel HuxleyRachel Huxley
Background: This population-based cross-sectional and panel study investigated disparities in the management of coronary heart disease (CHD) by level of socioeconomic status. Methods: CHD patients (aged ≥18 years), treated in 438 general practices in Australia, with ≥3 recent encounters with their general practitioners, with last encounter being during 2016–2018, were included. Secondary prevention prescriptions and number of treatment targets achieved were each modelled using a Poisson regression adjusting for demographics, socioeconomic indicators, remoteness of patient’s residence, comorbidities, lifetime follow-up, number of patient–general practitioner encounters and cluster effect within the general practices. The latter model was constructed using the Generalised Estimating Equations approach. Sensitivity analysis was run by comorbidity. Results: Of 137,408 patients (47% women), approximately 48% were prescribed ≥3 secondary prevention medications. However, only 44% were screened for CHD-associated risk factors. Of the latter, 45% achieved ≥5 treatment targets. Compared with patients from the highest socioeconomic status fifth, those from the lowest socioeconomic status fifth were 8% more likely to be prescribed more medications for secondary prevention (incidence rate ratio (95% confidence interval): 1.08 (1.04–1.12)) but 4% less likely to achieve treatment targets (incidence rate ratio: 0.96 (0.95–0.98)). These disparities were also observed when stratified by comorbidities. Conclusion: Despite being more likely to be prescribed medications for secondary prevention, those who are most socioeconomically disadvantaged are less likely to achieve treatment targets. It remains to be determined whether barriers such as low adherence to treatment, failure to fill prescriptions, low income, low level of education or other barriers may explain these findings.