An observational study of socioeconomic disparities in psychiatry consultation uptake in Australia, using routinely collected national data from 2015 to 2022

Background: The COVID-19 pandemic was associated with increased psychological distress and psychiatric service usage in Australia. Previous research into the first few months of the pandemic found severe inequality in telehealth psychiatry but no change in inequality for psychiatry service usage overall. However, it is unknown how inequality evolved over the remainder of the pandemic, as extended lockdowns continued in major Australian cities. Aims: To understand how socioeconomic inequality in psychiatric consultations changed during the COVID-19 pandemic, using new data from 2020 to 2022. Methods: We analysed routinely collected national Medicare data, provided to us as service counts per Statistical Area 3 (SA3) region by financial year from 2015 to 2016 to 2021 to 2022. We calculated daily rates of psychiatry attendances per 100,000 working age adults within each SA3 region, and we evaluated inequality in the distribution of consults using concentration indices and curves based on the Index of Relative Socio-economic Disadvantage (IRSD). Results: We analysed 7 years of Medicare data from 321 SA3 regions. The national consultation rate increased in 2020 to 2021 from 45.16 to 50.17, and then decreased slightly in 2021 to 22 to 48.65. Inequality as measured by concentration indices rose from 0.169 in 2020 to 2021 to 0.177 in 2021 to 2022. Consultation rates in the most disadvantaged IRSD quintile decreased by 15.9% in 2021 to 2022 compared to smaller decreases of between 1% and 4% in the top 4 quintiles. Conclusion: Our study shows that inequality in mental health service provision increased in the second year of the COVID-19 pandemic to the highest level seen in the 7 years of data we analysed. Individuals within the most disadvantaged IRSD quintile experienced a significant and disproportionate decline in service rates. Close monitoring and more granular data are needed to understand the drivers behind this inequity and its current status, and to inform interventions to improve it.