Project Description

Depression is a leading cause of disability worldwide and antidepressants are a common treatment widely prescribed in primary care. Many people with depression also have other health conditions and are prescribed several medicines (polypharmacy). Polypharmacy increases the risk of adverse outcomes such as serious adverse events, hospitalisation and mortality. However, the safety of polypharmacy regimens—especially those involving three or more concurrent medicines, including antidepressants—remains relatively underexplored.
This PhD project will investigate the risks and outcomes associated with antidepressant-related polypharmacy using several methods and integrating epidemiological analysis and qualitative research to inform safer prescribing practices.

Using large linked anonymised primary care records (CPRD) linked with nationally available datasets (such as HES, and ONS), the project will identify associations between specific medicine combinations and adverse outcomes. The project will require use of both traditional statistical methods and machine learning approaches to identify drug combinations with the highest risk.
The project will also use qualitative approaches recruiting people prescribed antidepressants, and specialist and generalist prescribers to explore lived experiences and decision-making processes related to antidepressant polypharmacy.

The project will collaborate with the PINCER prescribing safety initiative to embed findings into digital tools that support deprescribing and safer medication management. By combining large data epidemiology, and patient perspectives, this research will generate actionable insights to improve prescribing safety, address patient-identified priorities, and contribute to national guidance on polypharmacy in mental health care.