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Medication adherence in the UK population: determinants and influence on chronic disease outcomes and health inequalities using UK Biobank

  • 8 September 2025 to 2 December 2025
  • Project No: D26063
  • DPhil Project 2026

Background

Medication adherence is a critical determinant of health outcomes in chronic disease. Non-adherence, whether due to prescriptions not being dispensed (primary non-adherence), early discontinuation, or poor persistence, is associated with disease progression, avoidable hospitalisations, and premature mortality. However, in large population cohorts, exposure to medications is often inferred from prescribing data alone, which may not reflect what patients actually receive or take. Dispensing data, now increasingly available, provide a more accurate account of medication use, but are rarely harmonised with prescribing records. 

In the UK, discrepancies between prescribing and dispensing records may reflect system-level barriers (e.g. access to pharmacies), patient-level determinants (e.g. socioeconomic deprivation, ethnicity, multimorbidity), and clinical factors (e.g. drug class, side-effect profile, treatment switching). Understanding these determinants is essential for addressing adherence and for estimating the true population impact of preventive therapies in conditions such as cardiovascular disease, diabetes, and depression. 

This project will use UK Biobank (~500,000 participants with linked GP, hospital, and dispensing records) to characterise medication adherence, investigate its determinants, and evaluate the implications of non-adherence for chronic disease outcomes. 

research experience, research methods and skills training

The student will:

  1. Harmonise prescribing and dispensing data in UK Biobank, developing reproducible pipelines to clean, map, and align records (using BNF/DM+D codes).
  2. Identify discrepancies between prescribed and dispensed medications, including concordance, dispensing delays, and primary non-adherence.
  3. Develop adherence and persistence metrics, accounting for polypharmacy, switching, and discontinuation.
  4. Identify determinants of adherence, assessing how demographic, socioeconomic, geographic, and clinical factors shape patterns of medication use.
  5. Model impact of non-adherence on selected chronic disease outcomes and health inequalities, such as hospital admissions, disease progression, and mortality.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

The project includes placements with UK Biobank phenotyping/health outcomes team; training courses and workshops on secure data handling, drug-code mapping, and advanced analytic methods through the department and beyond. 

PROSPECTIVE STUDENT

The ideal candidate will have a Bachelor’s or Master’s degree in a relevant discipline (e.g., epidemiology, statistics, public health, data science, pharmacy) and demonstrable experience in large-scale data analysis. They should possess strong analytical skills, be comfortable with common programming languages, and have a keen interest in pharmacoepidemiology and interdisciplinary collaboration.

research group