CHE PhD studentship opportunity 2026/27

Supervisors: Vijay Gc, Laura Bojke, Mike Doyle

Poor mental health is the leading cause of disability in the UK. Yet, a large proportion of individuals with mental illness do not receive adequate treatment due to stigma, lack of providers, or system inefficiencies. Untreated or poorly treated mental health conditions lead to higher healthcare resource use, lost productivity, unemployment, homelessness and increased criminal justice involvement. This creates a significant economic burden on individuals, families and society, costing England £300 billion annually. Upfront investment is required to expand mental health services, and research shows that untreated mental illness costs far more in the long run through lost economic output and strain on social services.

This PhD will focus on the economic implications of unmet needs for mental healthcare. It could also explore, for example, how socioeconomic disparities drive unequal access, identify pockets of unmet need, and interventions targeting mental health gaps to reduce mental health inequalities. There will be an opportunity to work on a large research project on mental health inequality. This will include a mental health observatory and an opportunity to harness regional data on mental health needs and service provision, leading to innovative, applied research grounded in the lived experiences of communities in West Yorkshire to reduce mental health inequities.

Supervisors: Sebastian Hinde, Shainur Premji, Laura Bojke

Through the NIHR funded Applied Research Collaboration (ARC) programme we have developed numerous collaborations with local decision makers informing commissioning decisions. However, requests for support are typically associated with short deadlines and little capacity to develop detailed analyses. To address this, the PhD candidate will explore the role of generic system level models to inform rapid decision making in this setting.

As a member of the ARC programme the student will gain access to the Connected Bradford routine data repository, linked data across primary, secondary, community and social care, as well as education and crime settings for over 700,000 individuals. Through the course of the PhD, it is expected that this data environment will be expanded across Yorkshire. 

The overarching aim of the PhD will be to explore the opportunities to use such routine data to inform the creation of generic models across one or more of the ‘core themes’ of ARC (specifically prevention in children and families and mental health), working closely with Local Authority and Integrated Care System partners. In addition to this applied analysis, it is expected that the studentship will incorporate methodological research, such as exploring the challenges of generic models across settings and multi-sector trade-offs.

Supervisors: Maria Ana Matias, Adrián Villaseñor, Rowena Jacobs

Mental disorders account for a substantial proportion of the burden of disease, with prevention recognised as a significant priority. While preventive care and activities may reduce mental health system utilisation, research on this link remains limited. Literature reports strong social networks, healthy behaviours, and cohesive neighbourhoods can mitigate poor mental health outcomes. However, their impact on subsequent service use remains underexplored.

The English context offers a unique opportunity to address this gap. Understanding Society provides nationally representative longitudinal data on social support, socioeconomic status, health behaviours, and neighbourhood dynamics. When linked to the Mental Health Services Data Set and NHS Talking Therapies, these data allow evaluation of how social determinants impact incidence, timing, and intensity of service use.

Additionally, NHS England has expanded preventive programmes, namely social prescribing, where healthcare professionals refer patients to non-clinical community services. The staggered roll out of social prescribing across regions creates a natural experiment to test whether access modifies the relationship between preventive factors and service use.

By linking survey and administrative records, this project will provide evidence on how preventive care through social determinants and social prescribing might reduce reliance on specialist services in England, potentially achieving cost savings and improving mental health.

Supervisors: Julia Hatamyar and Andrea Manca

This PhD studentship offers a unique opportunity at the intersection of data science and health economics, focusing on the development and application of cutting-edge causal machine learning methods to evaluate health policies and support evidence-based decision making.

The project will explore novel methodologies to estimate causal effects from observational and experimental data, informing health policy impact evaluation and intervention design. Ideal applicants will have a background in economics, statistics, or computer science, with an interest in applied research for health. Experience with computer programming (e.g., R, Python) and/or a keen interest in policy-relevant questions are highly desirable, but we also welcome applications from those who are strong independent learners and eager to develop these skills.

This PhD has the potential to produce highly influential output. The successful candidate will work closely with interdisciplinary experts, contributing to real-world applications that inform resource allocation, policy development, and health technology assessment, developing skills in a supportive, collaborative environment.

Supervisors: Adriana Castelli, Anastasia Arabadzhyan, Rowena Jacobs

The World Health Organization estimated that nearly one billion people live with a mental health disorder, with the economic burden of illness estimated at $5 trillion in 2019.

The COVID-19 pandemic increased the number of people living with a mental health disorder and also accelerated a shift toward delivering mental health care via Digital Health Technologies (DHTs). Adoption of DHTs may provide opportunities to deliver mental health care to previously underserved groups, increase access, and reduce costs. On the other hand, it may depersonalise care and exacerbate the ‘digital divide’. It is, therefore, crucial to understand how the adoption of DHTs in mental health care provision impacts system- and patient-level outcomes.

There is a lack of evidence on whether and which mental health DHTs work and for whom in the English NHS. This PhD thesis will apply econometric analysis to provide evidence for future investment by key policy-makers and other stakeholders.

Possible research questions are:

• How do DHTs in mental health impact system capacity, access to services, waiting times, treatment engagement, and patient experience?
• Is digitally enabled mental health care as effective as conventional care in terms of patient outcomes?
• Is there evidence of a differential impact of DHTs across patients’ characteristics?

Supervisors: Beth Woods, Jessica Ochalek 

Appropriate design of pharmaceutical pricing policy is crucial to ensuring both allocative and dynamic efficiency. Current approaches to price regulation for new medicines largely focus on the launch prices of new medicines. Yet, there is increasing recognition that a medicine’s value is shaped across its entire lifecycle, including the period after patent expiry when generic and biosimilar products may enter the market.

A critical factor in determining the value captured by the health system is the speed at which these alternatives become available. However, there is limited UK evidence on the timeframes for generic and biosimilar entry, the determinants of market entry, or the policy mechanisms that could address delays or the absence of market entrants.

This PhD will address these gaps by examining whether and when generics and biosimilars emerge in the UK market, the factors influencing market entry, and the implications of alternative policies designed to address delayed entry and related market failures. It could also explore broader policy questions around how prices for new (originator) pharmaceuticals should be regulated once generic or biosimilar versions of comparators become available, and the global implications of alternative pricing policies. Using a combination of literature reviews, statistical methods (e.g. survival analysis, econometrics), and policy modelling, the research will generate new evidence to inform the design of more effective pharmaceutical pricing policies.

Supervisors: Marta Soares, Dina Jankovic

In-silico clinical trials (ISTs) are performed on cohorts of virtual patients using individualised computer simulations. An exemplar is the FD-PASS study. Early evidence suggests ISTs can replicate outcomes from conventional randomised controlled trials, establishing their predictive validity. 

ISTs can optimise the value of healthcare technologies, augment traditional clinical research, and extend the evidence-base submitted for healthcare decision making to, for example, patient subgroups that have not been trialled. ISTs may have profound implications for the societal value of health interventions:

• Early HTA & Research and Development (R&D): ISTs can be used alongside early economic models to optimize product development and refine in-human trial designs, generating better-targeted, higher-value products and increasing the probability of clinical and commercial success.
• Regulation & Reimbursement (R&R): ISTs can be submitted for R&R decisions, potentially accelerating patient access to innovative technologies, impacting market dynamics and societal welfare.

Despite this transformative potential, the expected economic and societal value of ISTs to support healthcare decision making remains unexplored. This PhD will develop this area using economic and decision modelling.