CHE PhD Studentship Opportunity 2027/28

Supervisors: Vijay Gc and Laura Bojke

Poor mental health negatively impacts participation in employment and remains the leading cause of disability and a significant driver of economic inactivity among working-age adults, costing the UK economy over £51 billion annually in lost productivity, sickness absence and reduced performance at work. Without action, these costs are expected to keep rising, with productivity losses linked with mental health projected to exceed £170 billion annually by 2030. Much of the work pertaining to mental health interventions for working-age adults has examined health outcomes or economic costs on their own, often failing to capture the full impact of mental health on longer-term productivity. There is a plethora of mental health interventions that show promise in improving such outcomes; however, their relative value for people working in particular professions, and for alternative working models, remains unknown. Additionally, the implications of poor mental health on health and economic outcomes do not fall evenly across groups, and interventions may need to be more targeted towards more particular population groups, for example those with more precarious employment or those supporting families.

This PhD project will bring these strands together and examine how mental health influences health outcomes among working-age adults, estimate the economic burden, and identify workplace and policy interventions that offer the best value for money. The project will also draw on secondary data and will also have an opportunity to collaborate with the NIHR Mental Health Research Group (MHRG).

Supervisors: Nils Gutacker and Joe Spearing

Artificial intelligence and large language models are likely to transform the production functions across many sectors of the economy, including healthcare. The integration of AI technologies into healthcare systems such as the English NHS raises several economic questions around endogenous adoption, productivity and efficiency effects, labour supply and labour demand dynamics, patient demand for AI-led or informed care, and optimal provider reimbursement mechanisms as marginal costs approach zero. A recent review of the evidence base in healthcare through an economic lens is available in a REAL Supply Unit report.

The economic evidence base for the impact of AI in healthcare remains limited, largely because real-world deployment is still nascent and the technology is evolving rapidly. We welcome PhD studentship applications exploring these topics with a particular focus on the English NHS. Interested applicants should submit a proposal outlining their specific research questions, proposed empirical or theoretical models, suitable data sources, and intended research settings (e.g. primary care, acute care, or labour supply).

Supervisors: Sebastian Hinde, Laura Bojke, Greig Taylor (University of Newcastle)

Despite being the most common non-communicable disease to affect children, dental caries are almost entirely preventable. It directly impacts a child’s quality of life, whilst indirectly contributing to significant healthcare costs and productivity loss. Inequitable access to care and unequal distribution of dental disease has led to increased levels of poor oral health. In turn, this has become a significant driver and symptom of poor general health in children, with tooth extraction the primary reason for school age children being admitted to hospital.

Conventional economic evaluation typically pre-defines a single perspective, often the health impact to the treated patients and the costs borne by the healthcare provider. This is the case for most existing evaluations of paediatric oral health interventions. However, the complexity of both the supply and demand sides of dental care in the UK, with impacts falling across multiple sectors and dental care often difficult or expensive to access, limit the relevance of this approach to engage all stakeholder perspectives.

This PhD will seek to utilise and extend existing methods of decision analysis to reconcile this complexity, including establishing cost-effective and equitable service provision within current and future care provision scenarios.

Supervisors: Francesco Longo and James Lomas

Public health, health and social care expenditures represent a substantial and fast-growing share of public spending. However, they should not be viewed simply as a fiscal burden: they generate beneficial effects across a range of socially valuable outcomes. Existing studies show that these categories of public expenditure can, for example, reduce mortality (Martin et al., 2021), increase care-related quality of life (Longo et al., 2021, Longo et al., 2025b), improve well-being (Longo et al., 2025c), and stimulate economic output (Longo et al., 2025a).

A key research challenge is to identify the mechanisms through which these effects are generated, as well as effects on other underexplored and policy-relevant outcomes. Addressing these questions requires robust methods for causal inference and may use local area-level data, individual-level data, or combinations of the two. The resulting evidence can inform analytical frameworks for assessing value for money across both individual interventions and broader categories of public expenditure (e.g., Longo et al., 2024).

We welcome applications proposing innovative empirical or methodological contributions to this growing and policy-relevant area of research.

References

LONGO, F., CLAXTON, K., GRIFFIN, S., MASON, A., WALKER, S. & WEATHERLY, H. 2024. Social Decision-Making Analysis: a general approach to inform decisions on resources in the public sector. Value in Health.

LONGO, F., CLAXTON, K., LOMAS, J. & MARTIN, S. 2021. Does public long-term care expenditure improve care-related quality of life of service users in England? Health Economics, 30, 2561-2581.

LONGO, F., CLAXTON, K., MASON, A., SALAS‐ORTIZ, A. & VILLASENOR‐LOPEZ, A. 2025a. Is caring productive? The effect of Adult Social Care on paid production in England. Health Economics, 34, 2182-2195.

LONGO, F., CLAXTON, K., SALAS-ORTIZ, A., LOMAS, J. & MARTIN, S. 2025b. Does publicly-funded Adult Social Care impact informal and unpaid carers’ quality of life in England? Health Economics.

LONGO, F., SALAS-ORTIZ, A., CLAXTON, K. & LOMAS, J. 2025c. What is the impact of fiscal devolution on adult social care in England? Evidence from a quasi-natural experiment.

MARTIN, S., LONGO, F., LOMAS, J. & CLAXTON, K. P. 2021. The causal impact of social care, public health and healthcare expenditure on mortality in England: cross-sectional evidence for 2013/14. BMJ Open.

Supervisors: Sumit Mazumdar, Vijay GC

Rising temperatures and longer, more frequent heatwaves are already reducing safe working hours in South Asia’s urban areas. The losses are concentrated among outdoor and informal workers in sectors such as construction, street vending, waste management and transport. These workers, who rely on daily wages, often lack access to cooling facilities, paid leave or social protection. Despite the critical implication of heat on productivity, existing economic assessments focus mainly on mortality. Estimates of lost working hours, foregone earnings, illness and household health-related expenses, and their distribution across different worker demographics remain poor. As South Asian governments begin to redefine heat as a major adverse climatic stressor that warrants public finance, key questions have arisen about the financial investments needed to develop adaptive mechanisms against extreme heat, how to allocate resources, and which interventions to prioritise.

We invite PhD proposals that apply health economics to these issues. We are particularly interested in projects that measure the labour-market and health (both physical and mental) consequences of heat, explore financing for heat adaptation, and evaluate adaptation measures with a focus on trade-offs and distributional impacts for decision-makers. Proposals that make creative use of existing secondary data, and whose findings travel beyond a single city or country, are especially welcome. 

Supervisors: Praveen Thokala, Dina Jankovic and Simon Walker

Health Technology Assessment (HTA) studies typically do not explicitly consider capacity constraints within the service pathway [1, 2]. However, in real clinical practice, there are resource constraints (e.g. staff, beds, test-kits, etc) which may result in lack of access, or delays, to treatment.

This PhD will focus on health systems modelling, which combines disease modelling with service pathway modelling, to incorporate capacity constraints formally in HTA and to make more accurate assessments of cost-effectiveness and population health impacts. Also, health systems models can provide a broader range of outputs (i.e. costs, health outcomes as well as system efficiency outcomes e.g. waiting times, coverage, etc) and allow for the consideration of a broader set of options to address how best to invest in a health system with resource constraints [3].

Case studies will be used to explore different issues including potentially different settings (e.g. primary vs secondary care, LMICs, high-income countries), data availability (e.g. sparse data vs comprehensive electronic health record data) and different modelling techniques (e.g. system dynamics vs process mining combined with discrete event simulation [4, 5])

Based on the lessons learnt from the case studies, guidance will be developed for future researchers interested in incorporating capacity constraints in HTA.

References

[1] Thokala, P., Dixon, S. & Jahn, B. Resource Modelling: The Missing Piece of the HTA Jigsaw?. PharmacoEconomics 33, 193–203 (2015). https://doi.org/10.1007/s40273-014-0228-9

[2] Thokala, P., Duarte, H., Wright, S. et al. Incorporating Resource Constraints in Health Economic Evaluations: Overview and Methodological Considerations. PharmacoEconomics Open 9, 161–178 (2025). https://doi.org/10.1007/s41669-024-00537-z

[3] Walker S, Fox A, Altunkaya J, et al. Program Evaluation of Population- and System-Level Policies: Evidence for Decision Making. Medical Decision Making. 2022;42(1):17-27. doi:10.1177/0272989X211016427

[4] Lacinova, K., Thokala, P., Nicholas, R. et al. ENTIMOS: A Discrete Event Simulation Model for Maximising Efficiency of Infusion Suites in Centres Treating Multiple Sclerosis Patients. Appl Health Econ Health Policy 20, 731–742 (2022). https://doi.org/10.1007/s40258-022-00733-0

[5] Salleh, S., Thokala, P., Brennan, A. et al. Discrete Event Simulation-Based Resource Modelling in Health Technology Assessment. PharmacoEconomics 35, 989–1006 (2017). https://doi.org/10.1007/s40273-017-0533-1