Modelling Upstream Prevention in Chronic Disease

Background and Rationale

Healthcare contributes 4–5% of global emissions. Shifting care 'upstream' through clinical prevention represents an unrecognised, highly powerful strategy for deep healthcare decarbonisation. The most sustainable healthcare is that which never needs to occur. By preventing chronic diseases, we reduce carbon-intensive treatments like hospitalisation, major surgeries, or lifelong dialysis. However, the environmental return on prevention is not systematically quantified. This lack of decision-ready models prevents policy-makers from integrating sustainability into clinical guidelines and Health Technology Assessment (HTA) methodologies, resulting in decision-making that overlooks the planetary co-benefits of preventive care.

Overall Aim and Objectives

The overall aim of the MSCA Doctoral Network is to train 15 interdisciplinary researchers to quantify, model, implement and translate into policy the environmental, clinical and economic return on investment (the ‘Triple Dividend’) of upstream prevention in chronic disease. Anchored in the Quadripartite Evaluation Framework, the network will generate standardised evidence across four interconnected pillars and develop the implementation and policy pathways required to translate this evidence into healthcare practice and decision-making.

1. Parameterise the Agent Footprint (Pillar 1 – Environmental Investment): Quantify the cradle-to-grave lifecycle environmental impacts of delivering five core preventive intervention types across European healthcare systems.

2. Quantify the Prevention Modifier (Pillar 2 – Clinical Efficacy): Model the effectiveness of preventive interventions in reducing disease incidence, progression, complications and subsequent healthcare demand across ten target clinical pathways.

3. Model the Population Context (Pillar 3 – Real-World Implementation): Determine how uptake, adherence, behavioural, social and environmental factors influence the real-world effectiveness of preventive interventions, and identify implementation and behaviour-change strategies that can increase their adoption by patients, professionals and healthcare organisations.

4. Standardise Comparative Resource Use (Pillar 4 – Longitudinal Lifecycle Return): Integrate clinical, health, resource-use, cost and environmental outcomes within longitudinal simulation and health-economic models to quantify the Triple Dividend of prevention and enable comparison across diseases and interventions.

5. Cross-cutting objective – Implementation and Policy Translation: Translate evidence generated across the four pillars into actionable strategies for clinical implementation and determine how environmental and Triple Dividend metrics can be embedded within clinical guidelines, HTA, commissioning, reimbursement and healthcare policy.

Research Questions and Scope

The network addresses four core research questions:

1. What are the lifecycle environmental impacts of preventive interventions, and how do these compare with the downstream healthcare they avert?

2. How do clinical effectiveness, population characteristics, uptake, adherence and wider social and environmental determinants modify the health, economic and environmental returns of prevention?

3. How can health, cost and environmental outcomes be standardised and integrated within longitudinal health-economic models to enable comparison across preventive interventions and disease areas?

4. How can evidence on the Triple Dividend of prevention be translated into behaviour change, clinical implementation and policy, and embedded within guidelines, HTA, commissioning and healthcare decision-making?

The network will apply this framework across 10 clinical pathways: chronic kidney disease, cardiovascular disease, diabetes, respiratory disease, cancer, maternity, neonatal, paediatric, oral and eye care. Across these pathways, the doctoral projects will investigate five core categories of prevention: targeted screening and preventive pharmacotherapy; lifestyle and behavioural interventions; digital and AI-enabled diagnostics; early-life interventions; and strategies to prevent disease progression and complications.