Evidence generation in CVRM: Quantifying burden of disease to support strategic decision-making 

In 2024, the World Health Organization (WHO) reported that noncommunicable diseases (NCDs) – including cardiovascular diseases, chronic kidney disease, obesity, hypertension, and diabetes – account for approximately 75% of all deaths worldwide. This positions cardiovascular, renal, and metabolic (CVRM) conditions as among the most pressing public health challenges globally. These conditions are chronic, complex, and interconnected – and their health and economic impact continues to grow. As populations age and rates of multimorbidity rise, health systems face increasing pressure to act earlier and more effectively. 

For pharmaceutical and biotech companies working in CVRMs, understanding future disease burden is essential for long-term strategy by informing decisions regarding where and when to invest resources for maximum impact of return. This is important when developing therapeutic interventions, but also for identifying where those interventions can have the greatest impact, and for making the case to act sooner.  

In this context, epidemiological modelling is playing a growing role in evidence generation. 

Why disease burden modelling matters 

While real-world data and clinical trial results remain foundational, they do not provide the broader picture of population-level disease dynamics over time. Epidemiological modelling allows teams to estimate future burden, and forecast how disease prevalence, incidence, and outcomes will evolve over time, as well as how interventions might change those trajectories. 

This is particularly relevant for CVRM diseases, which often remain undetected in patients until complications arise in more advanced disease stages. Whether it’s heart failure, chronic kidney disease (CKD), or type 2 diabetes, the same pattern often holds: late diagnosis results in costly management of the condition downstream. 

A central aim of disease burden modelling is to shift that paradigm – quantifying the benefits of earlier intervention and using this data to support the design of healthcare systems and interventions that diagnose and treat disease sooner (see Case Study 1, below).  

Turning evidence into action 

Using burden of disease studies to generate evidence is relevant to cross-functional teams within pharmaceutical and biotech companies, including those working in Medical Affairs, Policy and Public Affairs, Commercial Strategy and Business Development, and Real-World Evidence and Health and Economic Outcomes Research (HEOR) teams. Epidemiological modelling is increasingly being used to guide: 

1. R&D prioritisation 

• Target diseases where unmet need is projected to grow significantly 

• Align investments with long-term disease trends and patient needs 

2. Market access and commercial strategy 

• Support strategy with health economic data, including cost-effectiveness and budget impact analyses 

• Resource allocation: Prioritise markets based on forecasted disease burden 

• Differentiation: Use evidence to establish thought leadership in crowded markets 

3. Global health and equity planning 

• Identify and highlight underserved populations and geographies 

• Design interventions that address health inequalities 

4. Strategic foresight and policy engagement 

• Model “what-if” scenarios to test impact of policy or care pathway changes 

• Provide clear, data-driven input for health system design and planning 

• Identify shifts in disease patterns early to inform strategic pivots 

• Assess system vulnerability to chronic disease burdens 

5. Investor and stakeholder engagement 

• Build stronger, evidence-based investment narratives grounded in public health impact 

• Demonstrate that pipeline is aligned with long-term unmet needs, not just short-term market gaps 

In each case, the aim is the same: generate credible, scalable evidence that supports early action, shapes policy environments, and positions the company as a leader in disease management (see Case Study 2 below) – especially critical in the field of CVRM, where disease burden is rising but progress is often slow.  

Microsimulation: Capturing the complexity of CVRM 

The progression of CVRM conditions is shaped by dynamic, patient-level factors – including age, comorbidities, and treatment access – that evolve over time. Traditional modelling methods often miss this complexity, relying on static inputs and population-level assumptions. 

Microsimulation offers a more granular approach which makes it particularly well-suited to CVRM diseases. It models individual patients, capturing dynamic risk factors that can be modified over time. This allows for richer forecasting of outcomes – making microsimulation a valuable tool for policymakers who wish to consider how a specific demographic may be affected by a condition well into the future, as illustrated in Case Studies 3 and 4 below.  

Microsimulation captures: 

• Dynamic risk: Ageing, behavioural changes, treatment changes 

• Disease interactions: For example, where diabetes leads to CKD and cardiovascular events 

• Health system interactions: Delays in diagnosis, treatment access 

• Policy levers: Screening, early intervention, prevention measures 

What microsimulation delivers: 

• ‘What-if’ simulations to quantify the impact of new interventions, pathways, or access to treatments compared to current standards of care. 

• Clear quantification of future patient populations – and the upside of reaching them earlier. 

• Strategic evidence for pricing, positioning, and policy influence. 

For commercial, policy, and investor-facing teams, this brings a new level of clarity: sharper value stories, more confident market assessments, and a stronger case for early and equitable action. 

The evolving role of epidemiological modelling 

As pharmaceutical and biotech organisations expand their role in shaping health policy, demonstrating value beyond the product is critical. Burden of disease modelling, and microsimulation in particular, provides a way to generate robust, adaptable insights at scale to support this shift. 

In fragmented systems, modelling provides a shared, data-driven foundation for multi-stakeholder dialogue. In complex disease areas, it helps visualise long-term value. And in competitive therapeutic spaces, it strengthens positioning with clear, scenario-based insights. 

With CVRM conditions projected to account for an ever-larger share of global healthcare spending, the need to quantify unmet burden in this area is key, not just for meeting internal planning needs, but for shaping the environments in which new interventions are introduced, and the future of care. 

Contact HealthLumen today to learn about how our burden of disease modelling expertise can help generate evidence to support your strategic decision-making.

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