Deep expertise in the epidemiology of NCDs and rare genetic diseases, combined with entrepreneurial vision

Why work with us?

HealthLumen is committed to playing our part in understanding disease trends to support decision-making in the life sciences and ultimately improve health outcomes for the non-communicable disease (NCD) and rare genetic disease communities.

We bring to the table a highly qualified team and over a decade of experience.

And our entire focus is dedicated to using our precision epidemiology to bring data-driven insights to the decision-making process in the drug development lifecycle, helping to get new therapies to patients faster.

The NCD and rare genetic disease landscapes

NCDs – including heart disease, obesity, diabetes, respiratory disease and dementia – are the leading cause of death worldwide¹. They also represent a huge economic burden on patients, health systems, governments, and society. It is estimated that the financial burden of the 5 leading NCDs could contribute to a cumulative output loss across the globe of USD$47 trillion from 2010– 2030¹.

Rare genetic diseases, on the other hand, while individually rare are collectively common. A recent analysis estimated a population prevalence of 3.5–5.9%, which equates to 263–446 million people affected globally at any point in time².

And with so many of the rare community being misdiagnosed or slipping through healthcare systems completely, estimates of the rare disease patient population are often unreliable, affecting drug development mobilisation efforts and presenting yet another obstacle in addressing unmet needs and bringing therapies to the rare disease community.

The role of epidemiological modelling

At each stage of the drug development process it is crucial to understand:

• What is the real patient population size?
• How will the health and economic burden of disease change over time across different diseases, and what demographic changes and risk factors influence these changes?
• What effect could different prevention, screening or treatment interventions have on health and economic outcomes?

These are the issues we address.

Why use microsimulation modelling for epidemiological studies?

Microsimulation is a form of modelling based on computer-generated “virtual populations” that reproduce the characteristics and behaviour of a large sample of individuals representing the whole population of interest.

These models are dynamic, meaning that they can take into account any changes in the characteristics or behaviours of individuals over their life course to model the future burden of disease under different scenarios.

This is vital for epidemiological studies and, as highlighted by the OECD, “Only microsimulation approaches allow testing “what if” scenarios of the impact of changes in lifestyles.” ³

Microsimulation modelling for NCDs

NCDs are highly influenced by lifestyle factors. Microsimulation-based modelling enables interventions that modify a patient population’s exposure to risk factors – such as tobacco smoking, unhealthy eating or drinking habits, and lack of physical activity – to be quantified. It can also quantify the impacts of novel therapies on decreasing disease progression and mortality rates in patients. Understanding these intervention impacts is important, in particular:

• When we need to compare the economic burden of a disease with or without a proposed intervention. 
• When we need to know how a proposed intervention may impact the behaviours of different types of individuals.
• When the interventions are targeted to specific subgroups, for example, to individuals with a certain pre-existing condition, or part of a specific socio-economic group.

As set out in our recent publication co-authored with AstraZeneca on the epidemiological and economic impact of chronic kidney disease using patient-level microsimulation modelling, “Microsimulation analyses …complement clinical studies by conducting population-level analyses and projecting expected outcomes. A particular strength is that they allow for continuous, dynamic risk factors that can be modified over time.” ⁴

Informing cell and gene therapy development: closing the data-gaps by combining genetic database mining and microsimulation modelling for rare genetic diseases.

Genomic medicine offers a transformative approach to treatment, as interventions are tailored to the individual’s genetic makeup. However, understanding the current prevalence and future burden of rare genetic diseases is critical to all aspects of portfolio planning.

By applying genomic data mining methodology to large genetic datasets such as gnomAD and TOPMed, the data-gaps can be closed on inaccurate disease prevalence estimates that rely purely on literature reviews, data registries, and surveillance programs.

Robust estimates of the prevalence of genetic mutations causing rare disease can be calculated to reliably tell you how many rare disease patients you can expect to find within a population of interest.

By inputting these prevalence estimates into a microsimulation model, it can then be determined how many individuals would be expected to be diagnosed with a rare genetic disease in the future. 

As noted in a recent seminal review paper, “Genomics requires a form of modelling that can capture these individual genetic differences and responses to therapy – a purpose to which microsimulation is eminently suited. Conversely, the risk of using aggregate models of grouped diseases with crude averaged impacts as inputs can produce highly erroneous results”. ⁵

This improved accuracy in prevalence estimation and genomic disease burden modelling could be transformational for developing industry investment plans for new therapies, shaping policy guidelines, raising awareness of rare diseases, and patient advocacy.

HealthLumen’s microsimulation modelling capabilities

HealthLumen’s methodologies have a long and well-established heritage. From our origins in the public sector, we have gone on to model populations of up to 100 million people into the future, examining how different kinds of therapies or interventions will affect them.

Our work has extended to over 80 countries, examining over 25 NCDs – including obesity, type 2 diabetes, coronary heart disease, COPD, stroke, hypertension, cancer, liver disease, chronic kidney disease, asthma, dementia – and rare genetic conditions.

Extensively published and validated, it is a leading model for determining trends in rare diseases and NCDs and their risk factors, informing all stages of the “molecule to market” drug development process, from early-stage R&D decisions through to market access and launch,

Contact us to discuss how our data-driven epidemiology can support your product development, strategic planning and portfolio management.

References:

1. The Financial Burden of NCDs
2. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database
3. A Comparative Analysis of Health Forecasting Methods
4. Projecting the Epidemiological and Economic Impact of Chronic Kidney Disease Using Patient-Level Microsimulation Modelling: Rationale and Methods of Inside CKD
5. A brief, global history of microsimulation models in health: Past applications, lessons learned and future directions