HealthLumen is committed to playing our part in helping address the epidemic of non-communicable diseases (NCDs) in close collaboration with our clients.
We bring to the table a highly qualified team and over a decade of experience, all underpinned by our core values:
We are rigorous
We dive into challenges and think them through properly. We’re thoughtful and thorough. Which is why our customers trust us to always do what’s right.
We are inclusive
We bring together epidemiologists, mathematicians, product designers, engineers, data scientists and more. We’re a diverse community with diverse perspectives that assist our customers in helping make the right decisions to improve health outcomes.
We are adaptable
We listen, think and do. We’re open to feedback and adapt to change. We’re receptive, responsive and constructive – with each other and our customers.
And our entire focus is dedicated to addressing the NCD crisis.
NCDs including heart disease, obesity, diabetes, respiratory disease and dementia are the leading cause of death worldwide (World Health Organization, 2020 1). They also represent a huge economic burden on health systems, governments, employers and health insurers.
The financial burden of 5 major NCDs (cardiovascular disease, chronic respiratory disease, cancer, diabetes, and mental illness) could contribute a cumulative output loss of US$ 47 trillion in the two decades from 2011, representing a loss of 75% of global GDP in 2010 (US$ 63 trillion).1
To respond effectively, it is crucial to understand these epidemics better:
Computer-based modelling can help quantify the impact that different inputs, such as changes in behaviours, or new interventions, have on disease morbidity, mortality and costs. NCD modelling is a specific method for estimating the extent to which changes in one or more risk factors affects disease and health.
This then provides evidence to inform decisions, for example, made by payers about which technologies and/or services to fund, or by pharmaceutical companies developing new therapies, or public health bodies considering policy interventions.
To make these decisions effectively, the clinical and economic value of each option must be estimated, taking into account costs and outcomes into the future, for a complete range of population subgroups.
Modelling has two main applications to assist decision-making:
Modelling studies are cheaper and quicker than real world studies; furthermore, in many cases true randomised experiments are often not practical, cost-effective or ethical.
However, modelling should be seen as a complement to real world studies because they help get the best value out of these studies, often being used to integrate evidence from different studies and different domains.
Modelling can also inform empirical studies, for example to identify assumptions or key parts of evidence that may have a significant impact on model outcome (direct empirical study of these assumptions may give greater confidence in the model and its predictions). Modelling may also be used to estimate likely effect sizes to inform the size of evaluative studies.
Microsimulation models are based on computer-generated “virtual populations” that reproduce the characteristics and behaviour of a large sample of individuals representing the whole population of interest.
Microsimulation represents a heterogeneous population by reproducing the characteristics and behaviour of a large sample of individuals, using the best data available of population demographics, risk factor status, disease incidence, mortality etc and, can overcome gaps in existing knowledge or data.
Major life-course events can be represented in the lives of the simulated individuals and, in the case of dynamic models (such as HealthLumen’s), characteristics and behaviours can evolve over the life course.
There are many advantages to the microsimulation-based approach to modelling, helping to inform decision makers to make the best decisions possible, which include:
According to the OECD “Only microsimulation allow testing “what if” scenarios of the impact of changes in lifestyles3”
Microsimulation approaches enable interventions that modify a population’s exposure to risk factors, such as tobacco smoking, unhealthy eating, alcohol consumption and lack of physical activity, to be quantified in terms of their impact on health expenditures of investments in public health programmes, clinical interventions or medical treatments.
Critically, dynamic microsimulation is the only modelling approach that is applicable if an individual’s history matters. For example, an individual’s history of risk-taking behaviour, such as smoking, alcohol use and nutrition matters for the development of certain diseases. An individual’s history of disease matters for whether they live or die. Microsimulation models are designed to remember an individual‘s history and take it into account to influence their future life course.
HealthLumen’s microsimulation model has a long and well-established heritage.
The model was first developed for the UK Government’s Foresight Programme on “Tackling Obesities: Future Choices“ in 2005. The model has since been developed for over 70 national and regional populations covering over 20 NCDs including obesity, type 2 diabetes, coronary heart disease, COPD, stroke, hypertension, cancer, liver disease, chronic kidney disease, asthma, and dementia.
Extensively published and validated it is a leading model for determining trends in NCDs and risk factors, enabling the planning of interventions from prevention, management, treatment and long term care.
We model populations of up to 100 million people into the future, and examine how different kinds of health risks will affect them.
Our model works dynamically, tracking real change, showing how your chosen population’s health will evolve and transform over time.