RiskAnalytica The Science of Applied Risk Management Contact Us: 416-782-7475 or connect@riskanalytica.com

Population Health Analysis

What is it?

Linking the past, present and future quantification of the incidence and prevalence of health outcomes in a population to risk factors, continuums of care and macro policy decisions. Our Life at Risk simulation platform can forecast a single disease/injury types, or groups of disease/injury types along with co-morbidities.

Our models are built with the purpose of discerning the life and economic value propositions of change. Using either direct data, literature reviews, or proxy data, we are able to reliably provide a forecast baseline (from 1 year on to the next 30 years) for the incidence, prevalence, mortality, disability and economic consequences of most diseases and injuries across age groups, sex and regions. Then, using the simulation capabilities of our Life at Risk platform, we are able to test the value proposition of particular interventions as a form of ‘what-if’ analysis.

Many different types of interventions are anticipated, examples of which include: changes in continuums of care; new treatments; prevention programs; taxation policies on health care services and products, etc.

A full technical report of data, methods, validation and outcomes accompanies all of our work. Outcome measurements reported depend upon the nature of the research questions being asked. This may include:

  • Incidence, prevalence and mortality of the disease or injury stratified by age-group and by sex
  • Disability adjusted Life Years and Quality Adjusted Life Years stratified by age-group and by sex
  • Direct economic impacts attributable to the disease or injury including health care costs, care giving costs, and non-health related costs
  • Indirect productivity impacts attributable to the disease or injury including opportunity lost wages and opportunity lost fiscal revenues

Who is it for?

Organizations that are not content with the “status quo” and are looking to pursue evidence-based changes to the way that things are currently being done using population health measures.

How is it done?

A proven method of connecting and facilitating stakeholders and experts with data and mathematical modeling. At the heart of this four pillar approach is an agent-based, event-driven mathematical microsimulation model called the Life at Risk Platform. The model is of a class as described in section 4.1.65 of the OECD Health Working Papers No. 59 (A Comparative Analysis of Health Forecasting Methods). The life trajectories of individual people are followed, in which events such as “moving to Ontario”, “quitting smoking at age 42 and having started at age 20”, and “dying due to an unintentional fall at 89 years of age” can occur stochastically in a way which matches the known rates. This automatically generates self-consistent histories for each agent. Each agent can also be treated as a representative member of a class of similarly-situated agents, much like a compartmental model. However, rather than being fixed in age and disease state bins, each agent can move with the evolution of the population.

What are the benefits?

For the past decade we have worked with as many as 400 experts to generate over 70 high end, scientifically sound, quantitative decision analysis projects. Our process transforms data and the knowledge of experts and its clients into objective, quantitative, real world value propositions. The result is the generation of ownership of the analysis through the collaboration of different skill sets that provides the ability to:

  • Rank the relative value of knowledge
  • Unify stakeholders, experts and key opinion leaders through the language of population-based outcomes
  • Create connections (awareness, dialogue, action) between the different perspectives of a health problem through objective value propositions
  • Build the confidence to make a decision

Types of Analyses and Services

  • Epidemiological and demographic analyses
  • Demand for and supply of health care labour force
  • Health data
  • Policy evaluations and case analyses
  • Continuums of care analyses
  • Facilitation of stakeholder and expert collaborations
  • Literature reviews
  • Benchmarking analyses
  • Predictive modeling with scenario and sensitivity analyses
  • Manipulation and analysis of large, complex datasets
  • Actuarial modeling for R&D, risk assessment and insurance products