Life At Risk™
The trademark of RiskAnalytica, as well as its original approach in defining and solving problems specific to the population-based management of disease (from the viewpoint of healthcare policy agents), resides in the execution of its interdisciplinary and scientifically derived risk management framework. This framework is called Life at Risk™ .
Recently, the Life at Risk™ framework has been extended to the population-based management of cancer, circulatory diseases and chronic obstructive pulmonary diseases. Each disease application represents a modular concentration within Life at Risk™ framework.
Life at Risk™ framework has been successfully used to support several Canadian cancer control organizations (see Canadian Strategy for Cancer Control). The approach takes a holistic view of the disease control problem by considering the perspectives of various stakeholders, combined with their inherent motivations. In addition, the framework has many commonalities with the modern “system thinking” approach. The process takes into account not only the parts, or the major entities relevant to the problem, but also their complex interrelations as well as the system as a whole. In order to provide a proper analysis, the interrelations are translated into mathematical models. The results of the simulations do not provide point estimations (predictions) of the future states, (which can prove to be misleading), but rather a region of possibilities, which can be understood and managed by the various decision makers (after the process of knowledge formation has occurred).
The timeframe of analysis which can be conducted within the Life at Risk™ framework is a key attribute for efficient and effective disease control management and implementation. By mapping the short, medium and long term effects of potential policies on the life and economic factors, the forward looking nature of Life at Risk™ makes the observation of effects (which would otherwise not appear in an analysis within a short timeframe horizon) possible. The results of implementing specific interventions with such a long term orientation analysis and which are typically described in terms of “risk” and “rewards” are then expressed in specific managerial terms such as goals, objectives and tactics. Two main advantages of this process include (1) the underlying quantitative analysis (which are based on scenario simulation methodologies) become transparent for the decision makers and managers to whom the results of analysis are mostly dedicated, and (2) the creation of forward looking disease control management specific language.
Life at Risk™ computes the possible future (forward looking) burden of disease using a simulation platform that operates across four main modules (briefly described below). By incorporating the causal relationships among population, risk factors, epidemiology and economic impacts, the framework is able to generate the expected and possible risk factor exposure, its effects on the future state of cancer, and the associated economic burden.
The platform is divided into the following modules:
The population module simulates the expected and possible future states of the population, taking into account historical (both public and expert based information) data regarding population dynamics as well as the birth, mortality, immigration, emigration, and labour force dimensions (participation, unemployment and dependants). Depending on the required level of detail (focus of analysis), some other aspects including ethnicity or the status of mental health (within the population being considered) might be incorporated as well. The determinants of the future states of the population are simulated within a Monte Carlo process in which future states are randomly selected based on their respective transition probabilities (computed using the input data).
Risk Factors (with co-morbidity) Module
The population module along with the risk factor behaviour (prevalence) patterns (exhibited within the population) serves as a constraint on the simulated region of possible future risk prevalence associated with each of the risk factors. The input for this module is based on the population module as well as on the data available on each of the risk factors prevalence and the risk factors co-morbidity data. Within this module, the proposed policy is shown to have an impact on the future state of the risk factors (all of the risk factors considered and their co-morbid dependences). In general, a policy is shown to have an effect on various risk factors such as poor diet, smoking, lack of physical activity and obesity.
Within this module, disease incidence, mortality and associated prevalence are simulated as possible future states (based on the expected risk factor prevalence as well as on actual risk factor prevalence with the associated lag and latency periods). Data regarding disease incidence, mortality, survival statistics, staging, prevalence, co-morbidity and disability is required; existent forecasts regarding the status quo situation and the associated transition probabilities ensure our analysis will provide a detailed picture of the future possibility space of the disease states within the population.
Here, the results of the previous modules are used as inputs to estimate the economic burden associated with the presence of disease within the population (and the labour force as a subset). The immediate effects of disease within the population are related to health care costs, disability of the victims to perform work and earn wages, and even premature mortality and its associated economic consequences. Most of the relevant data for this module is provided through diverse National Statistics centers. These include data regarding some macroeconomic variables, inflation, labour force and earnings. In addition, in order to obtain a more accurate evaluation, some disease specific data is needed, such as economic disability weights within each disease stage (this mainly means data on how disease disability will affect a patient’s ability to work, for each disease stage) and the health costs due to each disease disability stage. By simulating the economic costs of various potential policies, an economic price of the presence of disease (within the population) will emerge.