As a society, we devote considerable resources to forecasting various phenomena—economic conditions, weather and climate, agricultural yields, and effects of various technologies. All such forecasting efforts are beset by inherent complexity, technical difficulty, and reliance on multiple assumptions. In the health-care sector, forecasting has been used to project the impact of approved and potential federal policies on health care, such as their effect on insurance coverage, federal Medicare and Medicaid utilization, costs, as well as the number, type, and distribution of health-care providers.1
With few notable exceptions, such as the development of the Future Elderly Model (FEM),2
relatively little effort has been invested in using forecasting to address questions about the health status of the population—the ultimate goal of health care, disease prevention, and health promotion. How much longer will they live? What will be the burden of morbidity? Will disparities in the distribution of morbidity and mortality widen or narrow?
The development of a population health-forecasting model has the potential to interject new and valuable information about the future health status of geopolitically or otherwise defined populations, based on current conditions, socioeconomic and demographic trends, and potential changes in policies and programs. More comprehensive modeling is now possible due to dramatic improvements in forecasting methodologies, including new techniques to assess and reduce forecasting error, support for sensitivity analyses, and better estimates of uncertainty enabled by improved computing power.3
A health-forecasting model can be a critical analytic and translation tool to infuse timely and relevant information into policy debates at all levels of government, as well as in the private sector.
Forecasting is not entirely new to public health. Current and previous efforts to forecast disease- and risk-specific aspects of human health aim to better understand the impact of interventions and provide actionable information to practitioners. Each of these efforts has tended to be limited, focusing on a specific aspect of health. For example, infectious disease modeling has examined disease transmission to project disease incidence and prevalence based on patterns of contact, mode of transmission, incubation period, and vectors;4
the results complement disease surveillance to support epidemic preparedness.5
Forecasting has also been used to examine trajectories of functional capacity in populations, using demographic trends and information on disability status and patterns in chronic conditions.6,7
Among forecasting models analyzing the effects of changes in behavioral risk factors and environmental conditions, tobacco use is by far the most extensively modeled behavioral risk factor, but physical activity, nutrition, and obesity are increasingly being modeled due to their linkage to diabetes and cardiovascular diseases.8
Some models include multiple risk factors and the estimated associated burden of disease for regional populations,9
while others focus on a narrow set of risk factors and trace disease trajectories for alternative intervention scenarios.10–17
Population health forecasting requires rich data, an understanding of the determinants of health and their interactions, and technically innovative modeling. The evidence base for such modeling is supported by systematic reviews of the environmental and policy determinants of health and meta-analyses of specific health risk factors and related interventions.18–20
Both give a sense of the extent of missed opportunities for health improvement and the high cost of not undertaking policies and programs of proven effectiveness. However, there continues to be a translation gap between research and practice.21,22
Policy researchers have identified many reasons for this gap, including a lack of understanding or awareness, uncertainty about the relevance of research for particular situations, and the lack of confidence in information sources.23,24
Most models that evaluate multiple health determinants and related outcomes are based on a microsimulation framework that allows modeling of individual units, usually individuals. Compared with approaches based on aggregate trends, microsimulation models are particularly suitable to evaluating different interventions and policy scenarios, by allowing the incorporation of data from disparate sources and inclusion of distributional information on variables of interest. Two major types are widely used. Static models use cross-sectional databases that provide a snapshot of the population at a point in time. In contrast, dynamic models build longitudinal databases of individual histories and allow behaviors and exposures to change over the time modeled.25,26
The Coronary Heart Disease Policy Model was one of the earliest microsimulation applications to evaluate policy and behavioral changes and their impact on population health. This model assesses the impact of policy and technological advances on the incidence, prevalence, and mortality from coronary heart disease, and related changes in health-care costs.15,16
Several other models have been developed to estimate the impact of policy changes on smoking patterns and outcomes.14
In Canada, a dynamic, continuous-time Population Health Model (POHEM) has been developed to assess the impact of different policy interventions and technologies on the health of the Canadian population.27
Using continuous-time modeling, such as used in Canada's POHEM, simplifies the modeling of multiple processes with many events that in a discrete-time model would result in an explosion of the number of possible state transitions. Continuous-time modeling reduces the complexity of modeling covarying behaviors, comorbidity, and competing risks, and it can incorporate joint distributions of variables that are determinants of health.
These existing models still have many limitations. Static models are limited in their application to different populations and time horizons. Other models have been limited in the number of variables, the scope of the model, or the exclusion of demographic and socioeconomic trends, providing only a partial picture of future health gains and changes in health-care costs. To obtain a more comprehensive understanding of the impact on health and associated outcomes, a more comprehensive model is required in which comorbid states and covarying behaviors are explicitly modeled, and unrelated interventions can be compared against a standardized baseline.
Probably the most concerted effort to forecast the health status of people in the United States is the FEM.2,28
This model is used to forecast the consequences of health trends and medical innovations for the Medicare population. It combines information on trends in health conditions, functional state, and risk factors (e.g., weight and smoking) with information on the availability of new medical technologies that may impact these trends, and the likely medical expenditures associated with the observed health conditions. This model exemplifies the value of building a more comprehensive model, as it can be used to more realistically anticipate future health-care expenditures.
Our approach is similar to the FEM in using simulation methods to incorporate information from different data sources and allowing for the incorporation of comorbid states and covarying health variables. We've expanded on this by (1) extending the age range that is modeled to birth to provide a full life-course model, (2) incorporating additional aspects of the dynamics of population demographics (e.g., changes due to migration), and (3) incorporating time-varying health risk factors (e.g., physical activity and obesity) to better evaluate the impact of public health programs and policies.
In this article, we focus primarily on the development of the model, its capabilities, and its application in public health practice. Although an outline of the technical working specifications is provided, a detailed technical working document and updates to the model are available at www.health-forecasting.org