All Shorenstein APARC News News July 16, 2020

Researchers Develop New Method for Projecting Future Wellness of Aging Populations

Asia Health Policy Director Karen Eggleston and her colleagues unveil a multistate transition microsimulation model that produces rigorous projections of the health and functional status of older people from widely available datasets.
An elderly individual travels in a cart up a street.
An elderly individual travels in a cart up a street. Galen Crout, Unsplash

The world’s population is aging at a faster rate and in larger cohorts than ever before. In countries like Japan that have low fertility rates and high life expectancy, population aging is a risk to social sustainability. Developing policies and healthcare infrastructure to support aging populations is now critical to the social, economic, and developmental wellbeing of all nations. As the COVID-19 pandemic has repeatedly shown, accurate projections of future population health status are crucial for designing sustainable healthcare services and social security systems.

Such projections necessitate models that incorporate the diverse and dynamic associations between health, economic, and social conditions among older people. However, the currently available models – known as multistate transition microsimulation models – require high-quality panel data for calibration and meaningful estimates. Now a group of researchers, including APARC Deputy Director and Asia Health Policy Program Director Karen Eggleston, has developed an alternative method that relaxes this data requirement.

In a newly published paper in Health Economics, Eggleston and her colleagues describe their study that proposes a novel approach using more readily-available data in many countries, thus promising more accurate projections of the future health and functional status of elderly and aging populations. This alternative method uses cross‐sectional representative surveys to estimate multistate‐transition contingency tables applied to Japan's population. When combined with estimated comorbidity prevalence and death record information, this method can determine the transition probabilities of health statuses among aging cohorts.

In comparing the results of their projections against a control, Eggleston and her colleagues show that traditional static models do not always accurately forecast the prevalence of some comorbid conditions such as cancer, heart disease, and stroke. While the sample sets used to test the new methodology originate in Japan, the proposed multistate transition contingency table method has important applications for aging societies worldwide. As rapid population aging becomes a global trend, the ability to produce robust forecasts of population health and functional status to guide policy is a universal need.

Read the full paper in Health Economics.

Learn more about Eggleston’s research projects >>

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