The future of death in America

Abstract

Population mortality forecasts are widely used for allocating public health expenditures, setting research priorities, and evaluating the viability of public and private pensions, and health care financing systems. In part because existing methods forecast less accurately when based on more information, most forecasts are still based on simple linear extrapolations that ignore known biological risk factors and other prior information. We adapt a Bayesian hierarchical forecasting model capable of including more known health and demographic information than has previously been possible. This leads to the first age- and sex-specific forecasts of American mortality that simultaneously incorporate, in a formal statistical model, the effects of the recent rapid increase in obesity, the steady decline in tobacco consumption, and the well known patterns of smooth mortality age profiles and time trends. Formally including new information in forecasts can matter a great deal. For example, we estimate an increase in male life expectancy at birth from 76.2 years in 2010 to 79.9 years in 2030, which is 1.8 years greater than the U.S. Social Security Administration projection and 1.5 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at birth over the next twenty years from 80.5 years to 81.9 years, which is virtually identical to the Social Security Administration projection and 2.0 years less than U.S. Census projections. We show that these patterns are also likely to greatly affect the aging American population structure. We offer an easy-to-use approach so that researchers can include other sources of information and potentially improve on our forecasts too.

Future Mortality in Low Mortality Countries

“…These conclusions are supported by the projections of Bongaarts (2006) and Soneji & King (2011). In many low mortality countries, national health policies are currently conducting intensive anti-smoking campaigns that can be expected to lead to further decreases of smoking rates among women and men.…”

Smoothing and projecting age-specific probabilities of death by TOPALS

“…One advantage of using death probabilities rather than rates is that probabilities are easy to interpret and can simply be used for forecasting (King and Soneji, 2011). Another advantage is that probabilities rather than rates are used for the calculation of life expectancy using a life table.…”

Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands

“…Comorbidities and dependencies among causes make such models even more complex. However, researchers like King and Soneji (2011) have emphasised the potential utility of including risk factors in forecasts, arguing that doing so will improve the quality, accuracy, and transparency of mortality forecasts. A classic example in which the determinant is known and can be measured is the dependence of lung cancer on smoking.…”

“…Epidemiological information can be included in the model in different ways (see, for instance, Pampel 2005;Bongaarts 2006;Stewart, Cutler, and Rosen 2009;Wang and Preston 2009;King and Soneji 2011;Janssen, van Wissen, and Kunst 2013). Forecasters first have to identify the correlation between the determinant and mortality, and then formulate assumptions about the future.…”

“…Other examples are forecasting methods using valuable medical knowledge and information on behavioural and environmental changes, such as smoking and/or obesity (e.g. Pampel 2005;Olshansky et al 2005;Bongaarts 2006;Stewart, Cutler, and Rosen 2009;Wang and Preston 2009;King and Soneji 2011;Janssen, van Wissen, and Kunst 2013). Although these new types of methods have many advantages, the more explanatory methods involve a large element of subjective judgment (see Section 6.5 for a further discussion).…”