The Future of Death in America

By King, Gary; Soneji, Samir | Demographic Research, July-December 2011 | Go to article overview

The Future of Death in America


King, Gary, Soneji, Samir, Demographic Research


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.

(ProQuest: ... denotes formulae omitted.)

1. Introduction

Since 1950, U.S. life expectancy at birth has grown from 68 to 78 years. The U.S. also experienced considerable population aging resulting mainly from declining fertility and mortality. The elderly (≥ 65 years) grew from 14% of the population in 1950 to 19% in 2000, while the working age population (18 to 64 years) declined from 60% to 56%. These developments in population aging had massive implications for American life, as well as for the allocation of medical expenditures, public health efforts, research priorities, pension programs, Social Security, economic growth, and health care financing (Lee and Tuljapurkar 1997). Future American mortality patterns are of understandably widespread interest, despite their uncertainties. In this paper, we offer the first formal statistical forecasts of age and sex-specific U.S. mortality to include knowledge about common demographic patterns in mortality, and large and well-studied biological risk factors.

The early demographic work of Graunt (1662), Huygens (Boyer 1947; Vollgraff 1950), Halley (1693) and Gompertz (1825) established two ubiquitous patterns that have continued to hold up across many countries and time periods. First, age-specific mortality usually declines smoothly and gradually over time, with few sharp jumps from one year to the next. Second, time-specific mortality across age groups, known as "age profiles," have a characteristic shape, with adjacent age groups having similar mortality rates. The vast majority of mortality forecasts to date have incorporated only the time-smoothness property; we make it possible to include as prior information (i.e., rather than either to ignore or require) both smoothness over time and age groups, when the data support it.

Smoking and obesity are two important risk factors, both with well-studied consequences for mortality and large changes over time. Smoking rates steadily declined over the last half century, and obesity rates have rapidly increased in the last thirty years. Most American mortality forecasts ignore these patterns and are based only on simple extrapolations without covariates, and so include these risk factors only indirectly. …

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