Population mortality forecasts, despite their uncertainties, are widely used for allocating public health expenditures, setting research priorities, and evaluating the viability of Social Security, private pensions, and health care financing systems. Although we know a great deal about patterns in and causes of mortality rates, most existing forecasts are still based on simple linear extrapolations that ignore covariates and other prior information. We adapt a Bayesian hierarchical forecasting model capable of including more known biological and demographic information than has previously been possible. This leads to the first age- and sex-specific forecasts of American mortality that simultaneously incorporates 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 75.1 years to 79.8 years over the next quarter-century, which is 1.7 years greater than the Social Security Administration projection and 1.4 years more than U.S. Census projection. For females, we estimate more modest gains in life expectancy at birth over the next quarter-century from 80.2 years to 82.7 years, which is 0.8 years greater than the Social Security Administration projection and 0.4 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 sophisticated, but easy-to-use, methods so that researchers can include other sources of information and potentially improve on our forecasts.
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