OLDEST-OLD MORTALITY--DEMOGRAPHIC MODELS AND ANALYSES

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How long will we live? The major biomedical uncertainty in forecasting life expectancy in the middle term from, say, twenty to eighty years from now arises from an unsettled controversy concerning: -- whether genetic factors limit human life expectancy to perhaps 85 years or so, or -- whether progress can be made in reducing mortality rates at all ages including advanced ages, such that life expectancy can increase to a century or more. The central aim of this program project is address this controversy by compiling and analyzing reliable data (Projects 1 - 4), by refining and developing appropriate statistical methods for this substantive research (Statistical Development Core), and by systematically structuring the controversy and integrating the research results (Paradigm Adjudication Core). An Administrative Core coordinates the various research activities. The data sets to be assembled, meticulously verified, published and archived pertain to Sweden and the U.S. (Project 1), Danish twins (Project 2), one million Mediterranean fruit flies raised under stringently controlled conditions (Project 3), and 50,000 Drosophila in ten inbred lines (Project 4). These data sets have been carefully chosen to supply the information on mortality rates over age and time and within and across genotypes that is needed to address the life-expectancy controversy. The data sets will be analyzed in the four projects by using lifetable methods and by using parametric survival models estimated by maximum likelihood methods. The analysis will focus on whether there is a genetically-determined senescent component of the force of mortality at older ages that can not be reduced by environmental changes or medical advances. It is the hypothesized existence of senescent mortality that is thought to limit human life expectancy. Some related research on oldest-old mortality is also proposed in the four projects, including research on male/female differentials and on the impact of debilitation vs. mortality selection.