James Oeppen


  • Associate Professor Emeritus, CPop

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Research areas

Forecasting Mortality by Cause of Death

Forecasting mortality is a difficult problem that is undertaken by many international, government, and financial institutions. It would be useful to disaggregate these forecasts by cause of death because that would allow policy makers to identify target areas for future reductions in mortality. In addition, the ultimate cause of death may be a good guide to the period of ill health that precedes most deaths and help to indicate its cost to society. At the moment, very few institutions try to forecast mortality by cause of death because of the technical difficulties.

One of the reasons it is more difficult is that even though the average age at death is generally rising, each person must die, so if the forecast suggests that a cause will become less important in the future, one or more of the other causes has to absorb extra deaths and not necessarily at the same age.

A second problem arises because we would like the sum of the forecasts for each cause to match the existing forecasts that do not separate the causes of death. This project undertakes basic research to explore new statistical methods that are explicitly designed to address these problems.


Forecasting Cancer Mortality, Prevalence, and Incidence.

Assuming that cancer mortality can be forecast within a coherent model of the other major competing causes of death, this project attempts to add forecasts of specific cancer prevalence and incidence that are consistent with the cancer mortality forecast. The initial phase has concentrated on lung cancer because of its importance to overall mortality and morbidity, its differential trajectories by sex and country, and its relatively well-defined etiology. Given a forecast of mortality by major causes, specifically separating out lung cancer mortality, we adapt the Preston-Glei-Wilmoth indirect estimation method to forecast the fraction of total mortality that may be attributed to smoking. The next step will try to integrate demographic, epidemiological and oncological approaches to estimate future patterns of lung cancer incidence and prevalence that are consistent with the forecast levels of lung cancer mortality.



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