ABSTRACT
BACKGROUND: Given the increased link between retirement age and payments to the development in life expectancy, a precise and regular forecast of life expectancy is of utmost importance. The choice of the jump-off rates, i.e. the rates in the last year of the fitting period, is essential for matching mortality forecasts to the most recently observed data. A general approach to the choice of the jump-off rates is currently lacking. OBJECTIVE: We evaluate six different options for the jump-off rates and examine their effects on the robustness and accuracy of the mortality forecast. DATA AND METHODS: Death and exposure numbers by age for eight European countries over the years 1960-2014 were obtained from the Human Mortality Database. We examined the use of model values as jump-off rates versus observed values in the last year or averaged over the last couple of years. The future life expectancy at age 65 is calculated for different fitting periods and jump-off rates using the Lee-Carter model and examined on accuracy (mean absolute forecast error) and robustness (standard deviation of the change in projected e65). RESULTS: The choice for the jump-off rates clearly influences the accuracy and robustness of the mortality forecast, albeit in different ways. For most countries using the last observed values as jump-off rates resulted in the most accurate method, which relates to the relatively high estimation error of the model in recent years. The most robust method is obtained by using an average of observed years as jump-off rates. The more years that are averaged, the better the robustness, but accuracy decreases with more years averaged. CONCLUSION: Carefully considering the best choice for the jump-off rates is essential when forecasting mortality. The best strategy for matching mortality forecasts to the most recently observed data depends on the goal of the forecast, the country-specific past mortality trends observed, and the model fit.
ABSTRACT
AIMS: We formally estimate future smoking-attributable mortality up to 2050 for the total national populations of England & Wales, Denmark and the Netherlands, providing an update and extension of the descriptive smoking-epidemic model. METHODS: We used smoking prevalence and population-level lung cancer mortality data for England & Wales, Denmark and the Netherlands, covering the period 1950-2009. To estimate the future smoking-attributable mortality fraction (SAF) we: (i) project lung cancer mortality by extrapolating age-period-cohort trends, using the observed convergence of smoking prevalence and similarities in past lung cancer mortality between men and women as input; and (ii) add other causes of death attributable to smoking by applying a simplified version of the indirect Peto-Lopez method to the projected lung cancer mortality. FINDINGS: The SAF for men in 2009 was 19% (44 872 deaths) in England & Wales, 22% (5861 deaths) in Denmark and 25% (16 385 deaths) in the Netherlands. In our projections, these fractions decline to 6, 12 and 14%, respectively, in 2050. The SAF for women peaked at 14% (38 883 deaths) in 2008 in England & Wales, and is expected to peak in 2028 in Denmark (22%) and in 2033 in the Netherlands (23%). By 2050, a decline to 9, 17 and 19%, respectively, is foreseen. Different indirect estimation methods of the SAF in 2050 yield a range of 1-8% (England & Wales), 8-13% (Denmark) and 11-16% (the Netherlands) for men, and 7-16, 12-26 and 13-31% for women. CONCLUSIONS: From northern European data we project that smoking-attributable mortality will remain important for the future, especially for women. Whereas substantial differences between countries remain, the age-specific evolution of smoking-attributable mortality remains similar across countries and between sexes.
