Forecasting cause-age specific mortality using, two random processes

Abstract

Mortality forecasts are critical information for assessing the health of a population and are necessary for making informed decisions about how best to direct health-related resources and activities. Timeliness in making health statistics available is crucial to identify and address current health problems. Being motivated to meet these needs, we propose a method to forecast the number of cause-age specific deaths through a two random processes model. Unlike the previous methods, the new method incorporates both cross-sectional and longitudinal correlations into our model without a high-dimensional problem. A bootstrap confidence interval is presented to measure the validity of our model and to detect an unusual occurrence of deaths. Our data analysis demonstrates that our method gives promising results compared with the true final counts.