Extended Mortality Forecasting Model

We used the mortality model previously published by Foreman and colleagues,²² (link) and extended it to 2100 with slight modifications. Briefly, the cause-specific model included three components: the underlying mortality, modelled as a function of the Socio-demographic Index (SDI), time, and additional cause-specific covariates where appropriate; a risk factor scalar that captured the combined risk factor effects for specific causes, based on the GBD 2017 cause-risk hierarchy and accounting for risk factor mediation;²⁴ (link) and an autoregressive integrated moving average (ARIMA) model²⁵ that accounted for unexplained residual mortality.
To accommodate long-range forecasts, we removed the spline on SDI and used a random walk with attenuated drift for the ARIMA model. Foreman and colleagues found that our mortality model had better out-of-sample predictive validity than the most widely used demographic forecasting model.²² (link) The method used to develop reference scenario values for each of the independent drivers in the mortality model was not modified from Foreman and colleagues.²² (link)