How far ahead the data we already hold can predict — deaths, health, healthcare demand — and what the record of those forecasts shows.
Official projections of life expectancy are always likely to be wrong. They were too low until the 2010s, when in several countries they became too high as improvement stalled. The question is whether anyone can forecast mortality better, and how one would know. The models are well described: extrapolative, cause-specific, expert-judgement, ensembles, and now machine-learning approaches that gather more from the same data. Their track records are less well described, as forecasts are seldom scored against outturns in public and the horizon that matters is decades away. What the evidence does show is that the errors come mostly from turning points — the stall, the pandemic — that no extrapolative model anticipates. Cause-specific and expert-judgement models fared little better. The same pattern holds wherever a projection is made from existing data rather than from new observation. A projection of one disease's future burden belongs on that disease's page. What belongs here is whether such projections have held up, and which kinds have held up better.
