Excess mortality is the number of deaths in a period above what would have been expected without some event: a pandemic, a heatwave, a severe influenza season. It is the measure that made the pandemic comparable across countries, because it does not depend on who was tested or how a death was certified. It is also a measure that two careful analysts can calculate differently by a factor of two, and it is worth understanding why.
The baseline. Expected deaths have to come from somewhere. The simplest baseline is the average of the previous five years. That was common in 2020 and produces a specific bias: populations are ageing, so a flat average understates the deaths that would have happened anyway, and overstates the excess. A trend-based baseline extends the pre-event pattern forward instead. Choice of baseline period matters too: whether 2020 and 2021 are inside the reference window changes every estimate for 2022 onwards, and analysts disagree about whether they should be.
Age standardisation. A country with more old people has more deaths. Comparing raw counts across countries, or across years within an ageing country, confuses population structure with risk. Standardised estimates apply age-specific death rates to a fixed population, so that a change means a change in risk rather than in the number of eighty-year-olds. The Office for National Statistics moved to an age-standardised, trend-based method in 2024 and revised its UK excess figures as a result; the two series are not comparable.
Registration. Deaths are counted when registered, not when they occur. In England and Wales, deaths referred to a coroner can register months late, so recent weeks are always incomplete and always revised upwards. Weekly series either wait, or model the delay, or carry a known undercount in the latest weeks. Provisional figures from the United States are similar.
Denominators. Excess mortality needs a population to divide by. Between censuses, population estimates are themselves estimates, and a census that finds fewer people than expected — as several did in 2021 and 2022 — rebases every rate calculated in the years before.
The models. Beyond the baseline and the standardisation, analysts choose a model for the expected count: a Poisson regression with seasonal terms, a Serfling-style cyclical fit, the Farrington algorithm used in outbreak surveillance. These matter less than the baseline choice but are not nothing, and they explain why EuroMOMO's figures, the Short-Term Mortality Fluctuations series and a national office can all be right and all differ.
Why the estimates differed so much. For 2020 and 2021, the World Health Organization's global estimate was close to three times the count of deaths reported as COVID-19. The gap is not fraud on either side. Reported deaths depend on testing and certification; the WHO figure is modelled, with wide intervals, and includes countries with no reliable registration at all. The Economist's model and the Institute for Health Metrics and Evaluation's produced different global totals from similar inputs. Where national data are good, the estimates converge; where they are not, the model is doing the work.
Reading an excess figure. Four questions settle most of it. What is the baseline period, and does it include the pandemic years? Is the figure age-standardised? Are the most recent weeks complete or provisional? And does the series revise its own history — because if it does, the figure you are reading may not be the figure in next year's series.