Phantoms Never Die: Living with Unreliable Population Data

Andrew John George Cairns, David Blake, Kevin Dowd, Amy Kessler

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)
29 Downloads (Pure)


The analysis of national mortality trends is critically dependent on the quality of the population, exposures and deaths data that underpin death rates. We develop a framework that allows us to assess data reliability and identify anomalies, illustrated, by way of example, using England & Wales (EW) population data. First, we propose a set of graphical diagnostics that help to pinpoint anomalies. Second, we develop a simple Bayesian model that allows us to quantify objectively the size of any anomalies. Two-dimensional graphical diagnostics and modelling techniques are shown to improve significantly our ability to identify and quantify anomalies. An important conclusion is that significant anomalies in population data can often be linked to uneven patterns of births in cohorts born in the distant past. In the case of EW, errors of more than 9% in the estimated size of some birth cohorts can be attributed to an uneven pattern of births. We propose methods that can use births data to improve estimates of the underlying population exposures. Finally, we consider the impact of anomalies on mortality forecasts and annuity values, and find significant impacts for some cohorts. Our methodology has general applicability to other population data sources, such as the Human Mortality Database.
Original languageEnglish
Pages (from-to)975-1005
Number of pages31
JournalJournal of the Royal Statistical Society Series A: Statistics in Society
Issue number4
Early online date25 Jan 2016
Publication statusPublished - Oct 2016


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