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Phantoms never die : living with unreliable population data.

Cairns, A. J. G. and Blake, D. and Dowd, K. and Kessler, A. (2016) 'Phantoms never die : living with unreliable population data.', Journal of the Royal Statistical series A : statistics in society., 179 (4). pp. 975-1005.

Abstract

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 to identify anomalies, illustrated, by way of example, using England and Wales 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 of people in cohorts born in the distant past. In the case of England and Wales, 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 effect of anomalies on mortality forecasts and annuity values, and we find significant effects for some cohorts. Our methodology has general applicability to other sources of population data, such as the Human Mortality Database.

Item Type:Article
Keywords:Baby boom, Cohort–births–deaths exposures methodology, Convexity adjustment ratio, Deaths, Graphical diagnostics, Population data.
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Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.1111/rssa.12159
Publisher statement:© 2016 The Author Journal of the Royal Statistical Society: Series A (Statistics in Society) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Record Created:27 Jan 2016 11:05
Last Modified:27 Oct 2017 09:48

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