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B0820
Title: Detecting life expectancy anomalies in England using a Bayesian hierarchical model Authors:  Areti Boulieri - Imperial College London (United Kingdom) [presenting]
Marta Blangiardo - Imperial College London (United Kingdom)
Abstract: In England, life expectancy has shown a steady increase over many years, however these improvements have recently started to slow down considerably. The aim is to investigate the changes in life expectancy in England over time and across its local authorities, and to identify local authorities with unusual time trends that might help with hypothesis generation and point to emerging risk factors. We analyse mortality count data in England for females at the local authority level (324 areas), from 2001 to 2016 (17 years), and by age group, assuming 19 age groups of 5 year bands. We develop a statistical model within the Bayesian hierarchical framework that accounts for spatial, temporal, and age effects, as well as for pairwise interactions. The space-time interaction parameter is used to detect areas whose time trends deviate from the national one. The detection rule that we specify focuses on areas that are detected as unusual over the last 5 years of the time period 2013-2017. The model is implemented in Integrated Nested Laplace Approximations (INLA). We found roughly 40 areas to be highlighted as unusual under the model, following a different time trend in the mortality rates compared to the national trend.