Modeling Changes in the Age Distribution of Opioid Mortality

• Activity Number: 414 - Advances in Estimation Methods
• Type: Contributed
• Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
• Sponsor: SSC
• Author(s): Patrick E Brown* and Ye Lennon Li
• Companies: University of Toronto and Public Health Ontario
• Keywords: Epidemiology; Mortality; Markov random fields; Bayesian inference; Opioids; Time series models

Abstract

Mortality from opioid use is not only increasing, but the age structure of the affected population is changing. A model able to quantify the changing age distribution requires an age effect, a time effect, and an interaction between the two. A two-dimensional Markov random field model is used for the age-time interaction, which assumes only that the mortality risk changes slowly by age and calendar year. Information in the data are weak, and strong Penalized Complexity priors and judicious use of constraints are required. Data are modelled monthly in time by single-year age group, and a Poisson model fit with Bayes and INLA is used to accommodate the low counts.

Deaths in Ontario up until the end of 2016 are modelled, and the age distribution has shifted from unimodal to bimodal in recent years.