Mapping disability‐adjusted life years: a Bayesian hierarchical model framework for burden of disease and injury assessment

Abstract

This paper presents a Bayesian disability-adjusted life year (DALY) methodology for spatial and spatiotemporal analyses of disease and/or injury burden. A Bayesian disease mapping model framework, which blends together spatial modelling, shared-component modelling (SCM), temporal modelling, ecological modelling, and non-linear modelling, is developed for small-area DALY estimation and inference. In particular, we develop a model framework that enables SCM as well as multivariate CAR modelling of non-fatal and fatal disease or injury rates and facilitates spline smoothing for non-linear modelling of temporal rate and risk trends. Using British Columbia (Canada) hospital admission-separation data and vital statistics mortality data on non-fatal and fatal road traffic injuries to male population age 20-39 for year 1991-2000 and for 84 local health areas and 16 health service delivery areas, spatial and spatiotemporal estimation and inference on years of life lost due to premature death, years lived with disability, and DALYs are presented. Fully Bayesian estimation and inference, with Markov chain Monte Carlo implementation, are illustrated. We present a methodological framework within which the DALY and the Bayesian disease mapping methodologies interface and intersect. Its development brings the relative importance of premature mortality and disability into the assessment of community health and health needs in order to provide reliable information and evidence for community-based public health surveillance and evaluation, disease and injury prevention, and resource provision.

Spline smoothing in Bayesian disease mapping

“…Their sum was proposed to form a deviance information criterion which may be used for comparing models for their overall performances, in terms of balancing complexity and fit (Spiegelhalter et al, 2003a). It is noteworthy that in this and similar studies the DIC calculation has shown considerable stability over different MCMC runs, and we observed considerable consistency in the pD-Dbar-DIC assessments with respect to knot and model selections (MacNab, 2007;MacNab and Gustafson, 2007).…”

Transferring injury data to decision makers in British Columbia

“…Each informant was presented with sample datasets on a laptop computer and asked to provide comments on the data presented. Injury mortality and hospitalisation counts and rates, stratified by gender and based on 1991-2000 populationbased administrative data (MacNab, 2007), were presented in Excel spreadsheets for major causes of injuries. Estimates of years of life lost, healthy years lost due to disability and disability adjusted life years were derived from disability weight and duration figures developed in the Global Burden of Disease study of 1990, with a 3% annual discount rate and zero age weighting (Murray & Lopez, 1996;MacNab, 2007).…”

“…Injury mortality and hospitalisation counts and rates, stratified by gender and based on 1991-2000 populationbased administrative data (MacNab, 2007), were presented in Excel spreadsheets for major causes of injuries. Estimates of years of life lost, healthy years lost due to disability and disability adjusted life years were derived from disability weight and duration figures developed in the Global Burden of Disease study of 1990, with a 3% annual discount rate and zero age weighting (Murray & Lopez, 1996;MacNab, 2007). The informants were also presented with two sample maps comparing (using colour-coding) annual iatrogenic injury risk estimates for children and youth aged 1 to 19 years across health regions.…”

A spatial structural equation model with an application to area health needs

“…To accommodate these features, a multivariate extension of the prior of Leroux et al [12] is used [13], but adapted to include regression terms η qi as, for example, in the spatial priors of Bell and Broemeling [14] and Stern and Cressie [15]. Thus denote…”