Restricted CAR Model for Reliable Life Expectancy Estimates in Philadelphia Census Tracts

Music City Center 

Reliable, and ideally smooth, age-specific all-cause mortality rate estimates are needed when estimating life expectancy. These rates, however, can be difficult to estimate in small areas, due to small counts of deaths when subsetting the population in each small area by age and sex. The conditional autoregressive (CAR) framework allows us to integrate spatial dependencies from the data, which helps us produce more reliable estimates, even when count data may be sparse. We estimated tract-level age- and sex-specific mortality rates using a Bayesian Poisson model adaptation of the TOPALS (tool for projecting age patterns using linear splines) – which is useful for producing smooth, age-specific rates – that includes spatial (CAR) random effects. Although smooth estimates are ideal for calculating life expectancy, this approach does come with the risk of oversmoothing rates. This study builds on recent work that developed a restricted CAR model to guard against producing overly smooth and overly precise estimated mortality rates, and extends it to the TOPALS-CAR framework for modelling age-specific rates in census tracts.

bayesian statistics

spatial statistics

spatial epidemiology

disease mapping 

Section on Statistics in Epidemiology