Title: Estimating Under Five Mortality in Space and Time in a Developing World Context,
Abstract: Accurate estimates of the under-5 mortality rate (U5MR) in a developing world
context are a key barometer of the health of a nation. This paper describes new
models to analyze survey data on mortality in this context. We are interested
in both spatial and temporal description, that is, wishing to estimate U5MR
across regions and years, and to investigate the association between the U5MR
and spatially-varying covariate surfaces. We illustrate the methodology by
producing yearly estimates for subnational areas in Kenya over the period 1980
- 2014 using data from demographic health surveys (DHS). We use a binomial
likelihood with fixed effects for the urban/rural stratification to account for
the complex survey design. We carry out smoothing using Bayesian hierarchical
models with continuous spatial and temporally discrete components. A key
component of the model is an offset to adjust for bias due to the effects of
HIV epidemics. Substantively, there has been a sharp decline in U5MR in the
period 1980 - 2014, but large variability in estimated subnational rates
remains. A priority for future research is understanding this variability.
Temperature, precipitation and a measure of malaria infection prevalence were
candidates for inclusion in the covariate model.