Comparison of Bayesian spatio-temporal models for small-area life expectancy: A simulation study.

Abstract

The purpose of this study was to assess the precision, uncertainty, and normality of small-area life expectancy estimates calculated using Bayesian spatio-temporal models. We hypothesized six scenarios in which all 247 districts of Korea had the same year-specific female population of 500, 1,000, 2,000, 5,000, 10,000, and 25,000 individuals during the study period (2013-2017). We generated 1,000 hypothetical datasets for each scenario and calculated district-year life expectancies. The precision and uncertainty of life expectancy estimates were compared between the two Bayesian spatio-temporal models and the traditional method and Bayesian spatial models. We examined the normality of the life expectancy distributions generated by each method and investigated an optimal cut-off value for the comparisons. The Bayesian spatio-temporal models produced precise life expectancy estimates. However, the 95% uncertainty interval contained the true value with a probability of less than 95%. The Bayesian spatio-temporal models violated the normality assumption in scenarios with small population sizes. Therefore, life expectancy comparisons should be conducted using a cut-off value that minimizes false-positive and false-negative rates. We proposed 0.8 as a cut-off value to determine the statistical significance of the difference in life expectancy.