Multivariable flexible modelling for estimating complete, smoothed life tables for sub-national populations. (December 2015)

• Record Type: Journal Article
• Authors: Rachet, Bernard; Maringe, Camille; Woods, Laura; Ellis, Libby; Spika, Devon; Allemani, Claudia
• Is Part Of: BMC public health. Volume 15:Number 1(2015)
• Page Start: 1
• Page End: 9
• Publication Date: 2015-12
• Subjects: Life tables -- Model life tables -- Mortality rates -- Life expectancy -- Generalised linear model -- Cubic splines -- Deprivation -- Small areas; Public health -- Periodicals; 362.105
• DOI: 10.1186/s12889-015-2534-3
• Languages: English
• ISSNs: 1471-2458

Abstract

Abstract Background The methods currently available to estimate age- and sex-specific mortality rates for sub-populations are subject to a number of important limitations. We propose two alternative multivariable approaches: a relational model and a Poisson model both using restricted cubic splines. Methods We evaluated a flexible Poisson and flexible relational model against the Elandt-Johnson approach in a simulation study using 100 random samples of population and death counts, with different sampling proportions and data arrangements. Estimated rates were compared to the original mortality rates using goodness-of-fit measures and life expectancy. We further investigated an approach for determining optimal knot locations in the Poisson model. Results The flexible Poisson model outperformed the flexible relational and Elandt-Johnson methods with the smallest sample of data (1%). With the largest sample of data (20%), the flexible Poisson and flexible relational models performed comparably, though the flexible Poisson model displayed a slight advantage. Both approaches tended to underestimate infant mortality and thereby overestimate life expectancy at birth. The flexible Poisson model performed much better at young ages when knots were fixeda priori . For ages 30 and above, results were similar to the model with no fixed knots. Conclusions The flexible Poisson model is recommended because it derives robust and unbiased estimates for sub-populations without making strong assumptions about age-specific mortality profiles. Fixing knotsa priori in the final model greatly improves fit at the young ages.