A General Age-Specific Mortality Model with An Example Indexed by Child or Child/Adult Mortality

Authors: Samuel J. Clark

Abstract: BACKGROUND. The majority of countries in Africa and nearly one third of all countries require mortality models to infer complete age schedules of mortality, required for population estimates, projections/forecasts and many other tasks in demography and epidemiology. Models that relate child mortality to mortality at other ages are important because all countries have measures of child mortality. OBJECTIVE. 1) Design a general model for age-specific mortality that provides a standard way to relate covariates to age-specific mortality. 2) Calibrate that model using the relationship between child or child/adult mortality and mortality at other ages. 3) Validate the calibrated model and compare its performance to existing models. METHODS. A general, parametrizable component model of mortality is designed using the singular value decomposition (SVD-Comp) and calibrated to the relationship between child or child/adult mortality and mortality at other ages in the observed mortality schedules of the Human Mortality Database. Cross validation is used to validate the model, and the predictive performance of the model is compared to that of the Log-Quad model, designed to do the same thing. RESULTS. Prediction and cross validation tests indicate that the child mortality-calibrated SVD-Comp is able to accurately represent the observed mortality schedules in the Human Mortality Database, is robust to the selection of mortality schedules used to calibrate it, and performs better than the Log-Quad Model. CONCLUSIONS. The child mortality-calibrated SVD-Comp is a useful tool that can be used where child mortality is available but mortality at other ages is unknown. Together with earlier work on an HIV prevalence-calibrated version of SVD-Comp, this work suggests that this approach is truly general and could be used to develop a wide range of additional useful models.

Submitted to arXiv on 01 Dec. 2016

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