Standard expected years of life lost as a measure of mortality: norms and reference to New Zealand data

Authors


Section of Epidemiology and Biostatistics, School of Population Health, Tanaki campus, University of Auckland, Auckland, New Zealand. e-mail: rj.marshall@auckland.ac.nz

Abstract

Objectives:Standard expected years of life lost (SEYLL) is a component of the disability adjusted life year (DALY) measure of disease burden. SEYLL is calculated from the expected remaining years, as specified by a normative survivorship that is derived from a model life table. Because every death has non-zero remaining years, years of life lost are never zero, even in a population that achieves the ideal life expectancy of the model life table itself. As zero is unattainable, what is an acceptable years of life lost? The study calculates norms and examines NZ data against them.

Method:Using model life tables with expectancy 80 years for men and 82.5 years for women, the years of life lost in cohorts that achieve the model life table expectancy are evaluated. Years of life lost per death and per head of population are considered and the effects of age-standardising, discounting and age-weighting are evaluated. Mortality and Census data are used to compute actual years of life lost in New Zealand, which are compared with the norms.

Results:Crude years of life lost per death is about 9–10 years in a population that achieves the model life table distribution. New Zealand European years of life lost are about five years in excess of the 9–10 year norm; Maori about 21 years. The effects of age standardising, using the Segi standard population, give a norm of about 21 years; both Maori and European values are about two years in excess of this figure.

Conclusions:Crude and age-standardised YLLs in New Zealand exceed the ideal set by the model life tables norms, especially so in Maori. Age-standardising has a strong influence on the statistics.

Implications:Years of life lost measures need to be considered against the established norms. Unless done so, the measures can appear more adverse than they actually are.

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