השוואת שיטות

	Healthy Life Expectancy Decomposition	Life Expectancy Decomposition
תחום	Social Epidemiology	Social Epidemiology
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2004	1984
הוגה השיטה	Wilma J. Nusselder & Caspar W. N. Looman; Daniel F. Sullivan	Eduardo E. Arriaga; John H. Pollard
סוג	Demographic decomposition pipeline for a health-expectancy difference	Demographic decomposition pipeline for differences in a summary measure
מקור מכונן	Nusselder, W. J., & Looman, C. W. N. (2004). Decomposition of differences in health expectancy by cause. Demography, 41(2), 315-334.	Arriaga, E. E. (1984). Measuring and explaining the change in life expectancies. Demography, 21(1), 83-96.
כינויים	Health Expectancy Decomposition, Nusselder-Looman Decomposition, Decomposition of Disability-Free Life Expectancy, Mortality and Disability Decomposition of Health Expectancy	Life Expectancy Decomposition Methods, Decomposition of Changes in Life Expectancy, Age and Cause Decomposition of Life Expectancy, Stepwise Life Expectancy Decomposition
קשורות	4	4
תקציר	Healthy (or disability-free) life expectancy combines how long people live with how much of that life is spent in good health, and differences in it between groups or over time reflect two distinct forces: changes in mortality and changes in the prevalence of disability. Healthy-life-expectancy decomposition separates these forces. Building on the Sullivan method — which weights life-table person-years by the age-specific share of life lived without disability — Wilma Nusselder and Caspar Looman's 2004 method splits the gap in health expectancy between two populations into an additive mortality component and a disability component for each age, and can further attribute each to specific causes. This resolves the central interpretive ambiguity of health expectancy: a population can have higher healthy life expectancy because its people die later, because they are less disabled at each age, or both, and only a decomposition can tell which.	Life-expectancy decomposition answers a question that a single number cannot: when life expectancy rises over time, or differs between two populations, exactly which ages and which causes of death are responsible? The family of methods takes two life tables and splits their gap in e0 (or ex at any age) into additive contributions from mortality differences in each age interval, with the contributions summing exactly to the total gap. Eduardo Arriaga's 1984 stepwise discrete method became the field standard because it is exact, intuitive, and easy to extend to a cause-of-death breakdown, separating a 'direct' effect of changed survival within an interval from an 'indirect plus interaction' effect that the change propagates to later ages. John Pollard's continuous formulation expresses the same decomposition as an integral of age-specific mortality differences weighted by their leverage on life expectancy, providing the theoretical underpinning and a cross-check. This page treats the general decomposition pipeline; the dedicated Arriaga and Pollard pages cover each estimator in depth.