1. MORTALITY CHANGE THE DETAILS ARE MESSY Year to year decline irregular Persistent, puzzling differentials Cause of death structure difficult to understand & to predict Poor understanding of causal relationship to driving forces Startling reversibility -- the Former Soviet Union

2. BUT… IN THE AGGREGATE (i.e., age/sex) OVER THE LONG-TERM ( >40 years) IN HIGHLY INDUSTRIALIZED NATIONS THERE APPEARS TO BE A Simple, general (?) pattern of decline

3. m(x,t) = central death rate log m(x,t) = s a(x) k(t) + r b(x) g(t) + … Singular Values s > r > … > 0 IF s >> r > … DOMINANT TEMPORAL PATTERN IS k(t) % VARIANCE EXPLAINED IS s 2 /(s 2 + r 2 + …)

4. AGGREGATE AGE/SEX MORTALITY CHANGE log m(x,t) = a(x ) + b(x) k(t) + e(t) G-7 = Canada, France, Germany, Italy, Japan, UK, US Period = 1950 TO 1994 Lee-Carter; Tuljapurkar-Li-Boe

5. IN ALL THE G 7 LEAD TEMPORAL COMPONENT k(t) EXPLAINS OVER 92 % OF VARIATION IN log m(x,t)

7. 1990200020102020203020402050 74 76 78 80 82 84 86 USA 95% 75% 50% 25% 5% H M L Year g

8. 1990200020102020203020402050 76 78 80 82 84 86 88 UK 95% 75% 50% 25% 5% H M L Year f

9. IMPROVING AGGREGATE FORECASTS 1. Take the uncertainty seriously. 2. Use higher components of temporal structure to improve forecasts. 3. Examine and forecast adult mortality separately from infant/child mortality. 4. Decompose differences (e.g., by sex) as deviations from the aggregate.

10. PROBLEMS WITH CAUSE OF DEATH (COD) METHODS 1. DEPENDENCE BETWEEN CAUSES Risk factors with multiple effects, complex states of health 2. CAUSE STRUCTURE SHIFTS OVER TIME Causes appear, peak, disappear. 3. LIMITED CAUSAL UNDERSTANDING OF RISK FACTOR DYNAMICS At population level, hard to use even smoking prevalence, intensity to predict 4. INACCURACY IN COD ASSIGNMENT