Potential magnitude of chronic mortality effects of air pollution

1. Potential magnitude of chronic mortality effects of air pollution J Fintan Hurley & Brian G Miller

2. HIA as Mathematical Modelling • We can draw on general modelling methodology • Focus on the big picture (multi-disciplinary work) • Identify component parts (and assess reliably) • Clarify links between component parts, e.g. • Pollution, baseline data, E-R functions, valuation….; • Model is not true, but can be useful • Saves time and fruitless argument. • Focus on estimates and their reliability (not on ‘truth’) • Focus on making model better (for what purpose) • Model testing, robustness: Does It Matter (DIM) • Highlights research needs

3. Hazard rates by sex and age

4. Source of effect estimates • Pope et al. (1995): The American Cancer Society Study • 7-year mortality: 550,000 adults • 151 metropolitan areas; most US states • Recent concentration data: sulphates, PM2.5 (subset) • Risk estimates: • All-cause mortality • Factor of 1.0064 per µg.m-3 PM2.5 • Recent major re-analysis in US

5. What the cohort studies provide • Basis for estimate of relative hazard (per µg.m-3 PM2.5) • Correctness of cohort study estimates depends on • Assumption/ judgement of causality of PM • Use of coefficient for PM2.5, sulphates • PM2.5 as a surrogate for other PM, other pollutants • Statistical variation (CIs) within and between studies • Adequacy of adjustment for confounders • Use of biologically relevant exposures • Assumption of proportional hazards • Transferability from US to UK, elsewhere • Users can modify cohort study values

6. Choosing specific values • Assume PM2.5= 0.6 PM10 (Dockery & Pope, 1994) • Reducing all-cause hazard by 0.99616 for 1µg.m-3 PM10

7. Life table data: England and Wales, 1995

8. Hazard rates by sex and age

9. Survival curve for newborn male population

10. Expectation of life, by age and sex. Estimated from baseline hazards for England and Wales, 1995.

11. Predictions of expectation of life and average gain in expectation, under various reductions in hazards

12. Scheme for calculating mortality projections

13. Assumptions in creating predictions • Assumptions for baseline scenario: to fill in matrix of hij • The mortality rates (hazards) for 1995 will remain constant throughout the future prediction period • Renewal of the population through new births will take place at the same rate as in 1995 • Migration affects neither hazard rates nor population sizes • Assumptions for alternative scenarios • Size of pollution effect • Age distribution of effects • Delay or phasing in • Thresholds • All assumptions may be subjected to sensitivity analyses

14. Outputs of mortality predictions

15. Predicted total gain in life-years (millions) under various assumed reductions in ambient PM10 pollution

16. Results on (in)sensitivity • Threshold or not • Choice of coefficient • PM10 from PM2.5 or sulphates? • Coefficient may reflect effect of higher historical pollution • Confounding over cities (results suggest not) • Are effects constant at all ages? • Cumulation of exposure with ageing • Late effects of early damage • Susceptibility, thresholds and phasing

17. Results on (in)sensitivity (cont.) • Life gains for an individual: • Almost exactly linear in pollution reduction (see Rabl) • Relatively insensitive to baseline rates (cf sexes, class, country) • Total life gained: • Insensitive to assumptions about very young and very old • Most of action between 40 and 90 • Sensitive to age-dependency assumptions • Scaled by size of populations • May be infinite if effects persist in perpetuity

18. Assumptions in valuation of outputs • There are no “extra” deaths; ultimate survival is zero • Value length (or amount) of life • Use monetary valuation; other options possible • Value of a Year of Life Lost (VLYL): assume £100k • Different VLYL by age: reductions from age 65 onwards • Discount rates for future values: try 0%, 3%, 11%

19. Monetary values per life-year, by age and year

20. Predicted total gain, at selected future discount rates, in value of life (£ billion) under a 10 µg.m-3 reduction in ambient PM10: full effect either immediate or accruing gradually over 30 years

21. What drives the answers? • Results are sensitive to assumptions/ decisions re: • causality • relative hazard per µg.m-3 PM • age-independence; transferability from US -UK (-elsewhere) • (no) threshold • lag time/ phasing-in of hazard reductions • discount rate • VLYL (+ age-adjustment) • Implies priorities for further work

22. Representing uncertainty in estimates • Some approaches we have used • Prose (non-mathematical) description at all stages • ‘Confidence Intervals’ (sampling uncertainty) for E-R functions • Sensitivity analyses of strategic options • Stratified presentation of results, according to reliability • (Quantitative compounding of uncertainties; based on subjective assessments of uncertainty) • (Reality check against observed changes in life expectancy) • Better representation of uncertainty is a priority • (Mis)use of range to express uncertainty

23. Some implications • Method is usable, in that results are • linear in pollution increment; hazard adjustment; valuation • insensitive to most population assumptions; can transfer changes in life expectancy and valuation per µg.m-3 PM, per 100,000 population • If life table work is not wildly wrong, cohort studies are not just an aggregate of acute effects; e.g. for 10 µg.m-3 PM, • up to 1% reduction in ‘acute’ mortality (usual time series) • up to 5 months life-years gained, on average, across all the population • if gained only on acute deaths, implies 500 months (40+years) YLY on average, per acute death. (No!!)