INSTITUTE OF OCCUPATIONAL MEDICINE EDINBURGH, EH8 9SU, UK Potential magnitude of chronic mortality effects of air pollution J Fintan Hurley & Brian G Miller

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

Hazard rates by sex and age

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, PM 2.5 (subset) Risk estimates: All-cause mortality Factor of per µg.m -3 PM 2.5 Recent major re-analysis in US

What the cohort studies provide Basis for estimate of relative hazard (per µg.m -3 PM 2.5 ) Correctness of cohort study estimates depends on Assumption/ judgement of causality of PM Use of coefficient for PM 2.5, sulphates PM 2.5 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

Choosing specific values Assume PM 2.5 = 0.6 PM 10 (Dockery & Pope, 1994) Reducing all-cause hazard by for 1µg.m -3 PM 10

Life table data: England and Wales, 1995

Hazard rates by sex and age

Survival curve for newborn male population

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

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

Scheme for calculating mortality projections

Assumptions in creating predictions Assumptions for baseline scenario: to fill in matrix of h ij 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

Outputs of mortality predictions

Predicted total gain in life-years (millions) under various assumed reductions in ambient PM 10 pollution

Results on (in)sensitivity Threshold or not Choice of coefficient PM 10 from PM 2.5 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

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

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%

Monetary values per life-year, by age and year

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

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

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

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!!)