potential magnitude of chronic mortality effects of air pollution

INSTITUTE OF OCCUPATIONAL MEDICINEEDINBURGH, 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

Age (years)

0 10 20 30 40 50 60 70 80 90 100

Haz

ard

rate

(lo

g sc

ale)

0.0001

0.001

0.01

0.1

1

MalesFemales

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

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

Choosing specific values

Assume PM2.5 = 0.6 PM10 (Dockery & Pope, 1994)

Reducing all-cause hazard by 0.99616 for 1µg.m-3 PM10

95% UIReduction inPM10 pollutionconcentration,

µg.m–3

Assumedreductionfactor inhazards Low High

5 0.98097 0.97206 0.98949

10 0.96230 0.94492 0.97910

15 0.94399 0.91852 0.96881

25 0.90840 0.86792 0.94856

Life table data: England and Wales, 1995 Mid-year populations DeathsAge

(years) Males Females Total Males Females Total 0 – 4 1,736,000 1,651,900 3,387,900 2,702 2,025 4,727 5 – 9 1,744,900 1,656,400 3,401,300 274 198 472

10 – 14 1,649,300 1,563,000 3,212,300 344 213 55715 – 19 1,557,000 1,469,100 3,026,100 929 394 1,32320 – 24 1,791,200 1,703,400 3,494,600 1,559 516 2,07525 – 29 2,092,300 2,001,900 4,094,200 1,881 765 2,64630 – 34 2,160,000 2,074,400 4,234,400 2,226 1,118 3,34435 – 39 1,843,900 1,810,600 3,654,500 2,498 1,440 3,93840 – 44 1,678,900 1,669,100 3,348,000 3,436 2,226 5,66245 – 49 1,830,400 1,828,200 3,658,600 5,711 3,863 9,57450 – 54 1,474,200 1,478,800 2,953,000 7,806 5,158 12,96455 – 59 1,321,600 1,339,100 2,660,700 11,959 7,386 19,34560 – 64 1,204,000 1,254,000 2,458,000 19,044 11,531 30,57565 – 69 1,107,300 1,245,800 2,353,100 30,492 19,867 50,35970 – 74 970,300 1,231,100 2,201,400 44,531 33,143 77,67475 – 79 622,100 933,600 1,555,700 45,003 40,232 85,23580 – 84 409,600 768,900 1,178,500 47,314 56,955 104,26985 – 89 182,800 468,100 650,900 31,479 56,976 88,45590 – 94 44,180 196,450 240,630 12,781 36,973 49,75495 – 99 5,520 40,900 46,420 2,534 12,324 14,858100 + 350 4,200 4,550 264 2,063 2,327

Total 25,425,850 26,388,950 51,814,800 274,767 295,366 570,133

Hazard rates by sex and age

Age (years)

0 10 20 30 40 50 60 70 80 90 100

Haz

ard

rate

(lo

g sc

ale)

0.0001

0.001

0.01

0.1

1

MalesFemales

Survival curve for newborn male population

Age

0 20 40 60 80 100 120

Sur

viva

l pro

babi

lity

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Expectation of life, by age and sex.

Estimated from baseline hazards for England and Wales, 1995. Male Female

Age atstart of

follow-up

Expectedlife

remaining(years)

Expectedsurvival to

age 65(%)

Expectedsurvival to

age 75(%)

Expectedlife

remaining(years)

Expectedsurvival to

age 65(%)

Expectedsurvival to

age 75(%)

0 74.18 81.00 56.02 79.43 88.01 71.03

10 64.82 81.72 56.52 69.98 88.63 71.53

20 55.06 82.06 56.75 60.11 88.81 71.68

30 45.51 82.79 57.26 50.29 89.11 71.92

40 35.98 83.77 57.93 40.59 89.71 72.40

50 26.77 85.96 59.45 31.20 91.27 73.67

60 18.34 92.39 63.90 22.38 95.50 77.08

70 11.41 100.00 79.42 14.61 100.00 87.43

80 6.46 100.00 100.00 8.47 100.00 100.00

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

Male FemaleReduction inPM10 pollutionconcentration,

µg.m–3

Assumedreductionfactor inhazards

Expectedlife

(years)

Lifegained(years)

Expectedlife

(years)

Lifegained(years)

None 74.18 79.43

5 0.98097 74.38 0.20 79.63 0.20

10 0.96230 74.59 0.41 79.83 0.40

15 0.94399 74.80 0.62 80.03 0.60

25 0.90840 75.22 1.04 80.43 1.00

Scheme for calculating mortality projections

YEAR1995 1996 - - - 1999 2000 2001 2002 - - - - j - - - - 2103 2104 2105 - - - 2135Age

Entrypopn

Births b1 - - - b5 b6 b7 b8 - - - bj - - - b108 b109 b110 - - - b140

0 e0 h0 h0 h0 h0 h0 h0 h0 h0 h0 h0 h0

1 e1 h1 h1 h1 h1 h1 h1 h1 h1 h1 h1 h1

2 e2 h2 h2 h2 h2 h2 h2 h2 h2 h2 h2 h2

¦

i ei hi hi hi hi hi hi hi hi hi hi hi

¦

103 e103 h103 h103 h103 h103 h103 h103 h103 h103 h103 h103 h103

104 e104 h104 h104 h104 h104 h104 h104 h104 h104 h104 h104 h104

105 e105 h105 h105 h105 h105 h105 h105 h105 h105 h105 h105 h105

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

Outputs of mortality predictions

YEARAge 1995 1996 - - - - 1999 2000 2001 2002 - - - - j - - - - 2103 2104 2105 - - - - 2135

0 d y d y d y d y d y d y d y d y d y d y d y



¦

i d y d y d y d y d y d y dij yij d y d y d y d y

¦




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

Delay to full effect (years)Population

Reduction inPM10

concentration,µg.m–3 0 5 10 20 30

5 8.94 8.20 7.58 6.30 4.97

10 17.89 16.41 15.17 12.61 9.96

15 26.85 24.64 22.78 18.94 14.97

Alive atstart of2000

25 44.81 41.13 38.03 31.63 25.01

5 8.02 8.00 7.96 7.87 7.76

10 16.02 15.95 15.88 15.72 15.48

15 23.97 23.88 23.77 23.52 23.17Born 2001

to 2135

25 39.77 39.61 39.45 39.04 38.46

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

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

YearAge 1995 1996 - - - 1999 2000 2001 2002 - - - - j - - - - 2103 2104 2105 - - - 2135

0 v0 v0 v0 v0 v0 v0 v0 v0 v0 v0 v0

1 v1 v1 v1 v1 v1 v1 v1 v1 v1 v1 v1

2 v2 v2 v2 v2 v2 v2 v2 v2 v2 v2 v2

¦

i vi vi vi vi vi vi vi,j vi vi vi vi

¦

103 v103 v103 v103 v103 v103 v103 v103 v103 v103 v103 v103

104 v104 v104 v104 v104 v104 v104 v104 v104 v104 v104 v104

105 v105 v105 v105 v105 v105 v105 v105 v105 v105 v105 v105

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

Delay to full effect(years)

PopulationDiscount rate

% pa0 30

0 909 691

3 320 195Alive atstart of2000 11 68 22

0 1042 1022

3 73 67Born 2001

to 213511 2 1

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!!)

potential magnitude of chronic mortality effects of air pollution

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