Norbert Englert
Federal Environmental Agency
Corrensplatz 1
D-14195 Berlin
Germany
Phone +49 30 8903 1805
Fax +49 30 8903 1830
e-mail norbert.englert@uba.de
12 February 2001
“The Measurement and Economic Valuation of the Health Effects of Air Pollution”
London, 19 and 20 February 2001
In economic evaluation of the health effects of air polution, mortality plays a very important
role. There is wide agreement that air pollution can lead to an "increase" in mortality.
What meaning do we have to associate with the term "increase" in this context and what
underlying mechanism can we think of?
"Increase" can mean
 an increase in the number of deaths during a defined period in a defined population
(number of deaths per year per million of persons; study type: time-series analysis)
 an increase in the number of deaths during a defined follow-up period in a defined
cohort (number of deaths per year per million of persons; study type: cohort study).
What "increase" cannot mean, however, is an absolute increase of deaths in a population
(everybody will die exactly 1.0 times). Therefore, the "mechanism" is a dislocation in time
(prematurity) which, statistically, results in reduced life expectancy.
Two questions need to be answered in this context:
A) What is the extent of prematurity (reduction in life expectancy) ?
B) What is the percentage of premature deaths (of all deaths) ?
To deal with these questions, two different epidemiological tools available: time-series
analyses and cohort studies. From the currently available studies we see that in the case of
PM10, these two tools provide numerically different relative risk estimates. Time-series
analyses show a mortality RR of 1.007 per 10 µg/m³ PM10, whereas cohort studies show a
mortality RR of about 1.05 per 10 µg/m³ PM10.
How can we use these findings to answer questions A and B?
Question A:
In a stable population, the relationship between mortality, mean life expectancy and
population size is as follows (with the time unit years or days):
Annual Mortality
Population Size / Mean Life Expectancy
=
(in case of annual mortality, life expectancy is given in years, in case of daily mortality, life
expectancy is given in days)
Example:
Population size 1,000,000 and annual mortality 13,333

life expectancy 75 years
Annual mortality increased by a factor of 1.007 to 13,426 
life expectancy
1,000,000 / 13,426 = 74.52 years.

RR of 1.007 based on time-series studies is associated with a life expectancy reduced
by 0.5 years.
Years Of Life Lost in a population of 1,000,000: 13,426 x 0.5 years = 6713 years
The RR of 1.05 based on cohort studies means that the age-specific probability of dying is
increased by 5 %.
Does this mean that the annual mortality in a population is increased by 5 %, too?
This would only be the case if 105 % of the persons constituting this population would die.
However, those who have died earlier cannot die again later.
Calculations based on life tables show that the increase in annual mortality is very similar to
that estimated on the basis of time-series analyses.
Result A:
The numerically different RRs (based on time-series studies or based on cohort studies)
describe a similar reduction in life expectancy. Therefore, in order to describe effects of air
pollution on mortality, reduction of life expectancy is an adequate metric.
Question B:
We start from the facts given above, i.e., 13,426 deaths observed and 13,333 deaths expected.
Three ways of calculation can be thought of.
1st estimate:
Difference between the two numbers  93 “additional” deaths = 93 premature deaths ?
But: If YOLL concern only these 93 persons  6713 years / 93 = 72.2 years / person.
2nd estimate:
5 % are supposed to be premature (cohort study-based RR = 1.05)
 93 “additional” deaths, 13,426 x 0.05 = 671 premature deaths ?
But: If YOLL concern these 671 persons  6713 years / 671 = 10 years / person.
Is this really probable? (considering that about 50 % die from cardiovascular disease, a cause
associated with relatively high life expectancy)
3rd estimate:
YOLL concern the whole population; on average, the prematurity amounts to 0.5 years
 100 % of deaths are premature.
But: What does it mean if 100 % of deaths are premature due to air pollution, and 100 % due
to malnutrition, 100 % due to inactivity and to several other reasons? In total at least 300 %?
Result B:
The same fact (reduction of life expectancy by half a year) can be described by very different
numbers with respect to the percentage of premature deaths (range: 0.7 % - 100 %).
Conclusions:


Reduction in life expectancy provides adequate information to describe health effects
of air pollution
Percentage of premature deaths provides inadequate information to describe health
effects of air pollution.