Health impact assessment of air pollution: AirQ+ life table manual

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Health impact assessment of air pollution: AirQ+ life table manual

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Health impact assessment of air pollution: AirQ+ life table manual

December 2020

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Abstract

AirQ+ is a software tool for quantifying the health burden and impact of air pollution developed by the WHO Regional Office for Europe. AirQ+ includes methodologies to assess the impacts of short- and long-term exposure to ambient air pollution.

The main methodologies use evidence generated by epidemiological cohort studies showing a relationship between average long-term air pollution concentration levels and the mortality risks in exposed populations. Assessing the impact of air pollution is suggested when evaluating the consequences of policies and interventions or of hypothetical scenarios. AirQ+

should always be used with the support of an epidemiologist or air pollution impact assessment expert. To facilitate users in their analyses, AirQ+ comes with manuals that require increasing levels of expertise. AirQ+ includes a module with life tables, designed to predict changes in health impacts anticipated from changes in long-term exposure to air pollution. AirQ+

allows estimating the years of life lost with calculations for one specific year and for the entire follow up period, for all-cause mortality due to specific air pollution exposure for a given population within an area. It also calculates the change in life expectancy attributed to long-term exposure to ambient air pollutants.

Keywords

AIR POLLUTANTS

AIR POLLUTION – exposure AIR POLLUTION – health impacts LIFE TABLES

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Document number: WHO/EURO:2020-1559-41310-56212

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The authors of this publication are: Pierpaolo Mudu (European Centre for Environment and Health, WHO Regional Office for Europe), Christian Gapp (WHO Regional Office for Europe), Ingu Kim (WHO European Centre for Environment and Health, WHO Regional Office for Europe), Michal Krzyzanowski (Kings College London, United Kingdom of Great Britain and Northern Ireland), Brian Miller (Institute of Occupational Medicine, United Kingdom of Great Britain and Northern Ireland) and Joseph Spadaro (Spadaro Environmental Research Consultants, Philadelphia, Pennsylvania, United States of America).

The WHO Regional Office for Europe gratefully acknowledges Magali Corso (Santé publique France) and Myriam Tobollik (German Environmental Agency) for their suggestions. The AirQ+ project was partially financed by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety.

Abbreviations

AQG air quality guidelines ASDR age-specific death rate ELR expected life remaining PM particulate matter

PM_2.5 particulate matter with a diameter of 2.5 µm or less YL years of life

YLL years of life lost

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Introduction

AirQ+ is a software tool for quantifying the health burden and impact of air pollution developed by the WHO Regional Office for Europe.¹ It includes a module with life tables, designed to predict changes in health effects anticipated from changes in long-term exposure to air pollution.² Please refer to the AirQ+ introductory manual for information on installation, prerequisites, test data and number formats.

Life table methods use evidence generated by epidemiological cohort studies, now run in different countries and continents, showing a relationship between average long-term air pollution concentration levels and the mortality risks in exposed populations. The underlying assumption of the methods is that the integrated exposure–response functions and the relative risks estimated from epidemiological studies are applicable to a target population (which may not be the same as that in the epidemiological study).³

This publication is composed of the following sections. The first section, “CountryLifeTable dataset”, describes the dataset used to explain the life table approach. The second one, “What is a life table?”, describes the theory behind the construction of a life table. All the formulas are explained in the third section, “Life table formulas in AirQ+”, which also describes how yearly values and relative risks are estimated. “Life table calculations in AirQ+” describes how to use the life table approach, and “Getting started with life tables in AirQ+” describes how to prepare and enter data in AirQ+. Lastly, the manual provides four examples, which are particularly relevant for expert users.

It is recommended to always use AirQ+ with the support of an epidemiologist or air pollution impact assessment expert.

1 For details on key terms, please refer to the AirQ+ Glossary, which is accessible on the main welcome window.

2 This version of AirQ+ has a revised life-table module; thus, this publication supersedes manuals provided with previous versions of the software.

3 Additional information on the health impact assessments of air pollution is available from the WHO Regional Office for Europe (2001; 2003).

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The CountryLifeTable dataset

In order to illustrate the functions of AirQ+, it comes with a test dataset “CountryLifeTable.csv” comprising sample data from a country with 51 814 800 inhabitants and a total of 570 133 deaths. The file contains one year of population, mortality and air pollution data (PM_2.5 yearly mean)⁴ by age range from a European country.

The data include:

population (total mid-year population, five-year age groups from 0 to 99 years and the age group 100 years and over); and

health data (all natural causes of mortality, five-year age groups from 0 to 99 years and the age group 100 years and over).

The file consists of population and mortality data by age range for one year.

Line 1 describes the names of the fields.

Lines 2–22 contain the data values.

Example data are:

line 1: from;to end;mid year;death

line 8: 30;34;4234400;3344.

This means that the age group 30–34 spans five years, includes people between 30 years of age and below 35, the mid-year population for the 30–34 age group is 4 234 400 and that age group had 3344 deaths in that year.

Note: a 35-year-old person belongs to the next age group (35–39 years).

The first line of an input file is mandatory and used by AirQ+ for its input module, but the content is quite flexible. For example, the first line “From age;Including age;Mid-year population, Number of deaths” would work as well.

4 PM_2.5: particulate matter with a diameter of 2.5 µm or less

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What is a life table?

At its simplest, a life table is a table describing the age structure of a real or hypothetical population, and the annual mortality within each age group (Preston et al., 2001). Life tables can be of different types, cohort or period. Cohort life tables describe the mortality experience of individuals born in the same year, with mortality recorded until the last member dies; such data are not easily obtained. More useful, and of interest for AirQ+, are period life tables built from a mid-year population and mortality data from a single (usually recent) period, often a single year, or average values for a period of few years, for example 3 or 5 years (WHO Regional Office for Europe, 2001).

The two types of life tables are:

complete (unabridged) with data for every year of life (not age ranges); and

abridged with data for selected age intervals (greater than one year) such as five-year age-groups.

The layout of a life table facilitates arithmetical calculations leading to the prediction of life expectancy.

Life expectancy at birth for a given time period and region is an estimate of the average number of years a newborn baby would survive if he/she experienced the particular region’s age-specific mortality rates for that time period throughout his/her life. Traditionally, the calculations were based on the expected mortality experience of an imaginary cohort of newborn babies, usually 100 000 newborns, who would be subject to the age-specific death rates (ASDRs) from the period life table.⁵

Life table calculations produce as their output an estimate of age-specific life expectancy (i.e. average remaining life expressed in life years (LY)) at birth, and the remaining life expectancy conditional on having reached the start of each age group. These are a direct function of the ASDRs (also known as hazard rates) in the life table, and it follows that changes to the ASDRs predict different life expectancies. This is the basis for estimating the impacts of changes in pollution levels; the epidemiological studies provide unit relative risks that can be applied to changes in mean pollution concentration values, and the resulting relative risks are applied to the ASDRs, and new mortality experience predicted.

AirQ+ predicts the impact of changes in pollution levels on the future mortality projected for a specified population. The ASDRs in a life table can be applied to any population, but AirQ+ defaults to the population providing the life table, and separate life table calculations are carried out for each age group. AirQ+ estimates the population size of each age group at the start of the prediction (“entry size”) as the sum of the mid-year population and half of the deaths in the year. Applying the life table calculations predicts the total LY that the whole population will accumulate; repeating the calculation with altered ASDRs (hazard rates) allows calculation of the LY predicted to be gained or lost as the pollution is assumed to decrease or increase. Table 1 shows an abridged life table layout, based on the CountryLifeTable data with standard notation (WHO, 2014).

5 The use of the “radix” 100 000 was a useful device to avoid decimals when the calculations were done by hand. Using computer programs today, the radix can just as easily be 1 and therefore be omitted.

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Table 1. AirQ+ CountryLifeTable dataset, example of an abridged life table

Age interval Mid-year population Deaths Fraction of last year lived Age-specific death rates Probability of dying Probability of surviving Number of persons alive Number of persons dying Number of persons years Total number of person- years lived Expect-ation of life

i–i + 4 (except

last interval)

m_i d_i a_i M_i Q_i P_i l_i d_i L_i T_i e_i

0–4 3387900 4727 0.5 0.00140 0.00695 0.99305 100 000 695 498 262 7 688 320 76.9 5–9 3401300 472 0.5 0.00014 0.00069 0.99931 99 305 69 496 352 7 190 058 72.4 10–14 3212300 557 0.5 0.00017 0.00087 0.99913 99 236 86 495 965 6 693 706 67.5 15–19 3026100 1323 0.5 0.00044 0.00218 0.99782 99 150 217 495 208 6 197 741 62.5 20–24 3494600 2075 0.5 0.00059 0.00296 0.99704 98 933 293 493 934 5 702 533 57.6 25–29 4094200 2646 0.5 0.00065 0.00323 0.99677 98 640 318 492 405 5 208 599 52.8 30–34 4234400 3344 0.5 0.00079 0.00394 0.99606 98 322 387 490 641 4 716 194 48.0 35–39 3654500 3938 0.5 0.00108 0.00537 0.99463 97 934 526 488 357 4 225 553 43.1 40–44 3348000 5662 0.5 0.00169 0.00842 0.99158 97 408 820 484 990 3 737 197 38.4 45–49 3658600 9574 0.5 0.00262 0.01300 0.98700 96 588 1 256 479 801 3 252 206 33.7 50–54 2953000 12964 0.5 0.00439 0.02171 0.97829 95 332 2 070 471 487 2 772 405 29.1 55–59 2660700 19345 0.5 0.00727 0.03570 0.96430 93 263 3 330 457 988 2 300 918 24.7 60–64 2458000 30575 0.5 0.01244 0.06032 0.93968 89 933 5 425 436 102 1 842 930 20.5 65–69 2353100 50359 0.5 0.02140 0.10157 0.89843 84 508 8 584 401 081 1 406 828 16.6 70–74 2201400 77674 0.5 0.03528 0.16212 0.83788 75 924 12 309 348 850 1 005 747 13.2 75–79 1555700 85235 0.5 0.05479 0.24094 0.75906 63 616 15 328 279 759 656 897 10.3 80–84 1178500 104269 0.5 0.08848 0.36225 0.63775 48 288 17 492 197 709 377 138 7.8 85–89 650900 88455 0.5 0.13590 0.50717 0.49283 30 795 15 619 114 931 179 429 5.8 90–94 240630 49754 0.5 0.20677 0.68153 0.31847 15 177 10 344 50 025 64 499 4.2 95–99 46420 14858 0.5 0.32008 0.88901 0.11099 4 833 4 297 13 424 14 473 3.0

100+ 4550 2327 0.5 0.51143 1.00000 0.00000 536 536 1 049 1 049 2.0

Note: the CountryLifeTable dataset contains four columns only: two to define the age interval, and one each for the mid-year population and the number of deaths.

In practice, the life table is built with the input of two types of data: mid-year population (m_i) and number of deaths (d_i). The fraction of the last age interval of life can be hypothesized equal to half year. All other values originate from these data (Table. 2).

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Table. 2. Definition of the life table input and calculations

Parameter Input or calculation Definition Age

interval Input Age intervals i to i + n where i = 0,1,5, . . . , 95, 100 years and n is the width of the age interval in years

m_i Input

The mid-year population is the sum of the mid-year population over the age interval. For example, 3,401,300 is interpreted as the sum of the mid-year populations in age cohorts 5, 6, 7, 8 and 9 years.

d_i Input Number of deaths among persons aged i to i + n

na_i Input

It is the average proportion of the time lived in the interval i to i + n. When an individual dies at a certain age he/she has lived only a fraction of the interval in which his/her age at death lies; the average of all of these fractions of the interval for all people dying in the interval is the fraction of the last age interval of life, a_i. Infant deaths tend to occur early in the first year of life and a_i value for this interval can be 0.3 or even 0.1. Adult intervals can be approximated with 0.5.

nM_i d_i/m_i

Age-specific deaths rates (hazard rates) calculated from information on deaths among persons aged i to i + n during a given year and the population aged i to i + n at the midpoint of the same year.

nQ_i n(_nm_i)/(1+n(1–_na_i)_nm_i) Probability of dying between exact ages i and i + n

nP_i 1 – _nq_i Probability of surviving between exact ages i and i + n

nl_i p_i–n * l_i-n Number of people alive at exact age i among a hypothetical birth cohort of 100 000 (or 1000 or 1). It is an arbitrary number called the radix.

nd_i l_i – l_i+1 Number of life table deaths in the age interval marked i to i + n

nL_i n(l_i + l_i+1)/2

Total number of person-years lived between exact ages i and i + n. It is referred usually as YL. The first years and the last open-ended age groups are usually calculated with additional assumptions.

T_i Sum _nL_i Total number of person-years lived after age i.

ê_i T_i/l_i Expected average number of years of life left for a person age i

Note: the subscript before a symbol defines the width of the interval. The variables l_i, T_i and ê_ido not have a left subscript as they refer to an exact age i.

In order to become familiar with the data used in all the examples in this manual, it is useful to visualize them as graphs that show the distribution of the age-specific mortality, the mid-year population, the annual deaths and the age-specific life expectancy (Figs. 1–4).

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Fig. 1. CountryLifeTable: age-specific mortality

Fig. 2. CountryLifeTable: mid-year population

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Fig. 3. CountryLifeTable: annual number of deaths

Fig. 4. CountryLifeTable: age-specific life expectancy

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Life table formulas in AirQ+

Formulas used by the AirQ+ module with life tables are based on “Annex 1. Life-table methods for predicting and quantifying long-term impacts on mortality” (WHO Regional Office for Europe, 2001) andAnnex 2 of Rothman & Greenland (1998).

Mid-year populations and the number of deaths for a specific year and specific age groups are taken from empirical data, and the user is expected to enter this information.

The life table calculations use the following definitions:

m: mid-year population d: number of deaths e: entry population h: hazard rate

s: survival probability.

The e, h and s values are computed based on empirical m and d data.

AirQ+ uses the following formulas for a single cohort (no additional births and no migration), moving from year i to year i + 1:

1. Entry population: e_i= m_i + 0.5d_i (half of the people die in the first half of the year); m_i in the mid-year population in year i, which is numerically equal to the number of years lived in year i

2. Hazard rate: h = d/m

3. All e_i persons are alive at the beginning of year i, di die over that year, thus the probability to survive year i is given below or with the help of formula (1) s_i = 1 – d_i/e_i

4. s_i = (2–h_i)/(2+h_i)⁶ 5. e_(i+1) = (s_i)*(e_i)

6. m_i = years of life in year i.

Closed, stationary populations follow formula (5) not just for cohorts from year to year, but also for age groups from age to age. In general, this is not true.

Hazard rates are calculated from empirical data using formula (2). With this, survival probabilities can be expressed and then the evolution of entry population can be computed, and total years of life predicted.

6 The survival probability [S_i=(2–h_i)/(2+h_i)] is important because in the population (without pollution effects) projections, a modified survival probability is calculated as a function of hazards divided by the relative risk.

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Estimation of yearly values

If age groups consist of more than one year, AirQ+ transforms them to yearly values (WHO Regional Office for Europe, 2001). As a first example, consider a data table with three-year intervals. Mid-period population values are sums of three mid-year population values (which are not known):

(i) M = m₁ + m₂ + m₃ and D = d₁ + d₂ + d₃

(ii) The weighted annual hazard rate over the three-year interval is H = D/M = (d₁ + d₂ + d₃)/(m₁ + m₂ + m₃) and the weighted annual survival rate is S = (2 – H)/(2 + H)

It is not the probability to survive 3 years (to calculate this, consider the special case of all three yearly hazard rates being equal, i.e. h_i = h, and d_i = h*m_i, which results in H = h).

The users need to consider the conversion of empirical data from a one-year to a five-year interval and the ways that entry population, probability of dying and survival probability are calculated. Table 1 with five-year interval data is altered by AirQ+ (Table 3) distributing the five-year interval values following these calculations:

1. Hazards (H_i), for the five-year distribution, where H_i = D_i/M_i

2. Probability of dying (Q_i), for the five-year distribution, where Q_i = H_i/(1+½H_i) 3. Survival probability (Si), for the five-year distribution, where S = 1–Qi

4. Start of year population nei, for the one-year distribution, calculated as: _ne_i = _nM_i/(½ + _nS_i + _nS_i^2 +

nS_i^3 + _nS_i^4 + ½ _nS_i^5), where n = 0, 1, 2 …

5. Mid-year population (m_i) for the one-year distribution, where m_i = ½(e_i+1 + e_i) 6. Deaths d_i for the one-year distribution calculated as d_i = 2(e_i – m_i)

except for the end of interval years calculated as the difference between D_i and the estimated d_i in the interval.

Table 3. CountryLifeTable data, five-year interval data converted to one-year interval data example for the interval of years 0–40

From To Mid-year

population Deaths e_x q_x p_x

0 0 679472.1 948 679946 0.00139 0.99861

1 1 678524.7 947 678998 0.00139 0.99861

2 2 677578.7 945 678051 0.00139 0.99861

3 3 676633.9 944 677106 0.00139 0.99861

4 4 678329.0 943 676162 0.00139 0.99861

5 5 680448.8 94 680496 0.00014 0.99986

6 6 680354.4 94 680402 0.00014 0.99986

7 7 680260.0 94 680307 0.00014 0.99986

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From To Mid-year

population Deaths e_x q_x p_x

8 8 680165.6 94 680213 0.00014 0.99986

9 9 661428.5 94 680118 0.00014 0.99986

10 10 642682.8 111 642739 0.00017 0.99983

11 11 642571.4 111 642627 0.00017 0.99983

12 12 642460.0 111 642516 0.00017 0.99983

13 13 642348.6 111 642404 0.00017 0.99983

14 14 624087.3 111 642293 0.00017 0.99983

15 15 605749.3 265 605882 0.00044 0.99956

16 16 605484.5 265 605617 0.00044 0.99956

17 17 605219.9 265 605352 0.00044 0.99956

18 18 604955.3 264 605088 0.00044 0.99956

19 19 652390.5 264 604823 0.00044 0.99956

20 20 699750.2 415 699958 0.00059 0.99941

21 21 699334.9 415 699542 0.00059 0.99941

22 22 698919.8 415 699127 0.00059 0.99941

23 23 698504.9 415 698712 0.00059 0.99941

24 24 759230.6 415 698298 0.00059 0.99941

25 25 819898.7 530 820164 0.00065 0.99935

26 26 819369.0 530 819634 0.00065 0.99935

27 27 818839.7 529 819104 0.00065 0.99935

28 28 818310.6 529 818575 0.00065 0.99935

29 29 833299.6 529 818046 0.00065 0.99935

30 30 848218.1 670 848553 0.00079 0.99921

31 31 847548.5 669 847883 0.00079 0.99921

32 32 846879.5 669 847214 0.00079 0.99921

33 33 846210.9 668 846545 0.00079 0.99921

34 34 789373.7 668 845877 0.00079 0.99921

35 35 732476.0 789 732871 0.00108 0.99892

36 36 731687.2 788 732081 0.00108 0.99892

37 37 730899.2 788 731293 0.00108 0.99892

38 38 730112.0 787 730505 0.00108 0.99892

39 39 701076.7 786 729719 0.00108 0.99892

40 40 671866.7 1136 672435 0.00169 0.99831

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Relative risks

Relative risks are usually expressed as the ratio by which risks are increased per given increase in pollution level. Relative risk for a unit change in pollution level is represented by the coefficient β, derived from empirical studies (and available in the Evaluation Parameters window of AirQ+).

Relative risk is a function of the difference in pollution level. Then for any change in pollution level from x₀ to x₁, the relative risk is given by the formula:

7) RR(x₁ – x₀) = exp (β*(x₁ – x₀))

Then the hazard rates {h} at pollution level x₁ are derived from those at level x₀ by:

8A) h(x₁) = RR(x₁ – x₀)*h(x₀)

The pollution level x₁ may be a target or cut-off level e.g. for which a policy or legislation is aiming, and it is likely to be lower than x₀.

Formula (7) can be used for cases in which x is less than x_reference, representing a drop in pollution level (RR <

1.0) and resulting in increased life expectancies. In fact, AirQ+ assumes that users are mainly interested in modelling decreases in levels of air pollution and the resulting changes (increases) in life expectancies. AirQ+

applies formula (8A) in the following manner:

8B) h_annualMean = exp (β * (x_annualMean – x_reference)) * h_reference h_annualMean: given, computed from empirical age distributions

x_annualMean: from measurements, entered in the Analysis Properties window x_reference: entered in the Evaluation Parameters window

h_reference: computed, i.e.:

8C) h_reference = h_annualMean/exp (β * (x_annualMean – x_reference))

Some additional definitions are helpful to complete the understanding of the estimates that are produced by the life-table approach.

YLL = Years of Life (reference) – Years of Life (entered data)

If the reference pollution level is less than the pollution level of entered data (i.e. the empirical measurements as they are entered), YLL is a positive quantity. It can also be viewed as the number of life years gained, if the pollution level drops from its current value to the reference level. YL values are computed using the mid-year population values (see the description of formulas).

Expected life remaining (ELR) is

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ELR (at AGE) = Σ (from AGE until end) YL/number of people at AGE

AirQ+ generates tables that present the ELR for the reference pollution levels, calculated with the hazard rates adjusted by the relative risk associated with the observed pollution levels. Since the relative risk will be different for total and cause-specific mortality, the delta ELR displayed in the tables will vary accordingly.

ELR lost = ELR (reference) – ELR (entered data)

AirQ+ also allows the comparison of years of life lost. “Years of Life Lost for starting year of simulation” and

“Years of Life Lost for first 10 years” compare the absolute numbers of YL based on the initial distribution. The formulas used are:

YL (first year) = Σ (age range) YL

YL (first 10 years) = Σ (first 10 years) [Σ (age range) YL])

These formulas use normalized values (per 100 000 population):

YL_norm (first year) = (100 000 * [Σ (age range)YL])) / (Σ (age range) Entries (first year))

YL_norm (first 10 years) = (100 000 * Σ(first 10 years) [Σ(age range) YL]))/(Σ(first 10 years) [Σ(age range) Entries (first year)]))

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Life table calculations in AirQ+

As described in the previous sections, the observed age structure of the population and age-specific mortality data are used to calculate the survival probability and number of deaths in each age category in future years.

The difference between the survival functions of the population at risk due to increased pollution and without risk enables the calculation of several parameters of impact. The program displays selected parameters (reduction of life expectancy at a certain age, expected years of life lost due to deaths in one year, and years of life lost in one year or in the entire period of follow-up due to the risk factor). Further impact parameters can be calculated from the life table results.

Once calculations are completed, results can be exported, using the export icon on the upper-left corner of the welcome window next to “Projects Overview”.

AirQ+ can calculate changes in survival related to the impact of air pollution on all (natural) causes of death for adults. Advanced features of the program allow the user to modify the pollution level (i.e. risk) taking into account time or future birth rates. Calculations of adult mortality are based on risk coefficients provided by the user or default to WHO-recommended values; this version⁷ uses the risk coefficients for PM_2.5recommended by the WHO Regional Office for Europe (2013).

The long-term effects of air pollution can be assessed by calculating years of life lost in a population exposed to a certain level of air pollution for a specified time period. Years of life lost are calculated for total (natural) mortality and can thus be attributable to this specific population exposure, all other factors being stable over the specified time period. Before describing in detail this analysis, it is important to understand how to start AirQ+ in order to use the module with life tables and how to prepare the data for analysis.

7 Previous versions of AirQ+ used coefficients from the American Cancer Society cohort study by Pope et al. (2002). AirQ+ will be updated with new coefficients as agreed upon and reviewed by international experts and it is advisable to check regularly the WHO Regional Office for Europe website for updates.

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Getting started with life tables in AirQ+

After installing the program (see the AirQ+ instruction manual), double-click the AirQ+ executable file or the shortcut if you created one. The AirQ+ welcome screen appears and you can click the “Create New Impact Assessment” button.

In the New Impact Assessment window, select ambient pollution, long-term effects, the pollutant PM_2.5.To run years of life lost (YLL) calculations, select Life Table Evaluation in the “Evaluation (optional)” drop-down box (Fig. 5).

Fig. 5. AirQ+ New Impact Assessment window for a life table evaluation of PM_2.5 – long-term – adult mortality

Data input for life table calculations

AirQ+ input and output data files are ASCII comma-separated values (csv) files that use the semicolon (;) as the separator character. The files can be viewed and edited by any standard program such as spreadsheets or text processors. Files that use the semicolon (;) as the separator character can be used for data input into AirQ+. Since commas are used as decimal separators in many languages, this can lead to confusion. AirQ+

always uses the semicolon (;) as the separator character.

AirQ+ processes and stores numerical data using decimal points, even if the language and number format settings of the target machine are different. For more information see the AirQ+ introductory manual.

Life table data input require four columns: the start of an age group (age from), end of an age group (age to), mid-period population and number of deaths (total mortality) for the corresponding age groups. This module cannot read data from other AirQ+ modules.

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Data must be entered by age group. The length of consecutive age groups may vary, but an age group must start with the age that follows the previous row’s “until age” value. Each row consists of one age group. This can be in one-year, five-year or any other available age intervals. Age groups must be consecutive otherwise the user will receive an error message during input.⁸ The age intervals must not overlap; example valid age intervals are: 0–4, 5–9, 10–14, . . . , 95–99, . . . 115–119. Data should cover the entire population age distribution of the selected location from 0 years to the oldest age in the population, up to the maximum age (120 years) considered by AirQ+. The age distribution can stop at any age below 120 years. The program splits the age groups into one-year classes by interpolating the distribution of the population and mortality within the specified age group.⁹

If possible, the first age group should be 0–1 years (population < 1 year of age). The last age group must be closed (e.g. 99–100, 100–104 or any other interval up to 120 years); it cannot be open-ended (e.g. 100+).

Empirical population data refer to the mid-year population of the selected location, a country in this example.

It is the absolute number of people in the middle of the calendar year. If mid-year population is not available, it can be replaced with the average of two consecutive first of January population numbers.

Total mortality is the number of deaths due to all causes in the corresponding calendar year. These values are always absolute numbers. Rates (e.g., deaths per 1000 population) should not be entered.

Population and mortality data must be entered by age group. Age groups must be identical for mortality and population data (see Fig. 6 and Fig. 7).

An age range with zero mortality – that is there are no deaths for a specific age group – creates problems in some computations. When AirQ+ detects zero mortality, it signals the lack of data in the Evaluation Parameters window; the user is requested to recheck the data and to add a low number such as 1 to empty cells or cells that display a zero. No empty cells are allowed for any of the age intervals and outcomes, and AirQ+ gives an error message if cells are empty after data are input.

Data tables for males, females and both sexes should be identical in structure but data must be entered separately for males, females and both sexes. To analyse males and females, the user has to create and run two different AirQ+ projects. This example considers the entire population (both males and females) of a country.

AirQ+ will calculate the life table matrices based on one-year age groups for a follow-up period of 120 years from the starting year of simulations, for a population distribution of 0–120 years, regardless of the user- defined age groups.

8 AirQ+ returns an error in case of irregular age intervals or for non-consecutive intervals (the 5–9 and 20–24 year intervals are missing) such as: 0;4;100;1, 10;14;100;3, 15;19;100;8, 25;29;100;12

9 AirQ assumes a uniform distribution of the population and mortality within the specified age group.

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Fig. 6. AirQ+ CountryLifeTable data in Notepad

Fig. 7. AirQ+ CountryLifeTable data in Excel before import into AirQ+

Data entry can be done in two ways: manually or by importing data from a text file with a “.csv” extension.

Manual entry is done by entering data directly into the AirQ+ window. In the Input Life Table window, the user can enter, in the empty fields provided, the “age interval” for the mid-year population and the number of deaths and proceed with filling in the data in the table. AirQ+ does not support Excel editing features. In order to enter data in these cells, the user must type in or import the data as described in this manual.

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Use data import when data are already available in a csv file and in the format needed by AirQ+: numbers are separated by semicolons and lines end with a return character (Fig. 6). To create an input data file (with the correct AirQ+ format) from an Excel file, open the file in Excel and use the Save As function to convert it to a

*.CSV file. Each row in the Excel table should have the required data: the start of an age group (age from), end of an age group (age to), mid-period population and number of deaths (total mortality) for the corresponding age groups.

To import data, click the Import Data button on the Input Life Table window (Fig. 8).

Fig. 8. CountryLifeTable data after import into AirQ+

As mentioned before, population and mortality data must have the same age structure. AirQ+ performs consistency checks when the user clicks the Configure Evaluation button in the Input Life Table window, which is described in the next section.

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The bottom of the Input Life Table window shows the “Total Population” and “Total Deaths” to facilitate the input data checking.

Box 1 shows an overview of the steps for life table calculations, which are discussed in detail in the following examples.

Box 1. Overview of steps for life table calculations

Select Ambient Pollution, Long-term Effects, PM_2.5 and Life Table Evaluation from the drop-down lists in the New Impact Assessment window.

Enter air pollution concentration values in the Analysis Property window.

Click the Create New Life Table Evaluation button in the Analysis Property window to go to the Input Life Table window to input data:

manually or

by clicking the Import Data button.

Enter parameters in the Evaluation Parameters window:

mortality, all (natural) causes (adults age 30+ years) from the Health Endpoint drop-down list for which the impact is to be calculated;

relative risk;

cut-off value; and

start year of the simulation.

Click the Calculate button in the Evaluation Parameters window. The user needs to click the Calculate button whenever making changes to the parameters.

Evaluation parameters

AirQ+ calculates the impact of one pollutant on one specified outcome at a time. The results of calculations are displayed on the same window, and detailed calculations are available in the Detailed Results window.

Mandatory fields in the Evaluation Parameters window (Fig. 9) are:

health endpoint

relative risk

cut-off value

start year of the simulation.

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The relative risk¹⁰ and cut-off values¹¹ have default recommended values. However, the user can override the default values and enter new values for relative risks and cut-off.

The cut-off value is the reference level above which the impact is calculated. This user can either accept the default value or enter a new one.

AirQ+ cut-off values for PM_2.5 are:

^{10 µg/m}³^(default)

0 µg/m3 (minimum)

This example assumes that the annual mean PM_2.5 concentration is 25.2 µg/m³; this value is displayed in the Evaluation Parameters window but cannot be changed here. Air quality data should be entered in the Analysis Properties window, because they are needed for calculations.

Users who wish to run advanced simulations can build different scenarios by changing the values for future pollution levels and birth rates.

Fig. 9. Mandatory data entry fields in the Evaluation Parameters window

10 The relative risk values and the 95% confidence interval (Lower and Upper) come from the results of the meta-analysis of 13 cohort studies by Hoek G, Krishnan RM, Beelen R, Peters A, Ostro B, Brunekreef B, et al. (2013). Long-term air pollution exposure and cardio-respiratory mortality: a review. Environ Health. 12(1):43. doi:10.1186/1476-069X-12-43. The calculation method defaults to the log-linear method but the linear-log method is also possible (please refer to the AirQ+ Glossary).

11 WHO Regional Office for Europe (2006). Air quality guidelines: global update 2005: particulate matter, ozone, nitrogen dioxide, and sulfur dioxide. Copenhagen: WHO Regional Office for Europe (http://www.euro.who.int/en/health-topics/environment- and-health/air-quality/publications/pre2009/air-quality-guidelines.-global-update-2005.-particulate-matter,-ozone,-nitrogen- dioxide-and-sulfur-dioxide, accessed 12 November 2019).

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Example calculations

The four life table examples require familiarity with epidemiological definitions related to life table calculations, such as mid-year population and deaths by age, hazard rates, survival probabilities, life expectancy, and years of life lost. Please consult standard literature epidemiological for life table concepts and methodologies.

Example CountryLifeTable analysis: ambient air pollution – PM

_2.5

– long-term – cut-off 7.5 µg/m

³

– adult mortality – use of life tables – years of life lost

This example consists of estimating the years of life lost for one specific year and for the entire follow-up period, for all-cause mortality due to specific air pollution exposure for a given population within a country.

The same calculations can also be done at the subnational or city levels. This example also calculates the change (reduction) in life expectancy at age x (Expected Life Remaining parameter) and the expected years of life lost in one year. These impact estimates can be attributed to long-term exposure to ambient air pollutants (PM_2.5in this example) for a population exposed to air pollution concentration levels above a certain reference level for the specified pollutant. Calculations are done on a yearly basis (population and air quality data).

This example uses data from the CountryLifeTable file assuming an annual mean PM_2.5 concentration of 25.2 µg/m³; select the Input Mean Value radio button and enter this value in the Mean Value field (Fig. 10) in the Analysis Properties window. The mean concentration is the annual mean country value (population weighted).

The user may use the default values or enter new ones in the Evaluation Parameters window for the range for which the relative risks can be applied and for the cut-off value.

This example uses a cut-off value for PM_2.5 of 7.5 µg/m³, which is lower than the value of 10 µg/m³ recommended by the 2005 WHO AQG.

Fig. 10. Analysis Properties window for the example data

Importing the data file (Fig. 8) into AirQ+ will create an unabridged life table to perform the calculations (not shown).

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To summarize, this example uses annual mean PM_2.5 values and total mortality (all causes) data to calculate the years of life lost due to long-term exposure to concentrations of PM_2.5 above the cut-off value of 7.5 µg/

m³, which is lower than the value recommended by the 2005 WHO air quality guidelines (AQG).¹²

Once calculations have been completed, AirQ+ automatically saves the results with a date-and-time stamp, which is also displayed in the Evaluation Parameters window (Fig. 11).

Fig. 11. Results showing the date and time of the last calculation

AirQ+ advanced scenarios and options

Click the Advanced button in the Evaluation Parameters window (Fig. 9) to display advanced options such as the betas for the selected health endpoints.

WHO default values for relative risks and betas are based on Hoek et al. (2013) and they apply to populations aged ≥ 30 years; for populations below 30 years, the hypothesis is that relative risk = 1 and there are no separate betas for males or females.

The life table parameter Start Year is the starting year of the simulation calculation. AirQ+ uses the data entered into the Analysis Properties window.

The user has to choose the age range for which relative risks are not equal to 1, for example, “Apply relative risk from Age: 30 until Age: 120”.

Users can run advanced scenarios that perform additional calculations taking into account the changes over time of air pollution or birth rate, for a specified time interval from year(x) to year(x+n).

Click the Scenarios button in the Evaluation Parameters window to display the two options: change in pollution level and change in birth numbers (Fig.12). The user enters the percentage change, start year and end year, and then checks the Use button to indicate which scenario to run.

Fig. 12. Advanced options in the Evaluation Parameters window

12 WHO Regional Office for Europe (2006). Air quality guidelines: global update 2005: particulate matter, ozone, nitrogen dioxide, and sulfur dioxide. Copenhagen: WHO Regional Office for Europe (http://www.euro.who.int/en/health-topics/environment- and-health/air-quality/publications/pre2009/air-quality-guidelines.-global-update-2005.-particulate-matter,-ozone,-nitrogen- dioxide-and-sulfur-dioxide, accessed 12 November 2019).

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Changes in air pollution levels

This option deals with a percentage change in pollution level from year 20xx to year 20zz. Basic calculations assume that air pollution remains stable over time. This may not be true in reality. In order to assess the impact of air pollution changing over time, the user can apply a gradual reduction or increase in air pollution levels, expressed as a percentage of a current year.

Two example calculations are shown below. Each example assumes that starting in 2020 air pollution will decrease by 5% annually until 2027. The user would enter “–5%” in the “Change per year” field, “2020” in the

“From year” and “2027” in the “To year”.

The first example assumes a starting PM_2.5 concentration level of 100 µg/m³ in year 2020. The output data pollution level in µg/m³ for each year are:

2020: 100

2021: 0.95*100 = 95 2022: 0.95 * 95 = 90.25 2023: 0.95*90.25 = 85.74 2024: 0.95*85.74 = 81.45 2025: 0.95*81.45 = 77.38 2025: 0.95*77.38 = 73.51 2026: 73.51, …

The second example assumes a starting PM_2.5 concentration level of 25.2 µg/m³ in year 2020. Example output data from pollution level in µg/m³ for each year are:

2020: 25.2

2021: 0.95*25.2 = 23.94 2022: 0.95 * 23.94 = 22.74 2023: 0.95*22.74 = 21.61 2024: 0.95*21.61 = 20.53 2025: 0.95*20.53 = 19.50 2026: 0.95*19.50 = 18.52 2027: 18.52, …

Changes in birth rate

This option deals with a percentage change in birth rates from year 20xx to year 20zz. Basic calculations assume that birth rates stay stable over time. This is not true in real life. In order to assess the impact of this change over time, the user can apply a reduction or increase in birth rates in a certain period, expressed as a percentage of a reference year. For example, to indicate that the birth rate increases by 10% each year starting from the 2019 level in the period 2020–2022 (and returns to the 2019 level in 2023), the user enters 10% in the corresponding cell (Fig. 13).

Fig. 13. Example scenario: change in birth number

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YLL – interpretation of results

Fig. 14 summarizes the main results of the life table calculations in the Evaluation Parameters window.

This example shows that the years of life lost due to the difference between the measured levels of air pollution and the target PM_2.5 level of 7.5 µg/m³ indicate that on average the life expectancy at birth is extended by 1.1 years (Fig. 14).¹³

The years of life lost due to death in the first year is the sum of all years lost by the different age groups by having a decreased expected life remaining because of the impacts of air pollution (79 704 years, Fig. 15).

This figure, 79 704 years, is the number of years of life lost in adults (aged 30 years and older) who died prematurely in 2003 and 2004 because their life expectancy was reduced due to air pollution. For the first 10 years of follow-up, 2 239 787 years of life are lost due to pollution exceeding the reference level. Click the Expected Life Remaining tab in the Evaluation Parameters window (Fig. 14) to see these results (Fig. 15).

This example shows that life expectancy (Expected Life Remaining) at birth at the current air pollution level is 76.8 years and it would be 77.9 years if the pollution level would decrease to the cut-off level of 7.5 µg/m³, which could be achieved with targeted policy. At age 65, life expectancy would be 17.4 years if the cut-off value of 7.5 µg/m³ is reached. The difference in life expectancy indicates the expected life remaining lost due to exposure to air pollution levels above the cut-off level. An average 65-year-old resident of this country is expected to lose 0.8 years (with upper and lower 95% confidence interval limits of: 0.54–1.10 years) due to the level of air pollution remaining at the observed concentration.

Fig. 14. Life table results for expected life remaining in the Evaluation Parameters window

13 Please note that the number of deaths attributable to the measured exposure in the one-year period is approximately = 27 204 x 2 = 54 408. Since 0.5 year of life lost is per death in the year of death (as each death case still counts as being alive for half a year in the year the death occurs), these 54 408 deaths can be attributed to long-term exposure to PM_2.5 pollution exceeding the reference level.

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Fig. 15. Life table results for years of life lost in the Evaluation Parameters window

Users can also explore the results in more detail by selecting the Detailed Results tab. For example, users can see how the currently analysed population moves toward a stable state by using a horizontal sliding bar for time change (not shown).

AirQ+ automatically saves the latest analysis. Click the Export button to export the results in a text format (Fig. 16). Results are saved by default in the AirQ+ folder, but the user can select a different folder.

Fig. 16. Export button

Health impact assessment of air pollution: AirQ+ life table manual

Health impact assessment of air pollution: AirQ+ life table manual

Health impact assessment of air pollution: AirQ+ life table manual

December 2020

Abstract

Keywords

Contents

Acknowledgments ... iv

Abbreviations ... iv

Introduction... 1

The CountryLifeTable dataset ... 2

What is a life table? ... 3

Life table formulas in AirQ+ ... 8

Life table calculations in AirQ+ ... 13

Getting started with life tables in AirQ+ ... 14

Example calculations ... 20

References ... 30

Annex 1. Structure of calculation matrices ... 31

Acknowledgments

Abbreviations

Introduction

The CountryLifeTable dataset

What is a life table?

Life table formulas in AirQ+

Estimation of yearly values

Relative risks

Life table calculations in AirQ+

Getting started with life tables in AirQ+

Data input for life table calculations

Evaluation parameters

Example calculations

Example CountryLifeTable analysis: ambient air pollution – PM

– long-term – cut-off 7.5 µg/m

– adult mortality – use of life tables – years of life lost

Health impact assessment of air pollution: AirQ+ life table manual

Health impact assessment of air pollution: AirQ+ life table manual

Health impact assessment of air pollution: AirQ+ life table manual

December 2020

Abstract

Keywords

Contents

Acknowledgments ... iv

Abbreviations ... iv

Introduction... 1

The CountryLifeTable dataset ... 2

What is a life table? ... 3

Life table formulas in AirQ+ ... 8

Life table calculations in AirQ+ ... 13

Getting started with life tables in AirQ+ ... 14

Example calculations ... 20

References ... 30

Annex 1. Structure of calculation matrices ... 31

Acknowledgments

Abbreviations

Introduction

The CountryLifeTable dataset









What is a life table?





Life table formulas in AirQ+

Estimation of yearly values

Relative risks

Life table calculations in AirQ+

Getting started with life tables in AirQ+

Data input for life table calculations























Evaluation parameters













Example calculations

Example CountryLifeTable analysis: ambient air pollution – PM

– long-term – cut-off 7.5 µg/m

– adult mortality – use of life tables – years of life lost