ERF of outdoor air pollution

This page is a knowledge crystal of subtype variable. The page identifier is Op_en2202
Moderator:Nobody (see all) Click here to sign up.
Upload data {{#opasnet_base_link:Op_en2202}}

Question

What is the the quantitative dose-response relationships between outdoor air PM2.5 concentration and mortality due to cardio-pulmonary, lung cancer, and other non-accidental causes (index Cause of death 2)?

Answer

This code gets the ovariable of this page and calculates some basic results.

+ Show code- Hide code library(OpasnetUtils) # A package with Opasnet functionalities library(ggplot2) # A package with fancy graph formats objects.latest("Op_en2202", "ERF_airpoll") # Get the latest ovariables from code calculations on page Op_en2202. ERF_airpoll <- EvalOutput(ERF_airpoll) summary(ERF_airpoll)

Rationale

Data

Exposure-response functions of air pollution(-)

Obs	Exposure agent	Response	Subgroup	Exposure	ER function	Scaling	Exposure unit	Threshold	ERF	Description
1	PM2.5	Natural mortality	Age:30+	Long-term exposure to outdoor pollutants	RR	None	10 µg/m3	0	1.08	Chen & Hoek, 2020
2	PM10	Annual number of days with bronchitis in children	Age:6-12 a	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.080 (0.980 – 1.190)	HRAPIE, WHO 2013
3	PM10	Incidence chronic bronchitis in adults	Age:18+ a	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.117 (1.040 – 1.189)	HRAPIE, WHO 2013
4	PM10	Incidence of asthma symptoms in asthmatic children	Age:5-19 a	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.028 (1.006 – 1.051)	HRAPIE, WHO 2013
5	PM2.5	Hospitalizations, cardiovascular diseases	Age:all ages	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.0091 (1.0017 – 1.0166)	HRAPIE, WHO 2013
6	PM2.5	Hospitalizations, respiratory diseases	Age:all ages	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.0190 (0.9982 – 1.0402)	HRAPIE, WHO 2013
7	PM2.5	Restricted activity days (RADs) (including sick-leave, hospital emergency admission, symptom days per person per year)	Age:all ages	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.0470 (1.0420 – 1.0530)	HRAPIE, WHO 2013
8	PM2.5	Work days lost per worker per year, working age population	Age:age 20-65 a	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.0460 (1.0390 – 1.0530)	HRAPIE, WHO 2013
9	PM2.5	Lung cancer	Age:age 30+ a	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.0900 (1.0400 – 1.1400)	Van der Zee et al., 2016
10	PM2.5	Low birth weight (< 2500 g at term)	Age:0 a	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.1900 (1.0000 – 1.4200)	Van der Zee et al., 2016
11	PM2.5	Decreased lung function (FEV1) in percentage (per 10 µg/m³)	Age:6-12 a	Long-term exposure to outdoor pollutant	ERS	None	10 µg/m3	0	0.015 (-0.003 - 0.032)	Van der Zee et al., 2016
12	PM10	Post-neonatal mortality	Age:1-12 months = age 0 a*11/12	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.040 (1.020 – 1.070)	HRAPIE, WHO 2013
13	PM10	YLL (premature deaths)	Age:30+ a	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.035 (1.004 – 1.066)	Hoek et al., 2013
14	PM2.5	YLL (premature deaths)	Age:30+ a	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.062 (1.041 – 1.084)	Hoek et al., 2013
15	NO2	YLL (premature deaths)	Age:30+ a	Long-term exposure to outdoor pollutant	RR	None	10 µg/m3	0	1.020 (1.010 – 1.030)	Atkinson et al., 2017
16	EC	YLL (premature deaths)	Age:30+ a	Long-term exposure to outdoor pollutant	RR	None	µg/m3	0	1.061 (1.049 – 1.073)	Hoek et al., 2013 Note! per 1 µg/m3
17	PM10	Decline in life expecancy in days	Age:30+ a	Long-term exposure to outdoor pollutant	ERS	None	10 µg/m3	0	120 (14 - 223)	Hoek et al., 2013
18	PM2.5	Decline in life expecancy in days	Age:30+ a	Long-term exposure to outdoor pollutant	ERS	None	10 µg/m3	0	210 (140 - 281)	Hoek et al., 2013
19	NO2	Decline in life expecancy in days	Age:30+ a	Long-term exposure to outdoor pollutant	ERS	None	10 µg/m3	0	69 (35 - 103)	Atkinson et al., 2017
20	EC	Decline in life expecancy in days	Age:30+ a	Long-term exposure to outdoor pollutant	ERS	None	µg/m3	0	206 (167 - 246)	Hoek et al., 2013 Note! per 1 µg/m3
21	PM2.5	Lung cancer mortality		Long-term exposure to outdoor pollutant	RR	None	µg /m3	0	1.014 (1.004-1.023)	Pope et al. 2002
22	PM2.5	Cardiopulmonary mortality		Long-term exposure to outdoor pollutant	RR	None	µg /m3	0	1.009 (1.003-1.016)	Pope et al. 2002
23	PM2.5	Total mortality		Long-term exposure to outdoor pollutant	RR	None	µg /m3	0	1.0062 (1.0014-1.011)	Pope?
24	Outdoor ozone	Total mortality		Long-term exposure to outdoor pollutant	RR	None	µg /m3	0	1.000299596 (1.000099955-1.000399282)
25	Outdoor ozone	Mild decreasing on general functioning		Long-term exposure to outdoor pollutant	UR	None	µg /m3	0	0.115 (0.044-0.186)

Ozone and PM2.5 used to be on page ERFs of environmental pollutants.

Cutoff values used in PAQ2018 tool: 3.9 µg/m³ for PM10, 2.5 µg/m³ for PM2.5, 5.0 µg/m³ for NO2, 0.3 µg/m³ for EC.

Studies used:

• Chen & Hoek, 2020
• HRAPIE
• WHO 2013
• Van der Zee et al., 2016
• Hoek et al., 2013
• Atkinson et al., 2017
• Dockery et al. 1993
• Pope et al. 2002

Classical literature

Recent literature on global PM2.5 disease burden estimates

• Lelieveld, J., Evans, J., Fnais, M. et al. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367–371 (2015). https://doi.org/10.1038/nature15371
• Richard Burnett, Hong Chen, Mieczysław Szyszkowicz, Neal Fann, Bryan Hubbell, C. Arden Pope III, Joshua S. Apte, Michael Brauer, Aaron Cohen, Scott Weichenthal, Jay Coggins, Qian Di, Bert Brunekreef, Joseph Frostad, Stephen S. Lim, Haidong Kan, Katherine D. Walker, George D. Thurston, Richard B. Hayes, Chris C. Lim, Michelle C. Turner, Michael Jerrett, Daniel Krewski, Susan M. Gapstur, W. Ryan Diver, Bart Ostro, Debbie Goldberg, Daniel L. Crouse, Randall V. Martin, Paul Peters, Lauren Pinault, Michael Tjepkema, Aaron van Donkelaar, Paul J. Villeneuve, Anthony B. Miller, Peng Yin, Maigeng Zhou, Lijun Wang, Nicole A. H. Janssen, Marten Marra, Richard W. Atkinson, Hilda Tsang, Thuan Quoc Thach, John B. Cannon, Ryan T. Allen, Jaime E. Hart, Francine Laden, Giulia Cesaroni, Francesco Forastiere, Gudrun Weinmayr, Andrea Jaensch, Gabriele Nagel, Hans Concin, and Joseph V. Spadaro. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. PNAS September 18, 2018 115 (38) 9592-9597; https://doi.org/10.1073/pnas.1803222115
• Karn Vohra, Alina Vodonos, Joel Schwartz, Eloise A.Marais, Melissa P.Sulprizio, Loretta J.Mickley. Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem https://doi.org/10.1016/j.envres.2021.110754
• Heli Lehtomäki, Antti Korhonen, Arja Asikainen, Niko Karvosenoja, Kaarle Kupiainen, Ville-Veikko Paunu, Mikko Savolahti, Mikhail Sofiev, Yuliia Palamarchuk, Ari Karppinen, Jaakko Kukkonen and Otto Hänninen. Health Impacts of Ambient Air Pollution in Finland. Int. J. Environ. Res. Public Health 2018, 15(4), 736; https://doi.org/10.3390/ijerph15040736

Pope CA et al. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA, 2002, 287:1132-1141

average exposure level in Europe to PM2,5	25	μg/m3 annual average
average inhalation rate	15	#/min
average tidal volume	0.6	L
inhaled air/person*year	4734	m3/a
average inhaled PM2,5 per year	0,12	g/a
population of Europe	515000000
total PM inhaled by European population	60945615	g/a
BoD increase from EU-26 population exposure to [BH90]	1,5E+06	DALY/a
Population CVD BoD increase	2,5E-02	DALY/1g PM2,5 intake
All-cause mortality increase	1,04	RR per 10 μg/m3 PM2,5 increase
LC mortality increase	1,14	RR per 10 μg/m3 PM2,5 increase
CVD mortality increase	1,09	RR per 10 μg/m3 PM2,5 increase
EU-26 weighed average from Global BoD estimates		Data from:[2]

⇤--#: . The end of this table contains calculations within the excel it was taken from, it only says #REF, because the frmulas have been broken somehow. --Heta (talk) 07:36, 10 June 2015 (UTC) (type: truth; paradigms: science: attack)

PM_2.5 are fine particles less than 2.5 μm in diameter. Exposure-response function can be derived from exposure modelling, animal toxicology, small clinical or panel studies, and epidemiological studies. Exposed population can be divided into subpopulations (e.g. adults, children, infants, the elderly), and exposure is assessed per certain time period (e.g. daily or annual exposure).

• Health effects related to short-term exposure
  • respiratory symptoms
  • adverse cardiovascular effects
  • increased medication usage
  • increased number of hospital admissions
  • increased mortality
• Health effects related to long-term exposure (more relevance to public health)
  • increased incidence of respiratory symptoms
  • reduction in lung function
  • increased incidence of chronic obstructive pulmonary disease (COPD)
  • reduction in life expectancy
    • increased cardiopulmonary mortality
    • increased lung cancer mortality

Sensitive subgroups: children, the elderly, individuals with heart and lung disease, individuals who are active outdoors.

Mortality effects of long-term (chronic) exposure to ambient air

In principle the ERFs for long-term exposure (produced by cohort studies) should also capture the mortality effects of short-term exposure (ERFs produced by time-series studies). In practice it is likely that they do not do so fully. This is due to the so-called "harvesting" phenomenon, i.e. it is possible that acute exposure, at least to some extent, only brings forward deaths that would have happened shortly in any case. However, adding effects of acute exposure to effects of long-term exposure is problematic because the risk of double-counting.

Pope et al. (2002)

• 6% increase in the risk of deaths from all causes (excluding violent death) (95% CI 2-11%) per 10 µg/m3 PM2.5 in age group 30+
• 12% increase in the risk of death from cardiovascular diseases and diabetes (95% CI 8-15%) per 10 µg/m3 PM2.5 in age group 30+
• 14% increase in the risk of death from lung cancer (95% CI 4-23%) per 10 µg/m3 PM_2.5 in age group 30+

Woodruff et al (1997)

• 4% (95% Cl 2%-7%) increase in all-cause infant mortality per 10 µg/m3 PM₁₀ (age 1 month to 1 year)

Tuomisto et al. 2008:

• A structured expert judgement study of the population mortality effects of PM_2.5 air pollution.
• Opinions of six European air pollution experts were elicited.
• Percent increase per 1 µg/m³ increase in PM_2.5:
  • Equal-weight decision-maker
    • Best estimate 0.97
    • 95% quantile 4.54
    • 5% quantile 0.02
  • Performance-based decision-maker
    • Best estimate 0.60
    • 95% quantile 3.80
    • 5% quantile 0.06

'Mortality effects of short-term (acute) exposure to ambient air PM

Anderson et al. 2004

• 0.6% (95% Cl 0.4%-0.8%) increase in all-cause mortality (excluding accidents) per 10 µg/m3 PM₁₀ in all ages
• 1.3% (95% Cl 0.5%-2%) increase in respiratory mortality per 10 µg/m3 PM₁₀ in all ages
• 0.9% (95% Cl 0.5%-1.3%) increase in cardiovascular mortality per 10 µg/m3 PM₁₀ in all ages

These coefficients are defined as distributions around estimates of central tendency for each cause of death.

Relative increase of mortality per 1 μgm-3 increase of outdoor PM2.5 concentration. Values were drawn with equal probability from the two distributions reported in ,

Uncertainties:

• Mortality estimate from Hoek et al. (2002) was not included due to many confounding factors related to mortality, e.g. road noise.
• Probability for PM2.5 assumed to be the true cause of the effects in 70 %, 90 %, and 10 % for cardiopulmonary, lung cancer and all other mortality, respectively (author judgement).
• Toxicity differences between ambient air particles and the particles generated by different bus types were not taken into account due to lack of comprehensive data.

• No threshold was assumed in the dose-response relationship.

Other

• Burnett RT, Pope CA 3rd, Ezzati M, Olives C, Lim SS, Mehta S, Shin HH, Singh G, Hubbell B, Brauer M, Anderson HR, Smith KR, Balmes JR, Bruce NG, Kan H, Laden F, Prüss-Ustün A, Turner MC, Gapstur SM, Diver WR, Cohen A. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure. Environ Health Perspect. 2014 Apr;122(4):397-403. doi:10.1289/ehp.1307049 Epub 2014 Feb 7. Erratum in Environ Health Perspect. 2014 Sep;122(9):A235. [3] [4]

Calculations

+ Show code- Hide code # This is code Op_en2202/ERF_airpoll on page [[ERF of air pollution]] library(OpasnetUtils) ERF_airpoll <- Ovariable("ERF_airpoll", ddata = "Op_en2202", subset="Exposure-response functions of air pollution") objects.store(ERF_airpoll) cat("Ovariable ERF_airpoll stored.\n")

Effect of infiltration

The mortality due to indoor exposure to outdoor pollutants can be described with this equation:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle log \Delta M_{in,j} = \frac{\Delta C_{out-in} t_{in}}{\Delta C_{out} t_{out} + \Delta C_{out-in} t_{in}} log \Delta M_{all,j} (1)}

where logΔM_{all, j} is the increase in mortality due to the j^th outcome associated with total PM exposure for each 10 μg/m³ increase in PM₁₀ or PM_2.5.

j represents three major health outcomes: all-cause, cardiovascular, and respiratory mortality.

ΔC_out is the increase in outdoor PM₁₀ or PM_2.5 concentrations, which is set as 10 μg/m³.

ΔC_out-in is the increase in outdoor-originated PM₁₀ or PM_2.5 concentrations found in the indoor environment.

t_out is the duration of direct exposure to outdoor PM pollution.

t_in is the duration of indoor exposure to PM of outdoor origin.

logΔM_{in, j} estimates the increase in mortality due to the j^th outcome associated with indoor exposure to outdoor-origin PM for each 10 μg/m³ increase in PM₁₀ or PM_2.5

When one compares changes in mortality in different countries and different infiltration factors, this is what I think actually happens:

If infiltration factor is relatively low such as for PM₁₀ in China, the people are exposed less than predicted based on outdoor concentrations. When an epidemiologic study is performed, the observed mortality rate is lower than in other places with higher infiltration factors, and this can be falsely interpreted that the dose-response slope (ΔM_all) is less steep. When this mortality estimate was combined with the lower exposure estimate (due to low infiltration) in equation 1, you get too low an estimate for the mortality due to indoor exposure to outdoor PM. In contrast, the local infiltration factor could and should be used to adjust the observed mortality rate.

In other words, what you actually observe in epidemiological studies is not ΔM_all but ΔM_obs, which is something close to ΔM_in because so much time is spent indoors. The work should be about estimating ΔM_all based on ΔM_obs and infiltration and not vice versa. (Here I interpret ΔM_all as the true risk estimate that we would observe, if the exposure measure was correct.)

More precisely, let's look at the mathematics of log-linear regression. We assume that the logarithms of the observed probabilities (or rates) of disease have the probability distribution of

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle log(p) \sim a + b C,}

where p is the probability of disease,

a is a constant describing the background probability,

b is a risk coefficient for the exposure, and

C is the exposure concentration in the population.

What happens with PM is that we observe the differences in the probability of disease due to differences in exposure C. However, we do NOT observe C itself (the actual exposure concentration) but only the surrogate C_obs, which in this case is the outdoor concentration of PM C_out. With a given difference in the probability of disease between the exposed and non-exposed groups, we therefore make a biased conclusion about b, and this bias can be described as:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle log(p_E) - log(p_0) \sim (a + b C_E) - (a + b C_0) = b (C_E - C_0) = b_{obs} (C_{E,obs} - C_{0,obs})}

where E is the exposed group,

0 is the non-exposed group, and

obs is the biased observed variable (in contrast to the actual variable we would observe if all measurements were correct). Let's look at the ratio of the biased and correct risk estimates:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \frac{b_{obs}}{b} = \frac{C_E - C_0}{C_{E,obs} - C_{0,obs}} = \frac{\Sigma_i C_{E,i} t_i - \Sigma_i C_{0,i} t_i}{C_{E,out} - C_{0,out}}}

where i means different microenvironments and t_i is the fraction of time spent in each microenvironment. If we use F_i to denote the relative exposure concentrations in different microenvironments i (in the case of only indoor and outdoor microenvironments F is 1 for outdoor and equal to infiltration factor for indoor), we get

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \frac{b_{obs}}{b} = \frac{\Sigma_i F_i C_{E,out} t_i - \Sigma_i F_i C_{0,out} t_i}{C_{E,out} - C_{0,out}} = \frac{(C_{E,out} - C_{0,out}) \Sigma_i F_i t_i}{C_{E,out} - C_{0,out}} = \Sigma_i F_i t_i }

As we can see, the observed b is biased downward if the population spends a lot of time in microenvironments with low infiltration factor. Therefore, it is a mistake to assume that the b_obs would reflect logΔM_all. Instead,

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle log \Delta M_{all} = \frac{log \Delta M_{obs}}{\Sigma_i F_i t_i}.}

and this should be used in any related calculations.

See also

• Major upgrade was made to this page on 2021-03-19. For previous content (dated 2015), see archive.
• Health aspects of air pollution. Results from the WHO project "Systematic review of health aspects of air pollution in Europe". World Health Organization, 2004.
• Pope et al. 2004. Cardiovascular mortality and long-term exposure to particulate air pollution. Circulation (109), 71-77.
• C. Puett, Joel Schwartz, Jaime E. Hart, Jeff D. Yanosky, Frank E. Speizer, Helen Suh, Christopher J. Paciorek, Lucas M. Neas and Francine Laden: Chronic Particulate Exposure, Mortality, and Coronary Heart Disease in the Nurses’ Health Study. American Journal of Epidemiology, doi:10.1093/aje/kwn232
• NEEDS - New Energy Externalities Developments for Sustainability, Deliverable 3.7 "A set of concentration-response function", Integrated Project, Sixth Framework Programme, Project no. 502687.